Methods for detecting cellular transitions

Abstract

The disclosure relates, inter alia, to methods for monitoring cellular transitions associated with or indicative of a presence or a stage of a myelofibrosis-related disease.

Description

[0001]CLNV-015PC / 132119-5015 METHODS FOR DETECTING CELLULAR TRANSITIONS TECHNICAL FIELD The present disclosure relates generally to methods for monitoring and / or identifying cellular transitions associated with or indicative of a presence or a stage of myelofibrosis-related diseases. CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to and benefit of U.S. Provisional Patent Application No.63 / 612,294, filed on December 19, 2023, the content of which is hereby incorporated by reference in its entirety. BACKGROUND The study of cellular mechanisms and the chemical compounds and intermediates underlying such biological processes is important for understanding the etiology, manifestation, and progression of disease. Existing drug discovery and patient selection methods, whether traditional high-throughput screens or methods employing in silico approaches remain inefficient and unable to meet existing medical needs. Myelofibrosis is a rare chronic myeloproliferative neoplasm characterized by progressive bone marrow fibrosis, and inefficient hematopoiesis. The myelofibrosis-associated consequences and medical complications often result in premature death from infection, thrombohemorrhagic events, cardiac or pulmonary failure, and leukemic transformation. The pathogenesis of myelofibrosis is multifactorial, involving multiple cell types, and key mechanisms of the disease and disease progression remains incompletely understood. There thus remains a need in the art for novel methods for understanding the underlying cellular processes causing myelofibrosis-related diseases, like primary myelofibrosis and secondary myelofibrosis, and their progression in order to develop effective methods and agents for treating myelofibrosis-related diseases, like primary myelofibrosis and secondary myelofibrosis. SUMMARY In aspects and embodiments, there is provided a method of detecting and / or monitoring a cellular transition associated with or indicative of a presence or a stage of a myelofibrosis-related disease, comprising: (a) analyzing cellular data from a plurality of human patients, at least some of the plurality of human patients DB1 / 142566607.11 1 CLNV-015PC / 132119-5015 being afflicted with the myelofibrosis-related disease; (b) optionally, analyzing cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease; and (c) generating a profile of cellular dysfunction based on the analyses of (a) and (b), wherein: the profile of cellular dysfunction is indicative of the cellular transition; and the cellular transition is associated with or indicative of a transition from an undiseased, pre-diseased, disease-treatable, or disease-reversible state to a disease-untreatable or disease-irreversible state. In aspects and embodiments, there is provided a method of detecting and / or monitoring a cellular transition associated with or indicative of a presence or a stage of a myelofibrosis-related disease, comprising: (a) analyzing cellular data from a plurality of human patients, at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease; (b) optionally, analyzing cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease; and (c) generating a profile of cellular dysfunction based on the analyses of (a) and (b), wherein: the cellular data of (a) and (b) is obtained from one or more cell types selected from Table 1; the cellular data of (a) and (b) comprises cellular data from one or more gene markers selected from Table 1; the profile of cellular dysfunction is indicative of the cellular transition; and the cellular transition is associated with or indicative of a transition from an undiseased, pre-diseased, disease-treatable, or disease-reversible state to a disease-untreatable or disease-irreversible state. In aspects and embodiments, there is provided a method of selecting a patient for treatment with an effective amount of an agent for a myelofibrosis-related disease, comprising: (a) determining the presence of a cellular transition associated with or indicative of a presence or a stage of a myelofibrosis-related disease in one or more samples from the patient by analyzing cellular data from the one or more samples; and (b) comparing the analysis of (a) to a profile of cellular dysfunction based on the analysis of cellular data from: (i) a plurality of human patients, at least some of the plurality of human patients being afflicted with the myelofibrosis- related disease; (ii) optionally, a plurality of animal disease models of the myelofibrosis-related disease; (iii) optionally, a plurality of in vitro disease models of the myelofibrosis-related disease; and / or (iv) optionally, a plurality of ex vivo disease models of the myelofibrosis-related disease; wherein: the profile of cellular dysfunction is indicative of the cellular transition; and the cellular transition is associated with or indicative of a transition from an undiseased, pre-diseased, disease-treatable, or disease-reversible state to a disease- untreatable or disease-irreversible state; wherein the patient is suitable for treatment if demonstrating that the cellular data of (a) is at least substantially similar to the profile of cellular dysfunction of (b). DB1 / 142566607.11 2 CLNV-015PC / 132119-5015 In aspects and embodiments, there is provided a method of selecting a patient for treatment with an effective amount of an agent for a myelofibrosis-related disease, comprising: (a) determining the presence of a cellular transition associated with or indicative of a presence or a stage of a myelofibrosis-related disease in a sample from the patient by analyzing cellular data from the sample; and (b) comparing the analysis of (a) to a profile of cellular dysfunction based on the analysis of cellular data from: (i) a plurality of human patients, at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease; (ii) optionally, a plurality of animal disease models of the myelofibrosis-related disease; (iii) optionally, a plurality of in vitro disease models of the myelofibrosis-related disease; and / or (iv) optionally, a plurality of ex vivo disease models of the myelofibrosis-related disease; wherein: the cellular data of (a) and (b) comprises cellular data from one or more cell types selected from Table 1; the cellular data of (a) and (b) comprises cellular data from one or more gene markers selected from Table 1; the profile of cellular dysfunction is indicative of the cellular transition; and the cellular transition is associated with or indicative of a transition from an undiseased, pre-diseased, disease-treatable, or disease-reversible state to a disease-untreatable or disease-irreversible state; wherein the patient is suitable for treatment if demonstrating that the cellular data of (a) is at least substantially similar to the profile of cellular dysfunction of (b). In aspects and embodiments, there is provided a method of evaluating translatability of an animal, in vitro, and / or ex vivo model of a myelofibrosis-related disease to a human, comprising: (a) determining the presence of a cellular transition associated with or indicative of a presence or a stage of the myelofibrosis- related disease in the animal, in vitro, and / or ex vivo model by analyzing cellular data from the animal, in vitro, and / or ex vivo model; and (b) comparing the analysis of (a) to a profile of cellular dysfunction based on the analysis of cellular data from a plurality of human patients, at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease wherein: the profile of cellular dysfunction is indicative of the cellular transition; and the cellular transition is associated with or indicative of a transition from an undiseased, pre-diseased, disease-treatable, or disease-reversible state to a disease-untreatable or disease- irreversible state; wherein the animal, in vitro, and / or ex vivo model is translatable to a human if demonstrating that the single nuclei data and / or single-cell transcription data of (a) is at least substantially similar to the profile of cellular dysfunction of (b). In aspects and embodiments, there is provided a method of translatability of an animal, in vitro, and / or ex vivo model of a myelofibrosis-related disease to a human, comprising: (a) determining the presence of a cellular transition associated with or indicative of a presence or a stage of a myelofibrosis-related disease in the animal, in vitro, and / or ex vivo model by analyzing cellular data from the animal, in vitro, and / or ex vivo DB1 / 142566607.11 3 CLNV-015PC / 132119-5015 model; and (b) comparing the analysis of (a) to a profile of cellular dysfunction based on the analysis of cellular data from a plurality of human patients, at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease wherein: the cellular data of (a) and (b) comprises cellular data from one or more cell types selected from Table 1; the cellular data of (a) and (b) comprises cellular data from one or more gene markers selected from Table 1; the profile of cellular dysfunction is indicative of the cellular transition; and the cellular transition is associated with or indicative of a transition from an undiseased, pre- diseased, disease-treatable, or disease-reversible state to a disease-untreatable or disease-irreversible state; wherein the animal, in vitro, and / or ex vivo model is translatable to a human if demonstrating that the single nuclei data and / or single-cell transcription data of (a) is at least substantially similar to the profile of cellular dysfunction of (b). In aspects and embodiments, there is provided a method of identifying an agent for treating a myelofibrosis- related disease, comprising: (a) administering the agent in an animal, in vitro, and / or ex vivo disease model of the myelofibrosis-related disease; (b) determining the presence of a cellular transition associated with or indicative of a presence or a stage of a myelofibrosis-related disease in the animal, in vitro, and / or ex vivo disease model by analyzing cellular data from the animal, in vitro, and / or ex vivo disease model; and (c) comparing the analysis of (b) to a profile of cellular dysfunction based on the analysis of cellular data from: (i) a plurality of human patients, at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease; (ii) a plurality of animal disease models of the myelofibrosis-related disease; (iii) a plurality of in vitro disease models of the myelofibrosis-related disease; and / or (iv) a plurality of ex vivo disease models of the myelofibrosis-related disease; wherein: the profile of cellular dysfunction is indicative of the cellular transition; and the cellular transition is associated with or indicative of a transition from a disease-untreatable or disease-irreversible state to an undiseased, pre-diseased, disease-treatable, or disease-reversible state; wherein the agent is suitable for treating the myelofibrosis-related disease if demonstrating that the cellular data of (b) is at least substantially similar to the profile of cellular dysfunction of (c). In aspects and embodiments, there is provided a method of identifying an agent for treating a myelofibrosis- related disease, comprising: (a) administering the agent in an animal, in vitro, and / or ex vivo disease model of the myelofibrosis-related disease; (b) determining the presence of a cellular transition associated with or indicative of a presence or a stage of a myelofibrosis-related disease in the animal, in vitro, and / or ex vivo disease model by analyzing cellular data from the animal, in vitro, and / or ex vivo disease model; and (c) comparing the analysis of (b) to a profile of cellular dysfunction based on the analysis of cellular data from: DB1 / 142566607.11 4 CLNV-015PC / 132119-5015 (i) a plurality of human patients, at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease; (ii) a plurality of animal disease models of the myelofibrosis-related disease; (iii) a plurality of in vitro disease models of the myelofibrosis-related disease; and / or (iv) a plurality of ex vivo disease models of the myelofibrosis-related disease; wherein: the cellular data of (b) and (c) comprises cellular data from one or more cell types selected from Table 1; the cellular data of (b) and (c) comprises cellular data from one or more gene markers selected from Table 1; the profile of cellular dysfunction is indicative of the cellular transition; and the cellular transition is associated with or indicative of a transition from a disease-untreatable or disease-irreversible state to an undiseased, pre-diseased, disease-treatable, or disease-reversible state; wherein the agent is suitable for treating the myelofibrosis-related disease if demonstrating that the single nuclei data and / or single-cell transcription data of (b) is at least substantially similar to the profile of cellular dysfunction of (c). In aspects and embodiments, there is provided a method of identifying a patient at risk for developing a myelofibrosis-related disease, comprising: (a) determining the presence of a cellular transition associated with or indicative of a presence or a stage of a myelofibrosis-related disease in a sample from the patient by analyzing cellular data from the sample; and (b) comparing the analysis of (a) to a profile of cellular dysfunction based on the analysis of cellular data from: (i) a plurality of human patients, at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease; (ii) optionally, a plurality of animal disease models of the myelofibrosis-related disease; (iii) optionally, a plurality of in vitro disease models of the myelofibrosis-related disease; and / or (iv) optionally, a plurality of ex vivo disease models of the myelofibrosis-related disease; wherein: the profile of cellular dysfunction is indicative of the cellular transition; and the cellular transition is associated with or indicative of a transition from an undiseased, pre- diseased, disease-treatable, or disease-reversible state to a disease-untreatable or disease-irreversible state; wherein the patient is at risk for developing the myelofibrosis-related disease if demonstrating that cellular data of (a) is at least substantially similar to the profile of cellular dysfunction of (b). In aspects and embodiments, there is provided a method of identifying a patient at risk for developing a myelofibrosis-related disease, comprising: (a) determining the presence of a cellular transition associated with or indicative of a presence or a stage of a myelofibrosis-related disease in a sample from the patient by analyzing cellular data from the sample; and (b) comparing the analysis of (a) to a profile of cellular dysfunction based on the analysis of cellular data from: (i) a plurality of human patients, at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease; (ii) optionally, a plurality of animal disease models of the myelofibrosis-related disease; (iii) optionally, a plurality of in vitro disease DB1 / 142566607.11 5 CLNV-015PC / 132119-5015 models of the myelofibrosis-related disease; and / or (iv) optionally, a plurality of ex vivo disease models of the myelofibrosis-related disease; wherein: the cellular data of (a) and (b) comprises cellular data from one or more cell types selected from Table 1; the cellular data of (a) and (b) comprises cellular data from one or more gene markers selected from Table 1; the profile of cellular dysfunction is indicative of the cellular transition; and the cellular transition is associated with or indicative of a transition from an undiseased, pre- diseased, disease-treatable, or disease-reversible state to a disease-untreatable or disease-irreversible state; wherein the patient is at risk for developing the myelofibrosis-related disease if demonstrating that cellular data of (a) is at least substantially similar to the profile of cellular dysfunction of (b). In aspects and embodiments, there is provided an atlas of cellular data, comprising: cellular data from a plurality of human patients, at least some of the plurality of human patients being afflicted with a myelofibrosis- related disease; and (b) optionally, cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease, wherein the cellular data is obtained from two or more cell types. In aspects and embodiments, there is provided an atlas of cellular data, comprising: cellular data from a plurality of human patients, at least some of the plurality of human patients being afflicted with a myelofibrosis- related disease; and (b) optionally, cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease of the myelofibrosis-related disease; wherein the cellular data of (a) and (b) is obtained from two or more cell types selected from Table 1; and the cellular data of (a) and (b) comprises cellular data from one or more gene markers selected from Table 1. In aspects and embodiments, there is provided a map of cellular data, comprising, cellular data from a plurality of human patients, wherein at least some of the plurality of human patients being afflicted with a myelofibrosis-related disease; and (b) optionally, cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease of the myelofibrosis- related disease; wherein the cellular data is obtained from a single cell type. In aspects and embodiments, there is provided a method of map of cellular data, comprising: cellular data from a plurality of human patients, at least some of the plurality of human patients being afflicted with a myelofibrosis-related disease; and (b) optionally, cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease of the myelofibrosis-related disease; wherein the cellular data of (a) and (b) is obtained from a single cell type DB1 / 142566607.11 6 CLNV-015PC / 132119-5015 selected from Table 1; and the cellular data of (a) and (b) of comprises cellular data from one or more gene markers selected from Table 1. In aspects and embodiments, there is provided a method for preparing an atlas of cellular data, comprising: obtaining cellular data from a plurality of human patients, at least some of the plurality of human patients being afflicted with a myelofibrosis-related disease, wherein the cellular data is obtained from two or more cell types; and assembling the cellular data of (a) into an atlas of cellular data. In aspects and embodiments, there is provided a method for preparing an atlas of cellular data, comprising: obtaining cellular data from a plurality of human patients, at least some of the plurality of human patients being afflicted with a myelofibrosis-related disease, wherein the cellular data is obtained from two or more cell types; and assembling the cellular data of (a) into an atlas of cellular data, wherein the cellular data is obtained from two or more cell types selected from Table 1; and the cellular data of (a) of comprises cellular data from one or more gene markers selected from Table 1. In aspects and embodiments, there is provided a method for preparing a map of cellular data, comprising: obtaining cellular data from a plurality of human patients, at least some of the plurality of human patients being afflicted with a myelofibrosis-related disease, wherein the cellular data is obtained from two or more cell types; assembling the cellular data of (a) into an atlas of cellular data; curating and / or filtering the cellular data of the atlas of (b) to obtain cellular data from a single cell type; and assembling the cellular data obtained in (c) into a map of cellular data. In aspects and embodiments, there is provided a method for preparing a map of cellular data, comprising: obtaining cellular data from a plurality of human patients, at least some of the plurality of human patients being afflicted with a myelofibrosis-related disease, wherein the cellular data is obtained from two or more cell types; assembling the cellular data of (a) into an atlas of cellular data; curating and / or filtering the cellular data of the atlas of (b) to obtain cellular data from a single cell type; and assembling the cellular data obtained in (c) into a map of cellular data wherein the cellular data of (a) is obtained from two or more cell types selected from Table 1; the cellular data of (b) is obtained from a single cell type selected from Table 1; and the cellular data of comprises cellular data from one or more gene markers selected from Table 1. In aspects and embodiments, there is provided a method of identifying a cellular behavior, the method comprising: selecting a desired cellular transition of a single cell type associated with or indicative of a presence or a stage of a myelofibrosis-related disease; selecting from a map of cellular data one or more cellular transitions at least substantially similar to the desired cellular transition of (a), wherein: the map DB1 / 142566607.11 7 CLNV-015PC / 132119-5015 comprises: cellular data from a plurality of human patients, wherein at least some of the plurality of human patients being afflicted with a myelofibrosis-related disease; and optionally, cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease; wherein the cellular data of (i) and (ii) are obtained from the single cell type; and (c) analyzing the one or more cellular transitions selected in (b); wherein the analyzing of (c) provides the cellular behavior. In aspects and embodiments, there is provided a method of identifying a cellular behavior, the method comprising: selecting a desired cellular transition of a single cell type associated with or indicative of a presence or a stage of a myelofibrosis-related disease; selecting from a map of cellular data one or more cellular transitions at least substantially similar to the desired cellular transition of (a), wherein: the map comprises: cellular data from a plurality of human patients, wherein at least some of the plurality of human patients being afflicted with a myelofibrosis-related disease; and optionally, cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease; wherein the cellular data of (i) and (ii) are obtained from the single cell type selected from Table 1; and the cellular data of (i) and (ii) comprises cellular data from one or more gene markers selected from Table 1; and (c) analyzing the one or more cellular transitions selected in (b); wherein the analyzing of (c) provides the cellular behavior. In aspects and embodiments, there is provided a method of identifying a mechanism of action (MOA), the method comprising: selecting one or more desired cellular transitions of a single cell type associated with or indicative of a presence or a stage of a myelofibrosis-related disease; selecting from a map of cellular data one or more cellular transitions at least substantially similar to the desired cellular transition of (a), wherein: the map comprises: cellular data from a plurality of human patients, wherein at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease; and optionally, cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease; wherein the cellular data of (i) and (ii) are obtained from the single cell type; (c) comparing the one or more cellular transitions selected in (b) with one or more cellular transitions associated with or indicative of one or more known mechanisms of action; and (d) selecting the mechanism of action from the one or more known mechanisms of action based on the comparison of (c), wherein one or more cellular transitions associated with or indicative of the selected mechanism of action is at least substantially similar to the one or more desired cellular transitions of (a). DB1 / 142566607.11 8 CLNV-015PC / 132119-5015 In aspects and embodiments, there is provided a method of identifying a mechanism of action (MOA), the method comprising: selecting one or more desired cellular transitions of a single cell type associated with or indicative of a presence or a stage of a myelofibrosis-related disease; selecting from a map of cellular data one or more cellular transitions at least substantially similar to the desired cellular transition of (a), wherein: the map comprises: cellular data from a plurality of human patients, wherein at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease; and optionally, cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease; wherein the cellular data of (i) and (ii) are obtained from the single cell type selected from Table 1; and the cellular data of (i) and (ii) comprises cellular data from one or more gene markers selected from Table 1; and (c) comparing the one or more cellular transitions selected in (b) with one or more cellular transitions associated with or indicative of one or more known mechanisms of action; and (d) selecting the mechanism of action from the one or more known mechanisms of action based on the comparison of (c), wherein one or more cellular transitions associated with or indicative of the selected mechanism of action is at least substantially similar to the one or more desired cellular transitions of (a). In aspects and embodiments, there is provided a method of identifying a molecular target, comprising: selecting one or more desired cellular transitions of a single cell type associated with or indicative of a presence or a stage of a myelofibrosis-related disease; selecting from a map of cellular data one or more cellular transitions at least substantially similar to the desired cellular transition of (a), wherein: the map comprises: cellular data from a plurality of human patients, wherein at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease; and optionally, cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease; wherein the cellular data of (i) and (ii) are obtained from the single cell type; comparing the one or more cellular transitions selected in (b) with one or more cellular transitions associated with or indicative of one or more known molecular targets; and selecting the molecular targets from the one or more known molecular targets based on the comparison of (c), wherein one or more cellular transitions associated with or indicative of the selected molecular target is at least substantially similar to the one or more desired cellular transitions of (a). In aspects and embodiments, there is provided a method of identifying a molecular target, comprising: selecting one or more desired cellular transitions of a single cell type associated with or indicative of a presence or a stage of a myelofibrosis-related disease; selecting from a map of cellular data one or more cellular transitions at least substantially similar to the desired cellular transition of (a), wherein: the map DB1 / 142566607.11 9 CLNV-015PC / 132119-5015 comprises: cellular data from a plurality of human patients, wherein at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease; and optionally, cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease; wherein the cellular data of (i) and (ii) are obtained from the single cell type selected from Table 1; and the cellular data of (i) and (ii) comprises cellular data from one or more gene markers selected from Table 1; and comparing the one or more cellular transitions selected in (b) with one or more cellular transitions associated with or indicative of one or more known molecular targets; and selecting the molecular targets from the one or more known molecular targets based on the comparison of (c), wherein one or more cellular transitions associated with or indicative of the selected molecular target is at least substantially similar to the one or more desired cellular transitions of (a). In aspects and embodiments, there is provided a method of identifying an agent, comprising: selecting one or more desired cellular transitions of a single cell type associated with or indicative of a presence or a stage of a myelofibrosis-related disease; selecting from a map of cellular data one or more cellular transitions at least substantially similar to the desired cellular transition of (a), wherein: the map comprises: cellular data from a plurality of human patients, wherein at least some of the plurality of human patients being afflicted with a myelofibrosis-related disease; and optionally, cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease; wherein the cellular data of (i) and (ii) are obtained from the single cell type; (c)comparing the one or more cellular transitions selected in (b) with one or more cellular transitions associated with or indicative of one or more known agents; and(d) selecting the agent from the one or more known agents based on the comparison of (c), wherein one or more cellular transitions associated with or indicative of the selected agent is at least substantially similar to the one or more desired cellular transitions of (a). In aspects and embodiments, there is provided a method of identifying an agent, comprising: selecting one or more desired cellular transitions of a single cell type associated with or indicative of a presence or a stage of a myelofibrosis-related disease; selecting from a map of cellular data one or more cellular transitions at least substantially similar to the desired cellular transition of (a), wherein: the map comprises: cellular data from a plurality of human patients, wherein at least some of the plurality of human patients being afflicted with a myelofibrosis-related disease; and optionally, cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease; wherein the cellular data of (i) and (ii) are obtained from the single cell type selected from Table 1; and the cellular data of (i) and (ii) comprises cellular data from one or more gene markers selected from Table 1; (c) comparing DB1 / 142566607.11 10 CLNV-015PC / 132119-5015 the one or more cellular transitions selected in (b) with one or more cellular transitions associated with or indicative of one or more known agents; and(d) selecting the agent from the one or more known agents based on the comparison of (c), wherein one or more cellular transitions associated with or indicative of the selected agent is at least substantially similar to the one or more desired cellular transitions of (a). In embodiments, the cellular transition is based on cellular behavior of at least one selected from hematopoietic stem cells (HSCs) (including but not limited to induced pluripotent stem cells (IPSCs)), hematopoietic precursor cells, hematopoietic progenitor cells, optionally basophil mast progenitor cell, common lymphoid progenitor, common myeloid progenitor, basophil mast progenitor cell, granulocyte monocyte progenitor cell, megakaryocyte-erythroid progenitor cell, erythroid progenitor cell, megakaryocyte progenitor cell, pro-T cell, pro-NK cell or pro-B cell, myeloid lineage cells, monocytes, macrophages, dendritic cells, optionally conventional type 1 dendritic cells (cDC1s), conventional type 2 dendritic cells (cDC2s), or plasmacytoid dendritic cells; neutrophils, eosinophils, erythroid lineage cell, megakaryocytes, basophils, mast cells, T-cells, natural killer (NK) cells, B-cells, plasma cells, mesenchymal cells, and endothelial cells. In embodiments, the cellular transition is based on cellular behavior of at least one selected from basophils / mast cells, monocyte, erythroid lineage cells, hematopoietic precursor cells, lymphoid lineage cells, megakaryocyte-erythroid progenitor cells, megakaryocytes, and myeloid lineage cells. In embodiments, the cellular transition is based on cellular behavior of at least one of a hematopoietic precursor cell and a megakaryocyte-erythroid progenitor cell. In embodiments, the myelofibrosis-related disease is selected from Primary Myelofibrosis, Secondary Myelofibrosis, Essential Thrombocythemia, Polycythemia Vera. DETAILED DESCRIPTION The present disclosure is based, in part, on the discovery that cellular processes and behavior demonstrating cellular dysfunction can be used to identify cellular transitions between non-diseased and diseased states. In embodiments, the cellular transitions can be useful to identify in a patient the progression of a disease in view of the transitions between the stages of the disease and / or agents useful to treat the disease, including but not limited to a myelofibrosis-related disease. Cellular behavior DB1 / 142566607.11 11 CLNV-015PC / 132119-5015 In aspects, the methods of the disclosure include the generation and / or the assembly of a profile of cellular behavior. In embodiments, the profile of cellular behavior comprises one or more changes (e.g. changes in gene expression) in one or more cells that are associated with or indicative of one or more cellular transitions (e.g. transitions of one or more cells from a first cellular state to a second cellular state). In embodiments, the one or more cells are a single type of cell. In embodiments, the one or more cells are two or more types of cells. In embodiments, the profile of cellular behavior comprises one or more cellular behaviors. In embodiments, a cellular behavior comprises one or more changes (e.g. changes in gene expression) in one or more cells that are associated with or indicative of a specific and / or singular cellular transition (e.g. a transition of one or more cells from a first cellular state to a second cellular state). In embodiments, the one or more cells are a single type of cell. In embodiments, the one or more cells are two or more types of cells. Non-limiting examples of changes include changes in gene expression (e.g. upregulation or downregulation of genes), changes in expression of biomarkers, genotypic changes, and phenotypic changes. Non-limiting examples of genotypic changes include genomic variations such as structural variations (SVs) and copy number variations (CNVs), simple nucleotide variations (SNVs), including single-nucleotide polymorphisms (SNPs), and small insertions and deletions (INDELs). Methods for detecting and / or measuring genotypic changes include, without limitation, next generation sequencing (NGS), amplification, polymerase chain reaction (PCR), real-time PCR (qPCR; RT-PCR), Sanger sequencing, next generation sequencing, restriction fragment length polymorphism (RFLP), pyrosequencing, DNA methylation analysis, or a combination thereof. In embodiments, a profile of cellular behavior comprises one or more cellular behaviors. In embodiments, each cellular behavior comprises one or more changes (e.g. a change in gene expression) in one or more cells that are associated with or indicative of a specific cellular transition (e.g. a transition of one or more cells from a first cellular state to a second cellular state). Non-limiting examples of phenotypic changes include changes in morphometric parameters. Non-limiting examples of morphometric parameters include cell shape, including cell roundness. The cell shape (cell roundness) can be analyzed using methods such as those described by Halir and Flusser, Proceeding of International Conference in Central Europe on Computer Graphics, Visualization and Interactive Digital Media: 125-132 (1998), which is incorporated by reference herein in its entirety. In embodiments, the cellular behavior and / or profile of cellular behavior is associated with cellular dysfunction (e.g. a profile of cellular dysfunction). In embodiments, the cellular behavior and / or profile of cellular dysfunction is associated with or indicative of a cellular transition, wherein the cellular transition is associated DB1 / 142566607.11 12 CLNV-015PC / 132119-5015 with or indicative of a transition from an undiseased, pre-diseased, disease-treatable, or disease-reversible state to a disease-untreatable or disease-irreversible state. In embodiments, the cellular behavior and / or profile of cellular dysfunction is associated with or indicative of a cellular transition associated with or indicative of a presence or a stage of a myelofibrosis-related disease. In embodiments, the cellular behavior and / or profile of cellular behavior is associated with cellular function (e.g. a profile of cellular function). In embodiments, the cellular behavior and / or profile of cellular function is associated with or indicative of a cellular transition, wherein the cellular transition is associated with or indicative of a transition from a disease-untreatable or disease-irreversible state to an undiseased, pre- diseased, disease-treatable, or disease-reversible state. In embodiments, the cellular behavior and / or profile of cellular dysfunction is associated with or indicative of a cellular transition from an undiseased, pre-diseased, disease-treatable, or disease-reversible state to a disease-untreatable or disease-irreversible state, wherein the cellular transition further comprises a transition between undiseased, pre-diseased, disease-treatable, and disease-reversible states and / or between disease-reversible, disease-untreatable, and / or disease-irreversible state. In embodiments, the cellular behavior and / or profile of cellular dysfunction is associated with or indicative of a cellular transition from an undiseased or pre-diseased state to a disease-treatable or disease-reversible state. In embodiments, the cellular behavior and / or profile of cellular dysfunction is associated with or indicative of a cellular transition from an undiseased or pre-diseased state to a disease-untreatable or disease-irreversible state. In embodiments, the cellular behavior and / or profile of cellular dysfunction is associated with or indicative of a cellular transition between two different disease-treatable or disease-reversible states. In embodiments, the cellular behavior and / or profile of cellular dysfunction is associated with or indicative of a cellular transition between a disease-treatable or disease-reversible state to a disease-untreatable or disease-irreversible state. In embodiments, the cellular behavior and / or profile of cellular function is associated with or indicative of a cellular transition from a disease-untreatable or disease-irreversible state to an undiseased, pre-diseased, disease-treatable, or disease-reversible state, wherein the cellular transition further comprises a transition between undiseased, pre-diseased, disease-treatable, and disease-reversible states and / or between disease-reversible, disease-untreatable, and / or disease-irreversible state. In embodiments, the cellular behavior and / or profile of cellular function is associated with or indicative of a cellular transition from a disease-treatable or disease-reversible state to an undiseased or pre-diseased state. In embodiments, the DB1 / 142566607.11 13 CLNV-015PC / 132119-5015 cellular behavior and / or profile of cellular function is associated with or indicative of a cellular transition from a disease-untreatable or disease-irreversible state to an undiseased or pre-diseased state. In embodiments, the cellular behavior and / or profile of cellular function is associated with or indicative of a cellular transition between two different disease-treatable or disease-reversible states. In embodiments, the cellular behavior and / or profile of cellular function is associated with or indicative of a cellular transition between a disease- untreatable or disease-irreversible state to a disease-treatable or disease-reversible state. In embodiments, the cellular transition is associated with or indicative of a transition from normal hematopoiesis to abnormal hematopoiesis. In embodiments, the cellular transition is associated with or indicative of a transition from normal red blood cell and / or platelet count to decreased red blood cell and / or platelet count. In embodiments, the cellular transition is associated with or indicative of a transition from healthy bone marrow to bone marrow fibrosis. In embodiments, the cellular transition is associated with or indicative of a transition from healthy blood cells to leukemia cells. In embodiments, the cellular behavior and / or profile of cellular behavior (e.g. profile of cellular dysfunction or cellular function) is associated with or indicative of a cellular transition from a quiescent state to an activated state. In embodiments, the cellular behavior and / or profile of cellular behavior (e.g. profile of cellular dysfunction or cellular function) is associated with or indicative of a cellular transition from an activated state to a quiescent state. In embodiments, the cellular behavior and / or profile of cellular behavior (e.g. profile of cellular dysfunction or cellular function) is associated with or indicative of a cellular transition from an active state to an exhausted state. In embodiments, the cellular behavior and / or profile of cellular behavior (e.g. profile of cellular dysfunction or cellular function) is associated with or indicative of a cellular transition from an exhausted state to an active state. In embodiments, the cellular behavior and / or profile of cellular behavior (e.g. profile of cellular dysfunction or cellular function) comprises common upregulation and / or downregulation of genes. In embodiments, the cellular behavior and / or profile of cellular behavior (e.g. profile of cellular dysfunction or cellular function) comprises common upregulation of about 1 or more, about 2 or more, about 3 or more, about 4 or more, about 5 or more, about 10 or more, about 15 or more, about 20 or more, about 25 or more, about 30 or more, about 35 or more, about 40 or more, about 45 or more, about 50 or more, about 55 or more, about 60 or more, about 65 or more, about 70 or more, about 75 or more, about 80 or more, about 85 or more, about 90 or more, about 95 or more, or about 100 or more genes, optionally about 1 or more or about 2 or more genes. In embodiments, the cellular behavior and / or profile of cellular behavior (e.g. profile of cellular dysfunction or DB1 / 142566607.11 14 CLNV-015PC / 132119-5015 cellular function) comprises common downregulation of about 1 or more, about 2 or more, about 3 or more, about 4 or more, about 5 or more, about 10 or more, about 15 or more, about 20 or more, about 25 or more, about 30 or more, about 35 or more, about 40 or more, about 45 or more, about 50 or more, about 55 or more, about 60 or more, about 65 or more, about 70 or more, about 75 or more, about 80 or more, about 85 or more, about 90 or more, about 95 or more, or about 100 or more genes, optionally about 1 or more or about 2 or more. In embodiments, the cellular behavior and / or profile of cellular behavior (e.g. profile of cellular dysfunction or cellular function) comprises common upregulation of about 1 or more, about 2 or more, about 3 or more, about 4 or more, about 5 or more, about 10 or more, about 15 or more, about 20 or more, about 25 or more, about 30 or more, about 35 or more, about 40 or more, about 45 or more, about 50 or more, about 55 or more, about 60 or more, about 65 or more, about 70 or more, about 75 or more, about 80 or more, about 85 or more, about 90 or more, about 95 or more, or about 100 or more genes, optionally about 2 or more, and common downregulation of about 1 or more, about 2 or more, about 3 or more, about 4 or more, about 5 or more, about 10 or more, about 15 or more, about 20 or more, about 25 or more, about 30 or more, about 35 or more, about 40 or more, about 45 or more, about 50 or more, about 55 or more, about 60 or more, about 65 or more, about 70 or more, about 75 or more, about 80 or more, about 85 or more, about 90 or more, about 95 or more, or about 100 or more genes, optionally about 1 or more or about 2 or more genes. In embodiments, the cellular behavior and / or profile of cellular behavior (e.g. profile of cellular dysfunction or cellular function) comprises common upregulation of about 1 or more genes or about 2 or more genes and / or common downregulation of about 1 or more genes or about 2 or more genes. In embodiments, the cellular behavior and / or profile of cellular behavior (e.g. profile of cellular dysfunction or cellular function) comprises common upregulation and / or downregulation of the expression of one or more biomarkers. In embodiments, the cellular behavior and / or profile of cellular behavior (e.g. profile of cellular dysfunction or cellular function) comprises common upregulation of the expression of about 1 or more, about 2 or more, about 3 or more, about 4 or more, about 5 or more, about 10 or more, about 15 or more, about 20 or more, about 25 or more, about 30 or more, about 35 or more, about 40 or more, about 45 or more, about 50 or more, about 55 or more, about 60 or more, about 65 or more, about 70 or more, about 75 or more, about 80 or more, about 85 or more, about 90 or more, about 95 or more, or about 100 or more biomarkers, optionally about 1 or more or about 2 or more. In embodiments, the cellular behavior and / or profile of cellular behavior (e.g. profile of cellular dysfunction or cellular function) comprises common downregulation of the expression of about 1 or more, about 2 or more, about 3 or more, about 4 or more, about 5 or more, about 10 or more, about 15 or more, about 20 or more, about 25 or more, about 30 or more, about 35 or more, DB1 / 142566607.11 15 CLNV-015PC / 132119-5015 about 40 or more, about 45 or more, about 50 or more, about 55 or more, about 60 or more, about 65 or more, about 70 or more, about 75 or more, about 80 or more, about 85 or more, about 90 or more, about 95 or more, or about 100 or more biomarkers, optionally about 1 or more or about 2 or more. In embodiments, the cellular behavior and / or profile of cellular behavior (e.g. profile of cellular dysfunction or cellular function) comprises common upregulation of the expression of about 1 or more, about 2 or more, about 3 or more, about 4 or more, about 5 or more, about 10 or more, about 15 or more, about 20 or more, about 25 or more, about 30 or more, about 35 or more, about 40 or more, about 45 or more, about 50 or more, about 55 or more, about 60 or more, about 65 or more, about 70 or more, about 75 or more, about 80 or more, about 85 or more, about 90 or more, about 95 or more, or about 100 or more biomarkers, optionally about 2 or more, and common downregulation of the expression of about 1 or more, about 2 or more, about 3 or more, about 4 or more, about 5 or more, about 10 or more, about 15 or more, about 20 or more, about 25 or more, about 30 or more, about 35 or more, about 40 or more, about 45 or more, about 50 or more, about 55 or more, about 60 or more, about 65 or more, about 70 or more, about 75 or more, about 80 or more, about 85 or more, about 90 or more, about 95 or more, or about 100 or more biomarkers, optionally about 1 or more or about 2 or more biomarkers. In embodiments, cellular behavior and / or the profile of cellular behavior (e.g. profile of cellular dysfunction or cellular function) comprises common upregulation of the expression of about 1 or more biomarkers or about 2 or more biomarkers and / or common downregulation of the expression of about 1 or more biomarkers or about 2 or more biomarkers. Methods for detecting cellular transitions In one aspect, the disclosure provides methods of detecting and / or monitoring a cellular transition associated with or indicative of a presence or a stage of a disease. In embodiments, the disease is a myelofibrosis- related disease. In embodiments, the method comprises: (a) analyzing cellular data from a plurality of human patients, at least some of the plurality of human patients being afflicted with the disease; (b) optionally, analyzing cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the disease; and (c) generating a profile of cellular dysfunction based on the analyses of (a) and (b), wherein: the profile of cellular dysfunction is indicative of the cellular transition; and DB1 / 142566607.11 16 CLNV-015PC / 132119-5015 the cellular transition is associated with or indicative of a transition from an undiseased, pre- diseased, disease-treatable, or disease-reversible state to a disease-untreatable or disease- irreversible state. In embodiments, cellular data (e.g. single nuclei data and / or single-cell transcription data), including but not limited to a profile of cellular dysfunction comprising same, comprises and / or identifies upregulation and / or downregulation of genes that are associated with and / or indicative of a disease, disease state, one or more stages of a disease, and / or transitions from an undiseased, pre-diseased, disease-treatable, or disease- reversible state to a disease-untreatable or disease-irreversible state. In embodiments, the method comprises analyzing cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the disease. In embodiments, the gene markers of the present disclosure are data-driven gene markers identified in the atlas of the present disclosure as opposed to known cell type markers. In embodiments, data-driven gene markers are gene markers that are increased and / or upregulated in a cell type and / or cell state of interest when compared to gene markers of all other cell types and / or cell states. In embodiments, non-limiting examples of data-driven markers are selected from Table 1. In embodiments, the cellular data comprises cellular data from one or more cell types. In embodiments, a cell type includes a class of cells (e.g. basophil / mast cells, megakaryocytes) and / or a subclass and / or subset of a class of cells. In embodiments, a cell type includes a cell state. In embodiments, a cell state includes cells transitioning from a first state to a second state. Non-limiting examples of cell types are shown in Table 1. In embodiments, the cellular data comprises cellular data from one or more cell types selected from Table 1. In embodiments, the cellular data comprises cellular data from one or more gene markers selected from Table 1. Table 1: Cell Types and Gene Markers Cell Type Gene Markers 3, D, DB1 / 142566607.11 17 CLNV-015PC / 132119-5015 Monocyte LYZ, S100A9, MPO, ANXA2, LY86, S100A8, SAMHD1, ANXA5, GRB2, VCAN, ITGB2, GPR183, NAIP, S100B, L AL 1 TNFR F1B PLA ZEB2 FRL1 T 1, G, R, X, 3. A, 1, B, N, 2, 1, 1, Z, In embodiments, the one or more cell types are selected from basophils / mast cells, monocyte, erythroid lineage cells, hematopoietic precursor cells, lymphoid lineage cells, megakaryocyte-erythroid progenitor cells, megakaryocytes, and myeloid lineage cells. In embodiments, the one or more cell types comprise and / or consist of basophils / mast cells, and the one or more gene markers are selected from CPA3, SRGN, CLC, HIST1H4C, HDC, DBI, RNASE2, CD63, PLIN2, HIST1H1E, SOX4, GATA2, CSF2RB, LGALS1, HPGD, ITM2A, LMO4, SLC45A3, SLC40A1 and PIM1. In embodiments, the one or more cell types comprise and / or consist of monocytes, and the one or more gene markers are selected from LYZ, S100A9, MPO, ANXA2, LY86, S100A8, SAMHD1, ANXA5, GRB2, VCAN, ITGB2, GPR183, NAIP, S100B, LGALS1, TNFRSF1B, PLAC8, ZEB2, OGFRL1, and CST3. In embodiments, the one or more cell types comprise and / or consist of erythroid lineage cells, and the one or more gene markers are selected from BLVRB, HBB, HBA1, APOC1, S100A6, HBD, MPC2, KLF1, TMEM14C, HBA2, CNRIP1, MPST, FCER1A, GATA1, PKIG, TXNIP, FBXO7, CST3, S100A4, and REXO2. DB1 / 142566607.11 18 CLNV-015PC / 132119-5015 In embodiments, the one or more cell types comprise and / or consist of hematopoietic precursor cells, and the one or more gene markers are selected from SPINK2, AVP, CD74, HLA-DPB1, CD52, MT-CO3, CSF3R, HLA-DRA, HLA-DPA1, SELL, CRHBP, EGFL7, MALAT, HOPX, ZFP36L2, CD44, HLA-DRB1, CALCOCO2, C1QTNF4, and IFITM3. In embodiments, the one or more cell types comprise and / or consist of lymphoid lineage cells, and the one or more gene markers are selected from LTB, HOPX, SPINK2, ITM2C, TSC22D1, CD52, CORO1A, MZB1, HCST, LST1, KLF6, TMSB4X, MEF2C, FOS, LSP1, TCF4, NKG7, CD74, ACY3, and SLC2A5. In embodiments, the one or more cell types comprise and / or consist of megakaryocyte erythroid progenitor cells, and the one or more gene markers are selected from FCER1A, GATA2, CPPED1, FTH1, LMO4, HIST1H1B, TPSAB1, CYTL1, CTNNBL1, H1F0, H3F3B, RNF130, PNN, HDC, RRM2, SNHG8, LIMS1, SOD2, EIF2AK1, and INSIG1 In embodiments, the one or more cell types comprise and / or consist of megakaryocytes, and the one or more gene markers are selected from HBD, PLEK, PRKAR2B, PDLIM1, CMTM5, STOM, HBG2, TMSB4X, HBB, THBS1, ITGA2B, MEF2C, RAB27B, TPM1, GP9, MMRN1, PPBP, LTBP1, FERMT3, TGFB1, and PF4. In embodiments, the one or more cell types comprise and / or consist of myeloid lineage cells, and the one or more gene markers are selected from MPO, TUBA1B, CFD, AZU1, MGST1, LYZ, S100A8, STMN1, ANPEP, HMGN2, AVP, NEAT1, SERPINB1, MCM7, H2AFZ, SPINK2, LITAF, GSTP1, MDK, and PKM. In embodiments, the profile of cellular dysfunction comprises common upregulation and / or downregulation of genes according to analysis of (a) and (b). In embodiments, the profile of cellular dysfunction comprises common upregulation of about 1 or more, about 2 or more, about 3 or more, about 4 or more, about 5 or more, about 10 or more, about 15 or more, about 20 or more, about 25 or more, about 30 or more, about 35 or more, about 40 or more, about 45 or more, about 50 or more, about 55 or more, about 60 or more, about 65 or more, about 70 or more, about 75 or more, about 80 or more, about 85 or more, about 90 or more, about 95 or more, or about 100 or more genes according to analysis of (a) and (b), optionally about 2 or more. In embodiments, the profile of cellular dysfunction comprises common downregulation of about 1 or more, about 2 or more, about 3 or more, about 4 or more, about 5 or more, about 10 or more, about 15 or more, about 20 or more, about 25 or more, about 30 or more, about 35 or more, about 40 or more, about 45 or more, about 50 or more, about 55 or more, about 60 or more, about 65 or more, about 70 or more, about 75 or more, about 80 or more, about 85 or more, about 90 or more, about 95 or more, or about 100 or more genes according to analysis of (a) and (b), optionally about 2 or more. In embodiments, the profile of cellular DB1 / 142566607.11 19 CLNV-015PC / 132119-5015 dysfunction comprises common upregulation of about 1 or more, about 2 or more, about 3 or more, about 4 or more, about 5 or more, about 10 or more, about 15 or more, about 20 or more, about 25 or more, about 30 or more, about 35 or more, about 40 or more, about 45 or more, about 50 or more, about 55 or more, about 60 or more, about 65 or more, about 70 or more, about 75 or more, about 80 or more, about 85 or more, about 90 or more, about 95 or more, or about 100 or more genes, optionally about 2 or more, and common downregulation of about 1 or more, about 2 or more, about 3 or more, about 4 or more, about 5 or more, about 10 or more, about 15 or more, about 20 or more, about 25 or more, about 30 or more, about 35 or more, about 40 or more, about 45 or more, about 50 or more, about 55 or more, about 60 or more, about 65 or more, about 70 or more, about 75 or more, about 80 or more, about 85 or more, about 90 or more, about 95 or more, or about 100 or more genes according to analysis of (a) and (b), optionally about 2 or more. In embodiments, the profile of cellular dysfunction comprises common upregulation of about 1 or more genes or about 2 or more genes and / or common downregulation of about 1 or more genes or about 2 or more genes. In embodiments, the analyzing of cellular data comprises comparing samples of cord blood from healthy HSPCs and myelofibrosis HSPCs. In embodiments, the method further comprises analyzing information and / or other data related to the patient, the disease, such as the myelofibrosis-related disease, and / or stages of disease progression. In embodiments, the analyzing of step (a) further comprises analyzing patient information. Non-limiting examples of patient information include disease stage, family history, nutrition, age, gender, comorbidities, and lifestyle parameters. In embodiments, the analyzing of step (b) further comprises analyzing phenotypic or functional parameters. In embodiments, the analyzing of step (a) further comprises analyzing sample collection data. In embodiments, the analyzing of step (a) further comprises analyzing technical covariates. Non-limiting examples of technical covariates include the quality and / or accuracy of the cellular data (including but not limited to quality and / or accuracy of the atlas and / or map comprising the cellular data). In embodiments, “cellular data” refers to data used to collectively characterize and / or quantify pools of biological molecules that translate into the structure, function, and dynamics of an organism or organisms, including but not limited to omic data. Non-limiting examples of omic data include transcriptomic data, genomic data, proteomic data and metabolomic data. In embodiments, the cellular data comprises and / or consists of transcriptomic (e.g. translational) data. In embodiments, the transcriptomic data comprises and / or consists of bulk transcriptomic data. DB1 / 142566607.11 20 CLNV-015PC / 132119-5015 In embodiments, the transcriptomic data comprises and / or consists of single nuclei data and / or single-cell transcription data. In embodiments, the transcriptomic data comprises and / or consists of single nuclei data. In embodiments, the transcriptomic data comprises and / or consists of single-cell transcription data. In embodiments, the transcriptomic data comprises and / or consists of single nuclei data and single-cell transcription data. In embodiments, the transcriptomic data comprises RNA sequencing data. RNA sequencing (RNA-seq) is a genomic approach for the detection and quantitative analysis of messenger RNA molecules in a biological sample that is useful for studying the cellular responses. Non-limiting examples of methods of conducting RNA-seq include single cell RNA-seq (scRNA-seq) and single nuclei RNA-seq (snRNA-seq), which are useful for conducting RNA-seq in a specific cell type, and bulk RNA-seq, which is useful for conducting RNA-seq from a sample of multiple cell types. In embodiments, the transcriptomic data and / or single nuclei data and / or single-cell transcription data comprise one or more of single-cell ribonucleic acid (RNA) sequencing (scRNA-seq) data, single nuclei ribonucleic acid (RNA) sequencing (snRNA-seq) data, scTag-seq data, single-cell assay data for transposase-accessible chromatin using sequencing (scATAC-seq), CyTOF / SCoP data, E-MS / Abseq data, miRNA-seq data, CITE-seq data, or any combinations thereof, or summaries of the same, including combinations, such as linear combinations, representing activated pathways in the single nuclei and / or single-cell cellular-component expression datasets. In embodiments, the single-cell transcription data is single-cell ribonucleic acid (RNA) sequencing (scRNA-seq) data. In embodiments, the single-cell transcription data is transposase-accessible chromatin using sequencing (scATAC-seq) data. In embodiments, the cellular data (e.g. transcriptomic data, including but not limited to single nuclei data and / or single-cell transcription data) comprises or consists of cellular data obtained from a text-based format of sequencing data, including but not limited to FASTQ, which refers to a text-based format for storing both a biological sequence (e.g. a nucleotide sequence) and its corresponding quality scores. In embodiments, (a) comprises and / or consists of analyzing cellular data from a plurality of human patients, at least some of the plurality of human patients being afflicted with the disease. In embodiments, (b) comprises and / or consists of analyzing cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the disease. In embodiments, (a) comprises and / or consists of analyzing single nuclei data. In embodiments, (b) comprises and / or consists of analyzing single nuclei data. In embodiments, (a) comprises and / or consists of analyzing single-cell transcription data. In embodiments, (b) comprises and / or consists of analyzing single- cell transcription data. In embodiments, (a) comprises and / or consists of analyzing single nuclei data and DB1 / 142566607.11 21 CLNV-015PC / 132119-5015 single-cell transcription data. In embodiments, (b) comprises and / or consists of analyzing single nuclei data and single-cell transcription data. In embodiments, (a) comprises and / or consists of analyzing single-cell transcription data from a plurality of human patients, at least some of the plurality of human patients being afflicted with the disease. In embodiments, (a) comprises and / or consists of analyzing single nuclei transcription data from a plurality of human patients, at least some of the plurality of human patients being afflicted with the disease. In embodiments, (a) comprises and / or consists of analyzing single-cell transcription data and single nuclei transcription data from a plurality of human patients, at least some of the plurality of human patients being afflicted with the disease. In embodiments, (b) comprises and / or consists of analyzing single-cell transcription data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the disease. In embodiments, (b) comprises and / or consists of analyzing single nuclei transcription data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the disease. In embodiments, (b) comprises and / or consists of analyzing single-cell transcription data and single nuclei transcription data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the disease. In embodiments, the plurality of human patients comprises about 3 or more patients, about 5 or more patients, about 10 or more patients, about 15 or more patients, about 20 or more patients, about 25 or more patients, about 30 or more patients, about 35 or more patients, about 40 or more patients, about 45 or more patients, about 50 or more patients, about 55 or more patients, about 60 or more patients, about 65 or more patients, about 70 or more patients, about 75 or more patients, about 80 or more patients, about 85 or more patients, about 90 or more patients, about 95 or more patients, or about 100 or more patients. In embodiments, the cellular data of (a) is obtained from 1 or more samples from each human patient from the plurality of human patients, such as about 1 or more samples, about 2 or more samples, about 3 or more samples, about 4 or more samples, about 5 or more samples, about 6 or more samples, about 7 or more samples, about 8 or more samples, about 9 or more samples, about 10 or more samples, about 11 or more samples, about 12 or more samples, about 13 or more samples, about 14 or more samples, or about 15 or more samples from each human patient from the plurality of human patients. In embodiments, the single-cell transcription data of (a) is obtained from 1 or more samples from each human patient from the plurality of human patients, such as about 1 or more samples, about 2 or more samples, about 3 or more samples, about 4 or more samples, about 5 or more samples, about 6 or more samples, about 7 or more samples, about 8 or more samples, about 9 or more samples, about 10 or more samples, about 11 or more samples, about 12 or more samples, about 13 or more samples, about 14 or more samples, or about 15 or more samples from each human patient DB1 / 142566607.11 22 CLNV-015PC / 132119-5015 from the plurality of human patients. In embodiments, the one or more samples are frozen samples. In embodiments, the single nuclei transcription data of (a) is obtained from one or more samples from each human patient from the plurality of human patients, such as about 1 or more samples, about 2 or more samples, about 3 or more samples, about 4 or more samples, about 5 or more samples, about 6 or more samples, about 7 or more samples, about 8 or more samples, about 9 or more samples, about 10 or more samples, about 11 or more samples, about 12 or more samples, about 13 or more samples, about 14 or more samples, or about 15 or more samples from each human patient from the plurality of human patients. In embodiments, the one or more samples are frozen samples. In embodiments, the single-cell transcription data and single nuclei transcription data of (a) is obtained from one or more samples from each human patient from the plurality of human patients, such as about 1 or more samples, about 2 or more samples, about 3 or more samples, about 4 or more samples, about 5 or more samples, about 6 or more samples, about 7 or more samples, about 8 or more samples, about 9 or more samples, about 10 or more samples, about 11 or more samples, about 12 or more samples, about 13 or more samples, about 14 or more samples, or about 15 or more samples from each human patient from the plurality of human patients. In embodiments, the one or more samples are frozen samples. In embodiments, metadata indicating one or more features is obtained from each human patient from the plurality of human patients. In embodiments, the metadata indicates one or more features selected from the group consisting of sex, race, age, and physical condition. In embodiments, the physical condition comprises one or more features selected from body mass index (BMI), medical history, family medical history, disease diagnostic information, medication profile, alcohol consumption, illicit drug use, and cause of death. In embodiments, the disease diagnostic information comprises one or more features selected from pathology notes and key features. In embodiments, one or more of the sex, race, age, and physical condition is represented in a balanced manner in the plurality of patients. In embodiments, the cellular data of (a) is sorted based on one or more of the metadata features. In embodiments, the cellular data of (a) is sorted based on sex, e.g., male and female. In embodiments, the cellular data of (a) is sorted based on race, e.g., non-Latino white, Latino, non-Latino black, Asian, and other. In embodiments, the cellular data of (a) is sorted based on age, e.g., less than 20 years old, 20 to 40 years old, 40 to 60 years old, and more than 60 years old. In embodiments, the cellular data of (a) is sorted based on BMI, e.g., less than 25, 25 to 30, 30 to 35, or more than 35. In embodiments, the cellular data of (a) is sorted based on medications. In embodiments, the cellular data of (a) is sorted based on alcohol consumption. In embodiments, the cellular data of (a) is sorted based on cause of death. In embodiments, DB1 / 142566607.11 23 CLNV-015PC / 132119-5015 the cellular data of (a) is sorted based on medical history, e.g., presence, absence, or degree of severity of one or more of Primary Myelofibrosis, Secondary Myelofibrosis, Essential Thrombocythemia, Polycythemia Vera, and the like. Non-limiting examples of methods for determining degree of severity of diseases (e.g. Primary Myelofibrosis, Secondary Myelofibrosis, Essential Thrombocythemia, Polycythemia Vera, and the like) include severity of disease scoring systems (e.g Acute Physiology and Chronic Health Evaluation (APACHE), the Mortality Probability Models (MPM), Simplified Acute Physiology Score (SAPS)), and the measurement and / or assessment of biomarkers, which can be measured and / or assessed by, for example, screening, monitoring of disease, and also measuring an increase or decrease of the biomarkers that probability can be attained by disease. In embodiments, the cellular data of (a) is sorted based on family medical history, e.g., family history of Primary Myelofibrosis, Secondary Myelofibrosis, Essential Thrombocythemia, Polycythemia Vera, and the like. In embodiments, the single-cell transcription data and / or single nuclei transcription data of (a) is sorted based on one or more of the metadata features. In embodiments, the single-cell transcription data and / or single nuclei transcription data of (a) is sorted based on sex, e.g., male and female. In embodiments, the single-cell transcription data and / or single nuclei transcription data of (a) is sorted based on race, e.g., non-Latino white, Latino, non-Latino black, Asian, and other. In embodiments, the single-cell transcription data and / or single nuclei transcription data of (a) is sorted based on age, e.g., less than 20 years old, 20 to 40 years old, 40 to 60 years old, and more than 60 years old. In embodiments, the single-cell transcription data and / or single nuclei transcription data of (a) is sorted based on BMI, e.g., less than 25, 25 to 30, 30 to 35, or more than 35. In embodiments, the single-cell transcription data and / or single nuclei transcription data of (a) is sorted based on medications. In embodiments, the single-cell transcription data and / or single nuclei transcription data of (a) is sorted based on alcohol consumption. In embodiments, the single-cell transcription data and / or single nuclei transcription data of (a) is sorted based on cause of death. In embodiments, the single-cell transcription data and / or single nuclei transcription data of (a) is sorted based on presence, absence, or degree of severity of one or more of Primary Myelofibrosis, Secondary Myelofibrosis, Essential Thrombocythemia, Polycythemia Vera, and the like. In embodiments, the single-cell transcription data and / or single nuclei transcription data of (a) is sorted based on family medical history, e.g., family history of Primary Myelofibrosis, Secondary Myelofibrosis, Essential Thrombocythemia, Polycythemia Vera, and the like. In embodiments, the disease diagnostic information comprises disease status. In embodiments, the disease status comprises histologically graded disease status. In embodiments, disease status is measured using any method described in Duminuco A, Nardo A, Giuffrida G, et al. Myelofibrosis and Survival Prognostic DB1 / 142566607.11 24 CLNV-015PC / 132119-5015 Models: A Journey between Past and Future. J Clin Med. 2023;12(6):2188. Published 2023 Mar 11. doi:10.3390 / jcm12062188, incorporated herein by reference in its entirety. In embodiments, the disease diagnostic information comprises one or more features selected from, International Prognostic Scoring System (IPSS), Dynamic IPSS (DIPSS), DIPSS Plus, Mutation-Enhanced International Prognostic Score System (MIPSS70), MIPSS70+, Genetically Inspired Prognostic Scoring System (GIPSS), Myelofibrosis Secondary to PV and ET-Prognostic Model (MYSEC-PM), Myelofibrosis Transplant Scoring System (MTSS), Response to Ruxolitinib after 6 Months (RR6), Artificial Intelligence Prognostic Scoring System for Myelofibrosis (AIPSS-MF), and pathology notes and key features. In embodiments, the cellular data (e.g. single-cell transcription data and / or single nuclei transcription data) of (a) and / or (b) is filtered to remove counts of ambient nucleic acid molecules, doublets and / or empty droplets, optionally ambient RNA molecules using any methods known to those skilled in the art. Ambient RNA is the pool of mRNA molecules that have been released in a cell suspension, likely from cells that are stressed or have undergone apoptosis during single nuclei or single cell sequencing. Cross- contamination occurs when the ambient RNA gets incorporated into droplets representing single nuclei or single cells, which did not originate the ambient RNA, and is barcoded and amplified along with the nucleus’ or cell’s native mRNA. Without wishing to be bound by theory, liver biopsies often comprise 90% hepatocytes and the ambient RNA from the hepatocytes tends to drown out other less prevalent cell types in the biopsies through such ambient RNA contamination. Accordingly, in embodiments, the cellular data (e.g. single-cell transcription data and / or single nuclei transcription data) are filtered to remove counts of ambient RNA molecules. Contamination from ambient RNA is evident when highly expressed cell type-specific genes are observed at low levels in other cell populations. Different proportions of contamination can be found in different droplets depending on the amount of ambient and native mRNA present. In embodiments, ambient RNA is filtered to remove counts of ambient RNA molecules by assuming that the cellular data (e.g. single-cell transcription data and / or single nuclei transcription data) in fact represents a mixture of counts from two multinomial distributions: (1) a distribution of native transcript counts from the nuclei’s actual population and (2) a distribution of contaminating transcript counts from all other nuclei populations captured in the assay. In some such embodiments, a program such as DecontX, Yang et al., 2020, “Decontamination of ambient RNA in single-cell RNA-seq with DecontX”, Genome Biology 21:57, is used to deconvolute a gene-by-nuclei count matrix and a vector of nuclei population labels into a matrix of contamination counts and a matrix of native counts that can be used in downstream analyses. DB1 / 142566607.11 25 CLNV-015PC / 132119-5015 In embodiments, ambient RNA is filtered to remove counts of ambient RNA molecules using Cellbender. See Fleming et al., 2019, “CellBender remove-background: a deep generative model for unsupervised removal of background noise from scRNA-seq datasets,” bioRxiv 791699, doi:10.1101 / 791699, which is hereby incorporated by reference. In embodiments, ambient RNA is filtered to remove counts of ambient RNA molecules using SoupX. See, Young and Behjati, 2020, “SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data,” Gigascience 9, doi:10.1093 / gigascience / giaa151, which is hereby incorporated by reference. In embodiments, ambient RNA is filtered to remove counts of ambient RNA molecules using any known method. Additional examples of such methods are disclosed in Caglayan et al., 2022, “Ambient RNA analysis reveals misinterpreted and masked cell types in brain single-nuclei datasets,” Neuron doi: 10.1016 / j.neuron.2022.09.010, which is hereby incorporated by reference. In embodiments, the cellular data (e.g. single-cell transcription data and / or single nuclei transcription data) is filtered to remove doublets. This arises when more than one cell (or nucleus) is captured in a droplet, also known as a “doublet” or “multiplet.” In microfluidic systems, the occurrence of doublets is proportional to the concentration of cells (or nuclei) in the suspension and capture rate of the device. In embodiments, Scrublet, Wolock et al., 2019, “Scrublet: computational identification of cell doublets in single-cell transcriptomic data,” Cell Syst.8(4):281–91, or DoubletFinder, McGinnis et al., 2019, “Doubletfinder: doublet detection in single- cell rna sequencing data using artificial nearest neighbors,” Cell Syst. 8(4):329–37, is used to remove doublets or multiplets in the single nucleic transcriptome data. These programs simulate artificial doublets from the original data coordinates in a reduced-dimensional representation, then create doublet score for each barcode by calculating the similarity of its representation with artificial doublets. In embodiments, demuxlet, Kang et al., 2018, “Multiplexed droplet single-cell rna-sequencing using natural genetic variation,” Nat Biotechnol.36(1):89, or scds, Bais and Kostka, 2019, “scds: computational annotation of doublets in single cell RNA sequencing data,” bioRxiv. 2019564021 available on the Internet at biorxiv.org / content / 10.1101 / 564021v1, is used to remove doublets or multiplets in the single nucleic transcriptome data. These programs model gene expression from the original data, then assign doublet to barcodes that have observed expression from genes that are likely to not occur simultaneously. In embodiments, any known method is used to remove doublets or multiplets in the single nucleic transcriptome data. DB1 / 142566607.11 26 CLNV-015PC / 132119-5015 In embodiments, the cellular data (e.g. single-cell transcription data and / or single nuclei transcription data) are filtered to remove empty droplets or droplets representing damaged nuclei. See Lun et al., 2019, “EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data,” Genome Biol 20, 63; and Heiser et al., 2021, “Automated quality control and cell identification of droplet- based single-cell data using dropkick,” Genome Res 31, 1742-1752. In embodiments, ambient material created during sample processing contain transcripts called “ambient RNA” that can be captured by empty droplets (droplets that do not contain real cells), making them appear non-empty in data analysis. As a result, separation of empty droplets from real cells becomes a significant problem in single-cell RNA-seq analysis. Since real cells / nuclei contain more transcripts (and more unique molecular identifiers -UMIs- that denote unique reads) than ambient RNAs captured in empty droplets, it is possible to apply a cutoff based on number of UMIs to retain real cells / nuclei. However, this can be inaccurate as UMI distribution of cell barcodes is not completely discrete which makes a hard cutoff arbitrary. Moreover, certain cell types may be transcriptomically more silent than others which could lead to filtering out by a UMI-based cutoff. In embodiments, this problem of distinguishing real cells / nuclei and empty droplets is addressed by using other metrics such as expression profile and nuclear fraction. In some such embodiments DropletQC, Muskovic and Powel, 2021, “DropletQC: improved identification of empty droplets and damaged cells in single-cell RNA-seq data.,” Genome Biol.22, 329, is used to remove empty droplets or droplets representing damaged nuclei in the single nucleic transcriptome data. In embodiments, any known method is used to remove empty droplets or droplets representing damaged nuclei in the single nucleic transcriptome data. In embodiments, generating the profile of cellular dysfunction of (c) further comprises analyzing biomarkers from the cellular data, and comparing the biomarkers from the cellular to a standardized set of biomarkers to identify a cell type associated with the cellular transition. In embodiments, generating the profile of cellular dysfunction of (c) further comprises analyzing biomarkers from the single-cell transcription data and / or single nuclei transcription data, and comparing the biomarkers from the single-cell transcription data and / or single nuclei transcription data to a standardized set of biomarkers to identify a cell type associated with the cellular transition. In embodiments, the method further comprises (d) obtaining genotypic data from each human patient from the plurality of human patients of (a). In embodiments, generating a profile of cellular dysfunction of (c) further comprises analyzing the cellular data of (a) with the genotypic data of (d). In embodiments, generating a profile of cellular dysfunction of (c) further comprises analyzing the single-cell transcription data and / or single DB1 / 142566607.11 27 CLNV-015PC / 132119-5015 nuclei transcription data of (a) with the genotypic data of (d). Non-limiting examples of genotypic data include the absence or presence of a major allele for a single nucleotide polymorphism (SNP) at a particular genetic locus in a genome, the absence or presence of a particular restriction fragment length polymorphism (RFLP) at a particular genetic locus in a genome, particular copy number variations, insertions, and / or or deletions, a particular haplotype, a particular microsatellite, and a short tandem repeat. Myelofibrosis-related Diseases In one aspect, the disclosure provides a method of detecting and / or monitoring a cellular transition associated with or indicative of a presence or a stage of a myelofibrosis-related disease, comprising: (a) analyzing cellular data from a plurality of human patients, at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease; (b) optionally, analyzing cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease; and (c) generating a profile of cellular dysfunction based on the analyses of (a) and (b), wherein: the profile of cellular dysfunction is indicative of the cellular transition; and the cellular transition is associated with or indicative of a transition from an undiseased, pre- diseased, disease-treatable, or disease-reversible state to a disease-untreatable or disease- irreversible state. In embodiments, the cellular data comprises and / or consists of one or more of transcriptomic data, genomic data, proteomic data and metabolomic data. In embodiments, the cellular data comprises and / or consists of transcriptomic (e.g. translational) data. In embodiments, the transcriptomic data comprises and / or consists of single nuclei data and / or single-cell transcription data. In embodiments, the transcriptomic data comprises and / or consists of bulk transcriptomic data. In embodiments, the bulk transcriptomic data comprises and / or consists of single nuclei data and / or single-cell transcription data. In embodiments, the method comprises analyzing cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the disease. In embodiments, the cellular data comprises cellular data from one or more cell types selected from Table 1. In embodiments, the cellular data comprises cellular data from one or more gene markers selected from Table 1. DB1 / 142566607.11 28 CLNV-015PC / 132119-5015 Non-limiting examples of myelofibrosis-related diseases include Primary Myelofibrosis, Secondary Myelofibrosis, Essential Thrombocythemia, and Polycythemia Vera. In embodiments, the myelofibrosis-related disease comprises a spectrum of diseases and / or disease states. In embodiments, the present methods inform interventions to prevent or retard a progression between any two or more diseases and / or disease states selected from the spectrum of diseases and / or disease states. In embodiments, the cellular transition is associated with or indicative of a transition between any two or more diseases and / or disease states selected from the spectrum of diseases and / or disease states. In embodiments, the myelofibrosis-related disease comprises myeloproliferative neoplasms. In embodiments, myeloproliferative neoplasms comprise Primary Myelofibrosis, Secondary Myelofibrosis, Essential Thrombocythemia, and Polycythemia Vera. In embodiments, myelofibrosis-related diseases comprise osteosclerosis, extramedullary hematopoiesis (EMH), inefficient hematopoiesis, and / or inflammation. In embodiments, myelofibrosis-related diseases comprise splenomegaly, cytopenia, and constitutional symptoms of myelofibrosis. In embodiments, myelofibrosis-related diseases comprise portal hypertension, thromboembolism, infection, and acute myeloid leukemia (AML). In embodiments, myelofibrosis-related diseases comprise varying degrees of bone marrow fibrosis. In embodiments, the present methods inform interventions to prevent or retard a progression between non- diseased cells to diseased cells with a CALR mutation. In embodiments, the present methods inform interventions to prevent or retard a progression between non-diseased cells to diseased cells with a JAK2 mutation. In embodiments, the present methods inform interventions to prevent or retard a progression between non-diseased cells to diseased cells with a MPL mutation. In embodiments, the present methods inform interventions to prevent or retard a progression between non-diseased cells to diseased cells with a gene mutation. In embodiments, the gene is selected from TP53, ASXL1, KIT, FLT3, NPM1, CEBPA, RAS, WT1, BAALC, ERG, MN1, DNMT, TET2, IDH, PTPN11 and CBL. In embodiments, the cellular transition is associated with or indicative of a transition from the absence of a CALR mutation to the presence of a CALR mutation. In embodiments, the cellular transition is associated with or indicative of a transition from a JAK2 mutation to the absence of a JAK2 mutation. In embodiments, the cellular transition is associated with or indicative of a transition from a cMPL mutation to the absence of a cMPL mutation. In embodiments, the cellular transition is associated with or indicative of a transition from the presence of anemia to the absence of anemia. In embodiments, the cellular transition is associated with DB1 / 142566607.11 29 CLNV-015PC / 132119-5015 or indicative of a transition from the absence of thrombocytopenia to the presence of thrombocytopenia. In embodiments, the cellular transition is associated with or indicative of a transition from absence of leukemia to presence of leukemia. In embodiments, the cellular transition is associated with or indicative of a differentiation and / or transition from myelofibrosis (MF) hematopoietic stem and progenitor cells (HSPCs) to aberrant MF megakaryocyte- erythroid progenitor cells. In embodiments, the cellular transition is associated with or indicative of a differentiation and / or transition from myelofibrosis (MF) hematopoietic stem and progenitor cells (HSPCs) to MF megakaryocytes. In embodiments, the cellular transition is associated with or indicative of a differentiation and / or transition from aberrant MF megakaryocyte-erythroid progenitor cells to MF megakaryocytes. In embodiments, the cellular transition is associated with or indicative of a differentiation and / or transition from non-diseased hematopoietic stem and progenitor cells (HSPCs) to MF HPSCs. In embodiments, the cellular transition is associated with or indicative of a differentiation and / or transition from aberrant MF megakaryocyte-erythroid progenitor cells to myelofibrosis (MF) hematopoietic stem and progenitor cells (HSPCs). In embodiments, the cellular transition is associated with or indicative of a differentiation and / or transition from MF megakaryocytes to myelofibrosis (MF) hematopoietic stem and progenitor cells (HSPCs). In embodiments, the cellular transition is associated with or indicative of a differentiation and / or transition from MF megakaryocytes to aberrant MF megakaryocyte-erythroid progenitor cells. In embodiments, the cellular transition is associated with or indicative of a differentiation and / or transition from MF HPSCs to non-diseased hematopoietic stem and progenitor cells (HSPCs). In embodiments, the cellular transition is associated with or indicative of a transition from normal hematopoiesis to abnormal hematopoiesis. In embodiments, the cellular transition is associated with or indicative of a transition from normal red blood cell and / or platelet count to decreased red blood cell and / or platelet count. In embodiments, the cellular transition is associated with or indicative of a transition from healthy bone marrow to bone marrow fibrosis. In embodiments, the cellular transition is associated with or indicative of a transition from healthy blood cells to leukemia cells. In embodiments, the cellular transition is associated with or indicative of a transition from abnormal hematopoiesis to normal hematopoiesis. In embodiments, the cellular transition is associated with or indicative of a transition from decreased red blood cell and / or platelet count to normal red blood cell and / or platelet count. In embodiments, the cellular transition is associated with or indicative of a transition from bone DB1 / 142566607.11 30 CLNV-015PC / 132119-5015 marrow fibrosis to healthy bone marrow. In embodiments, the cellular transition is associated with or indicative of a transition from leukemic cells to healthy blood cells. In embodiments, the disease-treatable state, disease-reversible state, disease-untreatable state, and / or disease-irreversible state is associated with Primary Myelofibrosis, Secondary Myelofibrosis, Essential Thrombocythemia, and Polycythemia Vera, a CALR mutation, a JAK2 mutation, anemia, thrombocytopenia, and / or leukemia. In embodiments, at least some of the plurality of human patients are healthy. In embodiments, at least some of the plurality of human patients are at risk of developing the myelofibrosis-related disease. In embodiments, at least some of the plurality of human patients are non-responsive to one or more established therapies for treating the myelofibrosis-related disease. In embodiments, at least some of the plurality of human patients have transitioned, are transitioning, or are at risk of transitioning from an undiseased or pre-diseased state to a disease-treatable or disease-reversible state. In embodiments, at least some of the plurality of human patients have transitioned, are transitioning, or are at risk of transitioning from an undiseased or pre-diseased state to a disease-untreatable or disease-irreversible state. In embodiments, at least some of the plurality of human patients have transitioned, are transitioning, or are at risk of transitioning between two different disease-treatable or disease-reversible states. In embodiments, at least some of the plurality of human patients have transitioned, are transitioning, or are at risk of transitioning between a disease-treatable or disease-reversible state to a disease-untreatable or disease-irreversible state. In embodiments, (a) comprises and / or consists of analyzing transcriptomic (e.g. translational) data. In embodiments, (b) comprises and / or consists of analyzing transcriptomic (e.g. translational) data. In embodiments, (a) comprises and / or consists of analyzing single nuclei data and / or single-cell transcription data. In embodiments, (b) comprises and / or consists of analyzing single nuclei data and / or single-cell transcription data. In embodiments, (a) comprises and / or consists of analyzing single nuclei data. In embodiments, (b) comprises and / or consists of analyzing single nuclei data. In embodiments, (a) comprises and / or consists of analyzing single-cell transcription data. In embodiments, (b) comprises and / or consists of analyzing single-cell transcription data. In embodiments, (a) comprises and / or consists of analyzing single nuclei data and single-cell transcription data. In embodiments, (b) comprises and / or consists of analyzing single nuclei data and single-cell transcription data. In embodiments, (a) comprises and / or consists of analyzing cellular data from a plurality of human patients, at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease. In DB1 / 142566607.11 31 CLNV-015PC / 132119-5015 embodiments, (a) comprises and / or consists of analyzing transcriptomic (e.g. translational) data from a plurality of human patients, at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease. In embodiments, (a) comprises and / or consists of analyzing single nuclei data and / or single-cell transcription data from a plurality of human patients, at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease. In embodiments, (a) comprises and / or consists of analyzing single-cell transcription data from a plurality of human patients, at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease. In embodiments, (a) comprises and / or consists of analyzing single nuclei transcription data from a plurality of human patients, at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease. In embodiments, (a) comprises and / or consists of analyzing single-cell transcription data and single nuclei transcription data from a plurality of human patients, at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease. In embodiments, (b) comprises and / or consists of analyzing cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease. In embodiments, (b) comprises and / or consists of analyzing transcriptomic (e.g. translational) data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease. In embodiments, (b) comprises and / or consists of analyzing single nuclei data and / or single-cell transcription data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease. In embodiments, (b) comprises and / or consists of analyzing single-cell transcription data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease. In embodiments, (b) comprises and / or consists of analyzing single nuclei transcription data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease. In embodiments, (b) comprises and / or consists of analyzing single-cell transcription data and single nuclei transcription data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease. In embodiments, the plurality of human patients comprises about 3 or more patients, about 5 or more patients, about 10 or more patients, about 15 or more patients, about 20 or more patients, about 25 or more patients, about 30 or more patients, about 35 or more patients, about 40 or more patients, about 45 or more patients, about 50 or more patients, about 55 or more patients, about 60 or more patients, about 65 or more patients, DB1 / 142566607.11 32 CLNV-015PC / 132119-5015 about 70 or more patients, about 75 or more patients, about 80 or more patients, about 85 or more patients, about 90 or more patients, about 95 or more patients, or about 100 or more patients. In embodiments, the cellular data (e.g. transcriptomic (e.g. translational) data) of (a) is obtained from 1 or more samples from each human patient from the plurality of human patients, such as about 1 or more samples, about 2 or more samples, about 3 or more samples, about 4 or more samples, about 5 or more samples, about 6 or more samples, about 7 or more samples, about 8 or more samples, about 9 or more samples, about 10 or more samples, about 11 or more samples, about 12 or more samples, about 13 or more samples, about 14 or more samples, or about 15 or more samples from each human patient from the plurality of human patients. In embodiments, the one or more samples are frozen samples. In embodiments, the single-cell transcription data and / or single nuclei transcription data of (a) is obtained from one or more samples from each human patient from the plurality of human patients, such as about 1 or more samples, about 2 or more samples, about 3 or more samples, about 4 or more samples, about 5 or more samples, about 6 or more samples, about 7 or more samples, about 8 or more samples, about 9 or more samples, about 10 or more samples, about 11 or more samples, about 12 or more samples, about 13 or more samples, about 14 or more samples, or about 15 or more samples from each human patient from the plurality of human patients. In embodiments, the one or more samples are frozen samples. In embodiments, the single-cell transcription data of (a) is obtained from 1 or more samples from each human patient from the plurality of human patients, such as about 1 or more samples, about 2 or more samples, about 3 or more samples, about 4 or more samples, about 5 or more samples, about 6 or more samples, about 7 or more samples, about 8 or more samples, about 9 or more samples, about 10 or more samples, about 11 or more samples, about 12 or more samples, about 13 or more samples, about 14 or more samples, or about 15 or more samples from each human patient from the plurality of human patients. In embodiments, the one or more samples are frozen samples. In embodiments, the single nuclei transcription data of (a) is obtained from one or more samples from each human patient from the plurality of human patients, such as about 1 or more samples, about 2 or more samples, about 3 or more samples, about 4 or more samples, about 5 or more samples, about 6 or more samples, about 7 or more samples, about 8 or more samples, about 9 or more samples, about 10 or more samples, about 11 or more samples, about 12 or more samples, about 13 or more samples, about 14 or more samples, or about 15 or more samples from each human patient from the plurality of human patients. In embodiments, the one or more samples are frozen samples. In embodiments, the single-cell transcription data and single nuclei transcription data of (a) is obtained from one or more samples from each human patient from the plurality of human patients, such as about 1 or more DB1 / 142566607.11 33 CLNV-015PC / 132119-5015 samples, about 2 or more samples, about 3 or more samples, about 4 or more samples, about 5 or more samples, about 6 or more samples, about 7 or more samples, about 8 or more samples, about 9 or more samples, about 10 or more samples, about 11 or more samples, about 12 or more samples, about 13 or more samples, about 14 or more samples, or about 15 or more samples from each human patient from the plurality of human patients. In embodiments, the one or more samples are frozen samples. In embodiments, metadata indicating one or more features is obtained from each human patient from the plurality of human patients. In embodiments, the metadata indicates one or more features selected from the group consisting of sex, race, age, and physical condition. In embodiments, the physical condition comprises one or more features selected from BMI, medical history, family medical history, disease diagnostic information, medication profile, alcohol consumption, illicit drug use, and cause of death. In embodiments, the disease diagnostic information comprises one or more features selected from International Prognostic Scoring System (IPSS), Dynamic IPSS (DIPSS), DIPSS Plus, Mutation-Enhanced International Prognostic Score System (MIPSS70), MIPSS70+, Genetically Inspired Prognostic Scoring System (GIPSS), Myelofibrosis Secondary to PV and ET-Prognostic Model (MYSEC-PM), Myelofibrosis Transplant Scoring System (MTSS), Response to Ruxolitinib after 6 Months (RR6), Artificial Intelligence Prognostic Scoring System for Myelofibrosis (AIPSS-MF), and pathology notes and key features. In embodiments, the physical condition comprises one or more risk factors associated with myelofibrosis. In embodiments, the risk factors comprise age (typically older than 50 years old), having another blood cell disorder (e.g., essential thrombocythemia or polycythemia vera), exposure to certain chemicals (e.g., toluene and benzene), and exposure to radiation. In embodiments, one or more of the sex, race, age, and physical condition is represented in a balanced manner in the plurality of patients. For instance, in embodiments a t-test is used. See Smith, 1991, Statistical Reasoning, Third Edition, Allyn and Bacon, Boston, Chapter 9, which is hereby incorporated by reference. In embodiments, a calculation of a p-value of 0.15 or less for the null hypothesis that one or more of the sex, race, age, and physical condition is balanced using the alternative statistical test. In embodiments, a calculation of a p-value of 0.10 or less for the null hypothesis that one or more of the sex, race, age, and physical condition is balanced using the alternative statistical test. In embodiments, a calculation of a p-value of 0.05 or less for the null hypothesis that one or more of the sex, race, age, and physical condition is balanced using the alternative statistical test. In embodiments, a calculation of a p-value of 0.01 or less for the null DB1 / 142566607.11 34 CLNV-015PC / 132119-5015 hypothesis that one or more of the sex, race, age, and physical condition are balanced using the alternative statistical test. In embodiments, a nonparametric test such as a sign test for median, Wilcoxon-Mann-Whitney Rank Sum test, Rank Correlation Test, or Runs test is used to ensure that one or more of the sex, race, age, and physical condition are balanced. See Smith, 1991, Statistical Reasoning, Third Edition, Allyn and Bacon, Boston, Chapter 17, which is hereby incorporated by reference. In embodiments, the cellular data (e.g. single nuclei data and / or single-cell transcription data) is examined relative to the cellular data (e.g. single nuclei data and / or single-cell transcription data) for the plurality of human patients to ensure that the human patients represented in the plurality of human patients are balanced for sex, race, age, and / or physical condition relative to the entire plurality of human patients. In embodiments, no balancing is performed for sex, race, age, and / or physical condition. In embodiments, sex is balanced additionally or alternatively in the plurality of human patients by ensuring that between 40 percent and 60 percent of the subjects are female. In embodiments, sex is balanced additionally or alternatively in the plurality of human patients by ensuring that between 45 percent and 55 percent of the subjects are female. In embodiments, sex is balanced in the plurality of human patients additionally or alternatively by ensuring that between 47.5 percent and 52.5 percent of the subjects are female. In embodiments, the cohort of subjects is not balanced for sex. In embodiments, sex is balanced in the cohort of subjects additionally or alternatively by ensuring that the participation to prevalence ratio (PPR) for woman (across the entire cohort) is between 0.80 and 1.20, where PPR is defined as:Percentage of woman in the plurality of human patients of subjectsPercentage of woman among disease population (^^. ^^. human .patients being afflicted with the disease)In embodiments, sex is balanced in the plurality of human patients additionally or alternatively by ensuring that the PPR for woman (across the entire plurality of human patients) is between 0.90 and 1.10. In embodiments, race is represented in a balanced manner additionally or alternatively by ensuring that the respective PPR of white subjects, black subjects, and Asian subjects (across the entire plurality of human patients) is between 0.80 and 1.20. The PPR of a given race in such embodiments is calculated as: DB1 / 142566607.11 35 CLNV-015PC / 132119-5015 Percentage of subjects in the plurality of human patients that are of race XPercentage of subjects among disease population (^^. ^^. human patients .being afflicted with the disease) that are of race XIn embodiments, race is represented in a balanced manner additionally or alternatively in the plurality of human patients by ensuring that the respective PPR of white subjects, black subjects, and Asian subjects (across the entire plurality of human patients) is between 0.90 and 1.10. In embodiments, age is represented in a balanced manner additionally or alternatively by ensuring that the respective PPR of particular age groups (across the entire plurality of human patients) is between 0.80 and 1.20. The PPR of a given age group in such embodiments is calculated as:Percentage of subjects in the plurality of human patients in age group XPercentage of subjects among disease population (^^. ^^. human patients being .afflicted with the disease) in age group XIn embodiments, age is represented in a balanced manner additionally or alternative by ensuring that the respective PPR of each respective age group in a particular set of age groups (across the entire plurality of human patients) is between 0.90 and 1.10. In embodiments, one of the age groups that is balanced in the plurality of human patients is the age group defined as being over 65 years in age. In embodiments, one of the age groups that is balanced in the plurality of human patients is the age group defined as being aged 45 to 64 years. In embodiments, one of the age groups that is balanced in the plurality of human patients is the age group defined as being aged 18-44 years. In embodiments, physical condition is balanced additionally or alternatively in the plurality of human patients by ensuring that the participation to prevalence ratio (PPR) for subjects with the physical condition is between 0.80 and 1.20, where PPR is defined as:Percentage of subjects in the plurality of human patients having the physical condition Percentage of subject among disease population .(^^.^^. , human patients being afflicted with the disease) with the physical conditionIn embodiments, physical condition is balanced in the plurality of human patients additionally or alternatively by ensuring that the participation to prevalence ratio (PPR) for subjects with the physical condition (across the entire cohort) is between 0.90 and 1.10. In embodiments, the physical condition is body mass index. In embodiments, the physical condition is smoking status. DB1 / 142566607.11 36 CLNV-015PC / 132119-5015 For further discussion of the use of PPR to analyze whether a plurality of human patients is balanced, see Varma et al., 2021, “Reporting of Study Participant Demographic Characteristics and Demographic Representation in Premarketing and Postmarketing Studies of Novel Cancer Therapeutics,” JAMA Netw Open Apr; 4(4): e217063, which is hereby incorporated by reference. In embodiments, the cellular data of (a) is sorted based on one or more of the metadata features. In embodiments, the cellular data of (a) is sorted based on sex, e.g., male and female. In embodiments, the cellular data of (a) is sorted based on race, e.g., non-Latino white, Latino, non-Latino black, Asian, and other. In embodiments, the cellular data of (a) is sorted based on age, e.g., less than 20 years old, 20 to 40 years old, 40 to 60 years old, and more than 60 years old. In embodiments, the cellular data of (a) is sorted based on BMI, e.g., less than 25, 25 to 30, 30 to 35, or more than 35. In embodiments, the cellular data of (a) is sorted based on medications. In embodiments, the cellular data of (a) is sorted based on alcohol consumption. In embodiments, the cellular data of (a) is sorted based on cause of death. In embodiments, the cellular data of (a) is sorted based on medical history, e.g., presence, absence, or degree of severity of one or more of Primary Myelofibrosis, Secondary Myelofibrosis, Essential Thrombocythemia, Polycythemia Vera, and the like. In embodiments, the cellular data of (a) is sorted based on family medical history, e.g., family history of Primary Myelofibrosis, Secondary Myelofibrosis, Essential Thrombocythemia, Polycythemia Vera, and the like. In embodiments, the cellular data of (a) is sorted based on disease diagnostic information, e.g. International Prognostic Scoring System (IPSS), Dynamic IPSS (DIPSS), DIPSS Plus, Mutation- Enhanced International Prognostic Score System (MIPSS70), MIPSS70+, Genetically Inspired Prognostic Scoring System (GIPSS), Myelofibrosis Secondary to PV and ET-Prognostic Model (MYSEC-PM), Myelofibrosis Transplant Scoring System (MTSS), Response to Ruxolitinib after 6 Months (RR6), Artificial Intelligence Prognostic Scoring System for Myelofibrosis (AIPSS-MF), or pathology notes and key features. In embodiments, the single-cell transcription data and / or single nuclei transcription data of (a) is sorted based on one or more of the metadata features. In embodiments, the single-cell transcription data and / or single nuclei transcription data of (a) is sorted based on sex, e.g., male and female. In embodiments, the single-cell transcription data and / or single nuclei transcription data of (a) is sorted based on race, e.g., non-Latino white, Latino, non-Latino black, Asian, and other. In embodiments, the single-cell transcription data and / or single nuclei transcription data of (a) is sorted based on age, e.g., less than 20 years old, 20 to 40 years old, 40 to 60 years old, and more than 60 years old. In embodiments, the single-cell transcription data and / or single nuclei transcription data of (a) is sorted based on BMI, e.g., less than 25, 25 to 30, 30 to 35, or more than 35. In embodiments, the single-cell transcription data and / or single nuclei transcription data of (a) is sorted based DB1 / 142566607.11 37 CLNV-015PC / 132119-5015 on medications. In embodiments, the single-cell transcription data and / or single nuclei transcription data of (a) is sorted based on alcohol consumption. In embodiments, the single-cell transcription data and / or single nuclei transcription data of (a) is sorted based on cause of death. In embodiments, the single-cell transcription data and / or single nuclei transcription data of (a) is sorted based on medical history, e.g., presence, absence, or degree of severity of one or more of Primary Myelofibrosis, Secondary Myelofibrosis, Essential Thrombocythemia, Polycythemia Vera, and the like. In embodiments, the single-cell transcription data and / or single nuclei transcription data of (a) is sorted based on family medical history, e.g., family history of Primary Myelofibrosis, Secondary Myelofibrosis, Essential Thrombocythemia, Polycythemia Vera, and the like. In embodiments, the single-cell transcription data and / or single nuclei transcription data of (a) is sorted based on disease diagnostic information, e.g. International Prognostic Scoring System (IPSS), Dynamic IPSS (DIPSS), DIPSS Plus, Mutation-Enhanced International Prognostic Score System (MIPSS70), MIPSS70+, Genetically Inspired Prognostic Scoring System (GIPSS), Myelofibrosis Secondary to PV and ET-Prognostic Model (MYSEC-PM), Myelofibrosis Transplant Scoring System (MTSS), Response to Ruxolitinib after 6 Months (RR6), Artificial Intelligence Prognostic Scoring System for Myelofibrosis (AIPSS-MF), or pathology notes and key features. In embodiments, the disease diagnostic information comprises disease status. In embodiments, the disease status comprises histologically graded disease status. In embodiments, disease status is measured using any method described in Duminuco A, Nardo A, Giuffrida G, et al. Myelofibrosis and Survival Prognostic Models: A Journey between Past and Future. J Clin Med. 2023;12(6):2188. Published 2023 Mar 11. doi:10.3390 / jcm12062188, incorporated herein by reference in its entirety. In embodiments, the disease diagnostic information comprises one or more features selected from International Prognostic Scoring System (IPSS), Dynamic IPSS (DIPSS), DIPSS Plus, Mutation-Enhanced International Prognostic Score System (MIPSS70), MIPSS70+, Genetically Inspired Prognostic Scoring System (GIPSS), Myelofibrosis Secondary to PV and ET-Prognostic Model (MYSEC-PM), Myelofibrosis Transplant Scoring System (MTSS), Response to Ruxolitinib after 6 Months (RR6), Artificial Intelligence Prognostic Scoring System for Myelofibrosis (AIPSS-MF), and pathology notes and key features. In embodiments, the cellular data and / or the single nuclei transcription data of (a) and / or (b) is filtered to remove counts of ambient nucleic acid molecules, doublets and / or empty droplets, optionally ambient RNA molecules using any methods known to those skilled in the art. DB1 / 142566607.11 38 CLNV-015PC / 132119-5015 In embodiments, generating the profile of cellular dysfunction of (c) further comprises analyzing biomarkers from the cellular data, and comparing the biomarkers from the cellular data to a standardized set of biomarkers to identify a cell type associated with the cellular transition. In embodiments, generating the profile of cellular dysfunction of (c) further comprises analyzing biomarkers from the single-cell transcription data and / or single nuclei transcription data, and comparing the biomarkers from the single-cell transcription data and / or single nuclei transcription data to a standardized set of biomarkers to identify a cell type associated with the cellular transition. In embodiments, the method further comprises (d) obtaining genotypic data from each human patient from the plurality of human patients of (a). In embodiments, generating a profile of cellular dysfunction of (c) further comprises analyzing the cellular data of (a) with the genotypic data of (d). In embodiments, generating a profile of cellular dysfunction of (c) further comprises analyzing the single-cell transcription data and / or single nuclei transcription data of (a) with the genotypic data of (d). Cellular Transitions Tissues are complex ecosystems of individual cells, where dysregulation of cell state is the basis of disease. Existing drug discovery efforts seek to characterize the molecular mechanisms that cause cells to transition from healthy to disease states, and to identify pharmacological approaches to reverse or inhibit these transitions. Past efforts have also sought to identify molecular signatures characterizing these transitions, and to identify pharmacological approaches that reverse these signatures. Cellular transitions refer to a transition in a cell’s state from a first cell state to an altered and / or second cell state (e.g., healthy to diseased). In embodiments, a cellular transition is marked by a change in cellular- component expression in the cell, and thus by the identity and quantity cellular-components (e.g., mRNA, transcription factors) produced by the cell. In embodiments, the cellular transition is characterized by an upregulation or a down-regulation of one or more cellular-components. For example, in embodiments, cellular transitions (i.e., a transition in a cell’s state from a first cell state to an altered and / or second cell state) are marked by a change in expression of cellular-components in the cell. In a non-limiting example, a cellular transition is marked by a change in cellular-component expression in the cell, and thus by the identity and quantity cellular-components (e.g., mRNA, transcription factors) produced by the cell. DB1 / 142566607.11 39 CLNV-015PC / 132119-5015 As another example, in embodiments, the one or more cellular-components comprises a plurality of genes, optionally measured at the RNA level. In embodiments, the plurality of genes comprises at least 2, at least 5, at least 10, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50, at least 55, at least 60, at least 65, at least 70, at least 75, at least 80, at least 85, at least 90, at least 95, or at least 100 genes. In embodiments, the plurality of genes comprises at least 50, at least 100, at least 200, at least 300, at least 400, at least 500, at least 600, at least 700, at least 800, at least 900, or at least 1000 genes. In embodiments, the plurality of genes comprises at least 1000, at least 2000, at least 3000, at least 4000, at least 5000, at least 10,000, at least 30,000, at least 50,000, or more than 50,000 genes. In embodiments, the plurality of genes comprises between 2 and 20, between 20 and 50, between 50 and 100, between 100 and 200, between 200 and 500, between 500 and 1000, between 1000 and 5000, between 5000 and 10,000 genes, or between 10,000 and 50,000 genes. In embodiments, the one or more cellular- components comprises a plurality of proteins. In embodiments, the plurality of proteins comprises at least 2, at least 5, at least 10, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50, at least 55, at least 60, at least 65, at least 70, at least 75, at least 80, at least 85, at least 90, at least 95, or at least 100 proteins. In embodiments, the plurality of proteins comprises at least 50, at least 100, at least 200, at least 300, at least 400, at least 500, at least 600, at least 700, at least 800, at least 900, or at least 1000 proteins. In embodiments, the plurality of proteins comprises at least 1000, at least 2000, at least 3000, at least 4000, at least 5000, at least 10,000, at least 30,000, at least 50,000, or more than 50,000 proteins. In embodiments, the plurality of proteins comprises between 2 and 20, between 20 and 50, between 50 and 100, between 100 and 200, between 200 and 500, between 500 and 1000, between 1000 and 5000, between 5000 and 10,000 proteins, or between 10,000 and 50,000 proteins. In embodiments, cellular- components of interest include nucleic acids, including DNA, modified (e.g., methylated) DNA, RNA, including coding (e.g., mRNAs) or non-coding RNA (e.g., sncRNAs), proteins, including post-transcriptionally modified protein (e.g., phosphorylated, glycosylated, myristoylated, etc. proteins), lipids, carbohydrates, nucleotides (e.g., adenosine triphosphate (ATP), adenosine diphosphate (ADP) and adenosine monophosphate (AMP)) including cyclic nucleotides such as cyclic adenosine monophosphate (cAMP) and cyclic guanosine monophosphate (cGMP), other small molecule cellular-components such as oxidized and reduced forms of nicotinamide adenine dinucleotide (NADP / NADPH), and any combinations thereof. In embodiments, a cellular transition is determined based upon a change in cytotoxicity, cell viability, gene toxicity, developmental toxicity, and / or mitochondrial toxicity in response to an agonism and / or antagonism of one or more cellular components of interest. Further examples of cellular-components, cell states, and / or DB1 / 142566607.11 40 CLNV-015PC / 132119-5015 methods for measuring the same are described in Huang R, 2016, “A Quantitative High-Throughput Screening Data Analysis Pipeline for Activity Profiling,” High-Throughput Screening Assays in Toxicology, Methods in Molecular Biology; 1473(1); Huang et al., 2016, “Modelling the Tox2110 K chemical profiles for in vivo toxicity prediction and mechanism characterization,” Nat Commun.7, p.10425; and Huang et al., 2018, “Expanding biological space coverage enhances the prediction of drug adverse effects in human using in vitro activity profiles,” Sci Rep.8(1):3783, each of which is hereby incorporated herein by reference in its entirety. In embodiments, the one or more cellular-components are obtained and / or quantified using single nuclei assay experiments, including but not limited to single nucleus ribonucleic acid (RNA) sequencing (snRNA- seq). In embodiments, single nuclei assay experiments are carried out with and / or performed on samples, including but not limited to frozen samples (e.g. an organ, a tissue, stem cells, human cells, cells from umbilical cord blood, cells from peripheral blood, bone marrow cells, cells from a solid tissue, and / or differentiated cells). In embodiments, frozen samples are samples that were harvested and / or obtained from a donor and stored under freezing conditions (e.g. in a freezer and / or after treatment with liquid nitrogen) about one day, about two days, about 3 days, about 1 week, about 2 weeks, about 3 weeks, about 4 weeks, about 2 months, about 3 months, about 6 months, about 1 year, about 2 years, or about 3 years before analysis. In embodiments, the samples are post-mortem samples (e.g. samples obtained by post-mortem dissections). In embodiments, the sample (e.g. post-mortem sample) weighs about 0.1 g to about 1 g, or about 0.3 g to about 0.7 g, or about 0.1 g, about 0.2 g, about 0.3 g, about 0.4 g, about 0.5 g, about 0.6 g, about 0.7 g, about 0.8 g, about 0.9 g, or about 1 g. In embodiments, the sample (e.g. post-mortem sample) weighs about 0.5 g. In a non-limiting example, the frozen sample is harvested and / or obtained from a deceased organ donor and then frozen (e.g. treated with and / or frozen in liquid nitrogen), and nuclei of cells from the samples are isolated and analyzed (e.g. sequenced) in single nuclei assay experiments (e.g. snRNA-seq). In embodiments, and without wishing to be bound by theory, single nuclei assay experiments provide improvements over single-cell assay experiments because single nuclei assay experiments are more amendable to scaling and / or performing within short timeframes, and obtaining frozen samples can be more facile than obtaining fresh samples. In embodiments, fresh samples are samples that were harvested and / or obtained from a donor less than about 1 week, about 3 days, about 2 days, about 1 day, or about 12 hours before analysis, and not subjected to any freezing procedures prior to analysis) samples. In embodiments, the single nuclei data is single-nucleus ribonucleic acid (RNA) sequencing (snRNA-seq) data. DB1 / 142566607.11 41 CLNV-015PC / 132119-5015 In embodiments, single nuclei sequencing and / or single cell sequencing is used to obtain a plurality of sequence reads from each nucleus / cell in one or more cells in the frozen sample (e.g. frozen liver sample). See Jiang et al., February 23, 2023, “Isolated nuclei from frozen tissue are the superior source for single cell RNA-seq compared with whole cells,” available on the Internet at doi.org / 10.1101 / 2023.02.19.529150, which is hereby incorporated by reference. In embodiments, the one or more cellular-components are obtained and / or quantified using single-cell assay experiments, including but not limited to single-cell ribonucleic acid (RNA) sequencing (scRNA-seq), scTag- seq, single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq), CyTOF / SCoP, E-MS / Abseq, miRNA-seq, CITE-seq, single-nucleus ribonucleic acid (RNA) sequencing (snRNA-seq), or any combinations thereof, or summaries of the same, including combinations, such as linear combinations, representing activated pathways in the single-cell cellular-component expression datasets. In embodiments, single-cell assay experiments are carried out with and / or performed on samples, including but not limited to fresh samples (e.g. an organ, a tissue, stem cells, human cells, cells from umbilical cord blood, cells from peripheral blood, bone marrow cells, cells from a solid tissue, and / or differentiated cells). In a non-limiting example, and without wishing to be bound by theory, fresh samples are more amenable to sorting cells by cell type as compared to frozen samples, and single-cell types are isolated and analyzed (e.g. sequenced) in single-cell assay experiments (e.g. scRNA-seq). In embodiments, the single-cell transcription data is single-cell ribonucleic acid (RNA) sequencing (scRNA-seq) data. In embodiments, the single-cell transcription data is transposase-accessible chromatin using sequencing (scATAC-seq) data. In embodiments, the one or more cellular-components are obtained and / or quantified from cellular data (e.g. transcriptomic data including but not limited to single nuclei data and / or single-cell transcription data) obtained from a text-based format of sequencing data, including but not limited to FASTQ, which refers to a text-based format for storing both a biological sequence (e.g. a nucleotide sequence) and its corresponding quality scores. In embodiments, the cellular-component measurements include gene expression measurements, such as RNA levels. In embodiments, the cellular-component expression measurement can be selected based on the desired cellular-component to be measured. In embodiments, the single nuclei transcription data is or comprises single-nucleus ribonucleic acid (RNA) sequencing (snRNA-seq) data. In embodiments, the single- cell transcription data is or comprises single-cell ribonucleic acid (RNA) sequencing (scRNA-seq) data. DB1 / 142566607.11 42 CLNV-015PC / 132119-5015 In embodiments, the cellular transition is identified by the presence of a diseased cell phenotype in the cell. In embodiments, the diseased cell phenotype is identified by a discrepancy between the diseased cell and a normal cell. For instance, in embodiments, the diseased cell phenotype can be identified by loss of a function of the cell, gain of a function of the cell, progression of the cell (e.g., transition of the cell into a differentiated state), stasis of the cell (e.g., inability of the cell to transition into a differentiated state), intrusion of the cell (e.g., emergence of the cell in an abnormal location), disappearance of the cell (e.g., absence of the cell in a location where the cell is normally present), disorder of the cell (e.g., a structural, morphological, and / or spatial change within and / or around the cell), loss of network of the cell (e.g., a change in the cell that eliminates normal effects in progeny cells or cells downstream of the cell), a gain of network of the cell (e.g., a change in the cell that triggers new downstream effects in progeny cells of cells downstream of the cell), a surplus of the cell (e.g., an overabundance of the cell), a deficit of the cell (e.g., a density of the cell being below a critical threshold, a difference in cellular-component ratio and / or quantity in the cell, a difference in the rate of transitions in the cell, or any combination thereof. In embodiments, the diseased cells include cell lines, biopsy sample cells, and cultured primary cells. In embodiments, the normal cells include cultured primary cells and biopsy sample cells. In embodiments, the cells are human cells. Any cell in which cellular transitions and / or cellular behavior can be detected and / or measured are contemplated by the present disclosure, as would be understood by one of ordinary skill in the art. Non-limiting examples of cells include hematopoietic stem cells (HSCs) (including but not limited to induced pluripotent stem cells (IPSCs)), hematopoietic precursor cells, hematopoietic progenitor cells, optionally basophil mast progenitor cell, common lymphoid progenitor, common myeloid progenitor, basophil mast progenitor cell, granulocyte monocyte progenitor cell, megakaryocyte-erythroid progenitor cell, erythroid progenitor cell, megakaryocyte progenitor cell, pro-T cell, pro-NK cell or pro-B cell, myeloid lineage cells, monocytes, macrophages, dendritic cells, optionally conventional type 1 dendritic cells (cDC1s), conventional type 2 dendritic cells (cDC2s), or plasmacytoid dendritic cells; neutrophils, eosinophils, erythroid lineage cell, megakaryocytes, basophils, mast cells, T-cells, natural killer (NK) cells, B-cells, plasma cells, mesenchymal cells, and endothelial cells. In embodiments, the cell type is selected from basophils / mast cells, monocyte, erythroid lineage cells, hematopoietic precursor cells, lymphoid lineage cells, megakaryocyte-erythroid progenitor cells, megakaryocytes, and myeloid lineage cells. DB1 / 142566607.11 43 CLNV-015PC / 132119-5015 In embodiments, the cells are isolated from and / or harvested from an organ, a tissue, stem cells, human cells, cells from umbilical cord blood, cells from peripheral blood, bone marrow cells, cells from a solid tissue, and / or differentiated cells. In embodiments, the cellular transition is based on a cellular behavior of a hematopoietic precursor cell and a megakaryocyte-erythroid progenitor cell. Any method for detecting and / or monitoring and / or determining and / or measuring cellular transitions are contemplated by the present disclosure, as would be understood by one of ordinary skill in the art. Non- limiting examples of methods include colorimetric measurement, a fluorescence measurement, a luminescence measurement, a resonance energy transfer (FRET) measurement, a measurement of a protein-protein interaction, a measurement of a protein-polynucleotide interaction, a measurement of a protein-small molecule interaction, mass spectrometry (e.g, liquid chromatography mass spectrometry (LCMS) or Matrix-assisted laser desorption / ionization-time of flight (MALDI-TOF) mass spectrometry (MS)), nuclear magnetic resonance (NMR) (e.g. proton NMR, carbon NMR, COSY, HSQC, HMBC and HSQC- TOCSY), or a microarray measurement. Animal Models Any animal model in which cellular transitions and / or cellular behavior can be detected and / or measured are contemplated by the present disclosure, as would be understood by one of ordinary skill in the art. In embodiments, the animal model is selected from one or more available models (e.g. available models), including a model that mimics etiology and / or natural history, a model that mimics histopathology, and a model that comprises genetic drivers. In embodiments, the model (e.g. available model) that mimics etiology and / or natural history is selected from a Jak2VF, MplW515L, Calrdel52, TPO, Gata-1low, and Trisomy 21 (Ts65Dn) model. Non-limiting examples of animal models are shown below in Table 1A-1C. Table 1A: Models of myelofibrosis generated by retroviral overexpression and bone marrow transplantation in mice. Model Strain Vector Phenotype Jak2VFC57Bl / 6 MEGIX PV with progression to MF Jak2VFC57Bl / 6 and Balb / c MSCV PV, progression to MF was only observed in Balb / c mice MplW515LBalb / C MSCV Features of ET with progression to MF DB1 / 142566607.11 44 CLNV-015PC / 132119-5015 Model Strain Vector Phenotype Calrdel52C57Bl / 6 MSCV ET with progression to MF TPO BDF1 MSCV ET with progression to MF TPO C57Bl / 6 MPZen2 ET with progression to MF ET: essential thrombocythemia, MF: myelofibrosis, TPO: thrombopoietin, PV: polycythemia vera. Table 1B: Transgenic and knock-in mouse models of myelofibrosis. BMT: bone marrow transplant, Model Type Strain Locus Activation Phenotype Line 1: Dcc intron 12 on chr18 Line 1: about Line 2: 50% PV or ET, Jak2VFTransgenic BDF1 between Mef2a and Lrrc28 on Constitutive 50% no clear chr7, expression under H-2Kb phenotype promoter Line 2: PMF Jak2VFTransgenic C57BL / 6 Locus n / a, expression XDBAunder Vav promoterConditional ET, PV or PMFVavCre: ET with Conditional progression to JAK2VFTransgenic C57Bl / 6 Human JAK2VF, expression (VavCre), MF under human JAK2 promoter inducible Mx1Cre: PV with (Mx1Cre) progression to MF PV with progression to in k-in 129 progression to Jak2VFKnoc Sv Endogenous Jak2 locus on C57Bl / 6chr19ConstitutiveMF in heterozygous mice PV, homozygosity Endogenous Jak2 locus on embryonically Jak2VFKnock-in C57Bl / 6 Conditional, chr19 (E2Acre) lethal, MF only in secondary recipients DB1 / 142566607.11 45 CLNV-015PC / 132119-5015 Model Type Strain Locus Activation Phenotype Human JAK2VFin Conditional, ET, PV and one JAK2VFKnock-in C57Bl / 6 endogenous Jak2 locus on inducible mouse with MF chr19 (Mx1Cre) in BMT AK2 Knock-in C57Bl / 6EnConditional PV with JVFdogenous Jak2 locus onchr19 (VavCre) progression to MF ET in heterozygous, Conditional, ET with Calrdel52Knock-in C57Bl / 6 Endogenous Calr locus on chr8 inducible progression to (Mx1Cre) MF in homozygous mice nsgenic BDF1Locus n / Features of ET TPO Traa, driven by the IgHpromoter Constitutive with progression to MF Knock-out >90% mortality (upstream in C57Bl / 6, Other Gata-1lowpromotor C57Bl / 6CD1 Endogenous promotor region normal life span on cConstitutivemodels regionhrXwith develop- of Gata1) ment of PMF in CD1 Trisomy Chr16 (harboring 2 / 3 o Down syndrome, 21 Chromosomal f human C57Bl / 6 chr21 features of ET translocati genes) translocation to Constitutive (Ts65Dn) on chr17 with progression to MF ET: essential thrombocythemia, MF: myelofibrosis, PMF: primary myelofibrosis, TPO: thrombopoietin, PV: polycythemia vera. Table 1C: Mouse models of myelofibrosis targeting multiple genes. Model Type Strain Jak2VFmodel (locus) Activation Phenotype Retroviral Jak2VF- overexpression Const PV with progression to K (MIG) / BMT C57BL6 itutive LN / J / MF is accelerated in (Jak2VF) (LNK knockout) double mutant Knockout (LNK) Transgenic JAK2VFJak2VFmice (hu Conditional, JAK2VF- man ET with progression to Ezh2 (Jak2VF) C57BL6 / J JAK2VF, expression under inducible MF is accelera out (Ezh2) human JAK2 promoter) (M ted in Knock x1Cre, SclCreER) double mutant DB1 / 142566607.11 46 CLNV-015PC / 132119-5015 Model Type Strain Jak2VFmodel (locus) Activation Phenotype PV (JAK2VF) shift ) Jak2VFmice Conditional s Jak2VF- Knock-in (Jak2VF, (endogenous Jak2 locus on ind towards ET in combined Ezh2Knockout (Ezh2)C57Bl / 6ucible Chr19) (Mx1Cre) model with rapid / - In vitro, In vivo and Ex vivo Models Any in vitro, in vivo and / or ex vivo model in which cellular transitions and / or cellular behavior can be detected and / or measured are contemplated by the present disclosure, as would be understood by one of ordinary skill in the art. In embodiments, the in vitro disease model is selected from a primary cell type system, a stem cell derived system, and a cell line system. In embodiments, the in vitro disease model comprises induced hematopoietic stem cells (iHSCs) derived from patient induced pluripotent stem cells (iPSC). Non-limiting examples of protocols for differentiation of HSCs from iPSC cells can be found in Coll et al., Cell Stem Cell. 23:101-113 (2018), which is incorporated by reference herein in its entirety. In embodiments, the in vivo disease model comprises patient derived xenograft models (PDX). In embodiments, the PDX model comprises samples from myelofibrosis and healthy donors. In one aspect, the disclosure provides methods of evaluating translatability of an animal, in vitro, in vivo and / or ex vivo model of a disease, including but not limited to a myelofibrosis-related disease. In embodiments, the method includes detecting and / or monitoring a cellular transition associated with or indicative of a presence or a stage of a disease, including but not limited to a myelofibrosis-related disease, as disclosed herein. DB1 / 142566607.11 47 CLNV-015PC / 132119-5015 In one aspect, the disclosure also provides methods of evaluating translatability of an animal, in vitro, in vivo and / or ex vivo model of a myelofibrosis-related disease to a human, comprising: (a) determining the presence of a cellular transition associated with or indicative of a presence or a stage of a myelofibrosis-related disease in the animal, in vitro, in vivo and / or ex vivo model by analyzing cellular data from the animal, in vitro, in vivo and / or ex vivo model; and (b) comparing the analysis of (a) to a profile of cellular dysfunction based on the analysis of cellular data from a plurality of human patients, at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease wherein: the profile of cellular dysfunction is indicative of the cellular transition; and the cellular transition is associated with or indicative of a transition from an undiseased, pre-diseased, disease-treatable, or disease-reversible state to a disease-untreatable or disease-irreversible state; wherein the animal, in vitro, in vivo and / or ex vivo model is translatable to a human if demonstrating that the single nuclei data and / or single-cell transcription data of (a) is at least substantially similar to the profile of cellular dysfunction of (b). In embodiments, the cellular data comprises and / or consists of one or more of transcriptomic data, genomic data, proteomic data and metabolomic data. In embodiments, the cellular data comprises and / or consists of transcriptomic (e.g. translational) data. In embodiments, the transcriptomic data comprises and / or consists of single nuclei data and / or single-cell transcription data. In embodiments, the transcriptomic data comprises and / or consists of bulk transcriptomic data. In embodiments, the bulk transcriptomic data comprises and / or consists of single nuclei data and / or single-cell transcription data. In embodiments, the cellular data comprises cellular data from one or more cell types selected from Table 1. In embodiments, the cellular data comprises cellular data from one or more gene markers selected from Table 1. In embodiments, the animal, in vitro, in vivo and / or ex vivo model is translatable to a human if demonstrating that the single nuclei data and / or single-cell transcription data of (a) is at least about 30%, about 35%, about 40%, about 45%, about 50%, about 55%, about 60%, about 65%, about 70%, about 75%, about 80%, about 85%, about 90%, about 95%, about 96%, about 97%, about 98%, about 99%, or greater than about 99% similar to the profile of cellular dysfunction of (b). DB1 / 142566607.11 48 CLNV-015PC / 132119-5015 In embodiments, (a) comprises and / or consists of analyzing single nuclei data and / or single-cell transcription data. In embodiments, (a) comprises and / or consists of analyzing single nuclei data. In embodiments, (a) comprises and / or consists of analyzing single-cell transcription data. In embodiments, (a) comprises and / or consists of analyzing single nuclei data and single-cell transcription data. In embodiments, (b) comprises and / or consists of comparing the analysis of (a) to a profile of cellular dysfunction based on the analysis of single nuclei data and / or single-cell transcription data from a plurality of human patients. In embodiments, (b) comprises and / or consists of comparing the analysis of (a) to a profile of cellular dysfunction based on the analysis of single nuclei data from a plurality of human patients. In embodiments, (b) comprises and / or consists of comparing the analysis of (a) to a profile of cellular dysfunction based on the analysis of single- cell transcription data from a plurality of human patients. In embodiments, (b) comprises and / or consists of comparing the analysis of (a) to a profile of cellular dysfunction based on the analysis of single nuclei data and single-cell transcription data from a plurality of human patients. Agents In one aspect, the disclosure provides methods for identifying an agent useful for treating a disease, including but not limited to a myelofibrosis-related disease. In embodiments, the method includes detecting and / or monitoring a cellular transition associated with or indicative of a presence or a stage of a disease, including but not limited to a myelofibrosis-related disease, as disclosed herein. In embodiments, the method informs the selection of an agent to treat the myelofibrosis-related disease. In embodiments, the method for identifying an agent useful for treating a myelofibrosis-related disease comprises: (a) administering the agent in an animal, in vitro, in vivo and / or ex vivo disease model of the myelofibrosis- related disease; (b) determining the presence of a cellular transition associated with or indicative of a presence or a stage of a myelofibrosis-related disease in the animal, in vitro, in vivo and / or ex vivo disease model by analyzing cellular data from the animal, in vitro, in vivo and / or ex vivo disease model; and (c) comparing the analysis of (b) to a profile of cellular dysfunction based on the analysis of cellular data from: (i) a plurality of human patients, at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease; DB1 / 142566607.11 49 CLNV-015PC / 132119-5015 (ii) a plurality of animal disease models of the myelofibrosis-related disease; (iii) a plurality of in vitro disease models of the myelofibrosis-related disease; and / or (iv) a plurality of ex vivo disease models of the myelofibrosis-related disease; wherein: the profile of cellular dysfunction is indicative of the cellular transition; and the cellular transition is associated with or indicative of a transition from a disease- untreatable or disease-irreversible state to an undiseased, pre-diseased, disease-treatable, or disease-reversible state; wherein the agent is suitable for treating the myelofibrosis-related disease if demonstrating that the single nuclei data and / or single-cell transcription data of (b) is at least substantially similar to the profile of cellular dysfunction of (c). In embodiments, the cellular transition is associated with or indicative of a transition from an undiseased, pre- diseased, disease-treatable, or disease-reversible state to a disease-untreatable or disease-irreversible state. In embodiments, the cellular data comprises and / or consists of one or more of transcriptomic data, genomic data, proteomic data and metabolomic data. In embodiments, the cellular data comprises and / or consists of transcriptomic (e.g. translational) data. In embodiments, the transcriptomic data comprises and / or consists of single nuclei data and / or single-cell transcription data. In embodiments, the transcriptomic data comprises and / or consists of bulk transcriptomic data. In embodiments, the bulk transcriptomic data comprises and / or consists of single nuclei data and / or single-cell transcription data. In embodiments, the cellular transition is associated with or indicative of a transition from a disease-treatable or disease-reversible state to an undiseased or pre-diseased state. In embodiments, the cellular transition is associated with or indicative of a transition from a disease-untreatable or disease-irreversible state to an undiseased or pre-diseased state. In embodiments, the cellular transition is associated with or indicative of a transition between two different disease-treatable or disease-reversible states. In embodiments, the cellular transition is associated with or indicative of a a transition between a disease-untreatable or disease- irreversible state to a disease-treatable or disease-reversible state. In embodiments, the cellular transition is associated with or indicative of a transition from abnormal hematopoiesis to normal hematopoiesis. In embodiments, the cellular transition is associated with or indicative of a transition from decreased red blood cell and / or platelet count to normal red blood cell and / or platelet count. In embodiments, the cellular transition DB1 / 142566607.11 50 CLNV-015PC / 132119-5015 is associated with or indicative of a transition from bone marrow fibrosis to healthy bone marrow. In embodiments, the cellular transition is associated with or indicative of a transition from leukemic cells to healthy blood cells. In embodiments, the method comprises in (c) comparing the analysis of (b) to a profile of cellular dysfunction based on the analysis of cellular data from: (i) a plurality of human patients, at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease; (ii) a plurality of animal disease models of the myelofibrosis-related disease; (iii) a plurality of in vitro disease models of the myelofibrosis-related disease; and / or (iv) a plurality of ex vivo disease models of the myelofibrosis-related disease. In embodiments, the cellular data comprises cellular data from one or more cell types selected from Table 1. In embodiments, the cellular data comprises cellular data from one or more gene markers selected from Table 1. In embodiments, the agent is suitable for treating the myelofibrosis-related disease if the agent corrects and / or reverses the profile of cellular dysfunction of (c) by at least about 1%, at least about 2%, at least about 3%, at least about 4%, at least about 5%, at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 45%, at least about 50%, or greater than at least about 50% as demonstrated by the single nuclei data and / or single-cell transcription data of (b). In embodiments, the agent is suitable for treating the myelofibrosis-related disease if demonstrating that the single nuclei data and / or single-cell transcription data of (b) is at least about 30%, at least about 35%, at least about 40%, at least about 45%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or greater than about 99% similar to the profile of cellular dysfunction of (c). In embodiments, the agent is one or more of a small molecule, a biologic, a protein, a protein combined with a small molecule, an antibody drug conjugate (ADC), a peptide, and a nucleic acid. In embodiments, the nucleic acid is one or more of an siRNA or interfering RNA, an antimiR, an mRNA, an aptamer, a cDNA over- DB1 / 142566607.11 51 CLNV-015PC / 132119-5015 expressing wild-type and / or mutant shRNA, a cDNA over-expressing wild-type and / or a gene-editing system (e.g. a mutant guide RNA (e.g., Cas9 system or other cellular-component editing system)). In embodiments, the agent is a particular gene, a particular mRNA associated with a gene, a carbohydrate, a lipid, an epigenetic feature, a metabolite, an antibody, a peptide, a protein, or a post-translationally modified protein. Non-limiting examples of post-translational modifications capable of producing modified proteins include glycosylation, phosphorylation, acetylation, and ubiquitylation. In embodiments, the agent is a peptide comprising up to about 30 amino acids, about 35 amino acids, about 40 amino acids, about 45 amino acids, or about 50 amino acids. In embodiments, the agent is a peptide comprising and / or consisting of about 35 to about 45 amino acids (e.g. about 35, about 36, about 37, about 38, about 39, about 40, about 41, about 42, about 43, about 44, or about 45 amino acids) and having a molecular weight of about 150 Daltons or less, about 140 Daltons or less, about 130 Daltons or less, about 120 Daltons or less, or about 110 Daltons or less. In embodiments, the agent is a peptide comprising and / or consisting of about 41 amino acids and having a molecular weight of about 110 Daltons or less. In embodiments, the agent is a large molecule composition (e.g. a protein) comprising and / or consisting of at least about 40, about 50, about 60, about 70, about 80, about 90, about 100, about 150, or about 200 amino acids. In embodiments, the agent is a large molecule composition (e.g. a protein) comprising and / or consisting of at least about 42 amino acids. In embodiments, the agent is an organic compound (e.g. a small molecule) having a molecular weight of less than about 2000, about 1500, about 1000, or about 500 Daltons. In embodiments, the agent is a small molecule that satisfies at least one rule of the four rules of Lipinski’s rule of Five, which include (i) not more than five hydrogen bond donors, (ii) not more than ten hydrogen bond donors, (iii) a molecular weight under about 500 Daltons, and (iv) a LogP under 5. In embodiments, the agent satisfies any two or more rules, any three or more rules, or all four rules of Lipinski’s rule of Five. In embodiments, the compound (e.g. small molecule) has enhanced oral bioavailability compared to a compound that does not satisfy one or more, two or more, three or more, or all four rules of Lipinski’s rule of Five. In embodiments, the agent causes an improvement and / or restoration of normal hematopoiesis when administered to a patient in need thereof. In embodiments, the agent causes reversal of abnormal hematopoiesis, when administered to a patient in need thereof. In embodiments, the myelofibrosis-related DB1 / 142566607.11 52 CLNV-015PC / 132119-5015 disease is characterized by abnormal hematopoiesis. In embodiments, the agent causes an improvement and / or management of symptoms to treat anemia, thrombocytopenia and / or splenomegaly. In embodiments, the agent causes an improvement and / or resolution of anemia, when administered to a patient in need thereof. In embodiments, the agent causes an improvement and / or resolution of thrombocytopenia, when administered to a patient in need thereof. In embodiments, (b) comprises and / or consists of analyzing single nuclei data and / or single-cell transcription data. In embodiments, (b) comprises and / or consists of analyzing single nuclei data. In embodiments, (b) comprises and / or consists of analyzing single-cell transcription data. In embodiments, (b) comprises and / or consists of analyzing single nuclei data and single-cell transcription data. In embodiments, (c) comprises and / or consists of comparing the analysis of (b) to a profile of cellular dysfunction based on the analysis of single nuclei data and / or single-cell transcription data from (i), (ii), (iii), and / or (iv). In embodiments, (c) comprises and / or consists of comparing the analysis of (b) to a profile of cellular dysfunction based on the analysis of single nuclei data from (i), (ii), (iii), and / or (iv). In embodiments, (c) comprises and / or consists of comparing the analysis of (b) to a profile of cellular dysfunction based on the analysis of single nuclei data from (i), (ii), (iii), and / or (iv). In embodiments, (c) comprises and / or consists of comparing the analysis of (b) to a profile of cellular dysfunction based on the analysis of single nuclei data from (i), (ii), (iii), and / or (iv). In embodiments, the method of treating myelofibrosis-related diseases with allogeneic stem cell transplant is further supplemented or supplanted with the agent of the present disclosure. In embodiments, the method of treating myelofibrosis-related diseases with blood transfusions is further supplemented or supplanted with the agent of the present disclosure. In embodiments, the method of treating myelofibrosis-related diseases with HU, JAK1 / 2 inhibitors (e.g. Ruxolitinib, Pacritinib) is further supplemented or supplanted with the agent of the present disclosure. In embodiments, the method of treating myelofibrosis-related diseases with oral small molecules, IV / SQ small molecules, biologics, and / or anemia specific drugs is further supplemented or supplanted with the agent of the present disclosure. In embodiments, the method of treating myelofibrosis-related diseases with immune / fibrotic agents, cytoreductive anticancer agents, and / or hematopoiesis modulators is further supplemented or supplanted with the agent of the present disclosure. In embodiments, the method of treating myelofibrosis-related diseases with one or more of BRD Inhibitor, Cell Tx, BRD Inhibitor, PIM Kinase Inhibitor, Triple Kinase Inhibitor, BRD / HUNK Inhibitor, Bcl-2 Inhibitor, PRMT5 Inhibitor, PD1 Inhibitor, HSP90 Inhibitor, P-Selectin Inhibitor, HJV Antibody, Anti-TGF beta, LOX DB1 / 142566607.11 53 CLNV-015PC / 132119-5015 Inhibitor, TIM3 Inhibitor, Hdm2 Inhibitor, PIMK Inhibitor, LSD1 Inhibitor, LOX2 Inhibitor, JAK2 Inhibitor, JAK1 Inhibitor, IAP Inhibitor, Hdm2 Inhibitor, IFNAR-2 Agonists, TGF-b Ligand Trap, ACVR1;ALK2 Inhibitor, ACTR- IIA ligand trap, Tyrosine Kinase Inhibitor, CRM1;XPO1 Antagonists, ACVR1;ALK2 Inhibitor, IFNAR-2 Agonists, SAP Mimetic TERT Inhibitor, JAK Inhibitor, Bcl-2 Inhibitor, PI3K Inhibitor, BET Inhibitor, and JAK1 / 2; ACVR1;ALK2 Inhibitor is further supplemented or supplanted with the agent of the present disclosure. In embodiments, the method of treating myelofibrosis-related diseases with one or more of ABBV-744, CK- 0804, INCB-057643, LGH-447, LNK-01002, Mivebresib, NWP-4-76, PRT-811, Spartalizumab, Zelavespib, Crizanlizumab, DISC-0974, NIS-793, PXS-5505, Sabatolimab, Siremadlin, TP-3654, Bomedemstat (IMG- 7289), GB-2064, Ilginatinib, itacitinib, adipate, LCL-161, Navtemadlin, peginterferon alfa-2b, Luspatercept, INCB-00928, KER-050, TL-895, Selinexor, INCB-00928, ropeginterferon alfa 2b, zinpentraxin alfa, imetelstat, Jaktinib, Navitoclax, Parsaclisib, Pelabresib, and Momelotinib is further supplemented or supplanted with the agent of the present disclosure. Patient Selection In one aspect, the disclosure provides methods of selecting a patient for treatment with an effective amount of an agent for a disease, including but not limited to a myelofibrosis-related disease. In embodiments, the method includes detecting and / or monitoring a cellular transition associated with or indicative of a presence or a stage of a disease, including but not limited to a myelofibrosis-related disease, as disclosed herein. In one aspect, the disclosure provides methods of selecting a patient for treatment with an effective amount of an agent for a myelofibrosis-related disease, comprising: (a) determining the presence of a cellular transition associated with or indicative of a presence or a stage of a myelofibrosis-related disease in a sample from the patient by analyzing cellular data from the sample; and (b) comparing the analysis of (a) to a profile of cellular dysfunction based on the analysis of cellular data from: (i) a plurality of human patients, at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease; (ii) optionally, a plurality of animal disease models of the myelofibrosis-related disease; (iii) optionally, a plurality of in vitro disease models of the myelofibrosis-related disease; and / or DB1 / 142566607.11 54 CLNV-015PC / 132119-5015 (iv) optionally, a plurality of ex vivo disease models of the myelofibrosis-related disease; wherein: the profile of cellular dysfunction is indicative of the cellular transition; and the cellular transition is associated with or indicative of a transition from an undiseased, pre- diseased, disease-treatable, or disease-reversible state to a disease-untreatable or disease- irreversible state; wherein the patient is suitable for treatment if demonstrating that the cellular data of (a) is at least substantially similar to the profile of cellular dysfunction of (b). In embodiments, the cellular data comprises and / or consists of one or more of transcriptomic data, genomic data, proteomic data and metabolomic data. In embodiments, the cellular data comprises and / or consists of transcriptomic (e.g. translational) data. In embodiments, the transcriptomic data comprises and / or consists of single nuclei data and / or single-cell transcription data. In embodiments, the transcriptomic data comprises and / or consists of bulk transcriptomic data. In embodiments, the bulk transcriptomic data comprises and / or consists of single nuclei data and / or single-cell transcription data. In embodiments, the method comprises in (b) comparing the analysis of (a) to a profile of cellular dysfunction based on the analysis of cellular data from: (i) a plurality of human patients, at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease; (ii) optionally, a plurality of animal disease models of the myelofibrosis-related disease; (iii) optionally, a plurality of in vitro disease models of the myelofibrosis-related disease; and / or (iv) optionally, a plurality of ex vivo disease models of the myelofibrosis-related disease. In embodiments, the cellular data comprises cellular data from one or more cell types selected from Table 1. In embodiments, the cellular data comprises cellular data from one or more gene markers selected from Table 1. In embodiments, the patient is suitable for treatment if demonstrating that the cellular data of (a) is at least about 30%, at least about 35%, at least about 40%, at least about 45%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or greater than about 99% similar to the profile of cellular dysfunction of (b). DB1 / 142566607.11 55 CLNV-015PC / 132119-5015 In embodiments, the patient is suitable for treatment if demonstrating that the single nuclei data and / or single- cell transcription data of (a) is at least about 30%, at least about 35%, at least about 40%, at least about 45%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or greater than about 99% similar to the profile of cellular dysfunction of (b). In one aspect, the disclosure provides methods of identifying a patient at risk for developing a disease, including but not limited to a myelofibrosis-related disease. In embodiments, the method includes detecting and / or monitoring a cellular transition associated with or indicative of a presence or a stage of a disease, including but not limited to a myelofibrosis-related disease, as disclosed herein. In one aspect, the disclosure provides methods for identifying a patient at risk for developing a myelofibrosis- related disease, comprising: (a) determining the presence of a cellular transition associated with or indicative of a presence or a stage of a myelofibrosis-related disease in a sample from the patient by analyzing cellular data from the sample; and (b) comparing the analysis of (a) to a profile of cellular dysfunction based on the analysis of cellular data from: (i) a plurality of human patients, at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease; (ii) optionally, a plurality of animal disease models of the myelofibrosis-related disease; (iii) optionally, a plurality of in vitro disease models of the myelofibrosis-related disease; and / or (iv) optionally, a plurality of ex vivo disease models of the myelofibrosis-related disease; wherein: the profile of cellular dysfunction is indicative of the cellular transition; and the cellular transition is associated with or indicative of a transition from an undiseased, pre- diseased, disease-treatable, or disease-reversible state to a disease-untreatable or disease- irreversible state; wherein the patient is at risk for developing the myelofibrosis-related disease if demonstrating that cellular data of (a) is at least substantially similar to the profile of cellular dysfunction of (b). DB1 / 142566607.11 56 CLNV-015PC / 132119-5015 In embodiments, the cellular data comprises and / or consists of one or more of transcriptomic data, genomic data, proteomic data and metabolomic data. In embodiments, the cellular data comprises and / or consists of transcriptomic (e.g. translational) data. In embodiments, the transcriptomic data comprises and / or consists of single nuclei data and / or single-cell transcription data. In embodiments, the transcriptomic data comprises and / or consists of bulk transcriptomic data. In embodiments, the bulk transcriptomic data comprises and / or consists of single nuclei data and / or single-cell transcription data. In embodiments, the method comprises in (b) comparing the analysis of (a) to a profile of cellular dysfunction based on the analysis of cellular data from: (i) a plurality of human patients, at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease; (ii) optionally, a plurality of animal disease models of the myelofibrosis-related disease; (iii) optionally, a plurality of in vitro disease models of the myelofibrosis-related disease; and / or (iv) optionally, a plurality of ex vivo disease models of the myelofibrosis-related disease. In embodiments, the cellular data comprises cellular data from one or more cell types selected from Table 1. In embodiments, the cellular data comprises cellular data from one or more gene markers selected from Table 1. In embodiments, the patient is at risk for developing the myelofibrosis-related disease if demonstrating that the cellular data of (a) is at least about 30%, at least about 35%, at least about 40%, at least about 45%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or greater than about 99% similar to the profile of cellular dysfunction of (b). In embodiments, the patient is at risk for developing the myelofibrosis-related disease if demonstrating that the single nuclei data and / or single-cell transcription data of (a) is at least about 30%, at least about 35%, at least about 40%, at least about 45%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or greater than about 99% similar to the profile of cellular dysfunction of (b). DB1 / 142566607.11 57 CLNV-015PC / 132119-5015 In embodiments, the patient has transitioned, is transitioning, or is at risk of transitioning from an undiseased or pre-diseased state to a disease-treatable or disease-reversible state. In embodiments, the patient has transitioned, is transitioning, or is at risk of transitioning from an undiseased or pre-diseased state to a disease-untreatable or disease-irreversible state. In embodiments, the patient has transitioned, is transitioning, or is at risk of transitioning between two different disease-treatable or disease-reversible states. In embodiments, the patient has transitioned, is transitioning, or is at risk of transitioning between a disease- treatable or disease-reversible state to a disease-untreatable or disease-irreversible state. In embodiments, the myelofibrosis-related disease is selected from Primary Myelofibrosis, Secondary Myelofibrosis, Essential Thrombocythemia, and Polycythemia Vera. In embodiments, samples comprise and / or consist of an organ, a tissue, stem cells, human cells, cells from umbilical cord blood, cells from peripheral blood, bone marrow cells, cells from a solid tissue, and / or differentiated cells, and / or cells are isolated from and / or harvested therein. In embodiments, (a) comprises and / or consists of analyzing cellular data from the sample. In embodiments, (a) comprises and / or consists of analyzing single nuclei data and / or single-cell transcription data from the sample. In embodiments, (a) comprises and / or consists of analyzing single nuclei data from the sample. In embodiments, (a) comprises and / or consists of analyzing single-cell transcription data from the sample. In embodiments, (a) comprises and / or consists of analyzing single nuclei data and single-cell transcription data from the sample. In embodiments, (b) comprises and / or consists of comparing the analysis of (a) to a profile of cellular dysfunction based on the analysis of cellular data from (i), (ii), (iii), and / or (iv). In embodiments, (b) comprises and / or consists of comparing the analysis of (a) to a profile of cellular dysfunction based on the analysis of single nuclei data and / or single-cell transcription data from (i), (ii), (iii), and / or (iv).In embodiments, (b) comprises and / or consists of comparing the analysis of (a) to a profile of cellular dysfunction based on the analysis of single nuclei data from (i), (ii), (iii), and / or (iv). In embodiments, (b) comprises and / or consists of comparing the analysis of (a) to a profile of cellular dysfunction based on the analysis of single nuclei data from (i), (ii), (iii), and / or (iv). In embodiments, (b) comprises and / or consists of comparing the analysis of (a) to a profile of cellular dysfunction based on the analysis of single nuclei data from (i), (ii), (iii), and / or (iv). Atlas In one aspect, the disclosure provides an atlas of cellular data. In embodiments, the atlas is prepared from a profile of cellular dysfunction generated from any of the methods and / or analyses disclosed herein, provided that the cellular data of the map is obtained from two or more cell types. In embodiments, the atlas is useful DB1 / 142566607.11 58 CLNV-015PC / 132119-5015 as a comparison to cellular data (e.g. single nuclei data and / or single-cell transcription data) taken from a sample, such as a patient sample and / or an animal, in vitro, in vivo and / or ex vivo disease model, in order to determine whether the cellular data (e.g. single nuclei data and / or single-cell transcription data) demonstrates cellular dysfunction. In embodiments, the cellular data comprises and / or consists of one or more of transcriptomic data, genomic data, proteomic data and metabolomic data. In embodiments, the cellular data comprises and / or consists of transcriptomic (e.g. translational) data. In embodiments, the transcriptomic data comprises and / or consists of bulk transcriptomic data. In embodiments, the bulk transcriptomic data comprises and / or consists of single nuclei data and / or single-cell transcription data. In embodiments, the transcriptomic data comprises and / or consists of single nuclei data and / or single-cell transcription data. In embodiments, the atlas comprises and / or consists of single nuclei data and / or single-cell transcription data. In embodiments, the atlas comprises and / or consists of single nuclei data. In embodiments, the atlas comprises and / or consists of single-cell transcription data. In embodiments, the atlas comprises and / or consists of single nuclei data and single-cell transcription data. In embodiments, the atlas comprises and / or consists of cellular data (e.g. single nuclei data and / or single- cell transcription data) obtained from two or more cell types (e.g. two or more, three or more, four or more, five or more, six or more, seven or more, or eight or more cell types). In embodiments, each of the two or more cell types is a different cell type from the remaining cell types. Non-limiting examples of cell types include hematopoietic stem cells (HSCs) (including but not limited to induced pluripotent stem cells (IPSCs)), hematopoietic precursor cells, hematopoietic progenitor cells, optionally basophil mast progenitor cell, common lymphoid progenitor, common myeloid progenitor, basophil mast progenitor cell, granulocyte monocyte progenitor cell, megakaryocyte-erythroid progenitor cell, erythroid progenitor cell, megakaryocyte progenitor cell, pro-T cell, pro-NK cell or pro-B cell, myeloid lineage cells, monocytes, macrophages, dendritic cells, optionally conventional type 1 dendritic cells (cDC1s), conventional type 2 dendritic cells (cDC2s), or plasmacytoid dendritic cells; neutrophils, eosinophils, erythroid lineage cell, megakaryocytes, basophils, mast cells, T-cells, natural killer (NK) cells, B-cells, plasma cells, mesenchymal cells, and endothelial cells. In embodiments, the cell type is selected from basophils / mast cells, monocyte, erythroid lineage cells, hematopoietic precursor cells, lymphoid lineage cells, megakaryocyte-erythroid progenitor cells, megakaryocytes, and myeloid lineage cells. DB1 / 142566607.11 59 CLNV-015PC / 132119-5015 In embodiments, the cells are isolated from and / or harvested from an organ, a tissue, stem cells, human cells, cells from umbilical cord blood, cells from peripheral blood, bone marrow cells, cells from a solid tissue, and / or differentiated cells. In embodiments, the cellular transition is based on a cellular behavior of a hematopoietic precursor cell and a megakaryocyte-erythroid progenitor cell. In embodiments, the two or more cell types are selected from Table 1. In one aspect, the disclosure provides an atlas of cellular data, comprising: (a) cellular data from a plurality of human patients, at least some of the plurality of human patients being afflicted with a myelofibrosis-related disease; and (b) optionally, cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease; wherein the cellular data is obtained from two or more cell types. In one aspect, the disclosure provides an atlas of single nuclei data and / or single-cell transcription data, comprising: (a) single nuclei data and / or single-cell transcription data from a plurality of human patients, at least some of the plurality of human patients being afflicted with a myelofibrosis-related disease; and (b) optionally, single nuclei data and / or single-cell transcription data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease, wherein the single nuclei data and / or single-cell transcription data is obtained from two or more cell types. In embodiments, the atlas comprises single nuclei data and / or single-cell transcription data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease. In embodiments, the atlas comprises and / or consists of cellular data (e.g. single nuclei data and / or single- cell transcription data) obtained from two or more cell types selected from Table 1. In embodiments, the atlas comprises and / or consists of cellular data (e.g. single nuclei data and / or single- cell transcription data) from one or more gene markers selected from Table 1. DB1 / 142566607.11 60 CLNV-015PC / 132119-5015 In embodiments, the atlas comprises single nuclei data and / or single-cell transcription data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease. Maps In one aspect, the disclosure provides a map of cellular data. In embodiments, the map is prepared from a profile of cellular dysfunction generated from any of the methods and / or analyses disclosed herein, provided that the cellular data of the map is obtained from a single cell type (i.e. one cell type only). In embodiments, the map is useful as a comparison to cellular data (e.g. single nuclei data and / or single-cell transcription data) taken from a sample, such as a patient sample and / or an animal, in vitro, in vivo and / or ex vivo disease model, in order to determine whether the cellular data (e.g. single nuclei data and / or single-cell transcription data) demonstrates cellular dysfunction. In embodiments, the cellular data comprises and / or consists of one or more of transcriptomic data, genomic data, proteomic data and metabolomic data. In embodiments, the cellular data comprises and / or consists of transcriptomic (e.g. translational) data. In embodiments, the transcriptomic data comprises and / or consists of bulk transcriptomic data. In embodiments, the bulk transcriptomic data comprises and / or consists of single nuclei data and / or single-cell transcription data.In embodiments, the transcriptomic data comprises and / or consists of single nuclei data and / or single-cell transcription data. In embodiments, the map comprises and / or consists of single nuclei data and / or single- cell transcription data. In embodiments, the map comprises and / or consists of single nuclei data. In embodiments, the map comprises and / or consists of single-cell transcription data. In embodiments, the map comprises and / or consists of single nuclei data and single-cell transcription data. In embodiments, the map comprises and / or consists of cellular data (e.g. single nuclei data and / or single-cell transcription data) obtained from a single cell type (i.e. one cell type only). Non-limiting examples of cell types include hematopoietic stem cells (HSCs) (including but not limited to induced pluripotent stem cells (IPSCs)), hematopoietic precursor cells, hematopoietic progenitor cells, optionally basophil mast progenitor cell, common lymphoid progenitor, common myeloid progenitor, basophil mast progenitor cell, granulocyte monocyte progenitor cell, megakaryocyte-erythroid progenitor cell, erythroid progenitor cell, megakaryocyte progenitor cell, pro-T cell, pro-NK cell or pro-B cell, myeloid lineage cells, monocytes, macrophages, dendritic cells, optionally conventional type 1 dendritic cells (cDC1s), conventional type 2 dendritic cells (cDC2s), or plasmacytoid dendritic cells; neutrophils, eosinophils, erythroid lineage cell, DB1 / 142566607.11 61 CLNV-015PC / 132119-5015 megakaryocytes, basophils, mast cells, T-cells, natural killer (NK) cells, B-cells, plasma cells, mesenchymal cells, and endothelial cells. In embodiments, the cell type is selected from basophils / mast cells, monocyte, erythroid lineage cells, hematopoietic precursor cells, lymphoid lineage cells, megakaryocyte-erythroid progenitor cells, megakaryocytes, and myeloid lineage cells. In embodiments, the cells are isolated from and / or harvested from an organ, a tissue, stem cells, human cells, cells from umbilical cord blood, cells from peripheral blood, bone marrow cells, cells from a solid tissue, and / or differentiated cells. In embodiments, the cellular transition is based on a cellular behavior of a hematopoietic precursor cell and a megakaryocyte-erythroid progenitor cell. In embodiments, the single cell type is selected from Table 1. In one aspect, the disclosure provides a map of cellular data, comprising: (a) cellular data from a plurality of human patients, at least some of the plurality of human patients being afflicted with a myelofibrosis-related disease; and (b) optionally, cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease; wherein the cellular data is obtained from a single cell type. In one aspect, the disclosure provides a map of single nuclei data and / or single-cell transcription data, comprising: (a) single nuclei data and / or single-cell transcription data from a plurality of human patients, at least some of the plurality of human patients being afflicted with a myelofibrosis-related disease; and (b) optionally, single nuclei data and / or single-cell transcription data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease, wherein the single nuclei data and / or single-cell transcription data is obtained from a single cell type. In embodiments, the map comprises single nuclei data and / or single-cell transcription data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease. DB1 / 142566607.11 62 CLNV-015PC / 132119-5015 In embodiments, the map comprises and / or consists of cellular data (e.g. single nuclei data and / or single-cell transcription data) obtained from two or more cell types selected from Table 1. In embodiments, the map comprises and / or consists of cellular data (e.g. single nuclei data and / or single-cell transcription data) from one or more gene markers selected from Table 1. In embodiments, the map comprises cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease. Methods of Preparing Atlases and Maps In one aspect, the disclosure provides methods for preparing atlases and / or maps of cellular data. In a non- limiting example, an atlas of the disclosure is first generated from cellular data obtained from two or more cell types from a plurality of human patients (e.g. wherein at least some of the plurality of human patients are afflicted with a myelofibrosis-related disease), and the cellular data is assembled into an atlas; then, the cellular data of the atlas (which includes cellular data from multiple cell types (e.g. two or more cell types)) is curated and / or filtered to obtain cellular data from a single cell type only, which can then be assembled into a map of cellular data, wherein the cellular data only includes cellular data from a single cell type (i.e. one type of cell only). In embodiments, the cellular data comprises and / or consists of transcriptomic (e.g. translational) data. In embodiments, the transcriptomic data comprises and / or consists of bulk transcriptomic data. In embodiments, the bulk transcriptomic data comprises and / or consists of single nuclei data and / or single-cell transcription data. In embodiments, the transcriptomic data comprises and / or consists of single nuclei data and / or single-cell transcription data. In embodiments, the map comprises and / or consists of single nuclei data and / or single-cell transcription data. In embodiments, the map comprises and / or consists of single nuclei data. In embodiments, the map comprises and / or consists of single-cell transcription data. In embodiments, the map comprises and / or consists of single nuclei data and single-cell transcription data. In embodiments, the disclosure provides a method for preparing an atlas of cellular data, comprising: (a) obtaining cellular data from a plurality of human patients, at least some of the plurality of human patients being afflicted with a myelofibrosis-related disease, wherein the cellular data is obtained from two or more cell types; and (b) assembling the cellular data of (a) into an atlas of cellular data. In embodiments, the disclosure provides a method for preparing a map of cellular data, comprising: DB1 / 142566607.11 63 CLNV-015PC / 132119-5015 (a) obtaining cellular data from a plurality of human patients, at least some of the plurality of human patients being afflicted with a myelofibrosis-related disease, wherein the cellular data is obtained from two or more cell types; (b) assembling the cellular data of (a) into an atlas of cellular data; (c) curating and / or filtering the cellular data of the atlas of (b) to obtain cellular data from a single cell type; and (d) assembling the cellular data obtained in (c) into a map of cellular data. In embodiments, the cellular data comprises cellular data from two or more cell types selected from Table 1. In embodiments, the cellular data comprises cellular data from a single cell type selected from Table 1. In embodiments, the cellular data comprises cellular data from one or more gene markers selected from Table 1. In embodiments, assembling the cellular data of (a) into an atlas of cellular data further comprises one or more of (i)-(iii): (i) obtaining metadata indicating one or more features from each human patient from the plurality of human patients; (ii) sorting the cellular data of (a) based on one or more of the metadata features; and (iii) filtering the cellular data of (a) to remove counts of ambient nucleic acid molecules, doublets and / or empty droplets, optionally ambient RNA molecules, optionally wherein the cellular data comprises and / or consists of single nuclei transcription data. In embodiments, curating and / or filtering the cellular data of (a) further comprises identifying cellular data of (a) associated with gene markers and / or cellular behaviors specific to the single cell type of interest, and obtaining the cellular data associated with the gene markers and / or cellular behaviors. In a non-limiting example, the obtained cellular data associated with these gene markers and / or cellular behaviors would include only cellular data to the single cell type of interest, which could then be assembled into a map of cellular data, wherein the cellular data is directed to only a single cell type (i.e. the single cell type of interest). In embodiments, the map comprises and / or consists of cellular data (e.g. single nuclei data and / or single-cell transcription data) obtained from a single cell type (i.e. one cell type only). In embodiments, the atlas comprises and / or consists of cellular data (e.g. single nuclei data and / or single-cell transcription data) obtained from two or more cell types (e.g. two or more, three or more, four or more, five or more, six or more, seven or more, or eight or more cell types). DB1 / 142566607.11 64 CLNV-015PC / 132119-5015 Non-limiting examples of cell types include hematopoietic stem cells (HSCs) (including but not limited to induced pluripotent stem cells (IPSCs)), hematopoietic precursor cells, hematopoietic progenitor cells, optionally basophil mast progenitor cell, common lymphoid progenitor, common myeloid progenitor, basophil mast progenitor cell, granulocyte monocyte progenitor cell, megakaryocyte-erythroid progenitor cell, erythroid progenitor cell, megakaryocyte progenitor cell, pro-T cell, pro-NK cell or pro-B cell, myeloid lineage cells, monocytes, macrophages, dendritic cells, optionally conventional type 1 dendritic cells (cDC1s), conventional type 2 dendritic cells (cDC2s), or plasmacytoid dendritic cells; neutrophils, eosinophils, erythroid lineage cell, megakaryocytes, basophils, mast cells, T-cells, natural killer (NK) cells, B-cells, plasma cells, mesenchymal cells, and endothelial cells. In embodiments, the cell type is selected from basophils / mast cells, monocyte, erythroid lineage cells, hematopoietic precursor cells, lymphoid lineage cells, megakaryocyte-erythroid progenitor cells, megakaryocytes, and myeloid lineage cells. In embodiments, the cells are isolated from and / or harvested from an organ, a tissue, stem cells, human cells, cells from umbilical cord blood, cells from peripheral blood, bone marrow cells, cells from a solid tissue, and / or differentiated cells. In embodiments, the cellular transition is based on a cellular behavior of a hematopoietic precursor cell and a megakaryocyte-erythroid progenitor cell. In embodiments, the single cell type is selected from Table 1. Methods for identifying a cellular behavior In aspects, a cellular behavior is identified in order to examine and / or understand the one or more changes (e.g. changes in gene expression) in one or more cells that are associated with or indicative of a desired cellular transition (e.g. a transition of one or more cells from a first cellular state to a second cellular state). In a non-limiting example, examining and understanding the one or more changes associated with or indicative of a desired cellular transition informs the selection of an agent useful to modulate the cellular behavior. In embodiments, the desired cellular transition is a cellular transition associated with or indicative of a presence or a stage of a myelofibrosis-related disease. In embodiments, the desired cellular transition and / or cellular behavior is associated with cellular dysfunction. In embodiments, the cellular dysfunction is associated with or indicative of a cellular transition, wherein the cellular transition is associated with or indicative of a transition from an undiseased, pre-diseased, disease- DB1 / 142566607.11 65 CLNV-015PC / 132119-5015 treatable, or disease-reversible state to a disease-untreatable or disease-irreversible state. In embodiments, the cellular dysfunction is associated with or indicative of a cellular transition associated with or indicative of a presence or a stage of a myelofibrosis-related disease. In embodiments, the desired cellular transition and / or cellular behavior is associated with cellular function. In embodiments, the cellular function is associated with or indicative of a cellular transition, wherein the cellular transition is associated with or indicative of a transition from a disease-untreatable or disease-irreversible state to an undiseased, pre-diseased, disease-treatable, or disease-reversible state. In embodiments, the disclosure provides a method of identifying a profile of cellular behavior. In embodiments, the method comprises: (a) selecting a desired cellular transition of a single cell type associated with or indicative of a presence or a stage of a myelofibrosis-related disease; (b) selecting from a map of cellular data one or more cellular transitions at least substantially similar to the desired cellular transition of (a), wherein: the map comprises: (i) cellular data from a plurality of human patients, wherein at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease; and (ii) optionally, cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease; wherein the cellular data of (i) and (ii) are obtained from the single cell type; and (c) assembling the one or more cellular transitions selected in (b) into the profile of cellular behavior. In embodiments, the map comprises cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease. In embodiments, the disclosure provides a method of identifying a cellular behavior. In embodiments, the method comprises: (a) selecting a desired cellular transition of a single cell type associated with or indicative of a presence or a stage of a myelofibrosis-related disease; DB1 / 142566607.11 66 CLNV-015PC / 132119-5015 (b) selecting from a map of cellular data one or more cellular transitions at least substantially similar to the desired cellular transition of (a), wherein: the map comprises: (i) cellular data from a plurality of human patients, wherein at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease; and (ii) optionally, cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease; wherein the cellular data of (i) and (ii) are obtained from the single cell type; and (c) analyzing the one or more cellular transitions selected in (b); wherein the analysis of (c) provides the cellular behavior. In embodiments, the map comprises cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease. In embodiments, the cellular data are obtained from a single cell type selected from Table 1. In embodiments, the cellular data comprises cellular data from one or more gene markers selected from Table 1. In embodiments, the cellular behavior and / or desired cellular transition is associated with or indicative of a transition from an undiseased or pre-diseased state to a disease-treatable or disease-reversible state. In embodiments, the cellular behavior and / or desired cellular transition is associated with or indicative of a transition from an undiseased or pre-diseased state to a disease-untreatable or disease-irreversible state. In embodiments, the cellular behavior and / or desired cellular transition is associated with or indicative of a transition between two different disease-treatable or disease-reversible states. In embodiments, the cellular behavior and / or desired cellular transition is associated with or indicative of a transition between a disease- treatable or disease-reversible state to a disease-untreatable or disease-irreversible state. In embodiments, the cellular behavior and / or desired cellular transition is associated with or indicative of a transition from normal hematopoiesis to abnormal hematopoiesis. In embodiments, the analyzing of (c) comprises identifying one or more changes (e.g. changes in gene expression) associated with or indicative of the desired cellular transition. Non-limiting examples of changes include changes in gene expression (e.g. upregulation or downregulation of genes), changes in expression DB1 / 142566607.11 67 CLNV-015PC / 132119-5015 of biomarkers, genotypic changes, and phenotypic changes. In embodiments, the analyzing of (c) comprises identifying common upregulation of about 1 or more, about 2 or more, about 3 or more, about 4 or more, about 5 or more, about 10 or more, about 15 or more, about 20 or more, about 25 or more, about 30 or more, about 35 or more, about 40 or more, about 45 or more, about 50 or more, about 55 or more, about 60 or more, about 65 or more, about 70 or more, about 75 or more, about 80 or more, about 85 or more, about 90 or more, about 95 or more, or about 100 or more genes, optionally about 1 or more or about 2 or more genes. In embodiments, the cellular transition is associated with or indicative of a transition from a disease-treatable or disease-reversible state to an undiseased or pre-diseased state. In embodiments, the cellular transition is associated with or indicative of a transition from disease-untreatable or disease-irreversible state to an undiseased or pre-diseased state. In embodiments, the cellular transition is associated with or indicative of a transition from two different disease-treatable or disease-reversible states. In embodiments, the cellular transition is associated with or indicative of a transition between a disease-untreatable or disease-irreversible state to a disease-treatable or disease-reversible state. In embodiments, the cellular transition is associated with or indicative of a transition from abnormal hematopoiesis to normal hematopoiesis. In embodiments, the cellular transition is associated with or indicative of a transition from decreased red blood cell and / or platelet count to normal red blood cell and / or platelet count. In embodiments, the cellular transition is associated with or indicative of a transition from bone marrow fibrosis to healthy bone marrow. In embodiments, the cellular transition is associated with or indicative of a transition from leukemic cells to healthy blood cells. In embodiments, the cellular behavior is at least partially modified and / or impacted by the modulation of one or more molecular targets. In embodiments, the cellular behavior is at least partially modified and / or impacted when the cellular behavior undergoes reproducible transcriptional changes related to modulation of the one or more molecular targets. In embodiments, reproducible transcriptional changes comprise reproducible upregulation and / or downregulation of one or more genes upon modulation of the one or more molecular targets. Non-limiting examples of a molecular target include a protein, nucleic acid, gene, and any other biological molecule. In embodiments, modulation of the one or more molecular targets is performed using an agent having affinity for and / or is capable of modulating the one or more molecular targets (including but not limited to agents disclosed herein). In embodiments, modulation of the one or more molecular targets occurs through one or more mechanisms of action. DB1 / 142566607.11 68 CLNV-015PC / 132119-5015 In embodiments, the method further comprises selecting an agent, wherein the agent has affinity for and / or is capable of modulating the molecular target. In embodiments, the agent exhibits a therapeutic effect and / or is useful for the treatment of one or more diseases or disorders (e.g. a myelofibrosis-related disease). Non- limiting examples of agents include one or more of a small molecule, a biologic, a protein, a protein combined with a small molecule, an antibody drug conjugate (ADC), a peptide, and a nucleic acid, and include, without limitation, any agents disclosed herein. In embodiments, the agent is capable of modulating the therapeutic target through one or more mechanisms of action (MOAs). In embodiments, the selected agent is a further modified to exhibit one or more properties of (a)-(f) compared to the unmodified agent (e.g. to provide a lead agent useful in drug discovery efforts): (a) increased activity strength and / or selectivity; (b) increased solubility and / or partition property; (c) increased metabolic and / or chemical stability; (d) modulation of pharmacokinetic parameters (including, without limitation, absorption, distribution, metabolism, and excretion (ADME)), optionally wherein the modulation provides increased or enhanced pharmacokinetic properties compared to the compound prior to the modulation; (e) modulation of pharmacodynamic parameters (including, without limitation, dose-response relationship (e.g. EC50)); optionally wherein the modulation provides increased or enhanced pharmacodynamic properties compared to the compound prior to the modulation; and (f) reduction or ablation of toxicity and / or adverse reactions. In embodiments, the cellular data comprises and / or consists of one or more of transcriptomic data, genomic data, proteomic data and metabolomic data. In embodiments, the cellular data comprises and / or consists of transcriptomic (e.g. translational) data. In embodiments, the transcriptomic data comprises and / or consists of single nuclei data and / or single-cell transcription data. In embodiments, the transcriptomic data comprises and / or consists of bulk transcriptomic data. In embodiments, the bulk transcriptomic data comprises and / or consists of single nuclei data and / or single-cell transcription data. Non-limiting examples of cells include hematopoietic stem cells (HSCs) (including but not limited to induced pluripotent stem cells (IPSCs)), hematopoietic precursor cells, hematopoietic progenitor cells, optionally basophil mast progenitor cell, common lymphoid progenitor, common myeloid progenitor, basophil mast progenitor cell, granulocyte monocyte progenitor cell, megakaryocyte-erythroid progenitor cell, erythroid DB1 / 142566607.11 69 CLNV-015PC / 132119-5015 progenitor cell, megakaryocyte progenitor cell, pro-T cell, pro-NK cell or pro-B cell, myeloid lineage cells, monocytes, macrophages, dendritic cells, optionally conventional type 1 dendritic cells (cDC1s), conventional type 2 dendritic cells (cDC2s), or plasmacytoid dendritic cells; neutrophils, eosinophils, erythroid lineage cell, megakaryocytes, basophils, mast cells, T-cells, natural killer (NK) cells, B-cells, plasma cells, mesenchymal cells, and endothelial cells. In embodiments, the cell type is selected from basophils / mast cells, monocyte, erythroid lineage cells, hematopoietic precursor cells, lymphoid lineage cells, megakaryocyte-erythroid progenitor cells, megakaryocytes, and myeloid lineage cells. In embodiments, the cells are isolated from and / or harvested from an organ, a tissue, stem cells, human cells, cells from umbilical cord blood, cells from peripheral blood, bone marrow cells, cells from a solid tissue, and / or differentiated cells. In embodiments, the cellular transition is based on a cellular behavior of a hematopoietic precursor cell and a megakaryocyte-erythroid progenitor cell. In embodiments, the single cell type is selected from Table 1. Methods for identifying mechanisms of action In one aspect, the disclosure provides methods for identifying a mechanism of action (MOA). In embodiments, the mechanism of action is associated with or indicative of one or more cellular transitions associated with or indicative of a myelofibrosis-related disease. In embodiments, the mechanism of action is associated with or indicative of a myelofibrosis-related disease. In aspects, the disclosure provides a method of identifying a mechanism of action (MOA). In embodiments, the method comprises: (a) selecting one or more desired cellular transitions of a single cell type associated with or indicative of a presence or a stage of a myelofibrosis-related disease; (b) selecting from a map of cellular data one or more cellular transitions at least substantially similar to the desired cellular transition of (a), wherein: the map comprises: DB1 / 142566607.11 70 CLNV-015PC / 132119-5015 (i) cellular data from a plurality of human patients, wherein at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease; and (ii) optionally, cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease; wherein the cellular data of (i) and (ii) are obtained from the single cell type; (c) comparing the one or more cellular transitions selected in (b) with one or more cellular transitions associated with or indicative of one or more known mechanisms of action; and (d) selecting the mechanism of action from the one or more known mechanisms of action based on the comparison of (c), wherein one or more cellular transitions associated with or indicative of the selected mechanism of action is at least substantially similar to the one or more desired cellular transitions of (a). In embodiments, the map comprises cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease. In embodiments, the cellular data comprises cellular data from a single cell type selected from Table 1. In embodiments, the cellular data comprises cellular data from one or more gene markers selected from Table 1. In embodiments, the mechanism of action (MoAs) is useful for the treatment of one or more diseases or disorders, including but not limited to myelofibrosis-related diseases. Non-limiting examples of mechanisms of action include adenosine receptor agonism, aldosterone-receptor antagonism (e.g. mineralocorticoid receptor antagonists), alpha-adrenoceptor agonism (e.g. alpha-agonists), blocking of alpha-adrenoceptor (e.g. alpha-blockers), inhibition of angiotensin-converting enzyme (ace) (e.g. ace inhibitors), blocking of angiotensin receptor (arbs), beta-adrenoceptor agonism (e.g. beta-agonists), blocking of beta-adrenoceptor (e.g. beta-blockers), blocking of calcium-channel (ccbs), inhibition of adrenergic receptor signaling, promotion of diuresis, endothelin receptor antagonism, nicotinic receptor antagonism, mineralocorticoid receptor antagonism, muscarinic receptor antagonism, Na+-K+- atpase pump inhibition (e.g. cardiac glycosides), inhibition of neprilysin, inhibition of phosphodiesterase inhibitors, blocking of potassium-channel, opening of DB1 / 142566607.11 71 CLNV-015PC / 132119-5015 potassium-channel, renin inhibition, blocking of sodium-channel, inhibition of sodium-glucose cotransporter 2, and activation of adrenergic receptors. In embodiments, the desired cellular transition is associated with or indicative of a transition from an undiseased or pre-diseased state to a disease-treatable or disease-reversible state. In embodiments, the desired cellular transition is associated with or indicative of a transition from an undiseased or pre-diseased state to a disease-untreatable or disease-irreversible state. In embodiments, the desired cellular transition is associated with or indicative of a transition between two different disease-treatable or disease-reversible states. In embodiments, the desired cellular transition is associated with or indicative of a transition between a disease-treatable or disease-reversible state to a disease-untreatable or disease-irreversible state. In embodiments, the cellular behavior and / or desired cellular transition is associated with or indicative of a transition from normal hematopoiesis to abnormal hematopoiesis. In embodiments, the method further comprises selecting an agent, wherein the agent exhibits a mechanism of action at least substantially similar to the selected mechanism of action. In embodiments, the agent exhibits a therapeutic effect and / or is useful for the treatment of one or more diseases or disorders (e.g. a myelofibrosis-related disease) based at least on the mechanism of action. Non-limiting examples of agents include one or more of a small molecule, a biologic, a protein, a protein combined with a small molecule, an antibody drug conjugate (ADC), a peptide, and a nucleic acid, and include, without limitation, any agents disclosed herein. In embodiments, the selected agent is a further modified to exhibit one or more properties of (a)-(f) compared to the unmodified agent (e.g. to provide a lead agent useful in drug discovery efforts): (a) increased activity strength and / or selectivity; (b) increased solubility and / or partition property; (c) increased metabolic and / or chemical stability; (d) modulation of pharmacokinetic parameters (including, without limitation, absorption, distribution, metabolism, and excretion (ADME)), optionally wherein the modulation provides increased or enhanced pharmacokinetic properties compared to the compound prior to the modulation; (e) modulation of pharmacodynamic parameters (including, without limitation, dose-response relationship (e.g. EC50)); optionally wherein the modulation provides increased or enhanced pharmacodynamic properties compared to the compound prior to the modulation; and DB1 / 142566607.11 72 CLNV-015PC / 132119-5015 (f) reduction or ablation of toxicity and / or adverse reactions. In embodiments, the cellular data comprises and / or consists of one or more of transcriptomic data, genomic data, proteomic data and metabolomic data. In embodiments, the cellular data comprises and / or consists of transcriptomic (e.g. translational) data. In embodiments, the transcriptomic data comprises and / or consists of single nuclei data and / or single-cell transcription data. In embodiments, the transcriptomic data comprises and / or consists of bulk transcriptomic data. In embodiments, the bulk transcriptomic data comprises and / or consists of single nuclei data and / or single-cell transcription data. Non-limiting examples of cells include hematopoietic stem cells (HSCs) (including but not limited to induced pluripotent stem cells (IPSCs)), hematopoietic precursor cells, hematopoietic progenitor cells, optionally basophil mast progenitor cell, common lymphoid progenitor, common myeloid progenitor, basophil mast progenitor cell, granulocyte monocyte progenitor cell, megakaryocyte-erythroid progenitor cell, erythroid progenitor cell, megakaryocyte progenitor cell, pro-T cell, pro-NK cell or pro-B cell, myeloid lineage cells, monocytes, macrophages, dendritic cells, optionally conventional type 1 dendritic cells (cDC1s), conventional type 2 dendritic cells (cDC2s), or plasmacytoid dendritic cells; neutrophils, eosinophils, erythroid lineage cell, megakaryocytes, basophils, mast cells, T-cells, natural killer (NK) cells, B-cells, plasma cells, mesenchymal cells, and endothelial cells. In embodiments, the cell type is selected from basophils / mast cells, monocyte, erythroid lineage cells, hematopoietic precursor cells, lymphoid lineage cells, megakaryocyte-erythroid progenitor cells, megakaryocytes, and myeloid lineage cells. In embodiments, the cells are isolated from and / or harvested from an organ, a tissue, stem cells, human cells, cells from umbilical cord blood, cells from peripheral blood, bone marrow cells, cells from a solid tissue, and / or differentiated cells. In embodiments, the cellular transition is based on a cellular behavior of a hematopoietic precursor cell and a megakaryocyte-erythroid progenitor cell. In embodiments, the single cell type is selected from Table 1. Methods for identifying molecular targets In one aspect, the disclosure provides a method of identifying a molecular target. In embodiments, the method comprises: DB1 / 142566607.11 73 CLNV-015PC / 132119-5015 (a) selecting one or more desired cellular transitions of a single cell type associated with or indicative of a presence or a stage of a myelofibrosis-related disease; (b) selecting from a map of cellular data one or more cellular transitions at least substantially similar to the desired cellular transition of (a), wherein: the map comprises: (i) cellular data from a plurality of human patients, wherein at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease; and (ii) optionally, cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease; wherein the cellular data of (i) and (ii) are obtained from the single cell type; (c) comparing the one or more cellular transitions selected in (b) with one or more cellular transitions associated with or indicative of one or more known molecular targets; and (d) selecting the molecular targets from the one or more known molecular targets based on the comparison of (c), wherein one or more cellular transitions associated with or indicative of the selected molecular target is at least substantially similar to the one or more desired cellular transitions of (a). In embodiments, the map comprises cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease. In embodiments, the cellular data of (b) comprises cellular data from one or more cell types selected from Table 1. In embodiments, the cellular data of (b) comprises cellular data from one or more gene markers selected from Table 1. In embodiments, a cellular behavior and / or profile of cellular behavior (e.g. a profile of cellular dysfunction or a profile of cellular function) is at least partially modified and / or impacted by the modulation of one or more molecular targets. In a non-limiting example, one or more changes in a cellular transition (e.g. changes in gene expression) can be promoted or inhibited upon modulation of the one or more molecular targets. In embodiments, modulation of the molecular target results in one or more changes (e.g. changes in gene expression) associated with or indicative of the desired cellular transition. In embodiments, modulation of the DB1 / 142566607.11 74 CLNV-015PC / 132119-5015 molecular target comprises common upregulation and / or downregulation of the expression of one or more biomarkers. In embodiments, modulation of the molecular target comprises common upregulation and / or downregulation of genes. In embodiments, modulation of the molecular target provides a desired cellular transition (e.g. a transition of one or more cells from a first cellular state to a second cellular state) that provides treatment of a disease or disorder (e.g. a myelofibrosis-related disease). In embodiments, modulation of the molecular target at least partially modifies one or more cellular transitions associated with or indicative of a myelofibrosis-related disease. In embodiments, modulation of the molecular target provides treatment of a myelofibrosis-related disease. Non-limiting examples of a molecular target include a protein, nucleic acid, gene, and any other biological molecule. In embodiments, modulation of the one or more molecular targets is performed using an agent having affinity for and / or is capable of modulating the molecular target (including but not limited to agents disclosed herein). In embodiments, the desired cellular transition is associated with or indicative of a transition from an undiseased or pre-diseased state to a disease-treatable or disease-reversible state. In embodiments, the desired cellular transition is associated with or indicative of a transition from an undiseased or pre-diseased state to a disease-untreatable or disease-irreversible state. In embodiments, the desired cellular transition is associated with or indicative of a transition between two different disease-treatable or disease-reversible states. In embodiments, the desired cellular transition is associated with or indicative of a transition between a disease-treatable or disease-reversible state to a disease-untreatable or disease-irreversible state. In embodiments, the cellular behavior and / or desired cellular transition is associated with or indicative of a transition from normal hematopoiesis to abnormal hematopoiesis. In embodiments, the cellular transition is associated with or indicative of a transition from normal red blood cell and / or platelet count to decreased red blood cell and / or platelet count. In embodiments, the cellular transition is associated with or indicative of a transition from healthy bone marrow to bone marrow fibrosis. In embodiments, the cellular transition is associated with or indicative of a transition from healthy blood cells to leukemia cells. In embodiments, the method further comprises selecting an agent, wherein the agent has affinity for and / or is capable of modulating the molecular target. In embodiments, the agent exhibits a therapeutic effect and / or is useful for the treatment of one or more diseases or disorders (e.g. a myelofibrosis-related disease). Non- limiting examples of agents include one or more of a small molecule, a biologic, a protein, a protein combined with a small molecule, an antibody drug conjugate (ADC), a peptide, and a nucleic acid, and include, without limitation, any agents disclosed herein. In embodiments, the agent is capable of modulating the therapeutic target through one or more mechanisms of action (MOAs). In embodiments, the selected agent is a further DB1 / 142566607.11 75 CLNV-015PC / 132119-5015 modified to exhibit one or more properties of (a)-(f) compared to the unmodified agent (e.g. to provide a lead agent useful in drug discovery efforts): (a) increased activity strength and / or selectivity; (b) increased solubility and / or partition property; (c) increased metabolic and / or chemical stability; (d) modulation of pharmacokinetic parameters (including, without limitation, absorption, distribution, metabolism, and excretion (ADME)), optionally wherein the modulation provides increased or enhanced pharmacokinetic properties compared to the compound prior to the modulation; (e) modulation of pharmacodynamic parameters (including, without limitation, dose-response relationship (e.g. EC50)); optionally wherein the modulation provides increased or enhanced pharmacodynamic properties compared to the compound prior to the modulation; and (f) reduction or ablation of toxicity and / or adverse reactions. In embodiments, the cellular data comprises and / or consists of one or more of transcriptomic data, genomic data, proteomic data and metabolomic data. In embodiments, the cellular data comprises and / or consists of transcriptomic (e.g. translational) data. In embodiments, the transcriptomic data comprises and / or consists of single nuclei data and / or single-cell transcription data. In embodiments, the transcriptomic data comprises and / or consists of bulk transcriptomic data. In embodiments, the bulk transcriptomic data comprises and / or consists of single nuclei data and / or single-cell transcription data. Non-limiting examples of cells include hematopoietic stem cells (HSCs) (including but not limited to induced pluripotent stem cells (IPSCs)), hematopoietic precursor cells, hematopoietic progenitor cells, optionally basophil mast progenitor cell, common lymphoid progenitor, common myeloid progenitor, basophil mast progenitor cell, granulocyte monocyte progenitor cell, megakaryocyte-erythroid progenitor cell, erythroid progenitor cell, megakaryocyte progenitor cell, pro-T cell, pro-NK cell or pro-B cell, myeloid lineage cells, monocytes, macrophages, dendritic cells, optionally conventional type 1 dendritic cells (cDC1s), conventional type 2 dendritic cells (cDC2s), or plasmacytoid dendritic cells; neutrophils, eosinophils, erythroid lineage cell, megakaryocytes, basophils, mast cells, T-cells, natural killer (NK) cells, B-cells, plasma cells, mesenchymal cells, and endothelial cells. DB1 / 142566607.11 76 CLNV-015PC / 132119-5015 In embodiments, the cell type is selected from basophils / mast cells, monocyte, erythroid lineage cells, hematopoietic precursor cells, lymphoid lineage cells, megakaryocyte-erythroid progenitor cells, megakaryocytes, and myeloid lineage cells. In embodiments, the cells are isolated from and / or harvested from an organ, a tissue, stem cells, human cells, cells from umbilical cord blood, cells from peripheral blood, bone marrow cells, cells from a solid tissue, and / or differentiated cells. In embodiments, the cellular transition is based on a cellular behavior of a hematopoietic precursor cell and a megakaryocyte-erythroid progenitor cell. In embodiments, the single cell type is selected from Table 1. Methods for identifying an agent In one aspect, the disclosure provides a method of identifying an agent. In embodiments, the method comprises: (a) selecting one or more desired cellular transitions of a single cell type associated with or indicative of a presence or a stage of a myelofibrosis-related disease; (b) selecting from a map of cellular data one or more cellular transitions at least substantially similar to the desired cellular transition of (a), wherein: the map comprises: (i) cellular data from a plurality of human patients, wherein at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease; and (ii) optionally, cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease; wherein the cellular data of (i) and (ii) are obtained from the single cell type; (c) comparing the one or more cellular transitions selected in (b) with one or more cellular transitions associated with or indicative of one or more known agents; and DB1 / 142566607.11 77 CLNV-015PC / 132119-5015 (d) selecting the agent from the one or more known agents based on the comparison of (c), wherein one or more cellular transitions associated with or indicative of the selected agent is at least substantially similar to the one or more desired cellular transitions of (a). In embodiments, the map comprises cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease. In embodiments, the cellular data of (b) comprises cellular data from one or more cell types selected from Table 1. In embodiments, the cellular data of (b) comprises cellular data from one or more gene markers selected from Table 1. In embodiments, the agent has affinity for and / or is capable of modulating one or more molecular targets, wherein modulation of the one or more molecular targets modifies a cellular behavior and / or a profile of cellular behavior and / or one or more cellular transitions. Non-limiting examples of a molecular target include a protein, nucleic acid, gene, and any other biological molecule. In embodiments, modulation of the one or more molecular targets occurs through one or more mechanisms of action. In embodiments, modulation of the one or more molecular targets results in one or more changes (e.g. changes in gene expression) associated with or indicative of the desired cellular transition. Non-limiting examples of changes include changes in gene expression (e.g. upregulation or downregulation of genes), changes in expression of biomarkers, genotypic changes, and phenotypic changes. In embodiments, the analyzing of (c) comprises identifying common upregulation of about 1 or more, about 2 or more, about 3 or more, about 4 or more, about 5 or more, about 10 or more, about 15 or more, about 20 or more, about 25 or more, about 30 or more, about 35 or more, about 40 or more, about 45 or more, about 50 or more, about 55 or more, about 60 or more, about 65 or more, about 70 or more, about 75 or more, about 80 or more, about 85 or more, about 90 or more, about 95 or more, or about 100 or more genes, optionally about 1 or more or about 2 or more genes. In embodiments, the agent exhibits a therapeutic effect and / or is useful for the treatment of one or more diseases or disorders (e.g. a myelofibrosis-related disease). In embodiments, the agent Non-limiting examples of agents include one or more of a small molecule, a biologic, a protein, a protein combined with a small molecule, an antibody drug conjugate (ADC), a peptide, and a nucleic acid, and include, without limitation, any agents disclosed herein. In embodiments, the agent is a further modified to exhibit one or more properties of (a)-(f) compared to the unmodified agent (e.g. to provide a lead agent useful in drug discovery efforts): DB1 / 142566607.11 78 CLNV-015PC / 132119-5015 (a) increased activity strength and / or selectivity; (b) increased solubility and / or partition property; (c) increased metabolic and / or chemical stability; (d) modulation of pharmacokinetic parameters (including, without limitation, absorption, distribution, metabolism, and excretion (ADME)), optionally wherein the modulation provides increased or enhanced pharmacokinetic properties compared to the compound prior to the modulation; (e) modulation of pharmacodynamic parameters (including, without limitation, dose-response relationship (e.g. EC50)); optionally wherein the modulation provides increased or enhanced pharmacodynamic properties compared to the compound prior to the modulation; and (f) reduction or ablation of toxicity and / or adverse reactions. In embodiments, the desired cellular transition is associated with or indicative of a transition from an undiseased or pre-diseased state to a disease-treatable or disease-reversible state. In embodiments, the desired cellular transition is associated with or indicative of a transition from an undiseased or pre-diseased state to a disease-untreatable or disease-irreversible state. In embodiments, the desired cellular transition is associated with or indicative of a transition between two different disease-treatable or disease-reversible states. In embodiments, the desired cellular transition is associated with or indicative of a transition between a disease-treatable or disease-reversible state to a disease-untreatable or disease-irreversible state. In embodiments, the cellular behavior and / or desired cellular transition is associated with or indicative of a transition from normal hematopoiesis to abnormal hematopoiesis. In embodiments, the cellular data comprises and / or consists of one or more of transcriptomic data, genomic data, proteomic data and metabolomic data. In embodiments, the cellular data comprises and / or consists of transcriptomic (e.g. translational) data. In embodiments, the transcriptomic data comprises and / or consists of single nuclei data and / or single-cell transcription data. In embodiments, the transcriptomic data comprises and / or consists of bulk transcriptomic data. In embodiments, the bulk transcriptomic data comprises and / or consists of single nuclei data and / or single-cell transcription data. Non-limiting examples of cells include hematopoietic stem cells (HSCs) (including but not limited to induced pluripotent stem cells (IPSCs)), hematopoietic precursor cells, hematopoietic progenitor cells, optionally basophil mast progenitor cell, common lymphoid progenitor, common myeloid progenitor, basophil mast progenitor cell, granulocyte monocyte progenitor cell, megakaryocyte-erythroid progenitor cell, erythroid DB1 / 142566607.11 79 CLNV-015PC / 132119-5015 progenitor cell, megakaryocyte progenitor cell, pro-T cell, pro-NK cell or pro-B cell, myeloid lineage cells, monocytes, macrophages, dendritic cells, optionally conventional type 1 dendritic cells (cDC1s), conventional type 2 dendritic cells (cDC2s), or plasmacytoid dendritic cells; neutrophils, eosinophils, erythroid lineage cell, megakaryocytes, basophils, mast cells, T-cells, natural killer (NK) cells, B-cells, plasma cells, mesenchymal cells, and endothelial cells. In embodiments, the cell type is selected from basophils / mast cells, monocyte, erythroid lineage cells, hematopoietic precursor cells, lymphoid lineage cells, megakaryocyte-erythroid progenitor cells, megakaryocytes, and myeloid lineage cells. In embodiments, the cells are isolated from and / or harvested from an organ, a tissue, stem cells, human cells, cells from umbilical cord blood, cells from peripheral blood, bone marrow cells, cells from a solid tissue, and / or differentiated cells. In embodiments, the cellular transition is based on a cellular behavior of a hematopoietic precursor cell and a megakaryocyte-erythroid progenitor cell. In embodiments, the single cell type is selected from Table 1. Definitions In general, terms used in the claims and the specification are intended to be construed as having the plain meaning understood by a person of ordinary skill in the art. Certain terms are defined below to provide additional clarity. In case of conflict between the plain meaning and the provided definitions, the provided definitions are to be used. Any terms not directly defined herein shall be understood to have the meanings commonly associated with them as understood within the art of the invention. Certain terms are discussed herein to provide additional guidance to the practitioner in describing the compositions, the devices, the methods and the like of aspects of the invention and how to make or use them. It will be appreciated that the same thing may be said in more than one way. Consequently, alternative language and synonyms may be used for any one or more of the terms discussed herein. No significance is to be placed upon whether or not a term is elaborated or discussed herein. Some synonyms or substitutable methods, materials and the like are provided. Recital of one or a few synonyms or equivalents does not exclude use of other synonyms or equivalents, unless it is explicitly DB1 / 142566607.11 80 CLNV-015PC / 132119-5015 stated. Use of examples, including examples of terms, is for illustrative purposes only and does not limit the scope and meaning of the aspects of the invention herein. As used herein, the terms “cell fate” and “cell state” are interchangeable and synonymous. The term “subject,” refers to an individual organism such as a human or an animal. In embodiments, the subject is a mammal (e.g., a human, a non-human primate, or a non-human mammal), a vertebrate, a laboratory animal, a domesticated animal, an agricultural animal, or a companion animal. In embodiments, the subject is a human (e.g., a human patient). In embodiments, the subject is a rodent, a mouse, a rat, a hamster, a rabbit, a dog, a cat, a cow, a goat, a sheep, or a pig. As used in this Specification and the appended claims, the singular forms “a,” “an” and “the” include plural referents unless the context clearly dictates otherwise. Unless specifically stated or obvious from context, as used herein, the term “or” is understood to be inclusive and covers both “or” and “and”. Likewise, the term “and / or” covers both “or” and “and”. Unless specifically stated or obvious from context, as used herein, the term “about” is understood as within a range of normal tolerance in the art, for example within 2 standard deviations of the mean. About is understood to be within 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1%, 0.5%, 0.1%, 0.05%, or 0.01% of the stated value. Unless otherwise clear from the context, all numerical values provided herein are modified by the term “about.” The invention will be further described in the following examples, which do not limit the scope of the invention described in the claims. EXAMPLES Example 1: Validation of model systems Data Preprocessing In a non-limiting example, quality control and preprocessing of internal and external datasets are performed as follows. Low quality, outlier and doublet cells as well as lowly expressed genes are filtered based on established metrics. The count data is be normalized and log-transformed to account for differences in sequencing depth, and all samples of a dataset will be concatenated to a single matrix. This filtered and normalized data matrix is used as input for all downstream analyses along with the associated cell level metadata. DB1 / 142566607.11 81 CLNV-015PC / 132119-5015 Atlases In a non-limiting example, atlases (e.g. a profile of cellular dysfunction) are generated using learning low- dimensional data representations. Different public and in-house dimensionality reduction and manifold learning tools are utilized to find a low-dimensional embedding of the data, which captures the biological variation of interest. Unwanted variation such as technical batch effects or donor-to-donor variability are removed with data integration techniques. Cell types / states are identified through unsupervised-clustering and subsequent annotation of clusters based on enriched marker genes. Atlases are iteratively refined and tailored to specific research questions to represent the most relevant aspects of the captured biology. In silico disease modelling and validation of model systems In a non-limiting example, a targetable in silico model of the disease biology is established using transcriptional feature learning. First, data maps are generated to identify the cell states associated with disease progression in in vivo preclinical and, if available, clinical models. The diseased states are characterized through features learned from the transcriptional and functional data, enabling the ability to decompose and compare the complex data sets of distinct in vivo preclinical and clinical models. Next, covariate and phenotypic modeling is performed. Shared features represent elements of the identified diseased states, which are reproducibly deregulated across replicates, donors, disease stages and / or models. Cross-system mapping of disease associated features is then performed. Whether these features are clinically relevant in the publicly available bulk RNAseq data sets of large-scale patient cohorts is further validated. Finally, using these features the different in vitro model systems are benchmarked to delineate which aspects of the disease biology can be modeled. In a non-limiting example, cell states of interest are identified in clinical data. The cell states are characterized through transcriptional (e.g. RNA seq data) and functional data. Cell states or features thereof associated with clinical variables (i.e. clinical cell states) are identified. Examples of clinical variables include, but are not limited to, compositional analysis, differential expression, covariate and phenotypic modeling. Clinical cell states and features thereof are then validated in large scale bulk data (e.g. bulk transcriptional data such as bulk RNAseq data). Next, cell states of interest in preclinical data are identified. Human cell state features are then mapped to preclinical data to annotate cell states (e.g. cross-system mapping), and preclinical-data specific cell states are annotated. Animal models and in vitro models are benchmarked through cell state representation and clinical feature activation. Small molecule predictions to target disease states DB1 / 142566607.11 82 CLNV-015PC / 132119-5015 To target the identified disease states, machine learning models are used to predict small molecule “Hit Compounds” from a digital small molecule library based on characterized transcriptional signature. Different prediction strategies are designed based on the biological hypotheses generated from the in silico disease models. The strategies aim at targeting different aspects of the disease state profile and proposed biological processes underlying disease progression. Hits expansion to improve the hit compound starting point Once small molecules hits are identified in vitro, two parallel strategies to expand and deepen the understanding of the targeted mechanisms of action (MoAs) and disease states are initiated. One strategy is transcriptional signature refinement-based design. Perturbational modeling tools are utilized to further refine transcriptional signature of the disease states that are targeted by the hit molecules. Based on this refinement, an expanded list of small molecules that target similar biological processes but with increased chemical and MoA diversity is iteratively proposed. A second strategy is a MoA / target-based design. An expanded list of molecules or genetic perturbations are systematically proposed for the validation of possible mechanisms that links the compound MoA to the target disease states. This is achieved with a combination of mechanism inference models leveraging network biology, genetics and SAR models. Small Molecule Drug Design The expanded lists of small molecule hits are used to inform the design of drugs having novel chemical structures. In a non-limiting example, functional groups common to small molecules that target similar biological processes and / or exhibit similar MoAs to the target disease states are identified and incorporated into the structure of small molecules designed to exhibit the desired biological properties and / or MoA. INCORPORATION BY REFERENCE All patents and publications referenced herein are hereby incorporated by reference in their entireties. The publications discussed herein are provided solely for their disclosure prior to the filing date of the present application. Nothing herein is to be construed as an admission that the present invention is not entitled to antedate such publication by virtue of prior invention. As used herein, all headings are simply for organization and are not intended to limit the disclosure in any manner. The content of any individual section may be equally applicable to all sections. DB1 / 142566607.11 83 CLNV-015PC / 132119-5015 EQUIVALENTS While the invention has been disclosed in connection with specific embodiments thereof, it will be understood that it is capable of further modifications and this application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains and as may be applied to the essential features hereinbefore set forth and as follows in the scope of the appended claims. Those skilled in the art will recognize, or be able to ascertain, using no more than routine experimentation, numerous equivalents to the specific embodiments disclosed specifically herein. Such equivalents are intended to be encompassed in the scope of the following claims. DB1 / 142566607.11 84

Claims

CLNV-015PC / 132119-5015 CLAIMS What is claimed is:

1. A method of detecting and / or monitoring a cellular transition associated with or indicative of a presence or a stage of a myelofibrosis-related disease, comprising: (a) analyzing cellular data from a plurality of human patients, at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease; (b) optionally, analyzing cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease; and (c) generating a profile of cellular dysfunction based on the analyses of (a) and (b), wherein: the profile of cellular dysfunction is indicative of the cellular transition; and the cellular transition is associated with or indicative of a transition from an undiseased, pre- diseased, disease-treatable, or disease-reversible state to a disease-untreatable or disease- irreversible state.

2. A method of detecting and / or monitoring a cellular transition associated with or indicative of a presence or a stage of a myelofibrosis-related disease, comprising: (a) analyzing cellular data from a plurality of human patients, at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease; (b) optionally, analyzing cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease; and (c) generating a profile of cellular dysfunction based on the analyses of (a) and (b), wherein: the cellular data of (a) and (b) is obtained from one or more cell types selected from Table 1; the cellular data of (a) and (b) comprises cellular data from one or more gene markers selected from Table 1; the profile of cellular dysfunction is indicative of the cellular transition; and DB1 / 142566607.11 85CLNV-015PC / 132119-5015 the cellular transition is associated with or indicative of a transition from an undiseased, pre- diseased, disease-treatable, or disease-reversible state to a disease-untreatable or disease- irreversible state.

3. The method of claims 1 or 2, wherein the cellular data comprises and / or consists of one or more selected from transcriptomic data, genomic data, proteomic data and metabolomic data.

4. The method of any one of claims 1-3, wherein the cellular data comprises and / or consists of transcriptomic data, optionally wherein the transcriptomic data comprises or consists of data obtained from a text-based format of sequencing data (e.g. FASTQ), optionally wherein the transcriptomic data comprise and / or consists of bulk transcriptomic data optionally comprising and / or consisting of single nuclei data and / or single-cell transcription data.

5. The method of claim 4, wherein the transcriptomic data comprises and / or consists of single nuclei data and / or single-cell transcription data.

6. The method of any one of claims 1-5, wherein the plurality of human patients comprises about 3 or more patients, about 5 or more patients, about 20 or more patients, about 50 or more patients, or about 100 or more patients.

7. The method of any one of claims 1-6, wherein the cellular data of (a) is obtained from one or more samples from each human patient from the plurality of human patients, optionally wherein the one or more samples are frozen samples, optionally wherein the cellular data comprises and / or consists of single-cell transcription data and / or single nuclei transcription data.

8. The method of any one of claims 1-7, wherein metadata indicating one or more features is obtained from each human patient from the plurality of human patients.

9. The method of claim 8, wherein the cellular data of (a) is sorted based on one or more of the metadata features.

10. The method of any one of claims 1-9, wherein the cellular data of (a) and / or (b) is filtered to remove counts of ambient nucleic acid molecules, doublets and / or empty droplets, optionally ambient RNA molecules, optionally wherein the cellular data comprises and / or consists of single nuclei transcription data.

11. The method of any one of claims 1-10, wherein generating the profile of cellular dysfunction of (c) further comprises analyzing biomarkers from the cellular data, and comparing the biomarkers from the cellular data to a standardized set of biomarkers to identify a cell type associated with the cellular transition. DB1 / 142566607.11 86CLNV-015PC / 132119-5015 12. The method of any one of claims 1-11, wherein the method further comprises (d) obtaining genotypic data from each human patient of the plurality of human patients of (a).

13. The method of claim 12, wherein generating a profile of cellular dysfunction of (c) further comprises analyzing the cellular data of (a) with the genotypic data of (d).

14. The method of any one of claims 1-13, wherein the analyzing of step (a) further comprises analyzing sample collection detail, technical covariates, patient information, optionally selected from disease stage, family history, nutrition, age, gender, comorbidities, and lifestyle parameters.

15. The method of any one of claims 1-14, wherein the analyzing of step (b) further comprises analyzing phenotypic or functional parameters.

16. The method of any one of claims 1-15, wherein the method informs the selection of an agent to treat the myelofibrosis-related disease.

17. The method of any one of claims 1-16, wherein the profile of cellular dysfunction comprises one or more changes in one or more cells that are associated with or indicative of the cellular transition.

18. The method of claim 17, wherein the one or more changes are selected from changes in gene expression (e.g. upregulation or downregulation of genes), changes in expression of biomarkers, genotypic changes, and phenotypic changes).

19. The method of any one of claims 1-18, wherein the profile of cellular dysfunction comprises common upregulation of about 1 or more genes or about 2 or more genes and / or common downregulation of about 1 or more genes or about 2 or more genes according to analysis of (a) and (b).

20. A method of selecting a patient for treatment with an effective amount of an agent for a myelofibrosis- related disease, comprising: (a) determining the presence of a cellular transition associated with or indicative of a presence or a stage of a myelofibrosis-related disease in one or more samples from the patient by analyzing cellular data from the one or more samples; and (b) comparing the analysis of (a) to a profile of cellular dysfunction based on the analysis of cellular data from: (i) optionally, a plurality of human patients, at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease; DB1 / 142566607.11 87CLNV-015PC / 132119-5015 (ii) optionally, a plurality of animal disease models of the myelofibrosis-related disease; (iii) optionally, a plurality of in vitro disease models of the myelofibrosis-related disease; and / or (iv) optionally, a plurality of ex vivo disease models of the myelofibrosis-related disease; wherein: the profile of cellular dysfunction is indicative of the cellular transition; and the cellular transition is associated with or indicative of a transition from an undiseased, pre- diseased, disease-treatable, or disease-reversible state to a disease-untreatable or disease- irreversible state; wherein the patient is suitable for treatment if demonstrating that the cellular data of (a) is at least substantially similar to the profile of cellular dysfunction of (b).

21. A method of selecting a patient for treatment with an effective amount of an agent for a myelofibrosis- related disease, comprising: (a) determining the presence of a cellular transition associated with or indicative of a presence or a stage of a myelofibrosis-related disease in a sample from the patient by analyzing cellular data from the sample; and (b) comparing the analysis of (a) to a profile of cellular dysfunction based on the analysis of cellular data from: (i) a plurality of human patients, at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease; (ii) optionally, a plurality of animal disease models of the myelofibrosis-related disease; (iii) optionally, a plurality of in vitro disease models of the myelofibrosis-related disease; and / or (iv) optionally, a plurality of ex vivo disease models of the myelofibrosis-related disease; wherein: the cellular data of (a) and (b) comprises cellular data from one or more cell types selected from Table 1; the cellular data of (a) and (b) comprises cellular data from one or more gene markers selected from Table 1; the profile of cellular dysfunction is indicative of the cellular transition; and DB1 / 142566607.11 88CLNV-015PC / 132119-5015 the cellular transition is associated with or indicative of a transition from an undiseased, pre- diseased, disease-treatable, or disease-reversible state to a disease-untreatable or disease- irreversible state; wherein the patient is suitable for treatment if demonstrating that the cellular data of (a) is at least substantially similar to the profile of cellular dysfunction of (b).

22. The method of claim 20 or 21, wherein the cellular data comprises and / or consists of one or more selected from transcriptomic data, genomic data, proteomic data and metabolomic data.

23. The method of any one of claims 20-22, wherein the cellular data comprises and / or consists of transcriptomic data, optionally wherein the transcriptomic data comprises or consists of data obtained from a text-based format of sequencing data (e.g. FASTQ), optionally wherein the transcriptomic data comprise and / or consists of bulk transcriptomic data optionally comprising and / or consisting of single nuclei data and / or single-cell transcription data.

24. The method of claim 23, wherein the transcriptomic data comprises and / or consists of single nuclei data and / or single-cell transcription data.

25. The method of any one of claims 20-24, wherein the plurality of human patients of (b)(i) comprises about 3 or more patients, about 5 or more patients, about 20 or more patients, about 50 or more patients, or about 100 or more patients.

26. The method of any one of claims 20-25, wherein the one or more samples of (a) are frozen samples.

27. The method of any one of claims 20-26, wherein the cellular data of (b)(i) is obtained from one or more samples from each human patient from the plurality of human patients, optionally wherein the one or more samples are frozen samples, optionally wherein the cellular data comprises and / or consists of single- cell transcription data and / or single nuclei transcription data.

28. The method of any one of claims 20-27, wherein metadata indicating one or more features is obtained from each human patient from the plurality of human patients of (b)(i).

29. The method of claim 28, wherein the metadata indicates one or more features selected from sex, race, age, and physical condition.

30. The method of claim 28 or 29, wherein the cellular data of (b) is sorted based on one or more of the metadata features. DB1 / 142566607.11 89CLNV-015PC / 132119-5015 31. The method of any one of claims 20-30, wherein the cellular data of (a) and / or (b) is filtered to remove counts of ambient nucleic acid molecules, doublets and / or empty droplets, optionally ambient RNA molecules, optionally wherein the cellular data comprises and / or consists of single nuclei transcription data.

32. The method of any one of claims 29-31, wherein one or more of the sex, race, age, and physical condition is represented in a balanced manner in the plurality of patients of (b)(i).

33. The method of any one of claims 20-32, wherein the analyzing of step (a) further comprises analyzing sample collection detail, technical covariates, patient information, optionally selected from disease stage, family history, nutrition, age, gender, comorbidities, and lifestyle parameters.

34. The method of any one of claims 20-33, wherein the analyzing of step (b) further comprises analyzing phenotypic or functional parameters.

35. The method of any one of claims 20-34, wherein the wherein the profile of cellular dysfunction comprises one or more changes in one or more cells that are associated with or indicative of the cellular transition.

36. The method of claim 35, wherein the one or more changes are selected from changes in gene expression (e.g. upregulation or downregulation of genes), changes in expression of biomarkers, genotypic changes, and phenotypic changes).

37. The method of any one of claims 20-36, wherein the profile of cellular dysfunction comprises common upregulation of about 1 or more genes or about 2 or more genes and / or common downregulation of about 1 or more genes or about 2 or more genes according to analysis of (a) and the profile of cellular dysfunction of (b).

38. A method of evaluating translatability of an animal, in vitro, in vivo and / or ex vivo model of a myelofibrosis-related disease to a human, comprising: (a) determining the presence of a cellular transition associated with or indicative of a presence or a stage of the myelofibrosis-related disease in the animal, in vitro, in vivo and / or ex vivo model by analyzing cellular data from the animal, in vitro, in vivo and / or ex vivo model; and (b) comparing the analysis of (a) to a profile of cellular dysfunction based on the analysis of cellular data from a plurality of human patients, at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease wherein: DB1 / 142566607.11 90CLNV-015PC / 132119-5015 the profile of cellular dysfunction is indicative of the cellular transition; and the cellular transition is associated with or indicative of a transition from an undiseased, pre- diseased, disease-treatable, or disease-reversible state to a disease-untreatable or disease-irreversible state; wherein the animal, in vitro, in vivo and / or ex vivo model is translatable to a human if demonstrating that the single nuclei data and / or single-cell transcription data of (a) is at least substantially similar to the profile of cellular dysfunction of (b).

39. A method of evaluating translatability of an animal, in vitro, in vivo and / or ex vivo model of a myelofibrosis-related disease to a human, comprising: (a) determining the presence of a cellular transition associated with or indicative of a presence or a stage of a myelofibrosis-related disease in the animal, in vitro, in vivo and / or ex vivo model by analyzing cellular data from the animal, in vitro, in vivo and / or ex vivo model; and (b) comparing the analysis of (a) to a profile of cellular dysfunction based on the analysis of cellular data from a plurality of human patients, at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease wherein: the cellular data of (a) and (b) comprises cellular data from one or more cell types selected from Table 1; the cellular data of (a) and (b) comprises cellular data from one or more gene markers selected from Table 1; the profile of cellular dysfunction is indicative of the cellular transition; and the cellular transition is associated with or indicative of a transition from an undiseased, pre-diseased, disease-treatable, or disease-reversible state to a disease-untreatable or disease-irreversible state; wherein the animal, in vitro, in vivo and / or ex vivo model is translatable to a human if demonstrating that the single nuclei data and / or single-cell transcription data of (a) is at least substantially similar to the profile of cellular dysfunction of (b).

40. The method of claims 38 or 39, wherein the cellular data comprises and / or consists of one or more selected from transcriptomic data, genomic data, proteomic data and metabolomic data. DB1 / 142566607.11 91CLNV-015PC / 132119-5015 41. The method of any one of claims 38-40, wherein the cellular data comprises and / or consists of transcriptomic data, optionally wherein the transcriptomic data comprises or consists of data obtained from a text-based format of sequencing data (e.g. FASTQ), optionally wherein the transcriptomic data comprise and / or consists of bulk transcriptomic data optionally comprising and / or consisting of single nuclei data and / or single-cell transcription data.

42. The method of any one of claims 38-41, wherein the transcriptomic data comprises and / or consists of single nuclei data and / or single-cell transcription data.

43. The method of any one of claims 38-42, wherein the plurality of human patients comprises about 3 or more patients, about 5 or more patients, about 20 or more patients, about 50 or more patients, or about 100 or more patients.

44. The method of any one of claims 38-43, wherein the cellular data of (b) is obtained from one or more samples from each human patient from the plurality of human patients, optionally wherein the one or more samples are frozen samples, optionally wherein the cellular data comprises and / or consists of single-cell transcription data and / or single nuclei transcription data.

45. The method of any one of claims 38-44, wherein metadata indicating one or more features is obtained from each human patient from the plurality of human patients.

46. The method of claim 45, wherein the cellular data of (a) is sorted based on one or more of the metadata features.

47. The method of any one of claims 38-46, wherein the cellular data of (a) and / or (b) is filtered to remove counts of ambient nucleic acid molecules, doublets and / or empty droplets, optionally ambient RNA molecules, optionally wherein the cellular data comprises and / or consists of single nuclei transcription data.

48. The method of any one of claims 38-47, wherein the cellular transition is associated with or indicative of a transition from normal hematopoiesis to abnormal hematopoiesis.

49. The method of any one of claims 38-48, wherein the analyzing of step (a) further comprises analyzing sample collection detail, technical covariates, patient information, optionally selected from disease stage, family history, nutrition, age, gender, comorbidities, and lifestyle parameters.

50. The method of any one of claims 38-49, wherein the analyzing of step (b) further comprises analyzing phenotypic or functional parameters. DB1 / 142566607.11 92CLNV-015PC / 132119-5015 51. The method any one of claims 38-50, wherein the method informs the selection of an agent to treat the myelofibrosis-related disease.

52. The method of any one of claims 38-51, wherein the profile of cellular dysfunction comprises one or more changes in one or more cells that are associated with or indicative of the cellular transition.

53. The method of claim 52, wherein the one or more changes are selected from changes in gene expression (e.g. upregulation or downregulation of genes), changes in expression of biomarkers, genotypic changes, and phenotypic changes).

54. The method of any one of claims 38-53, wherein the profile of cellular dysfunction comprises common upregulation of about 1 or more genes or about 2 or more genes and / or common downregulation of about 1 or more genes or about 2 or more genes according to analysis of (a) and the profile of cellular dysfunction of (b).

55. A method for identifying an agent for treating a myelofibrosis-related disease, comprising: (a) administering the agent in an animal, in vitro, in vivo and / or ex vivo disease model of the myelofibrosis- related disease; (b) determining the presence of a cellular transition associated with or indicative of a presence or a stage of a myelofibrosis-related disease in the animal, in vitro, in vivo and / or ex vivo disease model by analyzing cellular data from the animal, in vitro, in vivo and / or ex vivo disease model; and (c) comparing the analysis of (b) to a profile of cellular dysfunction based on the analysis of cellular data from: (i) a plurality of human patients, at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease; (ii) a plurality of animal disease models of the myelofibrosis-related disease; (iii) a plurality of in vitro disease models of the myelofibrosis-related disease; and / or (iv) a plurality of ex vivo disease models of the myelofibrosis-related disease; wherein: the profile of cellular dysfunction is indicative of the cellular transition; and DB1 / 142566607.11 93CLNV-015PC / 132119-5015 the cellular transition is associated with or indicative of a transition from a disease- untreatable or disease-irreversible state to an undiseased, pre-diseased, disease-treatable, or disease-reversible state; wherein the agent is suitable for treating the myelofibrosis-related disease if demonstrating that the cellular data of (b) is at least substantially similar to the profile of cellular dysfunction of (c).

56. A method for identifying an agent for treating a myelofibrosis-related disease, comprising: (a) administering the agent in an animal, in vitro, in vivo and / or ex vivo disease model of the myelofibrosis- related disease; (b) determining the presence of a cellular transition associated with or indicative of a presence or a stage of a myelofibrosis-related disease in the animal, in vitro, in vivo and / or ex vivo disease model by analyzing cellular data from the animal, in vitro, in vivo and / or ex vivo disease model; and (c) comparing the analysis of (b) to a profile of cellular dysfunction based on the analysis of cellular data from: (i) a plurality of human patients, at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease; (ii) a plurality of animal disease models of the myelofibrosis-related disease; (iii) a plurality of in vitro disease models of the myelofibrosis-related disease; and / or (iv) a plurality of ex vivo disease models of the myelofibrosis-related disease; wherein: the cellular data of (b) and (c) comprises cellular data from one or more cell types selected from Table 1; the cellular data of (b) and (c) comprises cellular data from one or more gene markers selected from Table 1; the profile of cellular dysfunction is indicative of the cellular transition; and the cellular transition is associated with or indicative of a transition from a disease- untreatable or disease-irreversible state to an undiseased, pre-diseased, disease-treatable, or disease-reversible state; DB1 / 142566607.11 94CLNV-015PC / 132119-5015 wherein the agent is suitable for treating the myelofibrosis-related disease if demonstrating that the single nuclei data and / or single-cell transcription data of (b) is at least substantially similar to the profile of cellular dysfunction of (c).

57. The method of claims 55 or 56, wherein the cellular data comprises and / or consists of one or more selected from transcriptomic data, genomic data, proteomic data and metabolomic data.

58. The method of any one of claims 55-57, wherein the cellular data comprises and / or consists of transcriptomic data, optionally wherein the transcriptomic data comprises or consists of data obtained from a text-based format of sequencing data (e.g. FASTQ), optionally wherein the transcriptomic data comprise and / or consists of bulk transcriptomic data optionally comprising and / or consisting of single nuclei data and / or single-cell transcription data.

59. The method of any one of claims 55-58, wherein the transcriptomic data comprises and / or consists of single nuclei data and / or single-cell transcription data.

60. The method of any one of claims 55-59, wherein the plurality of human patients of (c)(i) comprises about 3 or more patients, about 5 or more patients, about 20 or more patients, about 50 or more patients, or about 100 or more patients.

61. The method of any one of claims 55-60, wherein the one or more samples of (b) are frozen samples.

62. The method of any one of claims 55-61, wherein the single-cell transcription data and / or single nuclei transcription data of (c)(i) is obtained from one or more samples from each human patient from the plurality of human patients, optionally wherein the one or more samples are frozen samples, optionally wherein the cellular data comprises and / or consists of single-cell transcription data and / or single nuclei transcription data.

63. The method of any one of claims 55-62, wherein metadata indicating one or more features is obtained from each human patient from the plurality of human patients of (c)(i).

64. The method of claim 63, wherein the metadata indicates one or more features selected from sex, race, age, and physical condition.

65. The method of claim 63 or 64, wherein the cellular data of (b) is sorted based on one or more of the metadata features. DB1 / 142566607.11 95CLNV-015PC / 132119-5015 66. The method of any one of claims 55-65, wherein the cellular data of (b) and / or (c) is filtered to remove counts of ambient nucleic acid molecules, doublets and / or empty droplets, optionally ambient RNA molecules, optionally wherein the cellular data comprises and / or consists of single nuclei transcription data.

67. The method of any one of claims 64-66, wherein one or more of the sex, race, age, and physical condition is represented in a balanced manner in the plurality of patients of (c)(i).

68. The method of any one of claims 55-67 wherein the analyzing of step (a) further comprises analyzing sample collection detail, technical covariates, patient information, optionally selected from disease stage, family history, nutrition, age, gender, comorbidities, and lifestyle parameters.

69. The method of any one of claims 55-68, wherein the analyzing of step (b) further comprises analyzing phenotypic or functional parameters.

70. The method of any one of claims 55-69, wherein the method informs the selection of an agent to treat the myelofibrosis-related disease.

71. The method of any one of claims 55-70, wherein the profile of cellular dysfunction comprises one or more changes in one or more cells that are associated with or indicative of the cellular transition optionally wherein, the one or more changes are selected from changes in gene expression (e.g. upregulation or downregulation of genes), changes in expression of biomarkers, genotypic changes, and phenotypic changes).

72. The method of any one of claims 55-71, wherein the cellular transition is associated with or indicative of a transition selected from (a)-(h): (a) a transition from a disease-treatable or disease-reversible state to an undiseased or pre- diseased state; (b) a transition from a disease-untreatable or disease-irreversible state to an undiseased or pre- diseased state; (c) a transition between two different disease-treatable or disease-reversible states; (d) a transition between a disease-untreatable or disease-irreversible state to a disease- treatable or disease-reversible state; (e) a transition from abnormal hematopoiesis to normal hematopoiesis; DB1 / 142566607.11 96CLNV-015PC / 132119-5015 (f) a transition from decreased red blood cell and / or platelet count to normal red blood cell and / or platelet count; (g) a transition from bone marrow fibrosis to healthy bone marrow; or (h) a transition from leukemic cells to healthy blood cells.

73. The method of any one of claims 55-72, wherein the profile of cellular dysfunction comprises common upregulation of about 1 or more genes or about 2 or more genes and / or common downregulation of about 1 or more genes or about 2 or more genes according to analysis of (a) and the profile of cellular dysfunction of (b).

74. The method of any one of claims 8-19, 28-37, 45-54, and 63-73, wherein the metadata indicates one or more features selected from sex, race, age, and physical condition.

75. The method of claim 74, wherein the physical condition comprises one or more features selected from body mass index (BMI), medical history, family medical history, disease diagnostic information, medication profile, alcohol consumption, illicit drug use, and cause of death.

76. The method of claim 75, wherein the medical history and / or family medical history comprises presence, absence, or degree of severity of one or more of Primary Myelofibrosis, Secondary Myelofibrosis, Essential Thrombocythemia, Polycythemia Vera, and the like.

77. The method of claim 75 or 76, wherein the disease diagnostic information comprises disease status.

78. The method of claim 77, wherein the disease status comprises histologically graded disease status.

79. The method of any one of claims 75-78, wherein the disease diagnostic information comprises one or more features selected from International Prognostic Scoring System (IPSS), Dynamic IPSS (DIPSS), DIPSS Plus, Mutation-Enhanced International Prognostic Score System (MIPSS70), MIPSS70+, Genetically Inspired Prognostic Scoring System (GIPSS), Myelofibrosis Secondary to PV and ET-Prognostic Model (MYSEC-PM), Myelofibrosis Transplant Scoring System (MTSS), Response to Ruxolitinib after 6 Months (RR6), Artificial Intelligence Prognostic Scoring System for Myelofibrosis (AIPSS-MF), and pathology notes and key features.

80. The method of any one of claims 74-79, wherein one or more of the sex, race, age, and physical condition is represented in a balanced manner in the plurality of patients. DB1 / 142566607.11 97CLNV-015PC / 132119-5015 81. The method of any one of claims 1-80, wherein the cellular transition is associated with or indicative of a transition from normal hematopoiesis to abnormal hematopoiesis.

82. The method of any one of claims 1-80, wherein the cellular transition is associated with or indicative of a transition from normal red blood cell and / or platelet count to decreased red blood cell and / or platelet count.

83. The method of any one of claims 1-80, wherein the cellular transition is associated with or indicative of a transition from an undiseased or pre-diseased state to a disease-treatable or disease-reversible state.

84. The method of any one of claims 1-80, wherein the cellular transition is associated with or indicative of a transition from an undiseased or pre-diseased state to a disease-untreatable or disease-irreversible state.

85. The method of any one of claims 1-80, wherein the cellular transition is associated with or indicative of a transition between two different disease-treatable or disease-reversible states.

86. The method of any one of claims 1-80, wherein the cellular transition is associated with or indicative of a transition between a disease-treatable or disease-reversible state to a disease-untreatable or disease- irreversible state.

87. The method of any one of claims 1-80, wherein the cellular transition is associated with or indicative of a transition from normal hematopoiesis to abnormal hematopoiesis.

88. The method of any one of claims 1-80, wherein the cellular transition is associated with or indicative of a transition from normal red blood cell and / or platelet count to decreased red blood cell and / or platelet count.

89. The method of any one of claims 1-80, wherein the cellular transition is associated with or indicative of a transition from healthy bone marrow to bone marrow fibrosis.

90. The method of any one of claims 1-80, wherein the cellular transition is associated with or indicative of a transition from healthy blood cells to leukemia cells.

91. The method of any one of claims 1-90, wherein the cellular transition is based on cellular behavior of at least one selected from hematopoietic stem cells (HSCs) (including but not limited to induced pluripotent stem cells (IPSCs)), hematopoietic precursor cells, hematopoietic progenitor cells, optionally basophil mast progenitor cell, common lymphoid progenitor, common myeloid progenitor, basophil mast progenitor cell, DB1 / 142566607.11 98CLNV-015PC / 132119-5015 granulocyte monocyte progenitor cell, megakaryocyte-erythroid progenitor cell, erythroid progenitor cell, megakaryocyte progenitor cell, pro-T cell, pro-NK cell or pro-B cell, myeloid lineage cells, monocytes, macrophages, dendritic cells, optionally conventional type 1 dendritic cells (cDC1s), conventional type 2 dendritic cells (cDC2s), or plasmacytoid dendritic cells; neutrophils, eosinophils, erythroid lineage cell, megakaryocytes, basophils, mast cells, T-cells, natural killer (NK) cells, B-cells, plasma cells, mesenchymal cells, and endothelial cells.

92. The method of any one of claims 1-91, wherein the cellular transition is based on a cellular behavior of at least one selected from basophils / mast cells, monocytes, erythroid lineage cells, hematopoietic precursor cells, lymphoid lineage cells, megakaryocyte-erythroid progenitor cells, megakaryocytes, and myeloid lineage cells.

93. The method of any one of claims 1-92, wherein the cellular transition is based on a cellular behavior of at least one of a hematopoietic precursor cell and a megakaryocyte-erythroid progenitor cell.

94. The method of any one of claims 1-93, wherein the method comprises analyzing cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the disease and / or comparing the analysis to a profile of cellular dysfunction based on the analysis of cellular data from a plurality of animal disease models of the myelofibrosis-related disease, in vitro disease models of the myelofibrosis-related disease, and / or ex vivo disease models of the myelofibrosis-related disease.

95. The method of any one of claims 1-94, wherein the animal model is selected from a model that mimics etiology and / or natural history, a model that mimics histopathology, and a genetic model.

96. The method of claim 95, wherein the model that mimics etiology and / or natural history is selected from a Jak2VF, MplW515L, Calrdel52, TPO, Gata-1low, and Trisomy 21 (Ts65Dn) model.

97. The method of any one of claims 1-96, wherein the in vitro disease model is selected from a primary cell type system, a stem cell derived system, and a cell line system.

98. The method of claim 97, wherein the in vitro disease model comprises induced hematopoietic stem cells (iHSCs) derived from patient induced pluripotent stem cells (iPSC).

99. The method of any one of claims 1-96, wherein the in vivo model disease model comprises patient derived xenograft models (PDX).

100. The method of any one of claims 1-99, wherein the cellular data comprises and or consists of single nuclei data, wherein the single nuclei data is or comprises single-nucleus ribonucleic acid (RNA) sequencing DB1 / 142566607.11 99CLNV-015PC / 132119-5015 (snRNA-seq) data and / or the single-cell transcription data is or comprises single-cell ribonucleic acid (RNA) sequencing (scRNA-seq) data.

101. The method of any one of claims 16-37 or 51-100, wherein the agent is one or more of a small molecule, a biologic, a protein, a protein combined with a small molecule, an antibody drug conjugate (ADC), a peptide, and a nucleic acid.

102. The method of claim 101, wherein the nucleic acid is one or more of an siRNA or interfering RNA, an antimiR, an mRNA, an aptamer, a cDNA over-expressing wild-type and / or mutant shRNA, a cDNA over- expressing wild-type and / or a gene-editing system (e.g. a mutant guide RNA (e.g., Cas9 system or other cellular-component editing system)).

103. The method of any one of claims 101 or 102, wherein the agent causes an improvement and / or resolution of anemia, when administered to a patient in need thereof.

104. The method of any one of claims 101-103, wherein the agent causes an improvement and / or resolution of thrombocytopenia, when administered to a patient in need thereof.

105. The method of any one of claims 101-104, wherein the agent causes reversal of abnormal hematopoiesis, when administered to a patient in need thereof.

106. The method of any one of claims 1-105, wherein the myelofibrosis-related disease is characterized by abnormal hematopoiesis.

107. The method of any one of claims 1-106, wherein the myelofibrosis-related disease is selected from Primary Myelofibrosis, Secondary Myelofibrosis, Essential Thrombocythemia, and Polycythemia Vera, anemia, thrombocytopenia.

108. The method of any one of claims 1-107, wherein at least some of the plurality of human patients are healthy.

109. The method of any one of claims 1-108, wherein at least some of the plurality of human patients are at risk of developing the myelofibrosis-related disease.

110. The method of any one of claims 1-109, wherein at least some of the plurality of human patients are non-responsive to one or more established therapies for treating the myelofibrosis-related disease. DB1 / 142566607.11 100CLNV-015PC / 132119-5015 111. The method of any one of claims 1-110, wherein at least some of the plurality of human patients have transitioned, are transitioning, or are at risk of transitioning from an undiseased or pre-diseased state to a disease-treatable or disease-reversible state.

112. The method of any one of claims 1-111, wherein at least some of the plurality of human patients have transitioned, are transitioning, or are at risk of transitioning from an undiseased or pre-diseased state to a disease-untreatable or disease-irreversible state.

113. The method of any one of claims 1-112, wherein at least some of the plurality of human patients have transitioned, are transitioning, or are at risk of transitioning between two different disease-treatable or disease-reversible states.

114. The method of any one of claims 1-113, wherein at least some of the plurality of human patients have transitioned, are transitioning, or are at risk of transitioning between a disease-treatable or disease- reversible state to a disease-untreatable or disease-irreversible state.

115. A method of identifying a patient at risk for developing a myelofibrosis-related disease, comprising: (a) determining the presence of a cellular transition associated with or indicative of a presence or a stage of a myelofibrosis-related disease in a sample from the patient by analyzing cellular data from the sample; and (b) comparing the analysis of (a) to a profile of cellular dysfunction based on the analysis of cellular data from: (i) a plurality of human patients, at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease; (ii) optionally, a plurality of animal disease models of the myelofibrosis-related disease; (iii) optionally, a plurality of in vitro disease models of the myelofibrosis-related disease; and / or (iv) optionally, a plurality of ex vivo disease models of the myelofibrosis-related disease; wherein: the profile of cellular dysfunction is indicative of the cellular transition; and the cellular transition is associated with or indicative of a transition from a disease- untreatable or disease-irreversible state to an undiseased, pre-diseased, disease-treatable, or disease-reversible state; DB1 / 142566607.11 101CLNV-015PC / 132119-5015 wherein the patient is at risk for developing the myelofibrosis-related disease if demonstrating that cellular data of (a) is at least substantially similar to the profile of cellular dysfunction of (b).

116. A method of identifying a patient at risk for developing a myelofibrosis-related disease, comprising: (a) determining the presence of a cellular transition associated with or indicative of a presence or a stage of a myelofibrosis-related disease in a sample from the patient by analyzing cellular data from the sample; and (b) comparing the analysis of (a) to a profile of cellular dysfunction based on the analysis of cellular data from: (i) a plurality of human patients, at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease; (ii) optionally, a plurality of animal disease models of the myelofibrosis-related disease; (iii) optionally, a plurality of in vitro disease models of the myelofibrosis-related disease; and / or (iv) optionally, a plurality of ex vivo disease models of the myelofibrosis-related disease; wherein: the cellular data of (a) and (b) comprises cellular data from one or more cell types selected from Table 1; the cellular data of (a) and (b) comprises cellular data from one or more gene markers selected from Table 1; the profile of cellular dysfunction is indicative of the cellular transition; and the cellular transition is associated with or indicative of a transition from a disease- untreatable or disease-irreversible state to an undiseased, pre-diseased, disease-treatable, or disease-reversible state; wherein the patient is at risk for developing the myelofibrosis-related disease if demonstrating that cellular data of (a) is at least substantially similar to the profile of cellular dysfunction of (b).

117. The method of claims 115 or 116, wherein the cellular data comprises and / or consists of one or more selected from transcriptomic data, genomic data, proteomic data and metabolomic data. DB1 / 142566607.11 102CLNV-015PC / 132119-5015 118. The method of any one of claims 115-117, wherein the cellular data comprises and / or consists of transcriptomic data, optionally wherein the transcriptomic data comprises or consists of data obtained from a text-based format of sequencing data (e.g. FASTQ), optionally wherein the transcriptomic data comprise and / or consists of bulk transcriptomic data optionally comprising and / or consisting of single nuclei data and / or single-cell transcription data.

119. The method of any one of claims 115-118, wherein the transcriptomic data comprises and / or consists of single nuclei data and / or single-cell transcription data.

120. The method of any one of claims 115-119, wherein the plurality of human patients comprises about 3 or more patients, about 5 or more patients, about 20 or more patients, about 50 or more patients, or about 100 or more patients.

121. The method of any one of claims 115-120, wherein the cellular data of (a) is obtained from one or more samples from each human patient from the plurality of human patients, optionally wherein the one or more samples are frozen samples, optionally wherein the cellular data comprises and / or consists of single- cell transcription data and / or single nuclei transcription data.

122. The method of any one of claims 115-121, wherein metadata indicating one or more features is obtained from each human patient from the plurality of human patients.

123. The method of claim 122, wherein the metadata indicates one or more features selected from sex, race, age, and physical condition.

124. The method of claim 122 or 123, wherein the physical condition comprises one or more features selected from body mass index (BMI), medical history, family medical history, disease diagnostic information, medication profile, alcohol consumption, illicit drug use, and cause of death.

125. The method of claim 124, wherein the medical history and / or family medical history comprises presence, absence, or degree of severity of one or more of Primary Myelofibrosis, Secondary Myelofibrosis, Essential Thrombocythemia, Polycythemia Vera, and the like.

126. The method of claim 124 or 125, wherein the disease diagnostic information comprises disease status.

127. The method of claim 126, wherein the disease status comprises histologically graded disease status. DB1 / 142566607.11 103CLNV-015PC / 132119-5015 128. The method of claim 126 or 127, wherein the disease diagnostic information comprises one or more features selected from International Prognostic Scoring System (IPSS), Dynamic IPSS (DIPSS), DIPSS Plus, Mutation-Enhanced International Prognostic Score System (MIPSS70), MIPSS70+, Genetically Inspired Prognostic Scoring System (GIPSS), Myelofibrosis Secondary to PV and ET-Prognostic Model (MYSEC- PM), Myelofibrosis Transplant Scoring System (MTSS), Response to Ruxolitinib after 6 Months (RR6), Artificial Intelligence Prognostic Scoring System for Myelofibrosis (AIPSS-MF) and pathology notes and key features.

129. The method of any one of claims 122-128, wherein the cellular data of (a) is sorted based on one or more of the metadata features.

130. The method of any one of claims 122-129, wherein the cellular data of (a) and / or (b) is filtered to remove counts of ambient nucleic acid molecules, doublets and / or empty droplets, optionally ambient RNA molecules, optionally wherein the cellular data comprises and / or consists of single nuclei transcription data.

131. The method of any one of claims 122-130, wherein one or more of the sex, race, age, and physical condition is represented in a balanced manner in the plurality of patients.

132. The method of any one of claims 115-131, wherein generating the profile of cellular dysfunction of (c) further comprises analyzing biomarkers from the cellular data, and comparing the biomarkers from the cellular data to a standardized set of biomarkers to identify a cell type associated with the cellular transition, optionally wherein the cellular data comprises and / or consists of single-cell transcription data and / or single nuclei transcription.

133. The method of any one of claims 115-132, wherein the method further comprises (d) obtaining genotypic data from each human patient of the plurality of human patients of (a).

134. The method of claim 133, wherein generating a profile of cellular dysfunction of (c) further comprises analyzing the cellular data of (a) with the genotypic data of (d), optionally wherein the cellular data comprises and / or consists of single-cell transcription data and / or single nuclei transcription data.

135. The method of any one of claims 115-133, wherein the patient is at risk for developing the myelofibrosis-related disease if demonstrating that the cellular data of (a) is at least about 30%, at least about 35%, at least about 40%, at least about 45%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least DB1 / 142566607.11 104CLNV-015PC / 132119-5015 about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or greater than about 99% similar to the profile of cellular dysfunction of (b).

136. The method of any one of claims 115-133, wherein the method comprises analyzing cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the disease.

137. The method of any one of claims 115-133, wherein the one or more cell type comprises or consists of hematopoietic stem and progenitor cells.

138. An atlas of cellular data, comprising: (a) cellular data from a plurality of human patients, at least some of the plurality of human patients being afflicted with a myelofibrosis-related disease; and (b) optionally, cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease, wherein the cellular data is obtained from two or more cell types.

139. An atlas of cellular data, comprising: (a) cellular data from a plurality of human patients, at least some of the plurality of human patients being afflicted with a myelofibrosis-related disease; and (b) optionally, cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease; wherein the cellular data of (a) and (b) is obtained from two or more cell types selected from Table 1; and the cellular data of (a) and (b) comprises cellular data from one or more gene markers selected from Table 1.

140. The atlas of claims 138 or 139, wherein the cellular data comprises and / or consists of one or more selected from transcriptomic data, genomic data, proteomic data and metabolomic data.

141. The atlas of any one of claims 138-140, wherein the cellular data comprises and / or consists of transcriptomic data, optionally wherein the transcriptomic data comprises or consists of data obtained from a text-based format of sequencing data (e.g. FASTQ), optionally wherein the transcriptomic data comprise DB1 / 142566607.11 105CLNV-015PC / 132119-5015 and / or consists of bulk transcriptomic data optionally comprising and / or consisting of single nuclei data and / or single-cell transcription data.

142. The atlas of any one of claims 138-141, wherein the transcriptomic data comprises and / or consists of single nuclei data and / or single-cell transcription data.

143. The map of any one of claims 138-142, wherein atlas map comprises (b) cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease.

144. The atlas of any one of claims 138-143, wherein the plurality of human patients comprises about 3 or more patients, about 5 or more patients, about 20 or more patients, about 50 or more patients, or about 100 or more patients.

145. The atlas of any one of claims 138-144, wherein the cellular data of (a) is obtained from one or more samples from each human patient from the plurality of human patients, optionally wherein the one or more samples are frozen samples, optionally wherein the cellular data comprises and / or consists of single-cell transcription data and / or single nuclei transcription data.

146. The atlas of any one of claims 138-145, wherein metadata indicating one or more features is obtained from each human patient from the plurality of human patients.

147. The atlas of claim 146, wherein the metadata indicates one or more features selected from sex, race, age, and physical condition.

148. The atlas of claim 147, wherein the physical condition comprises one or more features selected from body mass index (BMI), medical history, family medical history, disease diagnostic information, medication profile, alcohol consumption, illicit drug use, and cause of death.

149. The atlas of claim 148, wherein the medical history and / or family medical history presence, absence, or degree of severity of one or more of Primary Myelofibrosis, Secondary Myelofibrosis, Essential Thrombocythemia, Polycythemia Vera, and the like.

150. The atlas of claim 148 or 149, wherein the disease diagnostic information comprises disease status.

151. The atlas of claim 150, wherein the disease status comprises histologically graded disease status.

152. The atlas of any one of claims 148-151, wherein the disease diagnostic information comprises one or more features selected from International Prognostic Scoring System (IPSS), Dynamic IPSS (DIPSS), DB1 / 142566607.11 106CLNV-015PC / 132119-5015 DIPSS Plus, Mutation-Enhanced International Prognostic Score System (MIPSS70), MIPSS70+, Genetically Inspired Prognostic Scoring System (GIPSS), Myelofibrosis Secondary to PV and ET-Prognostic Model (MYSEC-PM), Myelofibrosis Transplant Scoring System (MTSS), Response to Ruxolitinib after 6 Months (RR6), Artificial Intelligence Prognostic Scoring System for Myelofibrosis (AIPSS-MF), and pathology notes and key features.

153. The atlas of claim any one of claims 138-152, wherein the cellular data of (a) is sorted based on one or more of the metadata features.

154. The atlas of any one of claims 146-153, wherein the cellular data of (a) and / or (b) is filtered to remove counts of ambient nucleic acid molecules, doublets and / or empty droplets, optionally ambient RNA molecules, optionally wherein the cellular data comprises and / or consists of single nuclei transcription data.

155. The atlas of any one of claims 147-154, wherein one or more of the sex, race, age, and physical condition is represented in a balanced manner in the plurality of patients.

156. The atlas of any one of claims 143-155 wherein the animal model is selected from a model that mimics etiology and / or natural history, a model that mimics histopathology, and a genetic model.

157. The atlas of claim 156, wherein the model that mimics etiology and / or natural history is selected from a Jak2VF, MplW515L, Calrdel52, TPO, Gata-1low, and Trisomy 21 (Ts65Dn)model.

158. The atlas of any one of claims 138-157, wherein the in vitro disease model is selected from a primary cell type system, a stem cell derived system, and a cell line system.

159. The atlas of claim 158, wherein the in vitro disease model comprises induced hematopoietic stem cells (iHSCs) derived from patient induced pluripotent stem cells (iPSC).

160. The atlas of any one of claims 138-157, wherein the in vivo model disease model comprises patient derived xenograft models (PDX).

161. The atlas of any one of claims 138-160, wherein the myelofibrosis-related disease is characterized by abnormal hematopoiesis.

162. The atlas of any one of claims 138-161, wherein the myelofibrosis-related disease is selected from Primary Myelofibrosis, Secondary Myelofibrosis, Essential Thrombocythemia, and Polycythemia Vera, anemia, thrombocytopenia. DB1 / 142566607.11 107CLNV-015PC / 132119-5015 163. The atlas of any one of claims 138-162, wherein at least some of the plurality of human patients are healthy.

164. The atlas of any one of claims 138-163, wherein at least some of the plurality of human patients are at risk of developing the myelofibrosis-related disease.

165. The atlas of any one of claims 138-164, wherein at least some of the plurality of human patients are non-responsive to one or more established therapies for treating the myelofibrosis-related disease.

166. The atlas of any one of claims 138-165, wherein at least some of the plurality of human patients have transitioned, are transitioning, or are at risk of transitioning from an undiseased or pre-diseased state to a disease-treatable or disease-reversible state.

167. The atlas of any one of claims 138-166, wherein at least some of the plurality of human patients have transitioned, are transitioning, or are at risk of transitioning from an undiseased or pre-diseased state to a disease-untreatable or disease-irreversible state.

168. The atlas of any one of claims 138-167, wherein at least some of the plurality of human patients have transitioned, are transitioning, or are at risk of transitioning between two different disease-treatable or disease-reversible states.

169. The atlas of any one of claims 138-168, wherein at least some of the plurality of human patients have transitioned, are transitioning, or are at risk of transitioning between a disease-treatable or disease-reversible state to a disease-untreatable or disease-irreversible state.

170. The atlas of any one of claims 138-169, wherein the two or more cell types are selected from hematopoietic stem cells (HSCs) (including but not limited to induced pluripotent stem cells (IPSCs)), hematopoietic precursor cells, hematopoietic progenitor cells, optionally basophil mast progenitor cell, common lymphoid progenitor, common myeloid progenitor, basophil mast progenitor cell, granulocyte monocyte progenitor cell, megakaryocyte-erythroid progenitor cell, erythroid progenitor cell, megakaryocyte progenitor cell, pro-T cell, pro-NK cell or pro-B cell; myeloid lineage cells, monocytes, macrophages, dendritic cells, optionally conventional type 1 dendritic cells (cDC1s), conventional type 2 dendritic cells (cDC2s), or plasmacytoid dendritic cells; neutrophils, eosinophils, erythroid lineage cell, megakaryocytes, basophils, mast cells, T-cells, natural killer (NK) cells, B-cells, plasma cells, mesenchymal cells, and endothelial cells. DB1 / 142566607.11 108CLNV-015PC / 132119-5015 171. The atlas of any one of claims 138-170, wherein the two or more cell types are selected from basophils / mast cells, monocytes, erythroid lineage cells, hematopoietic precursor cells, lymphoid lineage cells, megakaryocyte-erythroid progenitor cells, megakaryocytes, and myeloid lineage cells.

172. A map of cellular data, comprising: (a) cellular data from a plurality of human patients, wherein at least some of the plurality of human patients being afflicted with a myelofibrosis-related disease; and (b) optionally, cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease; wherein the cellular data is obtained from a single cell type.

173. A map of cellular data, comprising: (a) cellular data from a plurality of human patients, at least some of the plurality of human patients being afflicted with a myelofibrosis-related disease; and (b) optionally, cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease; wherein the cellular data of (a) and (b) is obtained from a single cell type selected from Table 1; and the cellular data of (a) and (b) of comprises cellular data from one or more gene markers selected from Table 1.

174. The map of claims 172 or 173, wherein the cellular data comprises and / or consists of one or more selected from transcriptomic data, genomic data, proteomic data and metabolomic data.

175. The map of any one of claims 172-174, wherein the cellular data comprises and / or consists of transcriptomic data, optionally wherein the transcriptomic data comprises or consists of data obtained from a text-based format of sequencing data (e.g. FASTQ), optionally wherein the transcriptomic data comprise and / or consists of bulk transcriptomic data optionally comprising and / or consisting of single nuclei data and / or single-cell transcription data.

176. The map of any one of claims 172-175, wherein the transcriptomic data comprises and / or consists of single nuclei data and / or single-cell transcription data. DB1 / 142566607.11 109CLNV-015PC / 132119-5015 177. The map of any one of claims 172-176, wherein the plurality of human patients comprises about 3 or more patients, about 5 or more patients, about 20 or more patients, about 50 or more patients, or about 100 or more patients.

178. The map of any one of claims 172-177, wherein the cellular data of (a) is obtained from one or more samples from each human patient from the plurality of human patients, optionally wherein the one or more samples are frozen samples, optionally wherein the cellular data comprises and / or consists of single-cell transcription data and / or single nuclei transcription data.

179. The map of any one of claims 172-178, wherein metadata indicating one or more features is obtained from each human patient from the plurality of human patients.

180. The map of claim 179, wherein the metadata indicates one or more features selected from sex, race, age, and physical condition.

181. The map of claim 180, wherein the physical condition comprises one or more features selected from body mass index (BMI), medical history, family medical history, disease diagnostic information, medication profile, alcohol consumption, illicit drug use, and cause of death.

182. The map of claim 181, wherein the medical history and / or family medical history comprises presence, absence, or degree of severity of one or more of Primary Myelofibrosis, Secondary Myelofibrosis, Essential Thrombocythemia, Polycythemia Vera, and the like.

183. The map of claim 181 or 182, wherein the disease diagnostic information comprises disease status.

184. The map of claim 183, wherein the disease status comprises histologically graded disease status.

185. The map of any one of claims 183 or 184, wherein the disease diagnostic information comprises one or more features selected from International Prognostic Scoring System (IPSS), Dynamic IPSS (DIPSS), DIPSS Plus, Mutation-Enhanced International Prognostic Score System (MIPSS70), MIPSS70+, Genetically Inspired Prognostic Scoring System (GIPSS), Myelofibrosis Secondary to PV and ET-Prognostic Model (MYSEC-PM), Myelofibrosis Transplant Scoring System (MTSS), Response to Ruxolitinib after 6 Months (RR6), Artificial Intelligence Prognostic Scoring System for Myelofibrosis (AIPSS-MF), and pathology notes and key features.

186. The map of any one of claims 179-185, wherein the cellular data of (a) is sorted based on one or more of the metadata features. DB1 / 142566607.11 110CLNV-015PC / 132119-5015 187. The map of any one of claims 172-186, wherein the cellular data of (a) and / or (b) is filtered to remove counts of ambient nucleic acid molecules, doublets and / or empty droplets, optionally ambient RNA molecules, optionally wherein the cellular data comprises and / or consists of single nuclei transcription data.

188. The map of any one of claims 180-187, wherein one or more of the sex, race, age, and physical condition is represented in a balanced manner in the plurality of patients.

189. The map of any one of claims 180-188, wherein the map comprises (b) cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease.

190. The map of claim 189, wherein the animal model is selected from a model that mimics etiology and / or natural history, a model that mimics histopathology, and a genetic model.

191. The map of claim 190, wherein the model that mimics etiology and / or natural history is selected from a Jak2VF, MplW515L, Calrdel52, TPO, Gata-1low, and Trisomy 21 (Ts65Dn)model.

192. The map of any one of claims 189-191, wherein the in vitro disease model is selected from a primary cell type system, a stem cell derived system, and a cell line system.

193. The map of claim 192, wherein the in vitro disease model comprises induced hematopoietic stem cells (iHSCs) derived from patient induced pluripotent stem cells (iPSC).

194. The map of any one of claims 189-191, wherein the in vivo model disease model comprises patient derived xenograft models (PDX).

195. The map of any one of claims 172-194, wherein the myelofibrosis-related disease is characterized by abnormal hematopoiesis.

196. The map of any one of claims 172-195, wherein the myelofibrosis-related disease is selected from Primary Myelofibrosis, Secondary Myelofibrosis, Essential Thrombocythemia, and Polycythemia Vera, CALR mutation, JAK2 mutation, anemia, thrombocytopenia.

197. The map of any one of claims 172-196, wherein at least some of the plurality of human patients are healthy.

198. The map of any one of claims 172-197, wherein at least some of the plurality of human patients are at risk of developing the myelofibrosis-related disease. DB1 / 142566607.11 111CLNV-015PC / 132119-5015 199. The map of any one of claims 172-198, wherein at least some of the plurality of human patients are non-responsive to one or more established therapies for treating the myelofibrosis-related disease.

200. The map of any one of claims 172-199, wherein at least some of the plurality of human patients have transitioned, are transitioning, or are at risk of transitioning from an undiseased or pre-diseased state to a disease-treatable or disease-reversible state.

201. The map of any one of claims 172-200, wherein at least some of the plurality of human patients have transitioned, are transitioning, or are at risk of transitioning from an undiseased or pre-diseased state to a disease-untreatable or disease-irreversible state.

202. The map of any one of claims 172-201, wherein at least some of the plurality of human patients have transitioned, are transitioning, or are at risk of transitioning between two different disease-treatable or disease-reversible states.

203. The map of any one of claims 172-202, wherein at least some of the plurality of human patients have transitioned, are transitioning, or are at risk of transitioning between a disease-treatable or disease-reversible state to a disease-untreatable or disease-irreversible state.

204. The map of any one of claims 172-203, wherein the single cell type is selected from hematopoietic stem cells (HSCs) (including but not limited to induced pluripotent stem cells (IPSCs)), hematopoietic precursor cells, hematopoietic progenitor cells, optionally basophil mast progenitor cell, common lymphoid progenitor, common myeloid progenitor, basophil mast progenitor cell, granulocyte monocyte progenitor cell, megakaryocyte-erythroid progenitor cell, erythroid progenitor cell, megakaryocyte progenitor cell, pro-T cell, pro-NK cell or pro-B cell; myeloid lineage cells, monocytes, macrophages, dendritic cells, optionally conventional type 1 dendritic cells (cDC1s), conventional type 2 dendritic cells (cDC2s), or plasmacytoid dendritic cells; neutrophils, eosinophils, erythroid lineage cell, megakaryocytes, basophils, mast cells, T-cells, natural killer (NK) cells, B-cells, plasma cells, mesenchymal cells, and endothelial cells.

205. The map of any one of claims 172-204, wherein the single cell type is selected from basophils / mast cells, monocytes, erythroid lineage cells, hematopoietic precursor cells, lymphoid lineage cells, megakaryocyte-erythroid progenitor cells, megakaryocytes, and myeloid lineage cells.

206. The map of any one of claims 172-205, wherein the single cell type consists of hematopoietic stem and progenitor cells.

207. A method for preparing an atlas of cellular data, comprising: DB1 / 142566607.11 112CLNV-015PC / 132119-5015 (a) obtaining cellular data from a plurality of human patients, at least some of the plurality of human patients being afflicted with a myelofibrosis-related disease, wherein the cellular data is obtained from two or more cell types; and (b) assembling the cellular data of (a) into an atlas of cellular data.

208. A method for preparing an atlas of cellular data, comprising: (a) obtaining cellular data from a plurality of human patients, at least some of the plurality of human patients being afflicted with a myelofibrosis-related disease, wherein the cellular data is obtained from two or more cell types; and (b) assembling the cellular data of (a) into an atlas of cellular data, wherein the cellular data is obtained from two or more cell types selected from Table 1; and the cellular data of (a) of comprises cellular data from one or more gene markers selected from Table 1.

209. A method for preparing a map of cellular data, comprising: (a) obtaining cellular data from a plurality of human patients, at least some of the plurality of human patients being afflicted with a myelofibrosis-related disease, wherein the cellular data is obtained from two or more cell types; (b) assembling the cellular data of (a) into an atlas of cellular data; (c) curating and / or filtering the cellular data of the atlas of (b) to obtain cellular data from a single cell type; and (d) assembling the cellular data obtained in (c) into a map of cellular data.

210. A method for preparing a map of cellular data, comprising: (a) obtaining cellular data from a plurality of human patients, at least some of the plurality of human patients being afflicted with a myelofibrosis-related disease, wherein the cellular data is obtained from two or more cell types; (b) assembling the cellular data of (a) into an atlas of cellular data; (c) curating and / or filtering the cellular data of the atlas of (b) to obtain cellular data from a single cell type; and (d) assembling the cellular data obtained in (c) into a map of cellular data. wherein the cellular data of (a) is obtained from two or more cell types selected from Table 1; DB1 / 142566607.11 113CLNV-015PC / 132119-5015 the cellular data of (b) is obtained from a single cell type selected from Table 1; and the cellular data of comprises cellular data from one or more gene markers selected from Table 1.

211. The method of any one of claims 207-210, wherein the cellular data comprises and / or consists of one or more selected from transcriptomic data, genomic data, proteomic data and metabolomic data.

212. The method of any one of claims 207-211, wherein the cellular data comprises and / or consists of transcriptomic data, optionally wherein the transcriptomic data comprises data obtained from a text-based format of sequencing data (e.g. FASTQ), optionally wherein the transcriptomic data comprise and / or consists of bulk transcriptomic data optionally comprising and / or consisting of single nuclei data and / or single-cell transcription data.

213. The method of any one of claims 207-212, wherein the transcriptomic data comprises and / or consists of single nuclei data and / or single-cell transcription data.

214. The method of any one of claims 207-213, wherein the plurality of human patients of (a) comprises about 3 or more patients, about 5 or more patients, about 20 or more patients, about 50 or more patients, or about 100 or more patients.

215. The method of any one of claims 207-214, wherein the curating and / or filtering the cellular data of (a) further comprises identifying cellular data of (a) associated with gene markers and / or cellular behaviors specific to the single cell type of interest, and obtaining the cellular data associated with the gene markers and / or cellular behaviors.

216. The method of any one of claims 207-215, wherein the cellular data of (a) is obtained from one or more samples from each human patient from the plurality of human patients, optionally wherein the one or more samples are frozen samples, optionally wherein the cellular data comprises and / or consists of single- cell transcription data and / or single nuclei transcription data.

217. The method of any one of claims 207-216, wherein the assembling the cellular data of (a) into an atlas of cellular data further comprises obtaining metadata indicating one or more features from each human patient from the plurality of human patients of (a).

218. The method of claim 217, wherein the metadata indicates one or more features selected from sex, race, age, and physical condition. DB1 / 142566607.11 114CLNV-015PC / 132119-5015 219. The method of claim 217 or 218, wherein the assembling the cellular data of (a) into an atlas of cellular data further comprises sorting the cellular data of (a) based on one or more of the metadata features.

220. The method of any one of claims 207-219, wherein the assembling the cellular data of (a) into an atlas of cellular data further comprises filtering the cellular data of (a) to remove counts of ambient nucleic acid molecules, doublets and / or empty droplets, optionally ambient RNA molecules, optionally wherein the cellular data comprises and / or consists of single nuclei transcription data.

221. The method of any one of claims 218-220, wherein one or more of the sex, race, age, and physical condition is represented in a balanced manner in the plurality of patients of (a).

222. The method of any one of claims 207-221, wherein after (a) and before (b), the method further comprises analyzing sample collection detail, technical covariates, patient information, optionally selected from disease stage, family history, nutrition, age, gender, comorbidities, and lifestyle parameters.

223. The method of any one of claims 207-222, wherein after (a) and before (b), the method further comprises analyzing phenotypic or functional parameters.

224. The method of any one of claims 217-223, wherein the metadata indicates one or more features selected from sex, race, age, and physical condition.

225. The method of claim 224, wherein the physical condition comprises one or more features selected from body mass index (BMI), medical history, family medical history, disease diagnostic information, medication profile, alcohol consumption, illicit drug use, and cause of death.

226. The method of claim 225, wherein the medical history and / or family medical history comprises presence, absence, or degree of severity of one or more of Primary Myelofibrosis, Secondary Myelofibrosis, Essential Thrombocythemia, Polycythemia Vera, and the like.

227. The method of claim 225 or 226, wherein the disease diagnostic information comprises disease status.

228. The method of claim 227, wherein the disease status comprises histologically graded disease status.

229. The method of any one of claims 224-228, wherein the disease diagnostic information comprises one or more features selected from International Prognostic Scoring System (IPSS), Dynamic IPSS (DIPSS), DIPSS Plus, Mutation-Enhanced International Prognostic Score System (MIPSS70), MIPSS70+, Genetically Inspired Prognostic Scoring System (GIPSS), Myelofibrosis Secondary to PV and ET-Prognostic Model DB1 / 142566607.11 115CLNV-015PC / 132119-5015 (MYSEC-PM), Myelofibrosis Transplant Scoring System (MTSS), Response to Ruxolitinib after 6 Months (RR6), Artificial Intelligence Prognostic Scoring System for Myelofibrosis (AIPSS-MF), and pathology notes and key features.

230. The method of any one of claims 218-229, wherein one or more of the sex, race, age, and physical condition is represented in a balanced manner in the plurality of patients.

231. The method of any one of claims 209-230, wherein the cellular data comprises and or consists of single nuclei data, wherein the single nuclei data is or comprises single-nucleus ribonucleic acid (RNA) sequencing (snRNA-seq) data and / or the single-cell transcription data is or comprises single-cell ribonucleic acid (RNA) sequencing (scRNA-seq) data.

232. The method of any one of claims 209-231, wherein the myelofibrosis-related disease is characterized by abnormal hematopoiesis.

233. The method of any one of claims 209-232, wherein the myelofibrosis-related disease is selected from Primary Myelofibrosis, Secondary Myelofibrosis, Essential Thrombocythemia, and Polycythemia Vera, CALR mutation, JAK2 mutation, anemia, thrombocytopenia.

234. The method of any one of claims 207-233, wherein at least some of the plurality of human patients are healthy.

235. The method of any one of claims 207-234, wherein at least some of the plurality of human patients are at risk of developing the myelofibrosis-related disease.

236. The method of any one of claims 207-235, wherein at least some of the plurality of human patients are non-responsive to one or more established therapies for treating the myelofibrosis-related disease.

237. The method of any one of claims 207-236, wherein at least some of the plurality of human patients have transitioned, are transitioning, or are at risk of transitioning from an undiseased or pre-diseased state to a disease-treatable or disease-reversible state.

238. The method of any one of claims 207-237, wherein at least some of the plurality of human patients have transitioned, are transitioning, or are at risk of transitioning from an undiseased or pre-diseased state to a disease-untreatable or disease-irreversible state. DB1 / 142566607.11 116CLNV-015PC / 132119-5015 239. The method of any one of claims 207-238, wherein at least some of the plurality of human patients have transitioned, are transitioning, or are at risk of transitioning between two different disease-treatable or disease-reversible states.

240. The method of any one of claims 207-239, wherein at least some of the plurality of human patients have transitioned, are transitioning, or are at risk of transitioning between a disease-treatable or disease- reversible state to a disease-untreatable or disease-irreversible state.

241. The method of any one of claims 207-240, wherein the two or more cell types are selected from hematopoietic stem cells (HSCs) (including but not limited to induced pluripotent stem cells (IPSCs)), hematopoietic precursor cells, hematopoietic progenitor cells, optionally basophil mast progenitor cell, common lymphoid progenitor, common myeloid progenitor, basophil mast progenitor cell, granulocyte monocyte progenitor cell, megakaryocyte-erythroid progenitor cell, erythroid progenitor cell, megakaryocyte progenitor cell, pro-T cell, pro-NK cell or pro-B cell; myeloid lineage cells, monocytes, macrophages, dendritic cells, optionally conventional type 1 dendritic cells (cDC1s), conventional type 2 dendritic cells (cDC2s), or plasmacytoid dendritic cells; neutrophils, eosinophils, erythroid lineage cell, megakaryocytes, basophils, mast cells, T-cells, natural killer (NK) cells, B-cells, plasma cells, mesenchymal cells, and endothelial cells.

242. The method of any one of claims 207-240, wherein the two or more cell types are selected from basophils / mast cells, monocytes, erythroid lineage cells, hematopoietic precursor cells, lymphoid lineage cells, megakaryocyte-erythroid progenitor cells, megakaryocytes, and myeloid lineage cells.

243. The method of any one of claims 207-240, wherein the single cell type is selected from hematopoietic stem cells (HSCs) (including but not limited to induced pluripotent stem cells (IPSCs)), hematopoietic precursor cells, hematopoietic progenitor cells, optionally basophil mast progenitor cell, common lymphoid progenitor, common myeloid progenitor, basophil mast progenitor cell, granulocyte monocyte progenitor cell, megakaryocyte-erythroid progenitor cell, erythroid progenitor cell, megakaryocyte progenitor cell, pro-T cell, pro-NK cell or pro-B cell; myeloid lineage cells, monocytes, macrophages, dendritic cells, optionally conventional type 1 dendritic cells (cDC1s), conventional type 2 dendritic cells (cDC2s), or plasmacytoid dendritic cells; neutrophils, eosinophils, erythroid lineage cell, megakaryocytes, basophils, mast cells, T-cells, natural killer (NK) cells, B-cells, plasma cells, mesenchymal cells, and endothelial cells.

244. The method of any one of claims 207-240, wherein the single cell type is selected from basophils / mast cells, monocytes, erythroid lineage cells, hematopoietic precursor cells, lymphoid lineage cells, megakaryocyte-erythroid progenitor cells, megakaryocytes, and myeloid lineage cells. DB1 / 142566607.11 117CLNV-015PC / 132119-5015 245. The method of any one of claims 207-240, wherein the single cell type is hematopoietic stem and progenitor cells.

246. The method of any one of claims 207-245, wherein the atlas is an atlas of any one of claims 138- 171.

247. An atlas prepared by the method of any one of claims 207, 208 and 211-246.

248. A map prepared by the method of any one of claims 209-246.

249. A method of identifying a cellular behavior, the method comprising: (a) selecting a desired cellular transition of a single cell type associated with or indicative of a presence or a stage of a myelofibrosis-related disease; (b) selecting from a map of cellular data one or more cellular transitions at least substantially similar to the desired cellular transition of (a), wherein: the map comprises: (i) cellular data from a plurality of human patients, wherein at least some of the plurality of human patients being afflicted with a myelofibrosis-related disease; and (ii) optionally, cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease; wherein the cellular data of (i) and (ii) are obtained from the single cell type; and (c) analyzing the one or more cellular transitions selected in (b); wherein the analyzing of (c) provides the cellular behavior.

250. A method of identifying a cellular behavior, the method comprising: (a) selecting a desired cellular transition of a single cell type associated with or indicative of a presence or a stage of a myelofibrosis-related disease; (b) selecting from a map of cellular data one or more cellular transitions at least substantially similar to the desired cellular transition of (a), wherein: the map comprises: DB1 / 142566607.11 118CLNV-015PC / 132119-5015 (i) cellular data from a plurality of human patients, wherein at least some of the plurality of human patients being afflicted with a myelofibrosis-related disease; and (ii) optionally, cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease; wherein the cellular data of (i) and (ii) are obtained from the single cell type selected from Table 1; and the cellular data of (i) and (ii) comprises cellular data from one or more gene markers selected from Table 1; and (c) analyzing the one or more cellular transitions selected in (b); wherein the analyzing of (c) provides the cellular behavior.

251. The method of claims 249 or 250, wherein the analyzing of (c) comprises identifying one or more changes (e.g. changes in gene expression) associated with or indicative of the desired cellular transition.

252. The method of any one of claims 249-251, wherein the analyzing of (c) comprises identifying common upregulation and / or downregulation of about 1 or more, about 2 or more, about 3 or more, about 4 or more, about 5 or more, about 10 or more, about 15 or more, about 20 or more, about 25 or more, about 30 or more, about 35 or more, about 40 or more, about 45 or more, about 50 or more, about 55 or more, about 60 or more, about 65 or more, about 70 or more, about 75 or more, about 80 or more, about 85 or more, about 90 or more, about 95 or more, or about 100 or more genes, optionally about 1 or more or about 2 or more genes.

253. The method of any one of claims 249-252, wherein the cellular behavior is at least partially modified and / or impacted by the modulation of one or more molecular targets, optionally wherein the molecular target is selected from a protein, nucleic acid, gene, and any other biological molecule.

254. The method of claim 253, wherein the method further comprises selecting an agent, wherein the agent has affinity for and / or is capable of modulating the molecular target, optionally wherein the agent is selected from a small molecule, a biologic, a protein, a protein combined with a small molecule, an antibody drug conjugate (ADC), a peptide, and a nucleic acid. DB1 / 142566607.11 119CLNV-015PC / 132119-5015 255. The method of claim 253 or 254, wherein modulation of the molecular target occurs through one or more mechanisms of action.

256. The method of any one of claims 249-255, wherein the cellular behavior is associated with or indicative of a cellular transition associated with or indicative of a transition from an undiseased or pre- diseased state to a disease-treatable or disease-reversible state.

257. The method of any one of claims 249-255, wherein the cellular behavior is associated with or indicative of a cellular transition associated with or indicative of a transition from an undiseased or pre- diseased state to a disease-untreatable or disease-irreversible state.

258. The method of any one of claims 249-255, wherein the cellular behavior is associated with or indicative of a cellular transition associated with or indicative of a transition between two different disease- treatable or disease-reversible states.

259. The method of any one of claims 249-255, wherein the cellular behavior is associated with or indicative of a cellular transition associated with or indicative of a transition between a disease-treatable or disease-reversible state to a disease-untreatable or disease-irreversible state.

260. The method of any one of claims 249-255, wherein the cellular behavior is associated with or indicative of a cellular transition associated with or indicative of a transition from normal hematopoiesis to abnormal hematopoiesis.

261. The method of any one of claims 249-255, wherein the cellular behavior is associated with or indicative of a cellular transition associated with or indicative of a transition from normal red blood cell and / or platelet count to decreased red blood cell and / or platelet count.

262. A method of identifying a mechanism of action (MOA), the method comprising: (a) selecting one or more desired cellular transitions of a single cell type associated with or indicative of a presence or a stage of a myelofibrosis-related disease; (b) selecting from a map of cellular data one or more cellular transitions at least substantially similar to the desired cellular transition of (a), wherein: the map comprises: DB1 / 142566607.11 120CLNV-015PC / 132119-5015 (i) cellular data from a plurality of human patients, wherein at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease; and (ii) optionally, cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease; wherein the cellular data of (i) and (ii) are obtained from the single cell type; (c) comparing the one or more cellular transitions selected in (b) with one or more cellular transitions associated with or indicative of one or more known mechanisms of action; and (d) selecting the mechanism of action from the one or more known mechanisms of action based on the comparison of (c), wherein one or more cellular transitions associated with or indicative of the selected mechanism of action is at least substantially similar to the one or more desired cellular transitions of (a).

263. A method of identifying a mechanism of action (MOA), the method comprising: (a) selecting one or more desired cellular transitions of a single cell type associated with or indicative of a presence or a stage of a myelofibrosis-related disease; (b) selecting from a map of cellular data one or more cellular transitions at least substantially similar to the desired cellular transition of (a), wherein: the map comprises: (i) cellular data from a plurality of human patients, wherein at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease; and (ii) optionally, cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease; wherein the cellular data of (i) and (ii) are obtained from the single cell type selected from Table 1; and DB1 / 142566607.11 121CLNV-015PC / 132119-5015 the cellular data of (i) and (ii) comprises cellular data from one or more gene markers selected from Table 1; and (c) comparing the one or more cellular transitions selected in (b) with one or more cellular transitions associated with or indicative of one or more known mechanisms of action; and (d) selecting the mechanism of action from the one or more known mechanisms of action based on the comparison of (c), wherein one or more cellular transitions associated with or indicative of the selected mechanism of action is at least substantially similar to the one or more desired cellular transitions of (a).

264. The method of claim 262, wherein the method further comprises selecting an agent, wherein the agent exhibits a mechanism of action at least substantially similar to the selected mechanism of action, optionally wherein the agent is selected from a small molecule, a biologic, a protein, a protein combined with a small molecule, an antibody drug conjugate (ADC), a peptide, and a nucleic acid.

265. A method of identifying a molecular target, comprising: (a) selecting one or more desired cellular transitions of a single cell type associated with or indicative of a presence or a stage of a myelofibrosis-related disease; (b) selecting from a map of cellular data one or more cellular transitions at least substantially similar to the desired cellular transition of (a), wherein: the map comprises: (i) cellular data from a plurality of human patients, wherein at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease; and (ii) optionally, cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease; wherein the cellular data of (i) and (ii) are obtained from the single cell type; (c) comparing the one or more cellular transitions selected in (b) with one or more cellular transitions associated with or indicative of one or more known molecular targets; and DB1 / 142566607.11 122CLNV-015PC / 132119-5015 (d) selecting the molecular targets from the one or more known molecular targets based on the comparison of (c), wherein one or more cellular transitions associated with or indicative of the selected molecular target is at least substantially similar to the one or more desired cellular transitions of (a).

266. A method of identifying a molecular target, comprising: (a) selecting one or more desired cellular transitions of a single cell type associated with or indicative of a presence or a stage of a myelofibrosis-related disease; (b) selecting from a map of cellular data one or more cellular transitions at least substantially similar to the desired cellular transition of (a), wherein: the map comprises: (i) cellular data from a plurality of human patients, wherein at least some of the plurality of human patients being afflicted with the myelofibrosis-related disease; and (ii) optionally, cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease; wherein the cellular data of (i) and (ii) are obtained from the single cell type selected from Table 1; and the cellular data of (i) and (ii) comprises cellular data from one or more gene markers selected from Table 1; and (c) comparing the one or more cellular transitions selected in (b) with one or more cellular transitions associated with or indicative of one or more known molecular targets; and (d) selecting the molecular targets from the one or more known molecular targets based on the comparison of (c), wherein one or more cellular transitions associated with or indicative of the selected molecular target is at least substantially similar to the one or more desired cellular transitions of (a). DB1 / 142566607.11 123CLNV-015PC / 132119-5015 267. The method of claims 265 or 266, the method further comprising modulating the molecular target, wherein the modulation results in one or more changes (e.g. changes in gene expression) associated with or indicative of the desired cellular transition.

268. The method of any one of claims 265-267, wherein the one or more changes comprises identifying common upregulation and / or downregulation of about 1 or more, about 2 or more, about 3 or more, about 4 or more, about 5 or more, about 10 or more, about 15 or more, about 20 or more, about 25 or more, about 30 or more, about 35 or more, about 40 or more, about 45 or more, about 50 or more, about 55 or more, about 60 or more, about 65 or more, about 70 or more, about 75 or more, about 80 or more, about 85 or more, about 90 or more, about 95 or more, or about 100 or more genes, optionally about 1 or more or about 2 or more genes.

269. The method of any one of claims 265-268, wherein the molecular target is selected from a protein, nucleic acid, gene, and any other biological molecule.

270. The method of any one of claims 265-269, wherein the method further comprises selecting an agent, wherein the agent has affinity for and / or is capable of modulating the molecular target, optionally wherein the agent is selected from a small molecule, a biologic, a protein, a protein combined with a small molecule, an antibody drug conjugate (ADC), a peptide, and a nucleic acid.

271. The method of claim 270, wherein modulation of the molecular target occurs through one or more mechanisms of action.

272. A method of identifying an agent, comprising: (a) selecting one or more desired cellular transitions of a single cell type associated with or indicative of a presence or a stage of a myelofibrosis-related disease; (b) selecting from a map of cellular data one or more cellular transitions at least substantially similar to the desired cellular transition of (a), wherein: the map comprises: (i) cellular data from a plurality of human patients, wherein at least some of the plurality of human patients being afflicted with a myelofibrosis-related disease; and DB1 / 142566607.11 124CLNV-015PC / 132119-5015 (ii) optionally, cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease; wherein the cellular data of (i) and (ii) are obtained from the single cell type; (c) comparing the one or more cellular transitions selected in (b) with one or more cellular transitions associated with or indicative of one or more known agents; and (d) selecting the agent from the one or more known agents based on the comparison of (c), wherein one or more cellular transitions associated with or indicative of the selected agent is at least substantially similar to the one or more desired cellular transitions of (a).

273. A method of identifying an agent, comprising: (a) selecting one or more desired cellular transitions of a single cell type associated with or indicative of a presence or a stage of a myelofibrosis-related disease; (b) selecting from a map of cellular data one or more cellular transitions at least substantially similar to the desired cellular transition of (a), wherein: the map comprises: (i) cellular data from a plurality of human patients, wherein at least some of the plurality of human patients being afflicted with a myelofibrosis-related disease; and (ii) optionally, cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease; wherein the cellular data of (i) and (ii) are obtained from the single cell type selected from Table 1; and the cellular data of (i) and (ii) comprises cellular data from one or more gene markers selected from Table 1; (c) comparing the one or more cellular transitions selected in (b) with one or more cellular transitions associated with or indicative of one or more known agents; and DB1 / 142566607.11 125CLNV-015PC / 132119-5015 (d) selecting the agent from the one or more known agents based on the comparison of (c), wherein one or more cellular transitions associated with or indicative of the selected agent is at least substantially similar to the one or more desired cellular transitions of (a).

274. The method of claims 272 or 273, wherein the agent is capable of modulating one or more molecular targets, wherein the modulation results in one or more changes (e.g. changes in gene expression) associated with or indicative of the desired cellular transition.

275. The method of any one of claims 272-274, wherein the one or more changes comprise identifying common upregulation and / or downregulation of about 1 or more, about 2 or more, about 3 or more, about 4 or more, about 5 or more, about 10 or more, about 15 or more, about 20 or more, about 25 or more, about 30 or more, about 35 or more, about 40 or more, about 45 or more, about 50 or more, about 55 or more, about 60 or more, about 65 or more, about 70 or more, about 75 or more, about 80 or more, about 85 or more, about 90 or more, about 95 or more, or about 100 or more genes, optionally about 1 or more or about 2 or more genes.

276. The method of claims 274 or 275, wherein the one or more molecular targets are selected from a protein, nucleic acid, gene, and any other biological molecule.

277. The method of any one of claims 274-276, wherein modulation of the molecular target occurs through one or more mechanisms of action.

278. The method of claim 277, wherein the selected agent exhibits a mechanism of action at least substantially similar to the one or more mechanism of actions.

279. The method of any one of claims 272-278, wherein the agent is selected from a small molecule, a biologic, a protein, a protein combined with a small molecule, an antibody drug conjugate (ADC), a peptide, and a nucleic acid.

280. The method of any one of claims 254-261, 264, and 270-279, wherein the method further comprises modifying the agent to exhibit one or more properties of (a)-(f) compared to the unmodified agent (e.g. to provide a lead agent useful in drug discovery efforts): (a) increased activity strength and / or selectivity; (b) increased solubility and / or partition property; (c) increased metabolic and / or chemical stability; DB1 / 142566607.11 126CLNV-015PC / 132119-5015 (d) modulation of pharmacokinetic parameters (including, without limitation, absorption, distribution, metabolism, and excretion (ADME)), optionally wherein the modulation provides increased or enhanced pharmacokinetic properties compared to the compound prior to the modulation; (e) modulation of pharmacodynamic parameters (including, without limitation, dose-response relationship (e.g. EC50)); optionally wherein the modulation provides increased or enhanced pharmacodynamic properties compared to the compound prior to the modulation; and (f) reduction or ablation of toxicity and / or adverse reactions.

281. The method of any one of claims 249-280, wherein the desired cellular transition is associated with or indicative of a transition from an undiseased or pre-diseased state to a disease-treatable or disease- reversible state.

282. The method of any one of claims 249-280, wherein the desired cellular transition is associated with or indicative of a transition from an undiseased or pre-diseased state to a disease-untreatable or disease- irreversible state.

283. The method of any one of claims 249-280, wherein the desired cellular transition is associated with or indicative of a transition between two different disease-treatable or disease-reversible states.

284. The method of any one of claims 249-280, wherein the desired cellular transition is associated with or indicative of a transition between a disease-treatable or disease-reversible state to a disease-untreatable or disease-irreversible state.

285. The method of any one of claims 249-280, wherein the desired cellular transition is associated with or indicative of a transition from normal hematopoiesis to abnormal hematopoiesis.

286. The method of any one of claims 249-280, wherein the desired cellular transition is associated with or indicative of a transition from normal red blood cell and / or platelet count to decreased red blood cell and / or platelet count.

287. The method of any one of claims 249-286, wherein the cellular data comprises and / or consists of one or more selected from transcriptomic data, genomic data, proteomic data and metabolomic data.

288. The method of any one of claims 249-287, wherein the cellular data comprises and / or consists of transcriptomic data, optionally wherein the transcriptomic data comprises or consists of data obtained from a text-based format of sequencing data (e.g. FASTQ), optionally wherein the transcriptomic data comprise DB1 / 142566607.11 127CLNV-015PC / 132119-5015 and / or consists of bulk transcriptomic data optionally comprising and / or consisting of single nuclei data and / or single-cell transcription data.

289. The method of any one of claims 249-288, wherein the transcriptomic data comprises and / or consists of single nuclei data and / or single-cell transcription data.

290. The method of any one of claims 249-289, wherein the plurality of human patients of (a) comprises about 3 or more patients, about 5 or more patients, about 20 or more patients, about 50 or more patients, or about 100 or more patients.

291. The method of any one of claims 249-290, wherein the cellular data of the map of cellular data is obtained from one or more samples from each human patient from the plurality of human patients, optionally wherein the one or more samples are frozen samples, optionally wherein the cellular data comprises and / or consists of single-cell transcription data and / or single nuclei transcription data.

292. The method of any one of claims 249-291, wherein metadata indicating one or more features is obtained from each human patient from the plurality of human patients.

293. The method of claim 292, the metadata indicates one or more features selected from sex, race, age, and physical condition.

294. The method of claim 293, wherein the physical condition comprises one or more features selected from body mass index (BMI), medical history, family medical history, disease diagnostic information, medication profile, alcohol consumption, illicit drug use, and cause of death.

295. The method of claim 294, wherein the medical history and / or family medical history comprises presence, absence, or degree of severity of one or more of Primary Myelofibrosis, Secondary Myelofibrosis, Essential Thrombocythemia, Polycythemia Vera, and the like.

296. The method of claim 294 or 295, wherein the disease diagnostic information comprises disease status.

297. The method of claim 296, wherein the disease status comprises histologically graded disease status.

298. The method of any one of claims 296 or 297, wherein the disease diagnostic information comprises one or more features selected from International Prognostic Scoring System (IPSS), Dynamic IPSS (DIPSS), DIPSS Plus, Mutation-Enhanced International Prognostic Score System (MIPSS70), MIPSS70+, Genetically Inspired Prognostic Scoring System (GIPSS), Myelofibrosis Secondary to PV and ET-Prognostic Model DB1 / 142566607.11 128CLNV-015PC / 132119-5015 (MYSEC-PM), Myelofibrosis Transplant Scoring System (MTSS), Response to Ruxolitinib after 6 Months (RR6), Artificial Intelligence Prognostic Scoring System for Myelofibrosis (AIPSS-MF)and pathology notes and key features.

299. The method of any one of claims 249-298, wherein the cellular data of the map of cellular data is sorted based on one or more of the metadata features.

300. The method of any one of claims 249-299, wherein the cellular data of the map of cellular data is filtered to remove counts of ambient nucleic acid molecules, doublets and / or empty droplets, optionally ambient RNA molecules, optionally wherein the cellular data comprises and / or consists of single nuclei transcription data.

301. The method of any one of claims 293-300, wherein one or more of the sex, race, age, and physical condition is represented in a balanced manner in the plurality of patients.

302. The method of any one of claims 299-301, wherein the map comprises (ii) cellular data from a plurality of animal disease models, in vitro disease models, and / or ex vivo disease models of the myelofibrosis-related disease.

303. The method of claim 302, wherein the animal model is selected from a model that mimics etiology and / or natural history, a model that mimics histopathology, and a genetic model.

304. The method of claim 303, wherein the model that mimics etiology and / or natural history is selected from a Jak2VF, MplW515L, Calrdel52, TPO, Gata-1low, and Trisomy 21 (Ts65Dn) model.

305. The method of any one of claims 301-304, wherein the in vitro disease model is selected from a primary cell type system, a stem cell derived system, and a cell line system.

306. The method of claim 305, wherein the in vitro disease model comprises induced hematopoietic stem cells (iHSCs) derived from patient induced pluripotent stem cells (iPSC).

307. The method of any one of claims 301-304, wherein the in vivo model disease model comprises patient derived xenograft models (PDX).

308. The method of any one of claims 249-307, wherein the myelofibrosis-related disease is characterized by abnormal hematopoiesis. DB1 / 142566607.11 129CLNV-015PC / 132119-5015 309. The method of any one of claims 249-308, wherein the myelofibrosis-related disease is selected from Primary Myelofibrosis, Secondary Myelofibrosis, Essential Thrombocythemia, and Polycythemia Vera, CALR mutation, JAK2 mutation, anemia, and thrombocytopenia.

310. The method of any one of claims 249-309, wherein at least some of the plurality of human patients are healthy.

311. The method of any one of claims 249-310, wherein at least some of the plurality of human patients are at risk of developing the myelofibrosis-related disease.

312. The method of any one of claims 249-311, wherein at least some of the plurality of human patients are non-responsive to one or more established therapies for treating the myelofibrosis-related disease.

313. The method of any one of claims 249-312, wherein at least some of the plurality of human patients have transitioned, are transitioning, or are at risk of transitioning from an undiseased or pre-diseased state to a disease-treatable or disease-reversible state.

314. The method of any one of claims 249-313, wherein at least some of the plurality of human patients have transitioned, are transitioning, or are at risk of transitioning from an undiseased or pre-diseased state to a disease-untreatable or disease-irreversible state.

315. The method of any one of claims 249-314, wherein at least some of the plurality of human patients have transitioned, are transitioning, or are at risk of transitioning between two different disease-treatable or disease-reversible states.

316. The method of any one of claims 249-315, wherein at least some of the plurality of human patients have transitioned, are transitioning, or are at risk of transitioning between a disease-treatable or disease- reversible state to a disease-untreatable or disease-irreversible state.

317. The method of any one of claims 249-316, wherein the single cell type is selected from hematopoietic stem cells (HSCs) (including but not limited to induced pluripotent stem cells (IPSCs)), hematopoietic precursor cells, hematopoietic progenitor cells, optionally basophil mast progenitor cell, common lymphoid progenitor, common myeloid progenitor, basophil mast progenitor cell, granulocyte monocyte progenitor cell, megakaryocyte-erythroid progenitor cell, erythroid progenitor cell, megakaryocyte progenitor cell, pro-T cell, pro-NK cell or pro-B cell; myeloid lineage cells, monocytes, macrophages, dendritic cells, optionally conventional type 1 dendritic cells (cDC1s), conventional type 2 dendritic cells (cDC2s), or plasmacytoid DB1 / 142566607.11 130CLNV-015PC / 132119-5015 dendritic cells; neutrophils, eosinophils, erythroid lineage cell, megakaryocytes, basophils, mast cells, T-cells, natural killer (NK) cells, B-cells, plasma cells, mesenchymal cells, and endothelial cells.

318. The method of any one of claims 249-316, wherein the the single cell type is selected from basophils / mast cells, monocytes, erythroid lineage cells, hematopoietic precursor cells, lymphoid lineage cells, megakaryocyte-erythroid progenitor cells, megakaryocytes, and myeloid lineage cells.

319. The method of any one of claims 249-316, wherein the single cell type is hematopoietic stem and progenitor cells. DB1 / 142566607.11 131

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