Methods for analyzing cervicovaginal samples

Abstract

Provided are systems and methods for analyzing vaginaliy derived samples and nucleic acids therefrom.

Description

WSGR Docket No.50272-712.601 METHODS FOR ANALYZING CERVICOVAGINAL SAMPLES CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit and priority of U.S. Provisional Application No. 63 / 673,329, filed on July 19, 2024, which is incorporated by reference herein in its entirety. SUMMARY

[0002] In certain aspects, described herein is a method of analyzing a compound, comprising: identifying a first expression level of a switch from a first RNA sequencing dataset obtained from a first vaginally derived sample; identifying a second expression level of the switch from a second RNA sequencing dataset from a second vaginally derived sample; wherein the switch comprises a set of genes selected from genes co-expressed within a biological relevance parameter; wherein the first and second vaginally derived samples are derived from a same mixed population of cells, and wherein the compound is administered ex vivo to the first vaginally derived sample and / or the second vaginally derived sample prior to the generation of the first RNA sequencing dataset and / or the second RNA sequencing dataset; generating a first score for the switch based on the first expression level of the switch; and generating a second score for the switch based on the second expression level of the switch. In some embodiments, the second RNA sequencing dataset is obtained at a time period subsequent to administration of the compound to the second vaginally derived sample. In some embodiments, the first RNA sequencing dataset is obtained without administration of the compound to the first vaginally derived sample. In some embodiments, the first RNA sequencing dataset is obtained at a time period subsequent to administration of the compound to the first vaginally derived sample. In some embodiments, the amount of the compound administered to the first vaginally derived sample is different from the amount of the compound administered to the second vaginally derived sample. In some embodiments, the amount of the compound administered to the first vaginally derived sample is the same as the amount of the compound administered to the second vaginally derived sample. In some embodiments, the amount of time of incubation with the compound is different between the first vaginally derived sample and the second vaginally derived sample. In some embodiments, the mixed population of cells are not cultured or expanded prior to administration of the compound. In some embodiments, the compound is a therapeutic agent or a candidate therapeutic agent. In some embodiments, the first vaginally derived sample and the second vaginally derived sample comprise menstrual fluid, cervical- vaginal fluid or a combination thereof. In some embodiments, the first vaginally derived sample and the second vaginally derived sample are derived from a healthy or putatively healthy subject.WSGR Docket No.50272-712.601 In some embodiments, the first vaginally derived sample and the second vaginally derived sample are derived from one or more individuals having a diseased state or putative diseased state. In some embodiments, diseased state or putative diseased state is selected from the group consisting of endometriosis, cervical cancer, infertility, uterine cancer, ovarian cancer, autoimmune disease, inflammatory disease, and fibroids. In some embodiments, diseased state or putative diseased state is a bacterial infection, a fungal infection or a viral infection. In some embodiments, the one or more individuals exhibits one or more of inflammation, bleeding, heavy bleeding, anemia, change in regular menstruation cycle, pain and a change in microbiome diversity, microbial species or microbial abundance. In some embodiments, the biological relevance parameter is selected from a group consisting of cell type, tissue type, biological pathway, and biological function. In some embodiments, the first RNA sequencing dataset and / or the second RNA sequencing dataset comprises bulk RNA sequencing data. In some embodiments, the second RNA sequencing dataset comprises bulk RNA sequencing data. In some embodiments, the first score is calculated using a number of reads within the RNA sequencing dataset of the genes within the switch. In some embodiments, the second score is calculated using the number of reads within the RNA sequencing dataset of the set of genes within the switch. In some embodiments, the score, first score and / or second score is normalized by the level of one or more housekeeping genes in the RNA sequencing dataset. In some embodiments, the score, first score and / or second score indicates the presence or absence of the activity of one or more cell types in the first vaginally derived sample. In some embodiments, the first switch comprises at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 genes. In some embodiments, the first switch comprises at least 5, at least 10, at least 15, at least 20, or at least 25 genes. In some embodiments, the second switch comprises at least 2, 3, 4, 5, 6, 7, 8, 9, or l 0 genes. In some embodiments, the second switch comprises at least 5, at least 10, at least 15, at least 20, or at least 25 genes. In some embodiments, the first switch comprises at least one set of genes selected from Table 2. In some embodiments, the biological relevance parameter is a cell type and the cell type is one or more of stromal cells, monocytes, neutrophils, dendritic cells, macrophages, eosinophils, B cells, megakaryocytes, epithelial cells. progenitor cells, stem cells, lymphoid cells, non-lymphoid cells, hemopoietic cells, and non-hemopoietic cells. In some embodiments, the biological relevance parameter is a tissue type and the tissue type is one or more of cervical, endometrial, vaginal, uterine, blood, placental, muscle, ovarian, fetal, and maternal. In some embodiments, the biological relevance parameter is a biological pathway.

[0003] In certain aspects, described herein is a method of analyzing RNA sequencing data, comprising: identifying an expression level of a first switch from a first RNA sequencing dataset obtained from a first vaginally derived sample, wherein the first vaginally derived sample comprises a mixed population of cells, wherein the first switch comprises a set of genes selected from genes co- expressed within a biological relevance parameter; and generating a firstWSGR Docket No.50272-712.601 score for the switch based on the first expression level of the first switch. In some embodiments, the mixed population of cells are not cultured or expanded prior to obtaining the first RNA sequencing dataset. In some embodiments, the method comprises identifying an expression level of a second switch from the first RNA sequencing dataset, wherein the second switch comprises a second set of genes selected from genes co-expressed within a second biological relevance parameter; and generating a second score based on the expression level of the second switch. In some embodiments, the method comprises comparing the first score with the second score. In some embodiments, the method comprises identifying an expression level of a plurality of switches from the first RNA sequencing dataset, wherein each switch of the plurality of switches comprises a set of genes selected from genes co-expressed within a biological relevance parameter and generating a score for each of the plurality of switches. In some embodiments, the first vaginally derived sample comprises menstrual fluid, cervical- vaginal fluid or a combination thereof. In some embodiments, the first vaginally derived sample comprises a mixture of cell types or tissue types. In some embodiments, the method comprises identifying a second expression level of the first switch from a second RNA sequencing dataset from a second vaginally derived sample and generating a second score for the first switch based on the second expression level of the first switch. In some embodiments, the first vaginally derived sample and the second vaginally derived sample comprise menstrual fluid, cervical- vaginal fluid or a combination thereof. In some embodiments, the first vaginally derived sample and the second vaginally derived sample comprises a mixture of cell types or tissue types. In some embodiments, the method comprises comparing the first score with the second score. In some embodiments, the first RNA sequencing dataset is derived from one or more healthy or putative healthy individuals. In some embodiments, the second RNA sequencing dataset is derived from one or more individuals having a diseased state or putative diseased state. In some embodiments, the second RNA sequencing dataset is derived from one or more healthy or putative healthy individuals. In some embodiments, the first RNA sequencing dataset and the second RNA sequencing dataset are derived from a same individual. In some embodiments, the first RNA sequencing dataset and the second RNA sequencing dataset are derived from different time points or different sample types. In some embodiments, the first vaginally derived sample is obtained under a first condition and the second vaginally derived sample is obtained under a second condition. In some embodiments, the first condition is prior to a diagnosis of a disease or infection, exhibition, progression or recurrence of one or more symptoms, reduction or disappearance of one or more symptoms, or a medical treatment. In some embodiments, the first condition is prior to a diagnosis of a disease or infection, exhibition, progression or recurrence of one or more symptoms, reduction or disappearance of one or more symptoms, or a medical treatment, and the second condition is subsequent to the diagnosis of a disease, exhibition, progression or recurrence of one or more symptoms, reduction or disappearance of one or more symptoms, or a medical treatment. In someWSGR Docket No.50272-712.601 embodiments, the first condition is subsequent to a diagnosis of a disease or infection, exhibition, progression or recurrence of one or more symptoms, reduction or disappearance of one or more symptoms, or a medical treatment In some embodiments, the disease is selected from the group consisting of endometriosis, cervical cancer, infertility, uterine cancer, ovarian cancer, autoimmune disease, inflammatory disease and fibroids. In some embodiments, the infection comprises a bacterial infection, a fungal infection or a viral infection. In some embodiments, the one or more symptoms are selected from the group consisting of inflammation, bleeding, heavy bleeding, anemia, change in regular menstruation cycle, pain and a change in microbiome diversity, microbial species or microbial abundance. In some embodiments, the medical treatment is selected from the group consisting of surgery and administration of a therapeutic agent. In some embodiments, the first condition is a time period within a menstrual window. In some embodiments, the first condition is a time period outside a menstrual window. In some embodiments, the first condition is a time period within a menstrual window and the second condition is a time period outside the menstrual window. In some embodiments, the first condition and the second condition are a time period within a menstrual window, and wherein the first condition and the second condition are different days of menstruation. In some embodiments, the method comprises identifying a differential for the first switch, wherein the differential is identified for the first switch when the first score differs by more than the standard deviation of a reference score of the first switch in a reference score set. In some embodiments, the reference score set is derived from optimal RNA samples. In some embodiments, the reference score set is derived from an RNA sequencing dataset from one or more healthy or putative healthy individuals. In some embodiments, the reference score set is derived from an RNA sequencing dataset from one or more individuals having a diseased state or putative diseased state. In some embodiments, the biological relevance parameter is selected from a group consisting of cell type, tissue type, biological pathway, and biological function. In some embodiments, the first RNA sequencing dataset and / or the second RNA sequencing dataset comprises bulk RNA sequencing data. In some embodiments, the second RNA sequencing dataset comprises bulk RNA sequencing data. In some embodiments, the first score is calculated using a number of reads within the RNA sequencing dataset of the genes within the switch. In some embodiments, the second score is calculated using the number of reads within the RNA sequencing dataset of the set of genes within the switch. In some embodiments, the score, first score and / or second score is normalized by the level of one or more housekeeping genes in the RNA sequencing dataset. In some embodiments, the score, first score and / or second score indicates the presence or absence of the activity of one or more cell types in the first vaginally derived sample. In some embodiments, the first switch comprises at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 genes. In some embodiments, the first switch comprises at least 5, at least 10, at least 15, at least 20, or at least 25 genes. In some embodiments, the second switch comprises at least 2, 3, 4, 5, 6, 7, 8, 9, or l 0 genes. In some embodiments, the second switch comprises at least 5, at leastWSGR Docket No.50272-712.601 10, at least 15, at least 20, or at least 25 genes. In some embodiments, the first switch comprises at least one set of genes selected from Table 2. In some embodiments, the biological relevance parameter is a cell type and the cell type is one or more of stromal cells, monocytes, neutrophils, dendritic cells, macrophages, eosinophils, B cells, megakaryocytes, epithelial cells. progenitor cells, stem cells, lymphoid cells, non-lymphoid cells, hemopoietic cells, and non-hemopoietic cells. In some embodiments, the biological relevance parameter is a tissue type and the tissue type is one or more of cervical, endometrial, vaginal, uterine, blood, placental, muscle, ovarian, fetal, and maternal. In some embodiments, the biological relevance parameter is a biological pathway. INCORPORATION BY REFERENCE

[0004] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. BRIEF DESCRIPTION OF THE DRAWINGS

[0005] The novel features of the invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention are utilized, and the accompanying drawings of which:

[0006] FIG.1 depicts data stratification of transcriptional RNA expression from tampons.

[0007] FIG.2 depicts a heatmap of RNA expression data stratified by co-expressed genes throughout the menstrual cycle.

[0008] FIG.3 depicts a representative response specific index derived from a single tampon sample.

[0009] FIG.4 represents a comparison of baseline and response switches of a healthy reference.

[0010] FIG.5 depicts a patient profile compared to established noise bands.

[0011] FIG.6 depicts a menstrual health index used to identify health and unhealthy samples.

[0012] FIG.7 depicts a representative graph of viable and dead cells collected from menstrual effluence.

[0013] FIG.8 depicts RNA expression change organized into switches after incubation in PBS for 2 hours.

[0014] FIG.9 depicts 3 representative switches that make up a board.

[0015] FIG.10 depicts RNA-sequencing data and the uterine response profile in menstrual, vaginal and whole blood samples.

[0016] FIG.11 depicts the AUC of validation of a genomic classifier for endometriosis.

[0017] FIG.12 depicts the interaction of the classifier on different pathways.WSGR Docket No.50272-712.601

[0018] FIG.13 depicts the experimental assay for measuring response to compounds.

[0019] FIG.14 depicts the differential response.

[0020] FIG.15 depicts the response ratio.

[0021] FIG.16 depicts the response ratio barcodes of 6 experiments where aspirin was administered ex vivo to 5 samples.

[0022] FIG.17 depicts activation of mRNA switches in samples where aspirin was administered ex vivo.

[0023] FIG.18 depicts the uterine response model in samples treated ex vivo with aspirin and a novel component.

[0024] FIG.19 depicts JNK-inhibitor response by patient.

[0025] FIG.20 depicts hierarchical clustering of early and late response of patient samples.

[0026] FIG.21 depicts a scheme for creating switches and boards.

[0027] FIG.22 depicts UMAP clusters of 1905 single cells from menstrual samples.

[0028] FIG.23 depicts an exemplary probabilistic model for deconvolution of bulk sequencing data. The model includes the following assumptions: (i) the cell type fractions are influenced by the individual’s affection status (case / control / suspected) and the day of the cycle; (ii) each sample can be mapped to a universal state characterized by the cell type fractions of the sample; (iii) the gene expression of a sample is a linear combination of its cell type fractions and the gene expression of the cell types; and (iv) The gene expression of a cell type is approximately log-normally distributed.

[0029] FIG.24 depicts the cell factions deconvoluted using switch profiles of the RNA seq bulk sequencing data from the menstrual samples collected from patients on their menstrual cycle day 1, 2, 3, 4, and 5.

[0030] FIG.25 depicts an exemplary comparison of identification and quantification of cell types by individual genes (FIG.25A) as compared to methods using switches as described herein (FIG.25B).

[0031] FIG.26 depicts tSNE of the co-expression similarity between cell-specific RNA switches using aggregate data obtained from 197 tampon samples. The clustering illustrates that normal menstruation produces a pattern of clustering similar to an inflammatory response.

[0032] FIG.27 depicts a comparison of healthy and endometriosis samples using the tSNE of the co-expression similarity between cell-specific RNA switches using aggregate data obtained from 197 tampon samples (healthy) and 79 tampon samples (endometriosis).

[0033] FIG.28 depicts a switch-based quantification of cell activity (left panel) and quantification based on cell abundance (right panel).

[0034] FIG.29 depicts clustering using the switches describes herein and shows that clustering by cell activity preserves biologically relevant cell relatedness and lineages.

[0035] FIG.30 depicts clustering by cell abundance and shows that such clustering loses biologically relevant cell relatedness.WSGR Docket No.50272-712.601

[0036] FIG.31 depicts a random forest model.

[0037] FIG.32 depicts one embodiment of the method described herein. DETAILED DESCRIPTION

[0038] The uterus is hyper-responsive to changes in environment and health and can therefore be used as a tissue type for tracking disease and other biological conditions. Uterine tissue may be responsive to a variety of inputs, including, without limitations, medications, exercise, the environment, proximity to other menstruators, mental health, vaccines, smoking, and hormones.

[0039] Vaginally derived samples, such as menstrual and cervicovaginal fluid, contain a broad set of viable, transcriptionally active cells, including cells from uterine tissue, which can be useful to detect gene expression. However, these samples often contain a mixed population of cells, complicating the analysis of gene expression, particularly as it relates to patterns of gene expression, co-regulation and the relationship of such patterns to biologically relevant parameters.

[0040] Described herein are systems and methods for measuring, identifying, and predicting uterine responses using a vaginally derived sample such as cervicovaginal fluid or menstrual fluid, where the samples comprised mixed populations of cells or RNA sequencing data derived from a mixed population of cells.

[0041] In certain aspects, the use of a vaginally derived sample, such as menstrual fluid, allows for analysis to a stimulus, such as a response to a change in a health state, an inflammatory response, or a response to an interaction with one or microbes (e.g., a shift in the composition of a microbiome). Also provided herein are systems and methods for identifying and predicting mechanisms of actions of substances, such as drugs and other therapeutic agents, using vaginally derived samples such as cervicovaginal fluids in an ex vivo assay. In certain aspects, the use of a vaginally derived sample, such as cervicovaginal fluid or menstrual fluid, in the assays described herein allows for the analysis of multiple (i.e., mixed) cell types and their responses to the treatment. I. METHODS A. Methods of constructing switches and boards

[0042] In certain embodiments, the methods described herein comprise identifying and classifying a plurality of co-expressed nucleic acids. In certain embodiments, the methods comprise identifying and classifying a plurality of co-expressed genes that are co-expressed in response to a stimulus. Such stimulus can be a change in a biological state. Exemplary stimuli include a change in a health state, a progression in a biological event (such as a progression into and / or through the menstrual cycle, cell development and / or differentiation into functional cell types), an inflammatoryWSGR Docket No.50272-712.601 stimulus, a change in the profile of a microbiome associated with a particular tissue, and an external stimulus (such as the administration of a drug).

[0043] In certain embodiments, the methods described herein comprise selecting a plurality of genes to comprise a switch. In some embodiments, a switch comprises a plurality of genes with similar cellular activities. In some embodiments, a switch comprises a plurality of genes with shared activity in a shared cell type. In some embodiments, a switch comprises a plurality of genes with similar functional activity. In some embodiments, the associated genes within one of the one or more switches are associated by upregulation or overexpression in at least one cell type or by association to a molecular pathway, for example the genes within a switch are all upregulated or overexpressed in response to a stimulus (as compared to their state without the stimulus). In some embodiments, the associated genes within one of the one or more switches are associated by down regulation or reduced expression in at least one cell type or by association to a molecular pathway, for example the genes within a switch are all down regulated or reduced in expression in response to a stimulus (as compared to their state without the stimulus). An exemplary flowchart for a method of constructing switches and boards is shown in FIG.21. The method includes: (1) identifying co-expressed genes for a given labeled state, cell type, function or pathway type and constructing a list of the co-expressed genes; (2) calculating the median gene expression value for the genes withing the co-expression list; (3) grouping individual switches by functional activity and / or cell type to make one or more boards, where each board describes a cell type or function; and (4) calculating the median expression value of the grouped switches to create a board value. Examples of switches are provided in Table 2.

[0044] In some embodiments, the functional activity used for grouping genes into switches comprises cell growth, differentiation, migration, metaphorization, or apoptosis. In some embodiments, a switch comprises a plurality of genes with a shared or overlapping expression profile in a shared cell type (such genes may include different, similar, or the same functional activities). In some embodiments, the cell type comprises an immune cell, a blood cell, an endometrial cell, an endothelial cell, an epithelial cell, a bone cell, an adipose cell, a neuron, a brain cell, or a temporarily named human cell, or an unnamed human cell. In some embodiments, one or more genes found in one switch, may also be grouped into another (or more than one other switch). In some embodiments, a gene may be represented in only one switch.

[0045] The grouping of genes (as further described herein) that respond in a similar fashion to a stimulus can provide information that would not be readily available with other methods. For example, switch-based identification of cell types can provide a profile of the activity of certain cells (such as when such cells have high gene expression) that would not be obtained by profiling cells by quantifying the cell types per se. FIG.25 shows an exemplary comparison of identification and quantification of cell types by switches (FIG.25B) as compared to methods using individual gene expression (FIG.25A). Switch-based identification preserves biologically relevant cell relatedness and lineages. FIG.25A shows an example of t-distributed stochastic neighbor embedding (t-SNE)WSGR Docket No.50272-712.601 analysis using individual genes. In comparison, FIG.25B shows an example of t-SNE analysis of the same genes using the switches described herein. As shown in FIG.25B, the clustering by switches groups the various genes related to cell type such as epithelial, stromal, myeloid, lymphoid and progenitor / parent. FIG.26 shows an example of clustering genes for switches based on their co- expression and how such clustering method groups cells into related lineages (e.g., lymphocytes, non- lymphocytes and hematopoietic cells. FIG.27 shows this same analysis compared in putatively healthy (FIG.27A) and confirmed endometriosis (FIG.27B) samples.

[0046] In some embodiments, a switch comprises at least one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen or twenty associated genes. In some embodiments, a switch comprises at least about 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 250, 300, 350, 400, 450, 500 or more than 500 associated genes. In some embodiments, a switch comprises no more than two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen or twenty associated genes. In some embodiments, a switch comprises no more than about 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 250, 300, 350, 400, 450, 500 or more than 500 associated genes.

[0047] In certain embodiments, the method described herein comprise selecting a plurality of switches to comprise a board. In certain embodiments, the methods comprise analyzing the expression of at least one board. In certain embodiments, the board comprises a plurality of switches related to each other by cell-type expression or by molecular function.

[0048] In some embodiments, a board comprises at least one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen or twenty associated switches. In some embodiments, a board comprises no more than two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen or twenty associated switches. In some embodiments, the switches comprised within a board are related by cell-type, by molecular function, by molecular pathway or any combination thereof.

[0049] In certain aspects, described herein are methods for identifying and classifying a plurality of co-expressed nucleic acids (i.e., switches and boards) for a uterine response, such as a response in response to a biological state or condition. In certain embodiments, the methods comprise obtaining a cervicovaginal fluid sample (e.g., a menstrual sample) and analysis of the expression of genes using the switches and / or boards as described herein.

[0050] In some embodiments, the cervicovaginal fluid sample (e.g., a menstrual sample) is collected with a menstrual cup, a tampon, or a pad. In some embodiments, no preservation buffer is added. In some embodiments, fluid is extracted from a sample collector. In some embodiments, the fluid is retained in a sample collector. In some embodiments, phosphate buffered saline (PBS) or other buffer is added to the fluid. In some embodiments, a mucolytic agent (e.g. acetylcysteine) is applied toWSGR Docket No.50272-712.601 the sample. In some embodiments, the sample undergoes single cell dissociation. In some embodiments, single cell dissociation is performed by addition of a collagenase, proteinase, a DNAse, or a combination thereof. In some embodiments, the cells are filtered. In some embodiments, the sample is not further cultured or expanded prior to the preparation of the sample for obtaining expression data. In some embodiments, the sample is processed to extract or isolate nucleic acids, such as RNA or mRNA, and such nucleic acid is used for obtaining expression data.

[0051] In some embodiments, the method comprises identifying a plurality of genes that exhibit a parallel change in expression level in response to a stimulus and grouping such genes into switches and boards as described herein.

[0052] In certain embodiments, the method comprises: (a) obtaining sequencing data from a sample of cervicovaginal fluid or nucleic acids therefrom; (b) detecting the presence or level of a plurality of nucleic acids in the sample, e.g., RNA, to obtain a gene expression data for the sample; (c) analyzing the data to identify pluralities of genes exhibiting a co-expression pattern (e.g., increased expression / upregulation or reduced expression / down regulation); and (d) grouping the co-expressed genes into one or more switches by biological context (e.g., as related to a particular cell type, tissue or function).

[0053] In some embodiments, the method further comprises (e) validating the co-expression of the genes within a particular switch, such as by analysis in additional samples. In some embodiments, the additional samples for validation are from the same subject. In some embodiments, the additional samples for validation are from different subjects.

[0054] In some embodiments, step (a) of the method is performed with multiple samples and the data are aggregated. In some embodiments, the nucleic acid or sequencing data is obtained from a population of mixed cell types. B. Methods for Analyzing Expression Using Switches and Boards

[0055] The methods herein include methods for analyzing expression of sets of genes using the switches and boards described herein. In some embodiments, the methods include analyzing RNA sequencing data derived from a mixed population of cells. In some embodiments, the methods include obtaining a first set of RNA sequencing data (also referred to herein as a first transcriptome data) of a first sample, wherein the first sample comprises a mixed population of cells obtained under a first condition, identifying the expression level of a set of switches from the first set of RNA sequencing data, and generating a score for each switch based on the expression level of the genes within the switch. Each switch for such analysis comprises a set of genes selected co-expressed within a biological relevance parameter. In some embodiments, the biological relevance parameter is a cell type, tissue type, biological pathway, or a biological function. In some embodiments, the score for oneWSGR Docket No.50272-712.601 or more of the switches in the first sample indicates the presence or absence of the activity of one or more cell types in the first sample.

[0056] In some embodiments, the biological relevance parameter for use with the methods herein is a cell type or tissue type and the switch is selected from the switches set forth in Table 2 herein. In some embodiments, a switch for use with the methods herein is one of Switch IDs 1-489 set forth in Table 2 herein.

[0057] In some embodiments, the methods comprise obtaining expression data from a first cervicovaginal fluid sample, e.g., a first transcriptome data obtained from a first cervicovaginal fluid sample. In some embodiments, the methods comprise analyzing the expression of one or more switches in the first transcriptome data, wherein each switch comprises a selected group of associated genes.

[0058] In some embodiments, the methods comprise a method of analyzing RNA sequencing data that comprises identifying an expression level of a switch from a first RNA sequencing dataset obtained from a first vaginally derived sample, such as a cervicovaginal fluid sample, wherein the first vaginally derived sample comprises a mixed population of cells, wherein the switch comprises a set of genes selected from genes co-expressed within a biological relevance parameter; and generating a score for the switch based on the expression level of the switch. In some embodiments, the score represents (or is calculated from) the median (mean) expression level of the set of genes within a switch. In some embodiments, the score represents (or is calculated from) the average expression level of the set of genes within a switch. In some embodiments, the expression level and mean or average score is derived from the gene counts of the set of genes within a switch, i.e., identifying the number of sequences within the first RNA sequencing dataset of each gene within the switch, and then finding the mean or average number for the genes within a switch. In some embodiments, the score is derived from the genes or the RNA sequencing data from the genes within a switch that is one of Switch IDs 1-489 set forth in Table 2 herein.

[0059] In some embodiments, the score (e.g., gene counts, average or mean) are normalized by using one or more housekeeping gene expression levels, for example, 1, 2, 3, 4, 5 or more housekeeping gene expression levels. In some embodiments, the score is derived from a log transformation of the gene expression counts of the switch, and in some embodiments, normalized by a log transformation of the gene expression counts of the one or more housekeeping gene expression levels.

[0060] In some embodiments, the score is calculated by generating a score for each gene in the switch derived from RPKM, where RPKM = numReads / ( geneLength / 1000 * totalNumReads / 1,000,000 ) and numReads - number of reads mapped to a gene sequence, geneLength - length of the gene sequence, totalNumReads - total number of mapped reads of a sample (e.g., the first RNA sequencing dataset). In some embodiments, a score for each gene within a switch is normalized to one or more housekeeping gene expression levels, for example, 1, 2, 3, 4, 5 or moreWSGR Docket No.50272-712.601 housekeeping gene expression levels. In some embodiments, the score is calculated with the following equation 10 + log2(RPKM [GeneX]) – log2(geomean(RPKM[Gene-housekeeping])), where GeneX is a gene in the switch and Gene-housekeeping is the one or more housekeeping genes used for normalization. Exemplary housekeeping genes include genes where the expression is relatively constant or stable within the cells or tissues of interest, such as housekeeping genes having stable expression within vaginally derived samples (e.g. cervical-vaginal fluid or menstrual fluid) such as RAB7A, GAPDH, and ACTB.

[0061] In some embodiments, the methods comprise comparing the expression of the one or more switches in in the first transcriptome data to reference expression data, e.g., a reference transcriptome data. In some embodiments, the methods include identifying a differential for each switch in the first sample, wherein the differential is identified for the switch when the score of the switch in the first sample differs by more than the standard deviation of the score of the same switch in a reference score set. In some embodiments, the reference score set comprises a set of switch scores derived from optimal RNA samples.

[0062] In some embodiments, the reference transcriptome data set comprises transcriptional data from one or more healthy subjects. In some embodiments, the reference transcriptome data set comprises transcriptional data from one or more subjects diagnosed with a health condition. In some embodiments, the reference transcriptome data set comprises transcriptional data from one or more subjects who have undergone medical treatment. In some embodiments, the reference transcriptome data set comprises transcriptional data from the same subject as the subject source for the first cervicovaginal fluid sample. In some embodiments, the reference transcriptome data is obtained from one or more reference vaginally derived samples such as cervicovaginal fluid samples or menstrual fluid samples. In some embodiments, a health index is generated by a comparison between the expression of the switches in the first transcriptome data and the reference transcriptome data, where the reference transcriptome data is generated from a healthy or putative healthy state. In some embodiments, a health index is generated by a comparison between the expression of the switches in the first transcriptome data and the reference transcriptome data, where the reference transcriptome data is generated from a disease state or putative disease state. In some embodiments, the health index is generated by a comparison between the expression of the boards in the first transcriptome data and the reference transcriptome data. In some embodiments, the health index is generated by one or more processors using a machine learning model.

[0063] In some embodiments, the first sample comprises a mixture of cell types or tissue types. In some embodiments, the first transcriptome data is nucleic acid sequences derived from a mixture of cell types or tissue types. In some embodiments, the first transcriptome data comprises RNA sequencing data, such as bulk RNA sequencing data.

[0064] In some embodiments, the methods include comparing the score for each switch for the first sample with a score for each switch derived from a second set of RNA sequencing data. In someWSGR Docket No.50272-712.601 embodiments, the methods include obtaining a first transcriptome data of a first sample obtained under a first condition, and a second set of RNA sequencing data (also referred to herein as a second transcriptome data) of a second sample, identifying the expression level of a set of switches from the first transcriptome data and the second transcriptome data, and generating a score for each switch within such data sets based on the expression level of the genes within the switch, such score generated such as described herein. In some embodiments, the expression level of the genes within each switch are compared between the first and second transcriptome data. In some embodiments, the score for one or more of the switches is compared between the first and second transcriptome data.

[0065] In some embodiments, the second set of RNA sequencing data (second transcriptome data) is derived from a second sample obtained under a second condition. In some embodiments, the second transcriptome data is derived from a second sample obtained from a selected set of individuals or subjects. In some embodiments, the second sample is from one or an aggregate of more than one healthy or putatively health individuals. In some embodiments, the second sample is from one or an aggregate of more than one individual having a disease, condition or diagnosis or a disease or condition. In some embodiments, the second set of RNA sequencing data (second transcriptome data) is derived from a second sample obtained from a same individual as the first transcriptome data. In some embodiments, the second set of RNA sequencing data (second transcriptome data) is derived from a second sample obtained from different time points or different sample types from the individual. For example, different time points may include time points before or after disease onset, before or after diagnosis. In some embodiments, different time points may include samples from the individual taken at different days before, during and / or after the menstrual window. In some embodiments, the second transcriptome data is derived from a second sample obtained from different sample types from the individual, for example blood, cervicovaginal fluid, or tissue biopsy. In some embodiments, a first sample or first transcriptome data is obtained from a first condition that is prior to a diagnosis of a disease or infection, exhibition, progression or recurrence of one or more symptoms, reduction or disappearance of one or more symptoms, or a medical treatment. In some embodiments, a second sample or second transcriptome data is obtained subsequent to the diagnosis of a disease, exhibition, progression or recurrence of one or more symptoms, reduction or disappearance of one or more symptoms, or the medical treatment. In some embodiments, the first and / or second condition is a disease state or diagnosis of a disease, and the disease is selected from the group consisting of endometriosis, cervical cancer, infertility, uterine cancer, ovarian cancer and fibroids. In some embodiments, the first and / or second condition is an infection, such as a bacterial infection, a fungal infection or a viral infection. In some embodiments, the first and / or second condition is the appearance of one or more symptoms. In some embodiments, the one or more symptoms are selected from the group consisting of inflammation, bleeding, heavy bleeding, anemia, change in regular menstruation cycle, pain and a change in microbiome diversity, microbial species orWSGR Docket No.50272-712.601 microbial abundance. In some embodiments, the first and / or second condition the administration of a procedure or a substance, such as surgery or administration of a therapeutic agent.

[0066] In some embodiments, the methods include obtaining a first transcriptome data of a first sample of cervicovaginal fluid obtained under a first condition, and a second transcriptome data of a second sample of cervicovaginal fluid obtained under a second condition, identifying the expression level of a set of switches from the first transcriptome data and the second transcriptome data, and generating a score for each switch within such data sets based on the expression level of the genes within the switch. In some embodiments, the expression level of the genes within each switch are compared between the first and second transcriptome data. In some embodiments, the score for one or more of the switches is compared between the first and second transcriptome data. In some embodiments, the first condition is within the menstrual window and the second condition is outside the menstrual window. In some embodiments, the first condition is within the menstrual window and the second condition is within the menstrual window. In some embodiments, both the first condition and the second condition are within the menstrual window and wherein the first condition and the second condition are different days of menstruation. In some embodiments, the first condition is outside the menstrual window and the second condition is within the menstrual window. In some embodiments, both the first condition and the second condition are outside the menstrual window.

[0067] In some embodiments, the expression level of a set of switches from the first transcriptome data and / or the second transcriptome data uses one or more of the switches set forth in Table 2, such as one or more of Switch IDs 1-489 set forth in Table 2 herein. In some embodiments, the set of switches represents gene expression within a selected cell or tissue type, such as one or more of activated dendritic cells, adipocytes, astrocytes, B-cells, basophils, CD4+ memory T-cells, CD4+ naive T-cells, CD4+ T-cells, CD4+ central memory T-cells, CD4+ effector memory T-cells, CD8+ naive T-cells, CD8+ T-cells, CD8+ central memory T-cells, CD8+ effector memory T-cells, conventional dendritic cells, chondrocytes, class-switched memory B-cells, common lymphoid progenitors, common myeloid progenitors, dendritic cells, endothelial cells, eosinophils, epithelial cells, erythrocytes, fibroblasts, granulocyte-macrophage progenitors, hepatocytes, hematopoietic stem cells, immature dendritic cells, keratinocytes, lymphatic endothelial cells, macrophages, macrophages M1, macrophages M2, mast cells, megakaryocytes, melanocytes, memory B-cells, megakaryocyte– erythroid progenitors, mesangial cells, monocytes, multipotent progenitors, mesenchymal stem cells, microvascular endothelial cells, myocytes, naive B-cells, neurons, neutrophils, NK cells, natural killer T-cells, osteoblasts, plasmacytoid dendritic cells, pericytes, plasma cells, platelets, preadipocytes, pro B-cells, sebocytes, skeletal muscle cells, smooth muscle cells, gamma delta T-cells, type 1 T-helper cells, type 2 T-helper cells and regulatory T-cells.

[0068] In some embodiments, the set of switches used for the methods herein contains 2, 3, 4, 5, or more than 5 switches. In some embodiments, the set of switches are chosen from 2 switches, 3 switches, 4 switches, 5 switches, or more than 5 switches set forth in Table 2 as Switch IDs 1-489. InWSGR Docket No.50272-712.601 some embodiments, the set of switches are chosen to represent a single cell or tissue type, 2 cell or tissue types, 3 cell or tissue types, 4 cell or tissue types, 5 cell or tissue types, or more than 5 cell or tissue types. In some embodiments, the set of switches are chosen to represent a single cell or tissue type, 2 cell or tissue types, 3 cell or tissue types, 4 cell or tissue types, 5 cell or tissue types, or more than 5 cell or tissue types that are selected from one or more of activated dendritic cells, adipocytes, astrocytes, B-cells, basophils, CD4+ memory T-cells, CD4+ naive T-cells, CD4+ T-cells, CD4+ central memory T-cells, CD4+ effector memory T-cells, CD8+ naive T-cells, CD8+ T-cells, CD8+ central memory T-cells, CD8+ effector memory T-cells, conventional dendritic cells, chondrocytes, class-switched memory B-cells, common lymphoid progenitors, common myeloid progenitors, dendritic cells, endothelial cells, eosinophils, epithelial cells, erythrocytes, fibroblasts, granulocyte- macrophage progenitors, hepatocytes, hematopoietic stem cells, immature dendritic cells, keratinocytes, lymphatic endothelial cells, macrophages, macrophages M1, macrophages M2, mast cells, megakaryocytes, melanocytes, memory B-cells, megakaryocyte–erythroid progenitors, mesangial cells, monocytes, multipotent progenitors, mesenchymal stem cells, microvascular endothelial cells, myocytes, naive B-cells, neurons, neutrophils, NK cells, natural killer T-cells, osteoblasts, plasmacytoid dendritic cells, pericytes, plasma cells, platelets, preadipocytes, pro B-cells, sebocytes, skeletal muscle cells, smooth muscle cells, gamma delta T-cells, type 1 T-helper cells, type 2 T-helper cells and regulatory T-cells. C. Methods of Assaying Cervicovaginal Samples

[0069] In certain embodiments, the methods comprise obtaining a first vaginally derived sample as described herein. In certain embodiments, the methods comprise applying the substance to the sample ex vivo. In certain embodiments, the methods comprise detecting the presence or level of at least one nucleic acid in the sample subsequent to the application of the substance. In some embodiments, the first vaginally derived sample is a cervicovaginal fluid sample and / or is a menstrual fluid.

[0070] In certain aspects, described herein is a method for preparing a first vaginally derived sample, such as a cervicovaginal sample (e.g., menstrual fluid). In certain embodiments, the methods comprise obtaining a sample as described herein. In certain embodiments, the methods comprise applying the substance to the fluid sample ex vivo. In certain embodiments, the methods comprise detecting the presence or level of at least one nucleic acid in the sample subsequent to the application of the substance.

[0071] In some embodiments, the first vaginally derived sample is collected with a menstrual cup. In some embodiments, no preservation buffer is added. In some embodiments, PBS is added to the fluid. In some embodiments, a mucolytic agent (e.g. acetylcysteine) is applied to the sample. In some embodiments, the sample undergoes single cell dissociation. In some embodiments, single cell dissociation is performed by addition of a collagenase, proteinase, a DNase, or a combination thereof.WSGR Docket No.50272-712.601 In some embodiments, the cells are filtered. In some embodiments, the cells rest before the compound is added. In some embodiments, the sample is collected with a menstrual cup and aliquoted for the methods further described herein. In some embodiments, the sample is not further grown or expanded prior to application of the substance used in the methods.

[0072] The methods may further comprise comparing the presence of level of the at least one nucleic acid to a reference sample. In some embodiments, the reference sample is the fluid sample prior to the application of the substance. In some embodiments, the reference sample is one or more cervicovaginal fluid samples from a subject. In some embodiments, the reference sample is one or more cervicovaginal fluid samples from a plurality of subjects. In some embodiments, the reference sample is the cervicovaginal fluid sample or a portion thereof treated with a different substance. In some embodiments, the reference sample is cervicovaginal fluid associated with a disease state. Insome embodiments, the reference sample is cervicovaginal fluid associated with a healthy state. Insome embodiments, the reference sample is one or more menstrual fluid samples from a subject. Insome embodiments, the reference sample is one or more menstrual fluid samples from a plurality of subjects. In some embodiments, the reference sample is the menstrual sample or a portion thereof treated with a different substance. In some embodiments, the reference sample is a menstrual sample associated with a disease state. In some embodiments, the reference sample is a menstrual sample associated with a healthy state.

[0073] In some embodiments, the method comprises measuring the presence or level of a plurality of nucleic acids. In some embodiments, the method comprises measuring the presence or level of nucleic acids for at least one switch as described herein. In some embodiments, the method comprises measuring the presence or level of nucleic acids for at least one board as described herein.

[0074] In some embodiments, the nucleic acid comprises RNA. In some embodiments, the nucleic acid comprises mRNA. In some embodiments, the nucleic acid sequence comprises an RNA molecule or a fragmented RNA molecule (RNA fragments) selected from: a microRNA (miRNA), a pre-miRNA, a pri-miRNA, a mRNA, a pre-mRNA, a viral RNA, a viroid RNA, a virusoid RNA, circular RNA (circRNA), a ribosomal RNA (rRNA), a transfer RNA (tRNA), a pre-tRNA, a long non- coding RNA (lncRNA), a small nuclear RNA (snRNA), a circulating RNA, a cell-free RNA, an exosomal RNA, a vector-expressed RNA, an RNA transcript, a synthetic RNA, and combinations thereof.

[0075] In some embodiments, the substance comprises a drug, a drug candidate, a biologic, or a pathogen.

[0076] In some embodiments, the sample is not cultured or expanded prior to applying the substance to the sample. In some embodiments, the method further comprises detecting the presence or level of the at least one nucleic acid over a period of time subsequent to the application of the substance. In some embodiments, the method comprises identifying one or more genes exhibiting a change in expression level over the period of time.WSGR Docket No.50272-712.601

[0077] In certain embodiments, the method comprises: (a) obtaining a vaginally derived sample; (b) applying a substance to the vaginally derived sample ex vivo; and (c) detecting the presence or level of at least one nucleic acid in the sample subsequent to the application of the substance.

[0078] In certain embodiments, (a) comprises obtaining an initial vaginally derived sample and dividing the initial vaginally derived sample into multiple samples. In certain embodiments, (b) comprises treating two or more of the multiple samples with different amounts of the substance. In certain embodiments, (c) comprises detecting the presence or level of the at least one nucleic acid in each of the multiple samples subsequent to the application of the substance.

[0079] In certain embodiments, the method further comprises identifying one or more genes exhibiting a change in expression level between two or more of the multiple samples. In certain embodiments, the identified one or more genes are compared to the expression data for one or more switches as described herein. In certain embodiments, the method comprises identifying and analyzing at least one board as described herein.

[0080] In certain embodiments, the method comprises generating an index by finding a correlation between the expression of the one or more identified genes and the expression data for the one or more boards. In certain embodiments, the index is associated with a health condition, a mode of action, or a molecular pathway.

[0081] In certain embodiments, the method comprises measuring the expression of selected switches and / or selected boards for each experimental condition (e.g., time point or dose amount of the substance). In certain embodiments, the control expression value (from a sample that was not treated with the substance, such as treated with buffer only or no treatment) is subtracted for each switch to provide a difference in expression for each switch between each treated sample and the control. In certain embodiments, a response ratio of one or more switches and / or one or more boards is generated using the sample and control expression data. In some embodiments, a response index is generated by identifying which switches and / or boards show a response based on whether they are up- regulated or down-regulated from the control. In some embodiments, the selected switches are one or more of the switches set forth in Table 2 herein. In some embodiments, the one or more switches is one or more of Switch IDs 1-489 set forth in Table 2 herein.

[0082] In some embodiments, the selected switches represent gene expression within a selected cell or tissue type, such as one or more of activated dendritic cells, adipocytes, astrocytes, B-cells, basophils, CD4+ memory T-cells, CD4+ naive T-cells, CD4+ T-cells, CD4+ central memory T-cells, CD4+ effector memory T-cells, CD8+ naive T-cells, CD8+ T-cells, CD8+ central memory T-cells, CD8+ effector memory T-cells, conventional dendritic cells, chondrocytes, class-switched memory B- cells, common lymphoid progenitors, common myeloid progenitors, dendritic cells, endothelial cells, eosinophils, epithelial cells, erythrocytes, fibroblasts, granulocyte-macrophage progenitors,WSGR Docket No.50272-712.601 hepatocytes, hematopoietic stem cells, immature dendritic cells, keratinocytes, lymphatic endothelial cells, macrophages, macrophages M1, macrophages M2, mast cells, megakaryocytes, melanocytes, memory B-cells, megakaryocyte–erythroid progenitors, mesangial cells, monocytes, multipotent progenitors, mesenchymal stem cells, microvascular endothelial cells, myocytes, naive B-cells, neurons, neutrophils, NK cells, natural killer T-cells, osteoblasts, plasmacytoid dendritic cells, pericytes, plasma cells, platelets, preadipocytes, pro B-cells, sebocytes, skeletal muscle cells, smooth muscle cells, gamma delta T-cells, type 1 T-helper cells, type 2 T-helper cells and regulatory T-cells.

[0083] In some embodiments, the selected switches used for the methods herein contains 1, 2, 3, 4, 5, or more than 5 switches. In some embodiments, the selected switches are chosen from 1 switch, 2 switches, 3 switches, 4 switches, 5 switches, or more than 5 switches set forth in Table 2 as Switch IDs 1-489. In some embodiments, the selected switches are chosen to represent a single cell or tissue type, 2 cell or tissue types, 3 cell or tissue types, 4 cell or tissue types, 5 cell or tissue types, or more than 5 cell or tissue types. In some embodiments, the set of switches are chosen to represent a single cell or tissue type, 2 cell or tissue types, 3 cell or tissue types, 4 cell or tissue types, 5 cell or tissue types, or more than 5 cell or tissue types that are selected from one or more of activated dendritic cells, adipocytes, astrocytes, B-cells, basophils, CD4+ memory T-cells, CD4+ naive T-cells, CD4+ T- cells, CD4+ central memory T-cells, CD4+ effector memory T-cells, CD8+ naive T-cells, CD8+ T- cells, CD8+ central memory T-cells, CD8+ effector memory T-cells, conventional dendritic cells, chondrocytes, class-switched memory B-cells, common lymphoid progenitors, common myeloid progenitors, dendritic cells, endothelial cells, eosinophils, epithelial cells, erythrocytes, fibroblasts, granulocyte-macrophage progenitors, hepatocytes, hematopoietic stem cells, immature dendritic cells, keratinocytes, lymphatic endothelial cells, macrophages, macrophages M1, macrophages M2, mast cells, megakaryocytes, melanocytes, memory B-cells, megakaryocyte–erythroid progenitors, mesangial cells, monocytes, multipotent progenitors, mesenchymal stem cells, microvascular endothelial cells, myocytes, naive B-cells, neurons, neutrophils, NK cells, natural killer T-cells, osteoblasts, plasmacytoid dendritic cells, pericytes, plasma cells, platelets, preadipocytes, pro B-cells, sebocytes, skeletal muscle cells, smooth muscle cells, gamma delta T-cells, type 1 T-helper cells, type 2 T-helper cells and regulatory T-cells.

[0084] As pattern of regulation, such as a pattern of up- or down-regulation in one or more switches or one or more boards in response to the application of substance (or time course or dose response course) may provide a “barcode” or fingerprint that is specific to the substance. In certain embodiments, a barcode from application of one substance is compared to the barcode of another substance, for example comparing the barcode obtained with a drug candidate or novel drug to a known drug.

[0085] One example of the method is depicted in FIG.32. In some instances, a first sample 101 and a second sample 105 are obtained. The first sample and the second sample may be any samples described herein. In some instances, the first sample and the second sample are cervicovaginal fluidWSGR Docket No.50272-712.601 samples or menstrual samples. In some instances, the first sample and the second sample are from the same subject. In some instances, the first sample and the second sample are from different subjects. In some instances, the first sample and the second sample are from the same subjects at different points in the menstrual cycle. In some instances, a compound is applied 102 to the first sample and a compound is applied 106 to the second sample. In some instances, a compound is not applied to the first sample. In some instances, different compounds are applied. In some instances, different concentrations of the sample are applied. In some instances, the gene expression of sample 1103 is identified. In some instances, the gene expression of sample 2107 is identified. In some instances a score 1 is generated for sample 1 based on the gene expression 104, using the methods described herein. In some instances, a score 2 is generated for sample 2 based on the gene expression 108, using the methods described herein. In some instances, score 1 and score 2 are compared 109.

[0086] In certain embodiments, (a) comprises obtaining initial vaginally derived sample, such as cervicovaginal fluid samples or menstrual samples from 2 or more subjects, (b) comprises treating the samples (separately) with the substance, and (c) comprises detecting the presence or level of the at least one nucleic acid in each of the samples subsequent to the application of the substance. The method further comprises comparing expression data between the samples using the one or more switches and / or one or more boards as described herein. In certain embodiments, the comparison between samples can be used to identify different responses (e.g., strength, duration, type of response) between subjects, for example to identify responders vs. non-responders for a substance (such as a drug or drug candidate). D. Machine learning models

[0087] In certain aspects, the health index described herein is generated by one or more processors using a machine learning model.

[0088] In certain aspects, described herein is a method for detecting an endogenous condition in a subject. In certain embodiments, the method comprises obtaining, at one or more processors, data from vaginally derived sample(s) collected from the subject. In certain aspects, the method comprises analyzing, by the one or more processors, a transcriptome of the sample(s), thereby obtaining transcriptional data. In certain aspects, the method comprises detecting, by the one or more processors, the endogenous condition in the subject via analyzing the transcriptional data with a machine learning model.

[0089] In some embodiments, the machine learning model is trained using at least one switch as described herein. In some embodiments, the machine learning model is trained using at least 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 34, 50 or more than 50 switches. In some embodiments, the machine learning model is trained using at least one board as described herein. In some embodiments, the machine learning model is trained using at least 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 34, 50 or more than 50 boards.WSGR Docket No.50272-712.601

[0090] In some embodiments, the machine learning model is trained on a set of training data comprising a plurality of previously analyzed transcriptional data sets from samples associated with one or more health conditions.

[0091] In some embodiments, the machine learning model comprises a classifier. In some embodiments, the machine learning model comprises a decision tree. In some embodiments, the machine learning model comprises a random forest model.

[0092] As used in this specification and the appended claims, the terms “artificial intelligence,” “artificial intelligence techniques,” “artificial intelligence operation,” and “artificial intelligence algorithm” generally refer to any system or computational procedure that may take one or more actions that simulate human intelligence processes for enhancing or maximizing a chance of achieving a goal. The term “artificial intelligence” may include “generative modeling,” “machine learning” (ML), or “reinforcement learning” (RL).

[0093] As used in this specification and the appended claims, the terms “machine learning,” “machine learning techniques,” “machine learning operation,” and “machine learning model” generally refer to any system or analytical or statistical procedure that may progressively improve computer performance of a task. In some embodiments, ML may generally involve identifying and recognizing patterns in existing data in order to facilitate making predictions for subsequent data. ML may include a ML model (which may include, for example, a ML algorithm). Machine learning, whether analytical or statistical in nature, may provide deductive or abductive inference based on real or simulated data. The ML model may be a trained model. ML techniques may comprise one or more supervised, semi-supervised, self-supervised, or unsupervised ML techniques. For example, an ML model may be a trained model that is trained through supervised learning (e.g., various parameters are determined as weights or scaling factors). ML may comprise one or more of regression analysis, regularization, classification, dimensionality reduction, ensemble learning, meta learning, association rule learning, cluster analysis, anomaly detection, deep learning, or ultra-deep learning. ML may comprise, but is not limited to: k-means, k-means clustering, k-nearest neighbors, learning vector quantization, linear regression, non-linear regression, least squares regression, partial least squares regression, logistic regression, stepwise regression, multivariate adaptive regression splines, ridge regression, principal component regression, least absolute shrinkage and selection operation (LASSO), least angle regression, canonical correlation analysis, factor analysis, independent component analysis, linear discriminant analysis, multidimensional scaling, non-negative matrix factorization, principal components analysis, principal coordinates analysis, projection pursuit, Sammon mapping, t-distributed stochastic neighbor embedding, AdaBoosting, boosting, gradient boosting, bootstrap aggregation, ensemble averaging, decision trees, conditional decision trees, boosted decision trees, gradient boosted decision trees, random forests, stacked generalization, Bayesian networks, Bayesian belief networks, naïve Bayes, Gaussian naïve Bayes, multinomial naïve Bayes, hidden Markov models, hierarchical hidden Markov models, support vector machines,WSGR Docket No.50272-712.601 encoders, decoders, auto-encoders, stacked auto-encoders, perceptrons, multi-layer perceptrons, artificial neural networks, feedforward neural networks, convolutional neural networks, recurrent neural networks, long short-term memory, deep belief networks, deep Boltzmann machines, deep convolutional neural networks, deep recurrent neural networks, or generative adversarial networks.

[0094] Training the ML model may include, in some embodiments, selecting one or more untrained data models to train using a training data set. The selected untrained data models may include any type of untrained ML models for supervised, semi-supervised, self-supervised, or unsupervised machine learning. The selected untrained data models be specified based upon input (e.g., user input) specifying relevant parameters to use as predicted variables or other variables to use as potential explanatory variables. For example, the selected untrained data models may be specified to generate an output (e.g., a prediction) based upon the input. Conditions for training the ML model from the selected untrained data models may likewise be selected, such as limits on the ML model complexity or limits on the ML model refinement past a certain point. The ML model may be trained (e.g., via a computer system such as a server) using the training data set. In some embodiments, a first subset of the training data set may be selected to train the ML model. The selected untrained data models may then be trained on the first subset of training data set using appropriate ML techniques, based upon the type of ML model selected and any conditions specified for training the ML model. In some embodiments, due to the processing power requirements of training the ML model, the selected untrained data models may be trained using additional computing resources (e.g., cloud computing resources). Such training may continue, in some embodiments, until at least one aspect of the ML model is validated and meets selection criteria to be used as a predictive model.

[0095] In some embodiments, one or more aspects of the ML model may be validated using a second subset of the training data set (e.g., distinct from the first subset of the training data set) to determine accuracy and robustness of the ML model. Such validation may include applying the ML model to the second subset of the training data set to make predictions derived from the second subset of the training data. The ML model may then be evaluated to determine whether performance is sufficient based upon the derived predictions. The sufficiency criteria applied to the ML model may vary depending upon the size of the training data set available for training, the performance of previous iterations of trained models, or user-specified performance requirements. If the ML model does not achieve sufficient performance, additional training may be performed. Additional training may include refinement of the ML model or retraining on a different first subset of the training dataset, after which the new ML model may again be validated and assessed. When the ML model has achieved sufficient performance, in some embodiments, the ML may be stored for present or future use. The ML model may be stored as sets of parameter values or weights for analysis of further input (e.g., further relevant parameters to use as further predicted variables, further explanatory variables, further user interaction data, etc.), which may also include analysis logic or indications of model validity in some instances. In some embodiments, a plurality of ML models may be stored forWSGR Docket No.50272-712.601 generating predictions under different sets of input data conditions. In some embodiments, the ML model may be stored in a database (e.g., associated with a server).

[0096] As described above, the machine learning model may implement a decision tree. A decision tree may be a supervised ML algorithm that can be applied to both regression and classification problems. Decision trees may mimic the decision-making process of a human brain. For example, a decision tree may grow from a root (base condition), and when it meets a condition (internal node / feature), it may split into multiple branches. The end of the branch that does not split anymore may be an outcome (leaf). A decision tree can be generated using a training data set according to the following operations: (1) Starting from a root node (the entire dataset), the algorithm may split the dataset in two branches using a decision rule or branching criterion; (2) each of these two branches may generate a new child node; (3) for each new child node, the branching process may be repeated until the dataset cannot be split any further; (4) each branching criterion may be chosen to maximize information gain (e.g., a quantification of how much a branching criterion reduces a quantification of how mixed the labels are in the children nodes). The labels may be the data or the classification that is predicted by the decision tree.

[0097] A random forest regression is an extension of the decision tree model that tends to yield more robust predictions by stretching the use of the training data partition. Whereas a decision tree may make a single pass through the data, a random forest regression may bootstrap 50% of the data (e.g., with replacement) and build many trees. Rather than using all explanatory variables as candidates for splitting, a random subset of candidate variables may be used for splitting, which may enable trees that have completely different data and different variables (hence the term random). The predictions from the trees, collectively referred to as the “forest,” may be then averaged together to produce the final prediction. Many trees (e.g., one hundred trees) may be included in a random forest model, with a number (e.g., 3, 6, 10, etc.) of terms sampled per split, a minimum of number (e.g., 1, 2, 4, 10, etc.) of splits per tree, and a minimum split size (e.g., 16, 32, 64, 128, 256, etc.). Random forests may be trained in a similar way as decision trees. Specifically, training a random forest may include the following operations: (1) select randomly k features from the total number of features; (2) create a decision tree from these k features using the same operations as for generating a decision tree; and (3) repeat the previous two operations until a target number of trees is created.

[0098] FIG.31 illustrates a random forest 200. The random forest 200 (which may also be referred to as random forest model) is an ensemble of decision trees 205, 210, and 215 with randomly selected features in each of the decision trees 205, 210, and 215 so that it can provide more stable and accurate outcomes. Outcomes may be determined by majority voting in the case of a classification problem. In the example of FIG.31, the random forest 200, which has been trained previously by a training method, is used to decide between classifications A, B and C. For example, the random forestWSGR Docket No.50272-712.601 200, with only the three decision trees shown in FIG.31, would return the classification A by majority voting.

[0099] As also described above, the machine learning model may implement support vector machine learning techniques. In machine learning, support vector machines (SVMs) may be supervised learning models with associated learning algorithms that analyze data for classification and regression analysis. SVMs may be a robust prediction method, being based on statistical learning. SVMs may be well-suited for domains characterized by the existence of large amounts of data, noisy patterns, or the absence of general theories.

[0100] In general terms, SVMs may map input vectors into high dimensional feature space through non-linear mapping function, chosen a priori. In this high dimensional feature space, an optimal separating hyperplane may be constructed. The optimal hyperplane may then be used to determine things such as class separations, regression fit, or accuracy in density estimation. More formally, a SVM constructs a hyperplane or set of hyperplanes in a high or infinite-dimensional space, which can be used for classification, regression, or other tasks like outlier detection.

[0101] Support vectors may be defined as the data points that lie closest to the decision surface (or hyperplane). Support vectors may therefore be the data points that are most difficult to classify and may have direct bearing on the optimum location of the decision surface. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm may build a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier (although methods such as Platt scaling exist to use SVM in a probabilistic classification setting). SVM may map training examples to points in space so as to maximize the width of the gap between the two categories. New examples may then be mapped into that same space and predicted to belong to a category based on which side of the gap they fall. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces.

[0102] Within a support vector machine, the dimensionally of the feature space may be large. For example, a fourth-degree polynomial mapping function may cause a 200-dimensional input space to be mapped into a 1.6 billionth dimensional feature space. The kernel trick and the Vapnik- Chervonenkis dimension may allow the SVM to thwart the “curse of dimensionality” limiting other methods and effectively derive generalizable answers from this very high dimensional feature space. Accordingly, SVMs may assist in discovering knowledge from vast amounts of input data. E. Methods of collecting a biological fluid

[0103] In some embodiments of the methods and systems provided herein, a biological fluid sample, such as a vaginally derived sample e.g., a menstrual fluid sample, or a sample of another fluid, is collected from a subject using a sample collector which collects fluid from the vaginal cavity. In some embodiments, a sample collector is placed in the vagina or outside the vagina for sampleWSGR Docket No.50272-712.601 collection. In some embodiments, a sample collector collects a sample by pooling, holding, catching, directing, or absorbing the sample. In some embodiments, a sample collector is absorbent, semi- absorbent, or non-absorbent. In some embodiments, a sample collector is soluble in a buffer. In some embodiments, a sample collector is broken down, for example by exposing the sample collector to an acidic environment, a basic environment, or an enzyme. In some embodiments, sample collectors comprise a pad, a tampon, a vaginal cup, a cervical cap, a menstrual cup (also referred to as a Diva cup), a menstrual disk, a cervical disk, a sponge, or an interlabial pad. In some embodiments, more than one type of sample collector is used. In some embodiments, the sample collector comprises a menstrual cup.

[0104] In some embodiments of the methods and systems provided herein, a sample collector is left in place for a pre-determined amount of time to collect a biological sample. In some embodiments, at least 5 minutes, 10 minutes, 15 minutes, 30 minutes, 45 minutes, 1 hour, 1.5 hours, 2 hours, 3 hours, 4 hours, 5 hours, 6 hours, 7 hours, or 8 hours elapse. In some embodiments, at most 5 minutes, 10 minutes, 15 minutes, 30 minutes, 45 minutes, 1 hour, 1.5 hours, 2 hours, 3 hours, 4 hours, 5 hours, 6 hours, 7 hours, or 8 hours elapse while the sample collection device is left in place. In some embodiments, about 5 minutes, 10 minutes, 15 minutes, 30 minutes, 45 minutes, 1 hour, 1.5 hours, 2 hours, 3 hours, 4 hours, 5 hours, 6 hours, 7 hours, or 8 hours elapse while the sample collector is left in place.

[0105] In some embodiments of the methods and systems provided herein, a sample is collected during the menstrual window (the period) of a subject. In some embodiments, a sample collector is disposable. In some embodiments, a disposable sample collector is discarded or broken down after use. In some embodiments, a disposable sample collector is dissolvable, biodegradable, recyclable, or compostable. In some embodiments, one disposable sample collector is used to collect one sample from one subject. In some embodiments, a sample collector is reusable. In some embodiments, a reusable sample collector is washable, sterilizable, or autoclavable. In some embodiments, reusable sample collector is resistant to degradation, tearing, pore formation, or dissolution. In some embodiments, a reusable sample collector comprises anti-microbial, antibacterial, antiviral, or antifungal properties. In some embodiments, a reusable sample collector is used one or more times to collect one or more samples. In some embodiments, a reusable sample collector is used about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, or more times to collect one or more biological samples. In some embodiments, a reusable sample collector is used to repeatedly collect biological samples from one subject. In some embodiments, a reusable sample collector is used to collect samples from a plurality of subjects.

[0106] In some embodiments of the methods and systems provided herein, one or more samples is collected during one or more periods (menstrual windows) of a subject. In some embodiments, 1 sample is collected during 1 period cycle, 2 samples are collected during 1 period cycle, 3 samples are collected during 1 period cycle, 4 samples are collected during 1 period cycle, more than 4 samplesWSGR Docket No.50272-712.601 are collected during 1 period cycle, 2 samples are collected during 2 period cycles, 3 samples are collected during 2 period cycles, 4 samples are collected during 2 period cycles, 5 samples are collected during 2 period cycles, 6 samples are collected during 2 period cycles, 7 samples are collected during 2 period cycles, 8 samples are collected during 2 period cycles, more than 8 samples are collected during 2 period cycles, 3 samples are collected during 3 period cycles, 4 samples are collected during 3 period cycles, 5 samples are collected during 3 period cycles, 6 samples are collected during 3 period cycles, 7 samples are collected during 3 period cycles, 8 samples are collected during 3 period cycles, 9 samples are collected during 3 period cycles, 10 samples are collected during 3 period cycles, 11 samples are collected during 3 period cycles, 12 samples are collected during 3 period cycles, more than 12 samples are collected during 3 period cycles, 4 samples are collected during 4 period cycles, 5 samples are collected during 4 period cycles, 6 samples are collected during 4 period cycles, 7 samples are collected during 4 period cycles, 8 samples are collected during 4 period cycles, 9 samples are collected during 4 period cycles, 10 samples are collected during 4 period cycles, 11 samples are collected during 4 period samples, 12 samples are collected during 4 period cycles, 13 samples are collected during 4 period cycles, 14 samples are collected during 4 period cycles, 15 samples are collected during 4 period cycles, 16 samples are collected during 4 period cycles, or more than 16 samples are collected during 4 period cycles. In some embodiments, a plurality of samples is collected during more than 4 period cycles.

[0107] In some embodiments, samples are collected outside the menstrual window, e.g., between the time of the subject’s periods. In some embodiments, a non-menstrual fluid (e.g., cervicovaginal fluid) is collected using the sample collector. In some embodiments, non-menstrual fluid which is collected include vaginal secretions, cervical mucus, cervicovaginal fluid, spotting blood (i.e., from between periods), amniotic fluid, a mucus plug, or other vaginal discharge. In some embodiments, non-menstrual fluid is collected and analyzed using a protocol which is used to collect and analyze menstrual fluid.

[0108] In some embodiments, a sample is collected after a menstrual window has closed, e.g., after a period has ended. In some embodiments, a sample is collected on the same day a menstrual window closed. In some embodiments, a sample is collected about 1 day, about 2 days, about 3 days, about 4 days, about 5 days, about 6 days, about 7 days, about 8 days, about 9 days, about 10 days, about 11 days, about 12 days, about 13 days, about 14 days, about 15 days, about 16 days, about 17 days, about 18 days, about 19 days, about 20 days, about 21 days, about 22 days, about 23 days, about 24 days, about 25 days, about 26 days, about 27 days, about 28 days, about 29 days, or about 30 days after a menstrual window has closed. In some embodiments, a sample is collected at least 1 day, at least 2 days, at least 3 days, at least 4 days, at least 5 days, at least 6 days, at least 7 days, at least 8 days, at least 9 days, at least 10 days, at least 11 days, at least 12 days, at least 13 days, at least 14 days, at least 15 days, at least 16 days, at least 17 days, at least 18 days, at least 19 days, at least 20 days, at least 21 days, at least 22 days, at least 23 days, at least 24 days, at least 25 days, at least 26WSGR Docket No.50272-712.601 days, at least 27 days, at least 28 days, at least 29 days, or at least 30 days after a menstrual window has closed. In some embodiments, a sample is collected not more than 1 day, not more than 2 days, not more than 3 days, not more than 4 days, not more than 5 days, not more than 6 days, not more than 7 days, not more than 8 days, not more than 9 days, not more than 10 days, not more than 11 days, not more than 12 days, not more than 13 days, not more than 14 days, not more than 15 days, not more than 16 days, not more than 17 days, not more than 18 days, not more than 19 days, not more than 20 days, not more than 21 days, not more than 22 days, not more than 23 days, not more than 24 days, not more than 25 days, not more than 26 days, not more than 27 days, not more than 28 days, not more than 29 days, or not more than 30 days after a menstrual window has closed. In some embodiments, a sample is collected between 1 day and 30 days, between 1 day and 25 days, between 1 day and 20 days, between 1 day and 15 days, between 1 day and 10 days, between 1 day and 5 days, between 5 days and 30 days, between 5 days and 25 days, between 5 days and 20 days, between 5 days and 15 days, between 5 days and 10 days, between 10 days and 30 days, between 10 days and 25 days, between 10 days and 20 days, between 10 days and 15 days, between 15 days and 30 days, between 15 days and 25 days, between 15 days and 20 days, between 20 days and 30 days, between 20 days and 25 days, or between 25 days and 30 days after a menstrual window has closed.

[0109] In some embodiments, non-menstrual fluid collected between two menstrual windows is collected during various points during the reproductive cycle. Non-menstrual fluid is collected during a pre-ovulation phase, during ovulation, or during a post-ovulation phase. In some embodiments, non- menstrual fluid is collected during a proliferative phase, or during a luteal or secretory phase. In some embodiments, a phase of the reproductive cycle is an abnormal phase. In some embodiments, menstrual fluid and non-menstrual fluid is collected from the same subject.

[0110] In some embodiments, samples can be more vaginal-like. In some embodiments, vaginal- like samples may provide information related to lower reproductive tract disorders and cancers, pregnancy, menopause, and infectious disease. In some embodiments, samples can be more uterine- like. In some embodiments, uterine-like samples can provide information as to uterine disorders, fertility, upper reproductive tract cancers, and responsive disease detection.

[0111] In some embodiments, a sample is collected between two menstrual windows. In some embodiments, a sample is collected about halfway between two menstrual windows, before the halfway point between two menstrual windows, or after the halfway point between two menstrual windows.

[0112] In some embodiments, multiple samples are collected between two menstrual windows. In some embodiments, 2, 3, 4, 5, 6, 7, or 8 samples are collected between two menstrual windows. In some such embodiments, the multiple samples are collected from different times between the two menstrual windows.

[0113] In some embodiments, a sample is collected between two menstrual windows, while a second sample is collected between a second two menstrual windows. In further embodiments, a thirdWSGR Docket No.50272-712.601 sample is collected between a third two menstrual windows. In a general case, an nth sample is collected between n menstrual windows, where n is a positive integer which is equal to 1 or more.

[0114] In some embodiments, biological samples are collected from a subject both during a menstrual window and between a menstrual window. In some embodiments, a biological sample is collected from a subject during a menstrual window, and a second biological sample is collected from the same subject between two menstrual windows. In some embodiments, a biological sample is collected from a subject during a menstrual window and a second biological sample is collected from the same subject after the end of that menstrual window, and before the next menstrual window. In some embodiments, a biological sample is collected from a subject before the start of a menstrual window, and a second biological sample is collected from the same subject during that menstrual window.

[0115] In some embodiments, a volume of fluid, such as menstrual fluid or other fluid collected from a vaginal cavity, is determined using the sample collector. In some embodiments, a volume of menstrual fluid in a sample collector is determined for example by reading graduations on the sample collector. Graduations are at least 0.01 mL, 0.02 mL, 0.03 mL, 0.04 mL, 0.05 mL, 0.06 mL, 0.07 mL, 0.08 mL, 0.09 mL, 0.1 mL, 0.2 mL, 0.3 mL, 0.4 mL, 0.5 mL, 0.6 mL, 0.7 mL, 0.8 mL, 0.9 mL, or 1.0 mL. In some embodiments, a volume of menstrual fluid in a sample collector is determined by measuring the mass of fluid inside the sample collector.

[0116] In some embodiments, collected fluid such as menstrual fluid is extracted from the sample collector. In some embodiments, extraction occurs by pouring, pipetting, or suctioning of the fluid, which is appropriate, for example, when the sample collector comprises a menstrual cup or other non-absorbent reservoir. In some embodiments, extraction occurs by dissolving or otherwise breaking down and removing the sample collector from the sample, which is appropriate, for example, when the sample collector comprises a sponge, a tampon, a pad, or another absorbent material. In some embodiments, extraction occurs by squeezing, compressing the sample collector, eluting from the sample collector by placing the collector in a buffer such as an aqueous buffer. In some embodiments, the sample is extracted from the sample collector using the systems, methods, and devices described herein.

[0117] Described herein, in certain embodiments, are samples comprising one or more biomarkers, including without limitations, nucleic acids, proteins, and cells. In some embodiments, the one or more biomarkers comprise a cell. In some embodiments, cells from a menstrual fluid sample and a preservation solution (e.g., Biomatrica RNAgard®). In some embodiments, the sample is an endometrial cell sample comprising one or more endometrial cells. In some embodiments, the sample is an enriched cell sample. In some embodiments, the sample is collected using the systems or devices described herein. In some embodiments, the biomarkers display differential presence or level in cervicovaginal fluid or menstrual fluid as compared to peripheral blood or cervicovaginal tissue.WSGR Docket No.50272-712.601

[0118] In some embodiments, the one or more cells is from a biological sample. In some embodiments, the biological sample is taken from a female. In some embodiments, the biological sample is taken from an individual who is suffering from a reproductive disorder, such as for example, chronic pelvic pain, infertility, heavy menstrual bleeding, or a combination thereof. In some embodiments, the individual is a mammal. In some embodiments, the mammal is a human. In some embodiments, the individual is suspected of having endometriosis. In some embodiments, the individual has not received a surgical diagnosis of endometriosis. In some embodiments, the biological sample is taken on a second day of an individual’s menstrual cycle. In some embodiments, the biological sample is taken on a day of the individual’s menstrual cycle where the individual experiences a heavy flow of menstrual fluid. In some embodiments, the biological sample is taken from the individual prior to administering a treatment, such as a surgery or administration of a therapeutic composition, to the individual. In some embodiments, the treatment, or intervention, is a treatment for endometriosis. In some embodiments, the biological sample is taken from the individual after administering the treatment to the individual. In some embodiments, a first biological sample is taken prior to administering the treatment to the individual and a second biological sample is taken after administering the treatment to the individual. In some embodiments, the method comprises determining a difference in: an expression of one or more microRNAs, a methylation profile of one or more CpG sites, a measure of bacterial diversity, or a combination thereof between the first biological sample and the second biological sample.

[0119] In some embodiments, the biological sample comprises menstrual fluid. In some embodiments, the biological sample comprises a cervicovaginal fluid, a cervical fluid, or a vaginal fluid. In some embodiments, the biological sample comprises one or more endometrial cells. In some embodiments, the endometrial cells comprise endometrial stromal cells, endometrial epithelial cells, or a combination thereof. In some embodiments, the endometrial cells comprise endometrial stem cells. In some embodiments, the endometrial stem cells comprise menstrual blood mesenchymal stem cells. In some embodiments, the biological sample comprises a non-endometrial cell of the individual. In some embodiments, the non-endometrial cell of the individual comprises a macrophage, a glandular cell, a squamous cell, a cervical columnar cell, a leukocyte, a lymphocyte, a non- endometrial stromal cell, a non-endometrial endothelial cell, a fibroblast, an erythrocyte, a mesenchymal stem cell, an ova, or a combination thereof. In some embodiments, the biological sample comprises one or more spermatozoa.

[0120] In some embodiments, the biological sample comprises one or more bacterial cells. In some embodiments, the one or more bacterial cells comprise one or more bacterium from the phylum Bacteroidetes, Proteobacteria, Actinobaeria, Cyanobacteria, Fusobacteria, Spirochates, Tenericutes, Acidobacterua, TM7, or Syngerstetes. In some embodiments, the one or more bacterial cells comprise one or more bacteria from the genus Lactobacillus, Gardnerella, Fusobacterium, Staphylococcus, Streptococcus, Atopobium, Mageeibacillus, Mobiluncus, Mycoplasm, Bacteroides, Prevotella,WSGR Docket No.50272-712.601 Porphyeromonas, Dialister, Atopobium, Megasphaera, Propionibacterium, Porphyromonas, Dermabacter, Moraxella, Anaerococcus, Peptostreptococcus, Campylobacter, Corynebacterium, Facklamia, Klebsiella, Peptoniphilis, Sneathia, Ureaplasma, Finegoldia, Actinomyces, Clostridium, Veillonella, Peptinophilus, Adlercreurzia, Faecalibacterium, Haemophilus, Sphingomonasm Aerococcus, Weeksella, Biffidobacterium, Blautia, or a combination thereof. - In some embodiments, the one or more bacteria from the genus Lactobacillus is L. acidophilus, L. amylovorus ultunensis, L. coleohominis, L. crispatus, L. fermentum, L. gasseri, L. iners, L. jensenii, L. kitasatonis, L. mucosae, L. paracasei rhamnosus, L. plantarum, L. pontis, L. reuteri frumenti, Lactobacillus sp.3, Lactobacillus sp.9, or a combination thereof.

[0121] In some embodiments, the one or more bacteria from the genus Gardnerella is Gardnerella vaginalis. In some embodiments, the one or more bacteria from the genus Streptococcus is Streptococcus agalactiae or Streptococcus gallolyticus. In some embodiments, the one or more bacteria from the genus Sneathia is Sneathia sanguinegens. In some embodiments, the one or more bacteria from the genus Mobiluncus is Mobiluncus curtisii, Mobiluncus mulieris, or a combination thereof. In some embodiments, the one or more bacteria from the genus Mageeibacillus is Mageeibacillus indolicus. In some embodiments, the one or more bacteria from the genus Megashaera is Megashaera elsdenii micronuciformis, Megasphaera sp.1, Megasphaera sp.2, or a combination thereof. In some embodiments, the one or more bacteria in the genus Dialister is Dialister micraerophilus. In some embodiments, the one or more bacteria from the genus Propionibacterium is Propionibacterium acnes. In some embodiments, the one or more bacteria from the genus Porphyromonas is Porphyromonas somerae. In some embodiments, the one or more bacteria from the genus Dermabacter is Dermabacter vaginalis. In some embodiments, the one or more bacteria from the genus Moraxella is Moraxella catarrhalis.

[0122] In some embodiments, the one or more bacteria from the genus Anaerococcus is Anaerococcus tetradius or Anaerococcus prevotii. In some embodiments, the one or more bacteria from the genus Peptostreptococcus is Peptostreptococcus magnus or Peptostreptococcus anaerobius. In some embodiments, the one or more bacteria from the genus Campylobacter is Campylobacter ureolyticus or Camplyobacter fetus. In some embodiments, the one or more bacteria from the genus Cornyebacterium is Corynebacterium amycolatum or Corynebacterium fournierii. In some embodiments, the one or more bacteria from the genus Facklamia is Facklamia hominis or Facklamia massiliensis. In some embodiments, the one or more bacteria from the genus Klebsiella is Klebsiella pneumoniae. In some embodiments, the one or more bacteria from the genus Peptoniphilus is Peptoniphilus harei. In some embodiments, the one or more bacteria from the genus Porphyeromonas is Porphyeromonas asaccharolytica. In some embodiments, the one or more bacteria from the genus Prevotella is Prevotella buccalis, Prevotella amnii, Prevotella bivia, Prevotella disiens, Prevotella melaninogenica, or Prevotella timonensis. In some embodiments, the one or more bacteria from the genus Atopobium is A. deltae, A. minutum, A. parvulum, A. vaginae, or a combination thereof. InWSGR Docket No.50272-712.601 some embodiments, the biological sample comprises one or more fungal cells. In some embodiments, the fungal cells are a yeast. In some embodiments, the yeast is a yeast in the genus Candida. In some embodiments, the yeast in the genus Candida is Candida albicans, Candida glabrata, Candida parapsilosis, Candida fomata, or a combination thereof.

[0123] In some embodiments, the sample comprises at least one protein or fragment thereof derived from an endometrial cell, a non-endometrial cell from the individual, spermatozoa, bacterial cell, fungal cell, or a combination thereof. In some embodiments, the sample comprises at least one nucleic acid derived from an endometrial cell, a non-endometrial cell from the individual spermatozoa, bacterial cell, fungal cell, or a combination thereof. In some embodiments, the at least one nucleic acid is a cell-free nucleic acid. In some embodiments, the nucleic acid is DNA or RNA. In some embodiments, the RNA is an mRNA, tRNA, rRNA, miRNA, or siRNA. In some embodiments, the nucleic acid is a nucleic acid encoding the at least one protein or fragment thereof described herein.

[0124] In some embodiments, the sample comprises a portion of a sample collector. In some embodiments, a portion of the sample collector dissolves or breaks down into the sample. In some embodiments, the sample collector is a tampon, a pad, a vaginal cup, a menstrual cup, a cervical cap, a menstrual disk, a cervical disk, a sponge, or an interlabial pad. In some embodiments, the tampon is a light absorbency tampon. In some embodiments, the tampon comprises an applicator.

[0125] In some embodiments, the volume of the sample is between 2 ml and 15 ml. In some embodiments, the volume of the sample is between about 7 ml and 10 ml. In some embodiments, the volume of the sample is less than 20 ml, less than 15 ml, less than 10 ml, or less than 8 ml. In some embodiments, the volume of the sample is between 1 ml and 4 ml. In some embodiments, the volume of the menstrual fluid in the sample is between 2 ml and 3 ml. In some embodiments, the volume of the menstrual fluid in the sample is less than 5 ml, less than 4 ml, less than 3 ml, less than 2 ml, or less than 1 ml. In some embodiments, the volume of the menstrual fluid in the sample is between 2 ml and 15 ml. In some embodiments, the volume of the sample is between about 7 ml and 10 ml. In some embodiments, the volume of the sample is less than 20 ml, less than 15 ml, less than 10 ml, or less than 8 ml. In some embodiments, the volume of the menstrual fluid in the sample is between 1 ml and 4 ml. In some embodiments, the volume of the menstrual fluid in the sample is between 2 ml and 3 ml. In some embodiments, the volume of the menstrual fluid in the sample is less than 5 ml, less than 4 ml, less than 3 ml, less than 2 ml, or less than 1 ml. In some embodiments, the sample comprises less than 10 cells, less than 102cells, less than 103cells, less than 104cells, less than 105cells, less than 106cells, less than 107cells, less than 108cells, or less than 109cells. In some embodiments, the sample comprises no cells. In some embodiments, the sample comprises less than 105endometrial cells, less than 106endometrial cells, less than 107endometrial cells, less than 108endometrial cells, or less than 109endometrial cells. In some embodiments, the sample comprises greater than 105cells, greater than 106cells, greater than 107cells, greater than 108cells, or greater than 109cells. In someWSGR Docket No.50272-712.601 embodiments, the sample comprises greater than 105endometrial cells, greater than 106endometrial cells, greater than 107endometrial cells, greater than 108endometrial cells, or greater than 109endometrial cells. In some embodiments, the sample comprises less than 105endothelial cells, less than 106endothelial cells, less than 107endothelial cells, less than 108endothelial cells, or less than 109endothelial cells. In some embodiments, the sample comprises greater than 105cells, greater than 106cells, greater than 107cells, greater than 108cells, or greater than 109cells. In some embodiments, the sample comprises greater than 105endothelial cells, greater than 106endothelial cells, greater than 107endothelial cells, greater than 108endothelial cells, or greater than 109endothelial cells. In some embodiments, the sample comprises less than 105epithelial cells, less than 106epithelial cells, less than 107epithelial cells, less than 108epithelial cells, or less than 109epithelial cells. In some embodiments, the sample comprises greater than 105cells, greater than 106cells, greater than 107cells, greater than 108cells, or greater than 109cells. In some embodiments, the sample comprises greater than 105epithelial cells, greater than 106epithelial cells, greater than 107epithelial cells, greater than 108epithelial cells, or greater than 109epithelial cells. In some embodiments, the sample comprises less than 105leukocytes, less than 106leukocytes, less than 107leukocytes, less than 108leukocytes, or less than 109leukocytes. In some embodiments, the sample comprises greater than 105cells, greater than 106cells, greater than 107cells, greater than 108cells, or greater than 109cells. In some embodiments, the sample comprises greater than 105leukocytes, greater than 106leukocytes, greater than 107leukocytes, greater than 108leukocytes, or greater than 109leukocytes. In some embodiments, the sample comprises less than 105mesenchymal cells, less than 106mesenchymal cells, less than 107mesenchymal cells, less than 108mesenchymal cells, or less than 109mesenchymal cells. In some embodiments, the sample comprises greater than 105cells, greater than 106cells, greater than 107cells, greater than 108cells, or greater than 109cells. In some embodiments, the sample comprises greater than 105mesenchymal cells, greater than 106mesenchymal cells, greater than 107mesenchymal cells, greater than 108mesenchymal cells, or greater than 109mesenchymal cells.

[0126] In some instances, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, or at least 90% of the target cells in the sample are intact. In some embodiments, the target cells are endometrial cells. In some embodiments, the target cells are endothelial cells, epithelial cells, leukocytes, mesenchymal cells, or a combination thereof. In some instances, at least 95% of the target cells in the sample are intact. An intact cell is a cell which does not have a ruptured cell membrane. An intact cell is a cell in its native state. An intact cell is a viable cell, wherein the viable cell is cultured in a cell culture. In some embodiments, 0% of the target cells in the sample are intact.

[0127] In some instances, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, or at least 90% of the target cells in the sample are viable. In some embodiments, 0% of the target cells in the sample are viable. In some embodiments, the termWSGR Docket No.50272-712.601 “viable” means intact, living, and / or capable of proliferation. Viability of a plurality of cells is assessed by measuring membrane permeability, enzymatic activity, metabolic activity, DNA synthesis, membrane potential, proliferation marker expression, or a combination thereof. F. Health conditions

[0128] In some embodiments, the subject has, has been diagnosed with a health condition. In some embodiments, the subject is suspected of having a health condition. In some embodiments, the subject is diagnosed with a health condition using the methods described herein.

[0129] In some embodiments, the health condition is a uterine pathology, a vaginal disorder, a reproductive cancer, an autoimmune disease, an infection, a cardiac disorder, a blood or bleeding disorder, or a disorder of pregnancy. In some embodiments, the disorder is a uterine pathology. In some embodiments, the uterine pathology is Pelvic inflammatory disease (PID), Polycystic ovary syndrome (PCOS) , Uterine Fibroids, Endometriosis (Endometrioma), Adenomyosis, Amenorrhea, Hypermenorrhea, Abnormal uterine bleeding, Premature menarche, Delayed menarche, Hypothalamic amenorrhea , Dysmenorrhea, Ovarian failure, Ovarian cysts, Fibroids, Ectopic pregnancy, Infertility, Diminished ovarian reserve, Atypical endometrial hyperplasia, Premature menopause, Peri- Menopause, or Menopause. In some embodiments, the health condition is a vaginal disorder. In some embodiments, the vaginal disorder is Bacterial vaginosis, Clitoral pain (clitorodynia), Dyspareunia (painful intercourse), Genital warts, Lichen planus, Lichen sclerosus, Vaginal intraepithelial neoplasia (VAIN), or Vulva intraepithelial neoplasia (VIN). In some embodiments, the health condition is a reproductive cancer. In some embodiments, the reproductive cancer is Ovarian cancer, Cervical cancer, Vaginal cancer, Vulva cancer, Endometrial cancer, Primary peritoneal cancer, Uterine sarcoma, Breast Cancer, Leukemia. In some embodiments, the health condition is an autoimmune disease. In some embodiments, the autoimmune disease is Rheumatoid arthritis, Psoriasis, Lupus, Graves’ disease, Hashimoto’s thyroiditis, Celiac disease, Multiple sclerosis (MS), Addison’s disease, Type 1 diabetes, Crohn’s disease, Ulcerative colitis, Alopecia, Sarcoidosis. In some embodiments, the health condition is an infection. In some embodiments, the infection is Vaginitis, Bacterial vaginosis, Urinary Tract Infection (UTI), Vulvodynia, Atrophic Vaginitis, Yeast infection, HPV infection, HBV infection (hepatitis), HSV infection (Herpes), Covid-19 infection, HIV infection, Gonorrhea, Chlamydia, or Syphilis. In some embodiments, the health condition is a cardiac disorder. In some embodiments, the cardiac disorder is heart disease. In some embodiments, the health condition is a blood or bleeding disorder. In some embodiments, the blood or bleeding disorder is Anemia, High blood pressure, Clotting Disorders, Factor V Leiden, or Hemophilia. In some embodiments, the health condition is a disorder of pregnancy. In some embodiments, the disorder of pregnancy is Preterm labor, Pre-eclampsia, Infertility, or Gestational diabetes. In some embodiments, the health condition is alcohol use, tobacco use, THC use, or other drug use.WSGR Docket No.50272-712.601 G. Additional Embodiments

[0130] The methods for analyzing gene expression data, such as in bulk RNA sequencing data from a mixed population of cell types have applicability to a broad variety of cells and tissues. In some embodiments, the methods comprise identifying an expression level of a switch from a first RNA sequencing dataset obtained from a first sample, wherein the first sample comprises a mixed population of cells, wherein the switch comprises a set of genes selected from genes co-expressed within a biological relevance parameter; and generating a score for the switch based on the expression level of the switch. In some embodiments, the mixed population of cells is derived from a tissue sample, such a epithelial, nervous system, muscle or connective tissue sample. In some embodiments, the mixed population of cells is derived from an organ, a tissue, a biopsy sample, for example, the mixed population of cells is derived from one or more of adrenal gland, anus, appendix, bladder, bone, bone marrow, brain, breast, bronchi, diaphragm, ear, esophagus, eye, fallopian tubes, gallbladder, genitals, heart, hypothalamus, joint, kidney, large intestine, larynx, liver, lungs, lymph nodes, mammary gland, mesentery, mouth, nasal cavity, nose, ovaries, pancreas, pineal gland, parathyroid gland, pharynx, pituitary gland, prostate, rectum, salivary gland, skeletal muscle, skin, small intestine, spinal cord, spleen, stomach, teeth, thymus gland, thyroid, trachea, tongue, ureter, urethra, uterus, skeletal tissue, ligament, tendon, blood, vagina, hair, placenta, testes, nails, vas deferens, seminal vesicle, bulbourethral gland, penis, scrotum, thoracic duct, artery, vein, capillary, lymphatic vessel, tonsils, nervous system tissue, and fetal tissue. In some embodiments, the method includes identifying the expression level of a plurality of switches from a first RNA sequencing dataset, wherein each switch of the plurality of switches comprises a set of genes selected from genes co-expressed within a biological relevance parameter and the biological relevance parameter is selected from a group consisting of cell type, tissue type, biological pathway, and biological function.

[0131] In some embodiments, the method comprises: identifying an expression level of a switch from a first RNA sequencing dataset obtained from a first vaginally derived sample, wherein the first vaginally derived sample comprises a mixed population of cells, wherein the switch comprises a set of genes selected from genes co-expressed within a biological relevance parameter; and generating a score for the switch based on the expression level of the switch. In some embodiments, the method further comprises identifying the expression level of a second switch from the first RNA sequencing dataset, wherein the second switch comprises a second set of genes selected from genes co-expressed within a second relevance biological parameter; and generating a score for the second switch based on the expression level of the switch. In some embodiments, the method further comprises identifying the expression level of a plurality of switches from the first RNA sequencing dataset, wherein each switch of the plurality of switches comprises a set of genes selected from genes co-expressed within a biological relevance parameter and generating a score for each of the plurality of switches. In some embodiments, the biological relevance parameter is selected from a group consisting of cell type,WSGR Docket No.50272-712.601 tissue type, biological pathway, and biological function. In some embodiments, the vaginally derived sample comprises menstrual fluid, cervical-vaginal fluid or a combination thereof. In some embodiments, the vaginally derived sample comprises a mixture of cell types or tissue types. In some embodiments, the RNA sequencing dataset comprises bulk RNA sequencing data. In some embodiments, the score for each switch is calculated using the number of reads within the RNA sequencing dataset of the genes within the switch. In some embodiments, the score for each switch is normalized by the level of one or more housekeeping genes in the RNA sequencing dataset. In some embodiments, the method further comprises identifying a differential for a switch, wherein the differential is identified for the switch when the score of the switch in the first vaginally derived sample differs by more than the standard deviation of the score of the same switch in a reference score set. In some embodiments, the method further comprises identifying a differential for each switch in the plurality of switches, wherein the differential is identified for each of the switches when the score of the switch in the first vaginally derived sample differs by more than the standard deviation of the score of the same switch in a reference score set. In some embodiments, the reference score set comprises a set of switch scores derived from optimal RNA samples. In some embodiments, the score for one or more of the switches indicates the presence or absence of the activity of one or more cell types in the first vaginally derived sample. In some embodiments, the method further comprises identifying the expression level of a plurality of switches from a second RNA sequencing dataset, wherein each switch of the plurality of switches comprises a set of genes selected from genes co- expressed within a biological relevance parameter and generating a score for each of the plurality of switches. In some embodiments, the second RNA sequencing dataset is obtained from a second vaginally derived sample. In some embodiments, the vaginally derived sample comprises menstrual fluid, cervical-vaginal fluid or a combination thereof. In some embodiments, the method further comprises comprising comparing the score for each of the plurality of switches from the first RNA sequencing dataset with a score for each switch of the plurality of switches from the second RNA sequencing dataset. In some embodiments, the second RNA sequencing dataset is derived from one or more healthy or putative healthy individuals. In some embodiments, the second RNA sequencing dataset is derived from one or more individuals having a diseased state or putative diseased state. In some embodiments, wherein the first RNA sequencing dataset and the second RNA sequencing dataset are derived from a same individual. In some embodiments, the first RNA sequencing dataset and the second RNA sequencing dataset are derived from different time points or different sample types. In some embodiments, the first vaginally derived sample is obtained under a first condition and the second cervicovaginal sample is obtained under a second condition. In some embodiments, the first condition is prior to a diagnosis of a disease or infection, exhibition, progression or recurrence of one or more symptoms, reduction or disappearance of one or more symptoms, or a medical treatment. In some embodiments, the first condition is prior to a diagnosis of a disease or infection, exhibition, progression or recurrence of one or more symptoms, reduction or disappearance of one or moreWSGR Docket No.50272-712.601 symptoms, or a medical treatment and the second condition is subsequent to the diagnosis of a disease, exhibition, progression or recurrence of one or more symptoms, reduction or disappearance of one or more symptoms, or the medical treatment. In some embodiments, the first condition is subsequent to a diagnosis of a disease or infection, exhibition, progression or recurrence of one or more symptoms, reduction or disappearance of one or more symptoms, or a medical treatment. In some embodiments, the disease is selected from the group consisting of endometriosis, cervical cancer, infertility, uterine cancer, ovarian cancer and fibroids. In some embodiments, the infection comprises a bacterial infection, a fungal infection or a viral infection. In some embodiments, the one or more symptoms are selected from the group consisting of inflammation, bleeding, heavy bleeding, anemia, change in regular menstruation cycle, pain and a change in microbiome diversity, microbial species or microbial abundance. In some embodiments, the medical treatment is selected from the group consisting of surgery and administration of a therapeutic agent. In some embodiments, the first condition is a time period within the menstrual window. In some embodiments, the first condition is a time period outside the menstrual window. In some embodiments, the first condition is a time period within the menstrual window and the second condition is a time period outside the menstrual window. In some embodiments, the first condition and the second condition are a time period within the menstrual window, and wherein the first condition and the second condition are different days of menstruation. In some embodiments, the first vaginally derived fluid sample and the second vaginally derived sample are derived from a same mixed population of cells, and wherein the first condition is a time period prior to administration of a compound and the second condition is a time period subsequent to administration of the compound. In some embodiments, the compound is administered ex vivo to the mixed population of cells. In some embodiments, the compound is a therapeutic agent or a candidate therapeutic agent. In some embodiments, the first vaginally derived fluid sample and the second vaginally derived sample comprise menstrual fluid, cervical-vaginal fluid or a combination thereof. In some embodiments, the switch comprises at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 genes. In some embodiments, the switch comprises at least 5, at least 10, at least 15, at least 20 or at least 25 genes. In some embodiments, the second switch comprises at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 genes. In some embodiments, the second switch comprises at least 5, at least 10, at least 15, at least 20 or at least 25 genes. In some embodiments, each switch of the plurality of switches comprises 2, 3, 4, 5, 6, 7, 8, 9, or 10 genes. In some embodiments, each switch of the plurality of switches comprises at least 5, at least 10, at least 15, at least 20 or at least 25 genes. In some embodiments, the plurality of switches comprises at least one set selected from Table 2. In some embodiments, the biological relevance parameter is cell type and the cell type is one or more of stromal cells, monocytes, neutrophils, dendritic cells, macrophages, eosinophils, B cells, megakaryocytes, epithelial cells. progenitor cells, stem cells, lymphoid cells, non-lymphoid cells, hemopoietic cells, non-hemopoietic cells In some embodiments, the biological relevance parameter is tissue type and the tissue type is one or more of cervical, endometrial, vaginal, uterine, blood, placental, muscle, ovarian, fetal, and maternal. In someWSGR Docket No.50272-712.601 embodiments, the biological relevance parameter is a biological pathway and the biological pathway is one or more of inflammation and response to infection. II. DEFINITIONS

[0132] Unless defined otherwise, all terms of art, notations and other technical and scientific terms or terminology used herein are intended to have the same meaning as is commonly understood by one of ordinary skill in the art to which the claimed subject matter pertains. In some embodiments, terms with commonly understood meanings are defined herein for clarity and / or for ready reference, and the inclusion of such definitions herein should not necessarily be construed to represent a substantial difference over what is generally understood in the art.

[0133] Throughout this application, various embodiments may be presented in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the disclosure. Accordingly, the description of a range should be considered to have specifically disclosed all the possible subranges as well as individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numbers within that range, for example, 1, 2, 3, 4, 5, and 6. This applies regardless of the breadth of the range.

[0134] As used in the specification and claims, the singular forms “a”, “an” and “the” include plural references unless the context clearly dictates otherwise. For example, the term “a sample” includes a plurality of samples, including mixtures thereof.

[0135] The terms “determining,” “measuring,” “evaluating,” “assessing,” “assaying,” and “analyzing” are often used interchangeably herein to refer to forms of measurement. The terms include determining if an element is present or not (for example, detection). These terms can include quantitative, qualitative or quantitative and qualitative determinations. Assessing can be relative or absolute. “Detecting the presence of” can include determining the amount of something present in addition to determining whether it is present or absent depending on the context.

[0136] The terms “subject,” “individual,” or “patient” are often used interchangeably herein. A “subject” can be a biological entity containing expressed genetic materials. The biological entity can be a plant, animal, or microorganism, including, for example, bacteria, viruses, fungi, and protozoa. The subject can be tissues, cells and their progeny of a biological entity obtained in vivo or cultured in vitro. The subject can be a mammal. The mammal can be a human. The subject may be diagnosed or suspected of being at high risk for a disease. In some embodiments, the subject is not necessarily diagnosed or suspected of being at high risk for the disease.

[0137] The term “in vivo” is used to describe an event that takes place in a subject’s body.WSGR Docket No.50272-712.601

[0138] The term “ex vivo” is used to describe an event that takes place outside of a subject’s body. An ex vivo assay is not performed on a subject. Rather, a sample obtained from a subject and assayed in an environment outside of a subject’s body. An example of an ex vivo assay is the use of menstrual effluent samples obtained from a subject’s tampon treated in a laboratory setting.

[0139] The term “in vitro” is used to describe an event that takes places contained in a container for holding laboratory reagent such that it is separated from the biological source from which the material is obtained. In vitro assays can encompass cell-based assays in which living or dead cells are employed. In vitro assays can also encompass a cell-free assay in which no intact cells are employed.

[0140] As used herein, the term “about” a number refers to that number plus or minus 10% of that number. The term “about” a range refers to that range minus 10% of its lowest value and plus 10% of its greatest value.

[0141] As used herein, the terms “treatment” or “treating” are used in reference to a pharmaceutical or other intervention regimen for obtaining beneficial or desired results in the recipient. Beneficial or desired results include but are not limited to a therapeutic benefit and / or a prophylactic benefit. A therapeutic benefit may refer to eradication or amelioration of symptoms or of an underlying disorder being treated. Also, a therapeutic benefit can be achieved with the eradication or amelioration of one or more of the physiological symptoms associated with the underlying disorder such that an improvement is observed in the subject, notwithstanding that the subject may still be afflicted with the underlying disorder. A prophylactic effect includes delaying, preventing, or eliminating the appearance of a disease or condition, delaying or eliminating the onset of symptoms of a disease or condition, slowing, halting, or reversing the progression of a disease or condition, or any combination thereof. For prophylactic benefit, a subject at risk of developing a particular disease, or to a subject reporting one or more of the physiological symptoms of a disease may undergo treatment, even though a diagnosis of this disease may not have been made.

[0142] The section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described. III. EXAMPLES

[0143] The following examples are included for illustrative purposes only and are not intended to limit the scope of the invention. Example 1: Calculating a Menstrual Health Index

[0144] Data including cycle day tracking, tampon flow phenotype, menstrual flow phenotype, post-op reports, lifestyle, demography, family history, pain, mental health was collected along with corresponding menstrual samples. The menstrual samples were molecularly analyzed to detect clinical chemistry (e.g. alkaline hematin), mRNA expression, and miRNA expression. Sequencing data was stratified by molecular phenotypes. For example, RPC1 and RPC2 are the first two principalWSGR Docket No.50272-712.601 components of variation within the transcriptional RNA expression data from tampons and account for the majority of variability between samples as depicted in FIG.1. The samples were also normalized for the amount of blood collected and the biological timepoints in the menstrual cycle. A representative example is depicted in FIG.2.

[0145] mRNA signals were organized into a uterine response model. The Uterine Response model is comprised of a hierarchical organization of RNA expression profiles according to discrete biological attributes. Gene subsets were grouped into mRNA switches. mRNA switches are grouped into boards, which were then combined into a response index. A representative response index for a single tampon sample is depicted in FIG.3. FIG.4 depicts a response compared to baseline in a healthy reference. Data from an individual patient was then compared to the reference baseline as depicted in FIG.5.

[0146] In one example, pairwise analysis of mRNA switches between healthy and unhealthy samples was used to identify 18 boards to construct index MHI that was predictive at identifying unhealthy samples. Results are depicted in FIG.6. The initial power was validated with this weighted classifier on an additional set of 66 patients. The MHI had a density of 76.5% and a specificity of 51.9%. Example 2: Calculating a Menstrual Health Index

[0147] Menstrual samples were collected using the methods described herein. The samples included both viable and dead cells, as depicted in FIG.7. The menstrual samples were incubated in PBC at 37 degrees for over 2 hours and transcriptional data was collected. The transcriptomics data was organized into 500 mRNA switches, as depicted in FIG.8. Each switch is a specific list of genes that define a function. This could be based on cell type or on a molecular pathway. Each board is a roll-up of switches that define a specific function. A board is composed of 3-15 switches that, in aggregate, provide a comprehensive profile of a cell type or a molecular function. Each index is a list of boards that show significant difference in their activity for a specific disease state. FIG.9 depicts 3 representative switches that make up a board.

[0148] The mRNA switches are specifically designed for menstrual effluence. FIG.10 depicts a plot of RNAseq data from menstrual, vaginal and whole blood samples. Example 3: Developing a genomic classifier for endometriosis

[0149] Differential expression analysis revealed 47 dysregulated genes between 13 endometriosis cases and 22 putative healthy controls. The 47 genes were related to G1 / S arrest, inflammation, and degranulation. These genes were trained on both random forest and support vector machine learning approaches to weight the importance of each gene and build a classification algorithm.

[0150] Initial validation calculated an overall AUC of 0.896 as depicted in FIG.11. Validation was completed using 14 cases and 20 controls. The Uterine Response Model was then used to assessWSGR Docket No.50272-712.601 how the classifier aligned with the extant literature on endometriosis by examining relational interactions. The results are depicted in FIG.12.

[0151] In healthy samples, menstrual initiation was related to monocyte activity, and other innate immune cells were highly related to CD4+ T cell activity. In endometriosis samples, menstrual initiation was most related to class switched B Cell activity and recruits along Eosinophil activity responsible for degranulation Other innate immune cells were highly related to CD8+ Cytotoxic T cell activity. Example 4: A method of identifying methods of response in response to compounds

[0152] The experimental set up is depicted in FIG.13.0.5 mL-1 mL of menstrual blood was incubated with either drug and buffer or buffer only at 37 degrees. Transcriptional data was then collected and analyzed using the switches and boards described herein.

[0153] Fig.14 depicts the differential response. This was calculated by comparing the 30min drug – 30min sham = difference in expression at given concentration and time.

[0154] Fig.15 depicts the response ratio which describes the expression differences at each experimental condition / average (ABS) of all expression differences for each experimental condition within a patient. This metric scales the response between all condition and allows for better visualization of the most significant changes, both globally and at the mRNA switch level. The response ratio is then plotted in a barcode graph. Each barcode represents a specific condition from the experiment (1uM for 30min), and barcodes are grouped by patient to easy visualize changes by time or concentration. mRNA switches in blue show suppression while mRNA switches in red show activation.

[0155] The results of 6 experiments applying aspirin ex vivo to menstrual fluid from 5 subjects is depicted in FIG.16. Overall broad activation of mRNA switches was seen in all patients as depicted in FIG.17. Neutrophil switches were activated in most patients.

[0156] The uterine response model was used to evaluate the drug impact of novel compounds for endometriosis. The novel compound showed a drug specific response compared to aspirin, as depicted in FIG.18. JNK-inhibitor response by patient shows that there were both time and concentration dependent changes in the samples tested (FIG.19). Hierarchical clustering of samples from drug responses were patient specific, with overall trends of late suppression, as depicted in FIG.20. Two distinct phenotypes of activation or suppression were seen in early timepoints of drug exposure independent of concentration. These early phenotypes were patient specific. Late timepoints of drug expression showed overall suppression, with several samples showing consistent suppression in epithelial derived cell types.WSGR Docket No.50272-712.601 Example 5: Methods for Examples 6-9

[0157] Sample Collection: Menstrual blood was collected from participants using a Menstrual cup (participants consented into an IRB-approved study). Samples were poured into a conical tube and mixed to resuspend cells uniformly.500ul of each sample was aliquoted into 1.5ml microcentrifuge tubes according to the experimental conditions in Table 1.50ul of drug substrate was added to each tube and thoroughly mixed before incubation. After incubation, cell lysis buffer was added to each aliquot and samples were stored at -80oC for downstream processing. Table 1: Drug assay experimental conditions Diluent Concentrations Incubation Temp Incubation Times Aspirin 0 μM 5 min 1xPBS 180 μM 37o 30 min 900 μM C 60 min 1.8 mM 120 min

[0158] Clinical specimens: Additionally, 276 menstrual samples were collected from patients with surgically confirmed endometriosis and putatively healthy controls under IRB-approved collection protocols. All patients consented to the study before donating specimens. Menstrual samples were collected using a tampon collection system such as described in WO2016025332, WO2017180909 and WO2022140447. Samples were harvested, aliquoted into cryovials, and stored at -80oC for downstream processing.

[0159] Wet-bench methods and post-sequencing analysis: Samples were extracted using RNA extraction columns (Norgen Biotek). Samples were DNase treated to remove residual genomic DAN, and nucleic acid was quantified on a qubit fluorometer.500ng of RNA was used to prepare sequence libraries with RiboFree Total RNA sequencing kit (Zymo Research) according to the manufacturer's protocols. Libraries were quantified on the qubit fluorometer and fragment analyzer, size selected using AMPure XP beads, and pooled into sets of 24 samples for sequencing. Pooled libraries were sequenced on the Illumina NextSeq 2000 using Illumina’s P3200 cycle chemistry.

[0160] After sequencing, fastq files were generated, and aligned to the human genome, and gene counts were generated and converted to RPKM (Reads Per Kilobase per Million mapped reads). Samples were then normalized for degradation using the DegNorm (Xiong B, et al. DegNorm: normalization of generalized transcript degradation improves accuracy in RNA-seq analysis. Genome Biol.2019 Apr 16;20(1):75. PMCID: PMC6466807) to normalize for degraded samples. After degradation normalization, samples were reference gene normalized using GAPDH, ACTB, and RAB7A, and transformed into the natural log space.

[0161] Once gene counts were calculated, RNA switch values were derived for 489 cell-specific gene switch modules by calculating the median gene expression value for all genes within a switch, and the switch values were output into a count file. RNA board values were calculated from theWSGR Docket No.50272-712.601 switch values, where all the switches for a specific cell type were derived by taking the median expression value for all the switches within a cell-specific board. FIG.21 describes the overall schematic of calculating a switch and a board.

[0162] Each sample was then processed for cell deconvolution using the UCSF Xcell algorithm (Aran D, Hu Z, Butte AJ. xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biol.2017 Nov 15;18(1):220. PMCID: PMC5688663). The relative abundance of 64 unique cell types was calculated and output into a count file.

[0163] As further described here, the RNA switches allow for the measurement of cell-specific transcriptional activity. Organization of gene modules into cell-specific activity provides a measure how cell activity and cell phenotypes change due to a stimulus, or between disease cohorts. As described herein, RNA switches were utilized to examine co-expression, and whether the presence of a disease changes the association or co-expression of these RNA switches. Such analysis provides insights into the pathobiology of disease, biological meaning to a classifier gene set (such as a disease, e.g., endometriosis or other stimulus or change of condition), by analyzing the association of the genes within the classifier to specific cell types. Example 6: Identifying cell fractions in menstrual samples of mixed cell types using bulk RNA sequencing

[0164] This example provides an exemplar to deconvolute mixed cell types such as menstrual samples using a reference RNA seq profile for various cell populations. This reference RNA seq profile can be a known public dataset of various cell types or it can be generated through single cell RNA sequencing of mixed cell populations. In this example, a single cell sequencing using menstrual samples was used to obtain RNA seq data to build the reference RNA seq profile. Specifically, RPKM gene matrix for 1095 single cell RNA-Seq data were clustered using UMAP (FIG.22) to identify the 15 cell clusters. These 15 cell clusters (named as cluster 0, 1, …, 14) were designated as the reference cell types.

[0165] A probabilistic model (FIG.23) using RNA switch expressions from the single cell data was developed for a total of 489 switches as listed in Table 2 above with the average log2 of RPKM values of the genes in a switch. The probabilistic model was constructed with the following assumptions:

[0166] The cell type fractions are influenced by the individual’s affection status (case / control / suspected) and the day of the cycle.

[0167] Each sample can be mapped to a universal state characterized by the cell type fractions of the sample.

[0168] The gene expression of a sample is a linear combination of its cell type fractions and the gene expression of the cell types.

[0169] The gene expression of a cell type is approximately log-normally distributed.

[0170] The following are the variables of the model:WSGR Docket No.50272-712.601 s sample i(s)individual for s d(s) day of the cycle, in the current model, we assume that the day of the cycle can be directly mapped to its state a(i) affection status (case / control, or suspected endometriosis) of individual i g gene, G set of all genes in dataset c cell type, C set of all expected cell types Xg,s expression of gene g in sample s Zg,cexpression level of gene g in cell type c c,ts fraction of cell type c for state ts

[0171] The model was applied to deconvolute the mixed menstrual samples using RNA switch from the bulk sequencing to generate fractions of 15 different cell populations (cell clusters) according to the reference UMAP clusters. The resulting cell fractions are shown in FIG.24 Table 2: Switches Tissue wher 1e SwitchID switch is on Gene 1 aDC C1QA, C1QB, CCL13, CCL17, CCL19, CCL22, CD80, IL12B C1QA, C1QB, CCL13, CCL17, CCL19, CCL22, CD80, FPR3, 2 aDC HLA-DQA1, IL12B C1QA, C1QB, CCL13, CCL17, CCL19, CCL22, CD80, FPR3, 3 aDC HLA-DQA1, IL12B CCL13, CCL17, CCL19, CCL23, CCL8, CD209, CD80, CXCL9, HS3ST3B1, IL12B, IL3RA, LILRA5, PTGIR, SIGLEC1, 4 aDC SLAMF1, SOCS3, TNFRSF4, TRAF1, TXN ABCA6, ABI1, ACHE, ACOT9, ADPGK, ADPRH, ALDH1A2, ALOX15B, ANKFY1, ANXA5, ARF3, ARFRP1, ARHGEF11, ARL8B, ARPC4, ATP1B3, AZIN1, BCKDK, BCL2L11, BCL2L13, BLVRA, C1orf27, C1QA, C1QB, C3AR1, C5orf15, CAMK1G, CASP5, CCL1, CCL17, CCL18, CCL19, CCL22, CCL23, CCL24, CCL4, CCL7, CCL8, CCR5, CD209, CD300C, CD80, CD86, CHFR, CLIP1, CMKLR1, CUL1, CXCL13, CXCL9, CYTH4, DENND1A, DNAJA1, DNPEP, DOT1L, DYNLT1, EIF5, ENO1, ETV3, EXOC5, FAM175B, FBXL4, FCER1G, FCER2, FPR3, GMFB, GNG5, GPN2, GPR107, GPR132, GRB2, H6PD, HAMP, HCK, HLA-DQA1, HPS5, HRH2, HS3ST3B1, IL10, IL10RA, IL12B, IL19, IL2RA, IL3RA, IL9, IMPDH1, IRF4, KCNK13, KCNMB1, KDM6B, LAIR1, LILRA5, LILRB1, LILRB2, LILRB5, LOR, MAGT1, MAP2K1, MAP3K13, MED25, MFN1, MGRN1, MIIP, MMP25, MTF1, MTHFD2, NETO2, NEU3, NFE2L2, NFKB1, NFKBIB, NRAS, NSUN3, NUP62, OGFR, OPA3, OSM, P2RX7, PAK2, PGK 1.00, PITPNA, PTGIR, RAB21, RAB35, RAB5A, RAB8A, RAMP3, RAPGEF1, RELA, RHOG, RIN2, RPGRIP1, RRP1, S100A10, SCARF1, SCYL2, SEC24A, SIGLEC1, SIGLEC7, SIGLEC9, SLAMF1, SLC1A2, SLC25A28, SLC6A12, SLCO5A1, SOCS3, SPAG9, SRC, STAT2, 5 aDC TAX1BP1, TBC1D13, TBC1D22B, TCF21, TDRD7, TFEC,WSGR Docket No.50272-712.601 TMCC2, TMEM41B, TMSB10, TMX1, TNFRSF4, TNIP2, TOR1B, TPI1, TRAF1, TREX1, TRIP4, TXN, UBE2Z, VRK2, WTAP, XIAP, XPNPEP1, ZBTB17, ZNF654 ABI1, ABTB2, ACHE, ACOT9, ADPGK, ALDH1A2, ALOX15B, ARHGEF11, ARL8B, ARPC4, ATP1B3, AZIN1, BLVRA, C1orf27, C1QB, C5orf15, CAMK1G, CCL17, CCL19, CCL23, CCL7, CCL8, CD209, CD80, CD86, CHFR, CHMP5, CLIP1, CUL1, CXCL9, CYTH4, DENND1A, DOT1L, EIF5, ELL, ENO1, ETV3, FAM175B, FBXL4, GNG5, GPR107, GPR132, HCK, HS3ST3B1, IL10, IL10RA, IL12B, IL19, IL2RA, IL3RA, IL9, IRF4, LILRA5, MAP2K1, MAP3K13, MFN1, MIIP, MTF1, MTHFD2, N4BP1, NECAP2, NEU3, NFE2L2, NFKB1, NSUN3, NUP62, OGFR, P2RX7, PGK 1.00, PTGIR, RAB8A, RAPGEF1, RELA, RHOG, SEC24A, SIGLEC1, SLAMF1, SLC1A2, SLC6A12, SLCO5A1, SNX11, SOCS3, SPAG9, SRC, STAT2, TDRD7, TFEC, TMSB10, TNFRSF4, TNIP2, TOR1B, TRAF1, TRPC4AP, TXN, UBE2Z, WTAP, 6 aDC ZBTB17, ZNF654 ADH1B, ADIPOQ, CILP, COL5A3, DLAT, GPD1, LBP, PLIN1, 7 Adipocytes PNPLA2, PPP1R1A, PPP2R1B, PTGER3 ADH1B, ADIPOQ, ATP5G3, CILP, COL5A3, DLAT, GPD1, 8 Adipocytes HADHA, LBP, PLIN1, PNPLA2, PPP1R1A, PPP2R1B, PTGER3 ADH1B, ADIPOQ, ATP5G3, CILP, COL5A3, DBI, DLAT, 9 Adipocytes GPD1, LBP, PLIN1, PNPLA2, PPP2R1B, PTGER3 10 Adipocytes ADH1B, ADIPOQ, ATP1A2, GPD1, HP, LBP, PLIN1, TF 11 Adipocytes ADH1B, ADIPOQ, ATP1A2, GPD1, HP, LBP, PLIN1, TF 12 Adipocytes ADH1B, ADIPOQ, ATP1A2, C6, GPD1, HP, LBP, PLIN1, TF ADH1B, ADIPOQ, ATP5G3, CILP, COL5A3, DLAT, GPD1, 13 Adipocytes HADHA, LBP, PLIN1, PNPLA2, PPP1R1A, PPP2R1B, PTGER3 ADH1B, ADIPOQ, CILP, COL5A3, DLAT, GPD1, LBP, PLIN1, 14 Adipocytes PNPLA2, PPP1R1A, PPP2R1B, PTGER3 ADH1B, ADIPOQ, ATP5G3, CILP, COL5A3, DBI, DLAT, ECHDC1, GPD1, LBP, PLIN1, PNPLA2, PPP1R1A, PPP2R1B, 15 Adipocytes PTGER3, SLC25A6 ADAM12, AGPAT4, ANKRD1, ASAP3, CAND2, CENPI, CNN1, COL11A1, DAPK2, DCHS1, DNA2, DOK5, DPF3, FABP7, FBXL7, FSD1, FZD2, GABRQ, GLI2, GNG3, GPC4, GPX7, KIF18A, KIF20B, LOXL1, LRP4, MXD3, NCAPG, NNAT, NT5DC3, OLFML2A, PNMA2, PNMAL1, POU3F3, PSRC1, PTN, PTX3, RBP1, REC8, SDC2, ST8SIA2, SYT11, 16 Astrocytes TFAP2C, TNC, TRO, XRCC2, ZNF239 ACSS3, ADAM12, AGPAT4, ASAP3, ATF7IP2, CAND2, CBR3, CBX2, CLCN2, CNN1, COL11A1, DAPK2, DCHS1, DOK5, DPF3, FABP7, FAM64A, FBXL7, FSD1, FZD2, GABRQ, GINS4, GPC4, GRM7, H2AFV, HIST1H1D, KIFC1, LOXL1, MCM3, MXD3, NT5DC2, OLFML2A, PNMAL1, POU3F3, PSRC1, PTN, REC8, RECQL4, RIBC2, SCN5A, SDC2, ST8SIA2, STAC, TFAP2C, TMEM135, TNC, TRIM45, 17 Astrocytes TRIOBP, TRO, XRCC2, ZNF239 ADAM12, AGPAT4, ANKRD1, ASAP3, CNN1, COL11A1, DAPK2, DOK5, DPF3, FABP7, FZD2, GABRQ, GNG3, LRP4, 18 Astrocytes OLFML2A, PNMAL1, POU3F3, PSRC1, PTN, TNC ACTA2, ACTG2, ADAM12, ADAM19, ADAMTS2, AFF3, 19 Astrocytes ANKRD1, APLP1, BGN, BST1, BTN3A3, C1S, C9orf16,WSGR Docket No.50272-712.601 CACNA1H, CCL2, CDH11, CDH6, CFH, CFI, CHN1, CHRDL1, CLDN11, CLSTN2, CLU, CNN1, COL13A1, COL1A1, COL1A2, COL3A1, COL5A1, COL5A2, COL6A1, COL6A2, COL6A3, COL8A2, COPZ2, CPA4, CRABP2, CREB3L1, CRLF1, CXCL12, DACT1, DCN, DHRS3, DIO2, DIRAS3, DPYSL3, DUSP2, EDIL3, EFEMP2, FAP, FBLN2, FBN1, FGFR1, FGG, FILIP1L, FKBP10, FLNC, FN1, FZD2, FZD7, GATA6, GBP2, GEM, GFPT2, GLIPR1, GLT8D2, GNG11, GREM1, HEPH, HOXB2, HS3ST3A1, IGFBP2, IGFBP3, IGFBP5, IGFBP6, ITGA7, LAMA4, LIF, LOX, LOXL1, LRRC17, LUM, LYPD1, MAP1A, MATN2, MDK, MEG3, MEST, MFAP4, MFAP5, MGP, MMP2, MN1, MOXD1, MRC2, MXRA5, MXRA8, MYL9, MYLK, NAP1L3, NES, NFASC, NID2, NR2F1, OLFML3, OXTR, PADI2, PALM, PAX6, PCOLCE, PCOLCE2, PDE5A, PDGFRA, PDGFRB, PLN, PNMAL1, POSTN, PPP1R3C, PTGIS, PTN, PTPRN, RARB, RARRES1, RARRES2, RCN3, RGS4, RGS5, SCARA3, SEMA3E, SERPINF1, SERPINH1, SFRP4, SLC12A8, SLC14A1, SLC22A17, SPARC, SPON2, SRPX2, SULF1, SYNPO, SYTL2, TAGLN, TFPI, TGM2, THY1, TIMP1, TIMP2, TNFSF4, TNS1, TPM1, TPM2, TRPV2, UCHL1, VCAM1, VCAN, WNT5A, XYLT1, ZCCHC24 ACTA2, ACTG2, ADAM12, ADAM19, ADAMTS2, ANKRD1, BGN, CACNA1H, CCL2, CFH, CFI, CNN1, COL1A1, COL1A2, COL3A1, COL5A1, COL6A1, COL6A2, COL8A2, COPZ2, CPA4, CRLF1, CXCL12, DACT1, DHRS3, DIO2, DIRAS3, DPYSL3, EFEMP2, FBLN2, FGG, FLNC, FN1, FZD7, GFPT2, GLIPR1, GREM1, HOXB2, IGFBP2, IGFBP3, IGFBP5, LAMA4, LOXL1, LRRC17, LUM, MEST, MFAP4, MFAP5, MGP, MXRA5, MXRA8, MYL9, MYLK, NID2, OLFML3, OXTR, PADI2, PCOLCE, PDGFRA, PDGFRB, PLN, POSTN, PPP1R3C, PTGIS, PTN, PTPRN, RARRES1, RARRES2, RCN3, RGS4, RGS5, SEMA3E, SFRP4, SLC12A8, SLC14A1, SPARC, SULF1, TAGLN, TGM2, THY1, TNFSF4, TPM1, TPM2, 20 Astrocytes VCAM1, VCAN, XYLT1 ACTA2, ACTG2, ADAM12, ADAM19, ADAMTS2, ANKRD1, APLP1, BGN, BTN3A3, C1S, CACNA1H, CCL2, CDH11, CFH, CFI, CLDN11, CLSTN2, CLU, CNN1, COL13A1, COL1A1, COL1A2, COL3A1, COL5A1, COL5A2, COL6A1, COL6A2, COL6A3, COL8A2, COPZ2, CPA4, CREB3L1, CRLF1, CXCL12, DACT1, DCN, DHRS3, DIO2, DIRAS3, DPYSL3, DUSP2, EDIL3, EFEMP2, FAP, FBLN2, FBN1, FGG, FILIP1L, FKBP10, FLNC, FN1, FZD7, GFPT2, GLIPR1, GLT8D2, GREM1, HOXB2, HS3ST3A1, IGFBP2, IGFBP3, IGFBP5, IGFBP6, LAMA4, LOX, LOXL1, LRRC17, LUM, LYPD1, MAP1A, MDK, MEST, MFAP4, MFAP5, MGP, MRC2, MXRA5, MXRA8, MYL9, MYLK, NAP1L3, NES, NFASC, NID2, NR2F1, OLFML3, OXTR, PADI2, PCOLCE, PCOLCE2, PDE5A, PDGFRA, PDGFRB, PLN, POSTN, PPP1R3C, PTGIS, PTN, PTPRN, RARRES1, RARRES2, RCN3, RGS4, RGS5, SCARA3, SEMA3E, SFRP4, SLC12A8, SLC14A1, SPARC, SPON2, SRPX2, SULF1, TAGLN, TGM2, THY1, TNFSF4, 21 Astrocytes TPM1, TPM2, VCAM1, VCAN, XYLT1 BLK, CD19, CD22, CD37, CD79A, FCRL2, GGA2, MBD4, 22 B-cells MS4A1, PNOC, PWP1, SMC6, SNX2, SP140, STAG3, STAP1WSGR Docket No.50272-712.601 BLK, CD19, CD22, CD37, CD79A, FCRL2, GGA2, MBD4, 23 B-cells PNOC, SMC6, SP140, STAG3 BLK, BTK, CD19, CD22, CD37, CD79A, CSNK1G3, DEPDC5, FCRL2, GGA2, MBD4, MS4A1, PHKB, PNOC, PWP1, SMC6, 24 B-cells SNX2, SP140, STAG3, STAP1, TRAF3 AFTPH, ARHGAP17, BCL2L11, BLK, BTK, C12orf49, CD180, CD19, CD22, CD37, CD79A, CDC40, CSNK1G3, DEF8, DEPDC5, EGOT, FCRL2, GGA2, JMJD1C, MBD4, MCM9, MFN1, MIOS, MS4A1, P2RY10, PHKB, PIKFYVE, PNOC, POU2F1, PRDM2, PRDM4, PRKCB, PWP1, RRAS2, SIPA1L3, SLC24A1, SMC6, SNX2, SP140, STAG3, STAP1, TRAF3, 25 B-cells UBE2G1, WDR11 CD19, CD22, CD37, CD79A, FCRL2, GGA2, MS4A1, PRDM4, 26 B-cells SMC6, SNX2 BLK, CD19, FCRL2, HLA-DOA, SPIB, STAP1, TNFRSF13B, 27 B-cells VPREB3 ACTN2, AFF2, AICDA, ARHGAP17, ATF7IP, BLK, CD180, CD19, CD22, CD37, CD53, CD72, CD79A, CD79B, CNOT1, CNR1, CNR2, CXCR5, DAXX, DCLRE1C, DNASE1, FCRL2, GDI2, GPR18, HDAC7, HLA-DOA, HTR3A, IFNW1, IKZF3, IL17A, INPP5B, ITSN2, KCNN3, KIAA1033, LSM 6.00, LY86, MBD4, MGAT5, MS4A1, MYOT, PAX5, PGR, PIKFYVE, PNOC, POU2F2, QRSL1, RECQL5, RNGTT, S1PR2, SEC62, SIPA1L3, SLC30A4, SP140, SPIB, STAG3, STAP1, SYPL1, TCL1A, TCL1B, TCL6, TERT, TNFRSF13B, 28 B-cells TNFRSF17, UTP6, VPREB3 ANKMY1, AP3B1, BAIAP3, BLK, BMP8B, C5orf15, CCR6, CD180, CD19, CD37, CD53, CD72, CD79A, CD79B, CEACAM21, CEPT1, CHAD, CIITA, COL19A1, CR1, CSNK1G3, CXCR5, DCLRE1C, DEPDC5, FCRL2, GGA2, GPR25, GPRC5D, HLA-DOA, HLA-DPB1, HSPA4, HTR3A, IKZF3, KCNIP2, KIAA0430, KIAA1033, LY86, LY9, MAP3K9, MGAT5, MMP17, MRM1, MS4A1, NSUN5, NUP160, P2RY10, PAX5, PLA2G2D, POLR3K, QRSL1, RIC3, RPS11, RPS16, S1PR4, SMC6, SNX2, SP140, SPIB, STAP1, TCL1A, TCL1B, TLR7, TNFRSF13B, TNFRSF17, TROVE2, UTP6, VPREB3, WDR11, ZNF154, ZNF202, ZNF208, ZNF37A, ZNF638, 29 B-cells ZNF688, ZNF701, ZZZ3 ACTN2, AFF2, AICDA, ATF7IP, BLK, CD19, CD22, CD37, CD53, CD72, CD79A, CD79B, CNOT1, CNR1, CXCR5, DCLRE1C, FCRL2, GDI2, GPR18, HLA-DOA, HTR3A, IFNA2, IFNW1, IKZF3, IL17A, INPP5B, ITSN2, KCNN3, LSM 6.00, LY86, MBD4, MS4A1, MYOT, PAX5, PIKFYVE, PNOC, POU2F2, QRSL1, RECQL5, RNGTT, S1PR2, SLC13A2, SLC30A4, SP140, SPIB, STAG3, STAP1, SYPL1, 30 B-cells TCL1A, TCL1B, TCL6, TERT, TNFRSF17, UTP6, VPREB3 AHCTF1, AKAP4, ALAS2, AMOTL2, ARHGAP17, ARR3, ART3, ATXN3L, BMP10, C11orf16, C6orf10, C8A, CASS4, CD72, CLCA4, CXCR2, CXorf36, DEFA5, EFNA2, FBRS, FRS3, GATC, GPR3, HP, HPSE2, KCNA5, KCNJ13, LAMB4, LECT2, LEP, LRTM1, MPO, MYH1, NPY5R, OMG, ONECUT1, OTUD7B, PRKACG, PRL, SLC17A6, SLCO5A1, 31 Basophils SMCP, TCL1A, TEX12, TGM3WSGR Docket No.50272-712.601 ABCB11, ACSM5, ACTN2, ADAM7, ADAMDEC1, ADCY2, ADH1A, ADM2, ADPRH, ADRA1A, AGTR2, AHCTF1, AIPL1, AKAP4, ALDH1A2, ALDOB, AMOTL2, APOF, ARR3, ART1, ART3, ATXN3L, BMP10, BRDT, BRIP1, C11orf16, C6orf10, C8A, C9, CACNG3, CALY, CASS4, CCDC70, CCL21, CD177, CEACAM7, CETP, CLCA4, CLEC3B, CLEC4E, COL19A1, CPA1, CRB1, CRHR2, CTRL, CXCR2, CYP17A1, CYP19A1, CYP21A2, CYP2A7, CYP4F8, DAZL, DCT, DDX4, DNAH3, DNAJC28, DSP, EFNA2, ELSPBP1, EPHA3, F13B, F9, FBRS, FBXO22, FCN2, FGF23, FHL5, FRS3, FSTL4, GABRA1, GABRG3, GATC, GNG3, GPR3, GPR85, GRB2, GRIK4, GRIN2A, GRM4, GRM7, GUCA1B, HIST1H2BL, HJURP, HP, HPSE2, HTN3, HTR1D, HTR1E, HTR3B, HTR6, IBSP, IFNAR1, IFNW1, IL12RB1, IL26, INHA, INSL6, INVS, IRF4, KCNA4, KCNC3, KCND3, KCNJ1, KCNJ13, KERA, KHDRBS2, KRT19, KRT24, KRT33B, KRT83, KRT84, LECT2, LPO, MAGEB4, MAGEL2, MAPK12, MCF 2.00, MEPE, MOBP, MRPS15, MRPS7, MS4A4A, MUC7, MYF6, MYLK3, MYO15A, MYOC, MYOD1, MYOZ1, NEUROD6, NKX6-1, NPVF, NPY5R, NR1I3, NR2E3, NTRK1, NTRK2, ONECUT1, PAX7, PDE11A, PDHA2, PDPR, PDZRN4, PHOX2B, PLP1, PMP2, PNMA3, POU1F1, PPEF2, PRKACG, PRL, RBMXL2, RBPJL, RPP38, SCN7A, SEMG2, SI, SLC10A2, SLC13A1, SLC17A6, SLC24A2, SLC30A4, SLC5A4, SLC5A5, SLC6A1, SLC6A11, SLCO1C1, SLCO5A1, SMCP, SMR3B, SPACA1, SPO11, SPTBN5, SSX3, SSX5, TAC1, TACC3, TAS2R14, TCL1A, TEX12, TFDP3, TLL2, TNNT2, TNNT3, TNP2, TRIM48, TRPM1, TRPM3, UNC5C, USH2A, VPREB1, WASL, 32 Basophils ZKSCAN3, ZNF16, ZNF214, ZPBP, ZXDB C11orf16, NPY5R, NR2E3, PCDHA2, SCGB2A2, SSX3, SSX5, 33 Basophils TMEM212 BCL2L11, CD101, CLK1, CYSLTR2, DEFA4, DENND1A, ERN 1.00, FCN1, FGR, GNLY, GPR183, GRK6, GSTO1, GTF3C1, GZMB, GZMH, HRH2, JMJD6, KLRD1, LAIR2, LILRA1, LILRA2, MMP8, NAA16, NKG7, NMUR1, PADI4, PF4V1, PI4KB, PLCB2, POLDIP3, RETN, RFX2, RGS1, RNASE2, S100A12, TBK1, TUBB1, VAPA, VIM, WRAP53, 34 Basophils XCL1 BCL2L11, CEACAM8, DENND1A, DERL2, FCN1, NCR1, 35 Basophils NMUR1, NXT1, PI4KB, POLDIP3, WRAP53 BCL2L11, DENND1A, FCN1, NCR1, NMUR1, NXT1, 36 Basophils POLDIP3, WRAP53 AAMP, ACD, ACTL6A, ADSL, AHCTF1, AKT2, AMBRA1, ANP32B, ANXA7, API5, ARL 2.00, ARPC4, ATF1, ATG5, ATPIF1, ATXN10, BAG3, BCAS2, BTF3, BUB3, C11orf58, C12orf29, CBLL1, CBX3, CCNC, CCR4, CD2, CD28, CD2AP, CD5, CD6, CDC40, CDK9, CDKN2AIP, CDV3, CEP57, CETN3, CLPX, CMPK1, CNBP, COPS4, COPS5, COX7C, CPSF6, CSNK1A1, CSNK2A2, CTBP1, CTLA4, DAD1, DAP3, DBF4, DDX3X, DDX50, DENR, DNAJA2, DNAJB1, DOHH, DPM1, DR1, EID1, EIF2B5, EIF3E, EIF3L, EIF3M, EIF4G2, ERH, ESD, ETAA1, EXOC2, FCF1, FNTA, FUBP3, FXR1, GABPA, GALR2, GATAD2A, GLOD4, GLUD1, GPR132, CD4+ memory GPR183, GRPEL1, HDAC1, HINT1, HMGN4, HMOX2, 37 T-cells HNRNPA0, HNRNPH3, HNRNPU, ICOS, IMP3, INTS8, ISCA1,WSGR Docket No.50272-712.601 ITK, JAK3, KBTBD4, KIF22, KTN1, LDHB, LIN7C, MAEA, MAGOH, MATR3, METAP1, METTL5, MMADHC, MRPL11, MRPL20, MRPL44, MRPS18B, MRPS27, MRPS34, NAE1, NCL, NDUFS5, NKRF, NUPL2, OSGEP, PABPC4, PAPOLA, PCID2, PCNP, PDCD10, PFDN6, PFN1, PKP4, PLP2, POLD2, PPA2, PPID, PPIH, PPP1CB, PPP1CC, PPP2R5D, PPP6C, PREPL, PRPF18, PRPF19, PSMF1, PTGES3, PTPN11, RAD21, RANBP1, RANBP9, RBM3, RGS1, RPF1, RPL13, RPL13A, RPL36, RPL4, RPL5, RPL8, RPS19, RPS3, RPS6, RRP1B, RSL24D1, RUVBL1, RWDD1, SAFB2, SERP1, SH2D1A, SLAMF1, SMAD2, SMC5, SMU1, SOD1, SP3, SPAG16, SRP9, SSNA1, STK16, SUB1, SUCLG1, SURF2, TBL3, THAP11, THOC7, THOP1, TPP2, TPT1, TRA2A, TRMT112, TSN, TSSC1, TTC37, U2AF2, UBE2D2, UBE2D3, UBE2N, UBIAD1, UBQLN2, UNC45A, UQCRC2, USP39, UXT, WDR46, ZC3H15, ZDHHC6, ZZZ3 CD4+ memory CD28, CD2AP, CD6, DNAJA2, EIF3E, GPR183, ICOS, ITK, 38 T-cells PKP4, PREPL, PTGES3 ADSL, AMBRA1, ANP32B, ANXA7, ARPC4, ATF1, ATG5, BAG3, BCAS2, BTF3, BUB3, C11orf58, C12orf29, CBLL1, CBX3, CCR4, CD28, CD2AP, CD5, CD6, CDC40, CDKN2AIP, CDV3, CEP57, CMPK1, CNBP, COX7C, CSNK2A2, CTBP1, DAD1, DAP3, DDX3X, DDX50, DENR, DNAJA2, DNAJB1, DR1, EEF2, EID1, EIF3E, EIF3L, EIF3M, EIF4G2, ESD, ETAA1, EXOC2, FARS2, FCF1, FNTA, FUBP3, FXR1, GATAD2A, GLOD4, GLUD1, GPR132, GPR183, HDAC1, HINT1, HMGN4, HNRNPA0, HNRNPH3, ICOS, IMP3, ISCA1, ITK, KARS, KIF22, KTN1, LDHB, MATR3, METAP1, MRPL11, MRPS34, NAE1, NDUFS5, NKRF, PABPC4, PAPOLA, PCID2, PCNP, PFN1, PKP4, PLP2, PPP1CB, PPP1CC, PPP2R5D, PPP6C, PREPL, PRPF18, PRPF19, PSMF1, PTGES3, RAD21, RBM3, RBM34, RNF6, RPF1, RPL13A, RPL36, RPL5, RPL8, RPS19, RPS3, RPS6, RRP1B, RSL24D1, RUVBL1, RWDD1, SERP1, SH2D1A, SLAMF1, SLC25A38, SLC25A6, SMAD2, SMC5, SOD1, SP3, SRP9, SSNA1, SUB1, SURF2, THAP11, THOC7, THRAP3, TINF2, TRA2A, CD4+ memory TRMT112, TSN, TTC37, UBE2D2, UBE2D3, UBIAD1, 39 T-cells UBQLN2, UNC45A, USP39, UXT, WDR46, ZDHHC6, ZZZ3 CD4+ memory ARHGAP15, CCR4, CD28, CD3G, CD40LG, CTLA4, CXCR6, 40 T-cells DLEC1, GPR15, LIMS1, PDCD10, RBL2, TRAT1 ARHGAP15, CCR4, CD226, CD28, CD40LG, CTLA4, GPR15, CD4+ memory GPR171, GZMA, GZMK, HMGB2, PDCD1, RBL2, TRAT1, 41 T-cells UBASH3A ADSL, ARHGAP15, AURKAIP1, CCR4, CD28, CD40LG, CD96, CTLA4, DLEC1, GZMA, GZMK, HMGB2, HMGN4, CD4+ memory ICOS, LIMS1, MEN1, PTPN4, RNF34, SEC23IP, TRAT1, 42 T-cells ZNF236 CD4+ naive T- ANKRD55, CABIN1, CCR7, CD3E, CD6, CD7, CHMP7, DSC1, 43 cells GPSM3, JAK3, LIMD2, PLCG1, PLCL1, PLXDC1, ZAP70 SEPTIN9, ACAP1, ANKRD55, APBB1, ARFRP1, CABIN1, CCR7, CD27, CD3E, CD6, CD7, CDK10, CHMP7, CREBZF, CRLF3, CUBN, DSC1, FAM193B, GPSM3, GRK6, GZMM, IDUA, INSL3, IPCEF1, ITK, JAK3, KLHL3, LEPROTL1, CD4+ naive T- LIMD2, MLXIP, MSL3, NCK2, NDFIP1, NUMA1, OBSCN, 44 cells PACS1, PHF1, PIP4K2B, PLCG1, PLCL1, PLXDC1, PRMT2,WSGR Docket No.50272-712.601 RAPGEF6, RNPEPL1, RXRB, SELPLG, SH2B1, SIRPG, SIT 1.00, TNFSF8, TNK1, TRAT1, TSPAN32, WDR6, ZAP70, ZNF76 CD4+ naive T- ANKRD55, CD27, CHMP7, CLC, CTSW, DNAJB1, HAUS3, 45 cells RBL2, ZNF394 AAK1, ANKRD55, ATXN7, BMS1, CCR7, CD6, CEPT1, CHMP7, CLUAP1, COQ6, CUL1, DSC1, GIN1, GLG1, ICOS, IPCEF1, KDM3A, LY9, MAK, MTRF1, NAA16, NDFIP1, CD4+ naive T- NUDT9, PHF20L1, PLCL1, PLXDC1, POP5, RAPGEF6, RBL2, 46 cells RBMS1, SETD5, SORCS3, TMEM30B, TRAF3IP3, UBASH3A ANKRD55, CCR7, CEPT1, CHMP7, FKTN, IL16, KRT2, CD4+ naive T- MTRF1, NPAT, PHF20L1, PLXDC1, POP5, RAPGEF6, 47 cells RBMS1, RNF216, SORCS3, TRAF3IP3, TUG1, UTP20, VPS52 AAK1, ANKRD55, CCR7, CD6, CEPT1, CHMP7, FKTN, GIN1, IL16, KRT2, MTRF1, NOL9, NPAT, PHF20L1, PLCL1, CD4+ naive T- PLXDC1, POP5, RAPGEF6, RBMS1, SORCS3, TRAF3IP3, 48 cells TUG1, UTP20, VPS52 CD3G, CD4, CD5, CD7, GIMAP6, HMOX2, MKL1, MLH3, CD4+ naive T- NPAT, PARP11, RAB3GAP1, RPA3, RPL14, RPLP2, SIRPG, 49 cells SNPH, TBC1D5 CD4+ naive T- CD4, CD40LG, CD5, CD7, GIMAP6, INPP4A, MKL1, NPAT, 50 cells PARP11, SIRPG, TBC1D5, TRAF1 ACBD4, CCR7, CD2, CD247, CD27, CD3G, CD4, CD40LG, CD5, CD7, COPS7B, DDX31, DDX50, FCF1, GIMAP6, HMOX2, INPP4A, KRI1, LEPROTL1, MKL1, MLH3, NPAT, NUP50, PARP11, PLXDC1, PRMT2, PRMT3, RAB3GAP1, RPA3, RPAP2, RPL14, RPL38, RPLP2, RPRD2, RPS6, SIRPG, CD4+ naive T- SLTM, SNPH, TBC1D5, TRAF1, TRAT1, USP16, WDR82, 51 cells ZBTB40, ZNF264, ZNF609, ZNF780B CA5B, CASP8, CCR7, CD226, CD27, CD28, CD3E, CD4, CD40LG, CDK1, COPS7B, CTLA4, DPEP2, FNBP4, GIMAP6, GRAP2, HMOX2, ICOS, LAIR2, LEPROTL1, MKL1, NUDCD3, PHF20L1, PLCL1, PLXDC1, POLR3E, RBL2, CD4+ naive T- SIRPG, SIT 1.00, SNPH, STAP1, SUPV3L1, TEX264, 52 cells TPP2, TRAT1, USP16, ZBTB40, ZNF263, ZNF609 CD4+ naive T- APBB1, CHMP7, DIDO1, NDFIP1, PLXDC1, PRMT2, REV1, 53 cells RNF216, TATDN2 CD4+ naive T- ANKRD55, CABIN1, CCR7, CD3E, CD6, CD7, CHMP7, DSC1, 54 cells GPSM3, JAK3, LIMD2, PLCG1, PLCL1, PLXDC1, ZAP70 ATXN7L1, BAD, CCR4, CCR7, CD2, CD28, CD3G, CD40LG, CD5, CTLA4, DDX31, FBXO21, FNBP4, FOXP3, GOLGA4, HIPK1, HMOX2, ICOS, LEPROTL1, MGAT2, NFE2L2, NUDCD3, PLCL1, POLR3E, PPP2CA, RIC3, SIRPG, SLTM, 55 CD4+ T-cells SON, SUPV3L1, TRAF1, TRAT1, TSPYL1, USP36 ARID5B, ATXN7L1, BAD, C14orf169, CCR4, CCR7, CCR9, CD2, CD28, CD3E, CD3G, CD40LG, CD5, CD6, CTLA4, DDX31, DLEC1, DNAH6, DNAJB1, FBXO21, FNBP4, FOXP3, GOLGA4, HAUS3, HERC1, HIPK1, HMOX2, HNRNPU, ICOS, IL21R, LEPROTL1, LSM14A, MBNL1, MGAT2, MKL1, NFE2L2, NUDCD3, OFD1, PHF3, PLCL1, POLR3E, PPP2CA, RBM19, RBM25, RIC3, RSRC2, SFPQ, SIRPG, SLTM, SNX19, SON, SUPV3L1, THAP1, TOE1, TOR1AIP1, TPP2, TRAF1, TRAT1, TSPYL1, TUBGCP5, UBP1, USP36, USP47, WBP11, 56 CD4+ T-cells ZAP70, ZCCHC11, ZXDCWSGR Docket No.50272-712.601 BAD, CCR4, CD2, CD28, CD3G, CD40LG, CD5, CTLA4, DDX31, FOXP3, GOLGA4, HAUS3, HIPK1, HMOX2, ICOS, LEPROTL1, MGAT2, NFE2L2, NUDCD3, PLCL1, POLR3E, PPP2CA, SIRPG, SLTM, SUPV3L1, TRAT1, TSPYL1, USP36, 57 CD4+ T-cells WBP11 ANKRD55, APBB1, CCR7, CD28, CD40LG, CD5, CD6, CHMP7, CTLA4, CUBN, FNBP4, ICOS, ITK, KLHL3, MSL3, NOL9, OBSCN, PLCG1, PLCL1, PLXDC1, PSD, RAPGEF6, 58 CD4+ T-cells SNPH, SPEG, SSTR3, TTN APBB1, CD28, CHMP7, CTLA4, ICOS, ITK, NOL9, PHF3, 59 CD4+ T-cells PLCL1, PPWD1, RAPGEF6, SNPH, TRAT1 AAK1, ALG13, ANKRD55, ARHGAP15, ASXL2, ATXN7, CA5B, CBLL1, CCNT2, CCR2, CCR7, CCR8, CCR9, CD2, CD28, CD4, CDC14A, CHMP7, CRLF3, CUBN, DDX5, DIDO1, DSC1, EZH1, FBXO11, FCN1, FUBP1, GIMAP4, GIMAP6, GP5, GPR183, HMGN4, IPCEF1, ITIH4, ITK, KRIT1, LAX1, LEPROTL1, LY9, MORC2, MSL3, MTO1, NCK2, NFATC2IP, NR2C1, OBSCN, PABPC3, PLXDC1, PNMA3, POU6F1, PPM1B, PRMT2, PRPF38B, RAPGEF6, RBL2, RXRG, SACM1L, SGSM2, SIRPG, SIT 1.00, THUMPD1, TMEM123, TNFSF8, TPT1, TRAF3IP3, TRAT1, TRIM46, TRMT2B, TUG1, UBA3, UBASH3A, UBQLN2, USP33, 60 CD4+ T-cells ZBTB11, ZC3HAV1, ZFC3H1, ZNF611 ABCD2, ARHGEF1, BAD, CCR4, CD2, CD27, CD28, CD3D, CD3E, CD3G, CD40LG, CD5, CD7, CD96, CTLA4, FBXO21, FOXP3, GOLGA4, GPR171, HIPK1, HMOX2, ICOS, LEPROTL1, NFE2L2, NUDCD3, PLCL1, POLR3E, PPP2CA, RIC3, SIRPG, SON, SUPV3L1, TNFRSF4, TOMM20, TRAT1, 61 CD4+ T-cells USP36, ZNF335 ARHGAP15, ARHGEF1, BAD, CCR4, CD2, CD27, CD28, CD3D, CD3E, CD3G, CD40LG, CD5, CD6, CD7, CD96, CTLA4, FOXP3, GPR171, HAUS3, HIPK1, HMOX2, ICOS, LEPROTL1, NUDCD3, PLCL1, PPP2CA, SIRPG, SON, 62 CD4+ T-cells SUPV3L1, TSPYL1 ABCD2, ARHGEF1, BAD, CCR4, CD2, CD27, CD28, CD3D, CD3E, CD3G, CD40LG, CD5, CD7, CD96, CTLA4, FOXP3, GOLGA4, GPR171, HAUS3, HIPK1, HMOX2, ICOS, LEPROTL1, MGAT2, NUDCD3, PLCL1, PPP2CA, RIC3, 63 CD4+ T-cells SIRPG, SON, SUPV3L1, TNFRSF4, TRAF1, TRAT1, ZNHIT6 ADSL, AK1, ANKRD55, BAG3, CBLL1, CD247, CD40LG, CNIH4, CORO7, CSNK1D, CTLA4, DGCR14, DHX16, DNMT1, EDC4, EXOC1, FAM193B, FBXL8, GOLGB1, HUWE1, ICOS, ITIH4, KDM3A, MAN2C1, MTO1, MYO16, NFRKB, NR2C1, NXF1, PLCG1, POLR2A, PSMD2, RAD9A, RAE1, RBM5, SGSM2, SORCS3, SPTAN1, SUPT6H, TSC1, 64 CD4+ Tcm USP36, USP4, ZNF638 SEPTIN10, AAK1, AK1, ANKRD55, ARHGAP15, BAG3, CBLL1, CCR7, CD2, CD247, CD40LG, CNIH4, CTLA4, DHX16, DNMT1, EDC4, EXOC1, FAM193B, FBXL8, GOLGB1, ICOS, ITIH4, KDM3A, MSL3, MYO16, NFRKB, NR2C1, PLCG1, RBM5, SGSM2, SORCS3, SPTAN1, TPR, 65 CD4+ Tcm TRADD, TRAF3IP3, TSC1, USP36, WDR59, YLPM1 SEPTIN10, AAK1, ACAP1, ADSL, AK1, ANAPC5, ANKRD55, 66 CD4+ Tcm ARHGAP15, BAG3, CA5B, CAMSAP1, CBLL1, CCR7, CD2,WSGR Docket No.50272-712.601 CD247, CD40LG, CNIH4, COL5A3, CORO7, CSNK1D, CTLA4, DDX24, DGCR14, DHX16, DIDO1, DNMT1, EDC4, EIF2B5, EXOC1, FAM193B, FASTK, FBXL8, GGA3, GOLGB1, HUWE1, ICOS, INPP5E, IPCEF1, ITGB1BP1, ITIH4, KBTBD2, KDM3A, LY9, MAN2C1, MCF2L2, MSL3, MTO1, MYO16, NAP1L4, NFRKB, NME6, NPAT, NR2C1, NUP85, NXF1, OLAH, PCDHGA9, PIGG, PLCG1, PLCL1, POLR2A, PSMD2, RAD9A, RAE1, RBM5, SGSM2, SLAMF1, SMG5, SORCS3, SPTAN1, SUPT6H, TCF25, TPR, TRADD, TRAF3IP3, TSC1, UBASH3A, UBQLN2, USP10, USP36, USP39, USP4, WDR59, WDR6, YLPM1, ZNF200, ZNF638 ANKRD55, ARHGAP15, ARID5B, BMPR1A, CD28, CD4, CD40LG, CD48, CD5, CD6, CDC14A, CTLA4, DAB1, DLEC1, DNAI2, DVL1, ERN 1.00, FBXL8, FXYD7, GLTSCR2, GMEB2, GP5, GPR15, HNRNPUL1, ICOS, IDUA, ITK, KBTBD4, KRT1, MORC2, NCDN, NCK2, OBSCN, PNMA3, POU6F1, PSD, RPL38, RRS1, SARDH, SCNN1D, SLC4A5, SNPH, SPEG, TCF20, TMEM30B, TNFRSF4, TNFSF8, TPO, TRADD, TRAT1, TRIM46, TRMT61A, UBASH3A, XPC, 67 CD4+ Tcm ZC3HAV1, ZXDB SEPTIN9, ACAP1, ANKRD55, ARHGAP15, ARID5B, BMPR1A, CCR4, CD28, CD4, CD40LG, CD48, CD5, CD6, CDC14A, CDKN2AIP, CRY2, CTLA4, DAB1, DLEC1, DNAI2, DNAJB1, DVL1, ERN 1.00, FBXL8, FXYD7, GLTSCR2, GMEB2, GP5, GPR15, HNRNPUL1, ICOS, IDUA, IKZF1, ITK, JOSD1, KBTBD4, KRT1, LTA, MORC2, NCDN, NCK2, NDRG3, OBSCN, PNMA3, POU6F1, PSD, PURA, RPL38, RPS16, RPS21, RRS1, SARDH, SCNN1D, SIRPG, SLC4A5, SNPH, SNTG2, SPEG, STK11, TAB2, TCF20, TFAP4, THAP11, TMEM30B, TNFRSF4, TNFSF8, TOMM7, TPO, TRADD, TRAT1, TRIM46, TRMT61A, UBASH3A, XPC, ZC3HAV1, 68 CD4+ Tcm ZRSR2, ZXDB ANKRD55, ARHGAP15, CD28, CD40LG, CD48, CD5, CD6, CDC14A, CTLA4, DAB1, DLEC1, DNAI2, ERN 1.00, FBXL8, FXYD7, GLTSCR2, GP5, GPR15, ICOS, ITK, KRT1, MORC2, NCK2, OBSCN, POU6F1, PSD, RPL38, RRS1, SNPH, TMEM30B, TNFRSF4, TNFSF8, TPO, TRADD, TRAT1, 69 CD4+ Tcm TRMT61A, UBASH3A ANKRD55, CCR4, CCR8, CD40LG, CDC14A, CTLA4, DAB1, DLEC1, DNAJB1, ERN 1.00, FBXL8, FXYD7, GPR15, ICOS, KRT1, OBSCN, POU6F1, SIRPG, SLC4A5, TPO, 70 CD4+ Tcm TRADD, TRAT1 ANKRD55, CCR4, CCR8, CTLA4, DAB1, DLEC1, DNAJB1, ERN 1.00, FBXL8, FXYD7, GPR15, ICOS, KRT1, OBSCN, 71 CD4+ Tcm POU6F1, SIRPG, TPO, TRADD, TRAT1 SEPTIN9, ANKRD55, APBB1, ARHGAP15, CCR4, CCR6, CCR7, CCR8, CD28, CD40LG, CD48, CD5, CD6, CDC14A, COL5A3, CTLA4, DAB1, DLEC1, DNAI2, DNAJB1, DVL1, ERN 1.00, FBXL8, FXYD7, GLTSCR2, GP5, GPR15, GPR25, ICOS, IL2RA, ITK, KALRN, KRT1, MORC2, NCK2, OBSCN, PLCG1, PLCH2, PLXDC1, PNMA3, POU6F1, PSD, RPL38, RRS1, SARDH, SIRPG, SNPH, SNTG2, SOCS3, TMEM30B, TNFRSF4, TNFSF8, TPO, TRADD, TRAT1, 72 CD4+ Tcm UBASH3A, ZFYVE9, ZXDBWSGR Docket No.50272-712.601 ARHGAP15, ESYT1, MCF2L2, MYO16, RIC8A, RPN2, 73 CD4+ Tem SLAMF1, SPTAN1, TRADD, TRAPPC2L CCR9, FBXL8, GPR25, LZTR1, RNPEPL1, SELPLG, 74 CD4+ Tem SIT 1.00, SMARCC2, TNFSF8, TRADD SEPTIN10, CCR9, CD2, CDC14A, CLIP1, COLQ, CTLA4, DNAJB1, DYNLT1, FBXL8, HAUS3, ICOS, MCF2L2, MYO16, RANBP9, RGS1, SAMSN1, SLAMF1, SMAP1, SPTAN1, 75 CD4+ Tem TRADD, TRAT1 SEPTIN10, SEPTIN9, AHCTF1, APBA3, ARAF, ARHGAP15, ASB6, BAG3, BCAS2, BRPF1, C19orf53, CABIN1, CAPZB, CCNT1, CCR9, CD2, CD3E, CD4, CD40LG, CD5, CD52, CDC14A, CDC37, CDC73, CGRRF1, CNOT1, COG4, COL5A3, COLQ, COPB1, COPS6, CTLA4, CXCR3, DCTN6, DDX56, DGCR14, DNAJB1, DYNC1H1, DYNLT1, E4F1, EIF3A, EIF3G, ELAC2, EMD, ESYT1, FBXL8, FBXO31, FYCO1, GIPC1, GIT1, GOLGA7, GOLGB1, GORASP2, GPI, GPSM3, GRM3, HAUS3, IBTK, ICOS, IDE, IK, IL10RA, ITIH4, ITK, JMJD6, JTB, KIAA0368, MAML1, MAPKAPK5, MARK3, MCF2L2, MKNK1, MORC2, MTMR6, MUS81, MYO16, N4BP1, NDUFB2, NECAP2, NFKB1, NFRKB, NOL6, NSD1, PABPC3, PGK 1.00, PLCL1, PSMD9, PVRIG, RABGGTA, RANBP9, RBL1, RBM10, RGS1, RIC8A, RIPK1, RNF7, RNPEPL1, RPN2, RRM1, RRS1, RWDD1, S100A11, SAMSN1, SEC24C, SELPLG, SF3A1, SF3B2, SLAMF1, SLC35B1, SLC38A10, SLC9A6, SMAP1, SOS 1, SPSB3, SPTAN1, SRRT, SSR2, STX17, TAF10, TAF1B, TBCC, TCF20, TCF25, THUMPD1, TNIP2, TRADD, TRAF1, TRAPPC2L, TRAPPC4, TRAT1, TUFM, UBIAD1, USP10, USP36, VPS54, XPC, 76 CD4+ Tem ZC3HAV1, ZFYVE9, ZNF394 AIRE, ARHGAP15, CCR2, CCR4, CCR6, CCR8, CCR9, CD28, CD40LG, CD48, CD5, CD6, CDC14A, CTLA4, DLEC1, DNAI2, DNAJB1, ERN 1.00, ESYT1, FBXL8, FXYD7, GALNT8, GLTSCR2, GPR15, GPR171, GZMK, HIST1H3A, HMGN4, IL5RA, ITGB7, KBTBD4, KLRB1, KRT1, LTA, LTK, MCF2L2, NCDN, NCK2, OSM, PDCD1, RPL38, RPS21, RRS1, SAMSN1, SPTAN1, STUB1, TKTL1, TNFRSF4, TNFSF8, TPO, TRADD, 77 CD4+ Tem TRAT1, UBASH3A ARHGAP15, ESYT1, MCF2L2, MYO16, RIC8A, RPN2, 78 CD4+ Tem SLAMF1, SPTAN1, TRADD, TRAPPC2L AIRE, ARHGAP15, CCR2, CCR4, CCR6, CCR8, CCR9, CD28, CD40LG, CD48, CD5, CD6, CDC14A, CTLA4, DLEC1, DNAJB1, ERN 1.00, FBXL8, FXYD7, GALNT8, GLTSCR2, GPR15, GPR171, GZMK, HIST1H3A, IL5RA, ITGB7, KLRB1, KRT1, LTA, LTK, MCF2L2, NCK2, OSM, PDCD1, RPL38, RPS21, RRS1, SAMSN1, TNFRSF4, TNFSF8, 79 CD4+ Tem TPO, TRADD, TRAT1 AIRE, CCR2, CCR6, CCR8, CCR9, CD40LG, CD48, ERN 1.00, GALNT8, GLTSCR2, KLRB1, LTK, MCF2L2, 80 CD4+ Tem TPO, TRAT1 AIRE, CCR2, CCR6, CCR8, CCR9, CD40LG, CD48, ERN 1.00, GALNT8, GLTSCR2, KLRB1, LTK, MCF2L2, 81 CD4+ Tem TPO, TRAT1 CD8+ naive T- AMBN, BCAS2, BTNL8, BUD31, C19orf53, CA14, CCDC87, 82 cells CCR8, CD8A, CD8B, CDK5RAP1, CGRRF1, COX4I1,WSGR Docket No.50272-712.601 CWF19L1, DDX24, EEF1D, FXYD7, GDAP2, GJB4, GPR15, GPR52, HAUS3, HIGD2A, HIST1H3A, HIST1H4F, HTR1B, IL21R, JMJD6, JOSD1, KIAA1109, LIN28A, LUZP4, MED31, MOGAT2, MS4A5, MYL1, NDUFA1, NDUFA4, NDUFS5, NGDN, NKTR, OMG, PCIF1, PSG11, RNF7, RNMT, RPP38, RRH, RRP8, SETD2, SHFM1, SKI, SLC17A4, SLC35E1, SMCP, SMR3B, SON, SS18L2, TNKS2, TPP2, USP36, ZNHIT3 BUD31, C19orf53, CD8A, CD8B, CDK5RAP1, CGRRF1, DDX24, EEF1D, GPR15, GPR52, HAUS3, HIST1H3A, LIN28A, CD8+ naive T- MED31, MS4A5, MYL1, NDUFA4, NDUFS5, NGDN, PCIF1, 83 cells PSG11, RRP8, SETD2, SKI, SON, SS18L2, TNKS2, ZNHIT3 C19orf53, CA14, CD8A, CD8B, CILP, DDX24, EEF1D, FXYD7, GJB4, GPR15, GPR52, HAUS3, HIST1H3A, IL21R, JMJD6, KRT1, LIN28A, MED31, MS4A5, MYL1, NDUFA4, CD8+ naive T- NDUFS5, NGDN, PSG11, RFX2, SETD2, SKI, SLC17A4, 84 cells TNKS2 AAK1, CBY1, CCR7, CD27, CD8A, CD8B, CD96, CEPT1, CIAPIN1, CLUAP1, CRTAM, DIDO1, DSC1, IL16, MMP19, MSL3, MTRF1, NFKB1, NPAT, PCNT, PFN2, POP5, PRMT2, PURA, RAPGEF6, RBM34, RING1, TRAF3IP3, TSPAN32, 85 CD8+ T-cells YLPM1 AAK1, BTN2A1, CA6, CBY1, CCDC25, CCR7, CD27, CD7, CD8A, CD8B, CD96, CEPT1, CIAPIN1, CLUAP1, COG2, CRTAM, CTSW, DIDO1, DPP8, DSC1, FKTN, GGNBP2, IL16, MMP19, MSL3, MTRF1, MYOM1, NDFIP1, NDUFS2, NFKB1, NKRF, NPAT, PCNT, PFN2, PLXDC1, POLR3E, POP5, PRMT2, PURA, RAPGEF6, RBM34, RING1, S100B, SDCCAG3, TMEM41B, TRAF3IP3, TSPAN32, UBQLN2, 86 CD8+ T-cells UTP20, WDR82, YLPM1, ZNF200 ARHGEF1, CA6, CCR7, CD27, CD7, CD8A, CD8B, CIAPIN1, CLUAP1, CRTAM, CTSW, DSC1, MSL3, MTRF1, MYOM1, 87 CD8+ T-cells NAA16, NDFIP1, PCNT, PFN2, POP5, TRAF3IP3 AAK1, BTN2A1, CA6, CBY1, CCDC25, CCR7, CD27, CD7, CD8A, CD8B, CD96, CEPT1, CIAPIN1, CLUAP1, COG2, CRTAM, CTSW, DIDO1, DPP8, DSC1, FKTN, GGNBP2, IL16, MMP19, MSL3, MTRF1, MYOM1, NDFIP1, NDUFS2, NFKB1, NKRF, NPAT, PCNT, PFN2, PLXDC1, POLR3E, POP5, PRMT2, PURA, RAPGEF6, RBM34, RING1, S100B, SDCCAG3, TMEM41B, TRAF3IP3, TSPAN32, UBQLN2, 88 CD8+ T-cells UTP20, WDR82, YLPM1, ZNF200 CASP8, CD8A, CD8B, CRTAM, GIMAP4, GZMK, KLRG1, 89 CD8+ T-cells NPRL2, PTGDR, SLC1A7, TSPAN32, ZNF611 APBB1, CA6, CD8A, CD8B, CD96, CRTAM, DHX15, DSC1, FNBP4, GGNBP2, GJC2, GZMM, HNRNPA0, HNRNPL, KLHL3, KRT2, LSM14A, LY9, MED17, NDFIP1, PCNT, PLCG1, PLXDC1, PRL, PRMT2, PRPF4B, PSD, RASA2, RBL2, RPL37A, S100B, SFPQ, SHANK1, SSTR3, TTN, USP47, 90 CD8+ T-cells ZBTB11, ZC3HAV1, ZNF154, ZNF639 CD8A, CD8B, DPP8, FTO, GZMK, IRF3, KLRG1, LY9, 91 CD8+ T-cells SDAD1, TBCC, TSPAN32 C7orf26, CD160, CD3D, CD8A, CD8B, COG2, COPZ1, DPP8, DSC1, EML3, FTO, GZMK, IRF3, KLRG1, LY9, MKRN2, 92 CD8+ T-cells PCNT, RNF113A, SDAD1, TBCC, TSPAN32, UBE2Q1WSGR Docket No.50272-712.601 CD160, CD27, CD8A, CD8B, CTSW, CX3CR1, DNAJB1, EEF1D, FAM134C, FBXW4, GZMH, GZMK, IPCEF1, KLRB1, KLRG1, LAIR2, LY9, NAA16, PTGDR, PTPN4, RBL2, 93 CD8+ T-cells RWDD3, SIRPG, TOMM7, TSPAN32 CD8A, CD8B, CD96, DSC1, PCNT, PLCG1, PLXDC1, PSD, 94 CD8+ T-cells RBL2, SSTR3 CD27, CD8A, CD8B, CIAPIN1, CRTAM, DSC1, MMP19, 95 CD8+ T-cells MTRF1, MYOM1 CBY1, CCDC53, CD27, CD8A, CD8B, CIAPIN1, COG2, CRTAM, DSC1, GGNBP2, MMP19, MTRF1, MYOM1, NFKB1, 96 CD8+ T-cells NKRF, PCNT, RING1 CA6, CD8A, CD8B, CD96, CRTAM, DHX15, DSC1, FNBP4, GGNBP2, HNRNPA0, KRT2, LY9, NDFIP1, PCNT, PLCG1, PLXDC1, PRL, PRMT2, PSD, RASA2, RBL2, RPL37A, S100B, 97 CD8+ T-cells SHANK1, SSTR3, TTN, USP47, ZBTB11, ZC3HAV1, ZNF154 CD8A, CD8B, DPP8, FTO, GZMK, IRF3, KLRG1, LY9, 98 CD8+ T-cells SDAD1, TBCC, TSPAN32 APBB1, CA6, CD8A, CD8B, CD96, CRTAM, DHX15, DSC1, FNBP4, GGNBP2, GJC2, GZMM, HNRNPA0, HNRNPL, KLHL3, KRT2, LSM14A, LY9, MED17, NDFIP1, PCNT, PLCG1, PLXDC1, PRL, PRMT2, PRPF4B, PSD, RASA2, RBL2, RPL37A, S100B, SFPQ, SHANK1, SSTR3, TTN, USP47, 99 CD8+ T-cells ZBTB11, ZC3HAV1, ZNF154, ZNF639 CASP8, CD27, CRTAM, GPR171, GZMK, NCK1, PARP11, 100 CD8+ Tcm TMEM30B, TNFSF8 ABCD2, ADAT1, ADCYAP1R1, ALK, CASP8, CD27, CD28, CD48, CD8A, CD8B, CD96, CHST5, CRTAM, CXCR3, DUSP11, ESR2, GIMAP4, GIMAP6, GPR171, GZMK, HAO2, HELZ, LAG3, LEPROTL1, MMP11, NCK1, PARP11, PCDHA10, SEC24A, SH2D1A, SIT 1.00, SLC6A7, SMAP1, SYN2, TMEM30B, TNFSF8, TPP2, VPS33A, ZC3H13, 101 CD8+ Tcm ZMYND11 ADCYAP1R1, CASP8, CD27, CD8B, CD96, CRTAM, ESR2, GPR171, GZMK, NCK1, PARP11, SH2D1A, SIT 1.00, 102 CD8+ Tcm TMEM30B, TNFSF8, ZMYND11 ADAT1, CASP8, CD27, CD8A, CD8B, CDC14A, CGRRF1, CRTAM, DCTN6, DNAJB1, DYNLT1, GZMK, HAUS3, LAG3, NAA16, PARP11, PPWD1, RASA2, RGS1, SH2D1A, 103 CD8+ Tcm TMEM30B, TPP2, USP36 CD8A, CD8B, CRTAM, CTSW, GPR171, GZMK, IL2RB, 104 CD8+ Tcm SH2D1A, TMEM30B, USP36 ADAT1, ATF7IP, ATXN7, BMPR1A, C21orf2, CASP8, CD27, CD28, CD3E, CD8A, CD8B, CD96, CDC14A, CGRRF1, CRTAM, CTSW, CYP20A1, DCTN6, DNAJB1, DUSP11, DYNLT1, GPR171, GZMA, GZMH, GZMK, HAUS3, IL2RB, INPP4A, ISCA1, KIF2A, KLRD1, KLRG1, KRT1, LAG3, LRIG2, MED6, NAA16, PARP11, PPWD1, PTPN4, RASA2, RGS1, RPS6KB1, SFXN1, SH2D1A, TMEM30B, TNKS2, 105 CD8+ Tcm TNRC6B, TPP2, TRADD, USP36, ZAP70, ZBTB1 ABCD2, AKAP3, C21orf59, CD3E, CD7, CD8A, CD8B, CRTAM, CXCR3, ELP3, GIMAP4, GIMAP6, GNLY, GPR171, GZMK, HIST1H4F, HMGN4, INTS5, KLRG1, LAG3, PDCD1, PTPRCAP, S100B, SGCD, SH2D1A, STUB1, TBCC, 106 CD8+ Tcm TRAF3IP1, WDR18, XCL1WSGR Docket No.50272-712.601 ABCD2, AKAP3, CD3E, CD7, CD8A, CD8B, CRTAM, CXCR3, ELP3, GNLY, GPR171, GZMK, HIST1H4F, INTS5, KLRG1, LAG3, PDCD1, PTPRCAP, S100B, SGCD, SH2D1A, STUB1, 107 CD8+ Tcm TRAF3IP1, XCL1 CD8B, CRTAM, CXCR3, ELP3, GZMK, HIST1H4F, INTS5, 108 CD8+ Tcm PTPRCAP, SGCD, STUB1 COLQ, CXCR6, DHX8, GZMH, GZMK, LAG3, PVRIG, 109 CD8+ Tem PYHIN1, RGS9, ZAP70 ABCF1, ABCF3, ABT1, ACADVL, AHNAK, AMBRA1, ARHGEF1, ARPC5L, B4GALT3, BIN3, BTN2A1, C14orf169, CAPZB, CCDC85C, CCL4, CCR5, CD160, CD2, CD3D, CD3G, CD7, CD8A, CD8B, CHST12, CIAO1, COL5A3, COLQ, COPB1, CROCC, CRTAM, CTSW, CX3CR1, CXCR6, CYTH4, DAXX, DEFB126, DHX16, DHX8, DIAPH1, DMWD, DNAJB1, DYNC1H1, DYNLT1, EMD, ERAL1, EXOC2, FYCO1, GNLY, GOLGA1, GPR171, GPR65, GTF3C1, GYG1, GZMA, GZMH, GZMK, GZMM, HAUS3, HMGXB3, HMOX2, IDH3B, IFNG, IL10RA, IL18RAP, IMP3, ITGAL, JMJD6, KIAA0196, KLRB1, KLRD1, KLRG1, LAG3, LAIR2, LZTR1, MAML1, MAP7D1, MARK3, MKNK1, MRPL22, MRPS22, MT2A, MUS81, MYO1F, N4BP1, NCR3, NDUFB1, NDUFB2, NDUFS6, NKG7, NOL6, NRBP1, PABPC3, PCNT, PHKG2, PPP2R5C, PREB, PSMC5, PSME1, PTGDR, PTPN4, PTPRA, PUF60, PVRIG, PYHIN1, PZP, RGS1, RGS9, RHOG, RHOT2, RIC8A, RIPK1, RNF113A, RNF167, RPN2, RWDD1, SEC16A, SEC24C, SF3A1, SF3B2, SLAMF1, SLC25A20, SLC35B1, SNRNP200, SNTB2, SPSB3, SRRT, SSR2, STX18, STX4, TAF10, TIMM22, TMEM184B, TNIP2, TRAPPC4, TRNAU1AP, TUFM, UBE2Q1, 110 CD8+ Tem UBN1, WAS, WSB2, WWP2, ZAP70, ZMYM1, ZNF394 AHNAK, BIN2, CALM1, CCDC130, CCL4, CD160, CD2, CD300A, CD3D, CD3G, CD8A, CD8B, COL5A3, COLQ, CROCC, CRTAM, CTDSP1, CTSW, CXCR6, DMWD, FYCO1, GNLY, GPR171, GPR65, GYG1, GZMA, GZMB, GZMH, GZMK, GZMM, IFNG, IL18RAP, IL2RB, ITGAL, KLRB1, KLRD1, KLRG1, LAG3, MAP7D1, MAPK13, MT2A, MYO1F, NKG7, NPRL2, PPP1CA, PRF1, PTGDR, PTPN4, PTPRA, PVRIG, PYHIN1, PZP, RGS9, RIC8A, SASH3, SH2D1A, SLAMF1, SLC25A20, SNTB2, STX4, TBX21, UBE2Q1, 111 CD8+ Tem ZMYM1, ZNF394 AAK1, ABCD2, ACTR1B, ADAT1, AHNAK, ALKBH4, ANAPC2, ARFRP1, ARHGEF1, ARHGEF5, ASTE1, ATR, BMPR1A, C1orf35, C21orf2, C2orf42, C7orf26, C8G, CACNB1, CAPN10, CAPN2, CASP8, CCR5, CD160, CD2, CD3D, CD3G, CD8A, CD8B, CD96, CDK10, CDKN2AIP, CEP250, CHD8, CNNM2, COL5A3, COLQ, CORO7, CREBZF, CROCC, CSNK1G2, CSTF2T, CTR9, CX3CR1, CXCR3, CXCR6, DDX18, DLEC1, DNAJC24, DPP8, DUSP8, E4F1, ELP3, FASLG, FBXO3, FBXW4, FLT4, FYCO1, GCC1, GIMAP4, GIMAP6, GIPR, GOLGA4, GPKOW, GPR171, GPR52, GPR65, GPR68, GRAP2, GSDMD, GTF3C1, GZMA, GZMB, GZMH, GZMK, GZMM, HLA-A, HLCS, HMGN4, IFNG, IKBKAP, IKZF3, IMP3, INPP5E, KIF22, KLHDC4, KLHL11, KLRD1, KLRG1, KRI1, LAG3, LIPT1, LTB4R2, LTK, LZTR1, MAN2C1, MAP3K10, MDN1, MEN1, MRFAP1L1, MSH3, 112 CD8+ Tem MTO1, MUS81, NKG7, NMUR1, OSBPL7, OTOF, PAPOLG,WSGR Docket No.50272-712.601 PDCD1, PLCG1, PLXDC1, PMS1, PNMA3, POLG, PPP2R5C, PRDM8, PRF1, PRKG2, PRMT7, PSTPIP1, PTGDR, PTPN4, PTPRC, PTPRCAP, PURA, PVRIG, PWWP2A, PYHIN1, RBL2, RFX1, RGS9, RIPK1, RNF25, RPUSD2, S100B, SART3, SCAP, SH2D1A, SIT 1.00, SLC1A7, SLC25A12, SLC38A1, SPSB3, SRCAP, STUB1, TBCC, TBX21, TCOF1, TRMT2A, TULP4, URB2, URGCP, USP1, USP34, USP47, VPS4A, WDR82, XCL1, ZAP70, ZBTB1, ZBTB39, ZNF142, ZNF276, ZNF335, ZNF428, ZNF549, ZNF639, ZNF79 AAK1, ABCD2, ACTR1B, ADAT1, ANAPC2, ANGEL2, ARFRP1, ARHGEF1, ARHGEF5, ASTE1, ATR, BMPR1A, C21orf2, C8G, CACNB1, CAPN10, CAPN2, CASP8, CCR5, CD160, CD2, CD3D, CD3E, CD3G, CD8A, CD8B, CD96, CDC14A, CDKN2AIP, CEP250, COL5A3, COLQ, CORO7, CREBZF, CROCC, CSTF2T, CTBP1, CTR9, CX3CR1, CXCR3, CXCR6, DIDO1, DLEC1, DNAJC24, DUSP8, E4F1, ELK4, ERN 1.00, EXOC2, FASLG, FBXO31, FBXW4, FLT4, FYCO1, GCC1, GIMAP4, GIPR, GNLY, GOLGA4, GPR171, GPR52, GPR65, GPR68, GRAP2, GSDMD, GTF3C1, GZMA, GZMB, GZMH, GZMK, GZMM, HIVEP3, HLA-A, HLCS, HMGN4, IFNG, IKBKAP, IKZF3, IL12RB1, IL18RAP, IRF3, ITGAL, KIAA1109, KLHDC4, KLHL11, KLRB1, KLRD1, KLRG1, KRI1, LAG3, LTB4R2, LTK, LZTR1, MAN2C1, MAP3K10, MDN1, MEN1, MRFAP1L1, MUS81, NCR1, NCR3, NKG7, NMUR1, OSBPL7, OTOF, OTUD7B, PANK4, PDCD1, PLCG1, PLXDC1, PNMA3, POLG, PPP2R5C, PRDM8, PRF1, PSTPIP1, PTGDR, PTPN4, PTPRC, PTPRCAP, PURA, PVRIG, PYHIN1, RBL2, RFX1, RGS9, RHOT2, RIPK1, RNF126, RPUSD2, SACM1L, SBF1, SGSM2, SH2D1A, SIT 1.00, SLAMF1, SLC1A7, SLC38A1, SPSB3, STK25, STUB1, TBCC, TBX21, TCOF1, TRAF3IP3, TRMT2A, TRMU, TTC22, TULP4, UBTF, URB2, VPS4A, WDR82, XCL1, ZAP70, ZBTB39, 113 CD8+ Tem ZNF142, ZNF276, ZNF335, ZNF428, ZNF639, ZNF696 ABCD2, ADAT1, ARHGEF5, ASTE1, BMPR1A, C8G, CACNB1, CASP8, CD8A, CDK10, CEP250, COL5A3, CXCR6, DUSP8, FBXW4, FYCO1, GCC1, GIMAP4, GIMAP6, GPR171, GZMH, HMGN4, IFNG, IKZF3, KLRG1, LAG3, LTK, MRFAP1L1, MSH3, PDCD1, PPP2R5C, PURA, PVRIG, RPUSD2, S100B, SH2D1A, SLC25A12, TBCC, TCOF1, XCL1, 114 CD8+ Tem ZNF428 C8G, CACNB1, CD8A, CD8B, COL5A3, CX3CR1, CXCR6, FASLG, GZMB, GZMH, GZMK, IFNG, KLRG1, LAG3, LTK, NMUR1, PDCD1, PRF1, PTGDR, PYHIN1, RBL2, SH2D1A, 115 CD8+ Tem SLC1A7, TBX21, ZAP70 C8G, CD8A, CD8B, COL5A3, CX3CR1, CXCR6, FASLG, GZMB, GZMH, GZMK, IFNG, KLRG1, NMUR1, PDCD1, 116 CD8+ Tem PRF1, PTGDR, PYHIN1, SLC1A7, TBX21, ZAP70 C8G, CACNB1, CD8A, CD8B, COL5A3, CX3CR1, CXCR6, FASLG, GZMB, GZMH, GZMK, IFNG, KLRG1, NMUR1, PDCD1, PRF1, PTGDR, PYHIN1, SH2D1A, SLC1A7, TBX21, 117 CD8+ Tem ZAP70 ALCAM, ANXA1, CCDC88A, CD1C, CD1E, CLEC10A, DBI, 118 cDC FCER1A, ITGAX, PITPNA, RAB7A, SLAMF8, SSR1, TCTN3 ACTR3, ALCAM, CCDC88A, CD1C, CD1E, CLEC10A, DBI, 119 cDC FCER1A, ITGAX, RAB7A, SLAMF8, SSR1WSGR Docket No.50272-712.601 CD163, CD1C, CD86, CD93, CLEC10A, CLEC4A, FCER1A, 120 cDC FGL2, FLT3, S100A10, WDFY3 CCL17, CCL24, CD1A, CD1B, CD1C, CD1E, CD209, CD80, 121 cDC DNASE1L3, FCER1A, GFRA2, KCNK13, RRP1B CCL17, CCL24, CD1A, CD1B, CD1C, CD1E, CD209, CD80, 122 cDC DNASE1L3, FCER1A, GFRA2, KCNK13 ALDH1A2, ALOX15, CCL13, CCL17, CCL23, CCL24, CD1A, CD1B, CD1C, CD1E, CD209, CD80, CD86, CLEC10A, CRH, 123 cDC DNASE1L3, FCER1A, GFRA2, KCNK13, RRP1B ACAN, ARL 1.00, COL14A1, COMP, ERG, FBXW11, ISLR, LAPTM4A, MORF4L2, MPHOSPH6, OGN, PGK 1.00, PODNL1, PRELP, REM1, RGS11, SCRG1, 124 Chondrocytes SNUPN, TBX4, ZNF471 ACAN, ARL 1.00, COL14A1, COMP, ERG, FBXW11, GLG1, GOSR2, ISLR, LAPTM4A, MORF4L2, MPHOSPH6, OGN, PGK 1.00, PODNL1, PRELP, PTP4A2, REM1, 125 Chondrocytes RGS11, SCRG1, SNUPN, TBX4, ZNF471 ACAN, ANGPTL7, CCNB1, CILP, COL10A1, COL14A1, COL5A3, COMP, CRTAC1, CSNK1A1, DSTN, DYNLRB1, ERG, FBXW11, FOXD2, FZD9, GDF5, HSPB7, IQSEC2, ISLR, LAPTM4A, LPAR4, LRRC15, MORF4L2, MPHOSPH6, MYOC, NDFIP1, NFATC4, NKX3-1, NPLOC4, OMD, PODNL1, PRELP, PRG4, PTP4A2, REM1, SCRG1, SLC38A3, SNUPN, STAT2, TBX4, TMEM59, TNFSF11, TNXB, WASL, 126 Chondrocytes WWP2, ZNF471 SEPTIN10, ABCC9, ABCD1, ABI2, ACAN, ACBD3, ADM2, ADRA1D, AK1, ANGPTL7, APOC3, ARCN1, ARL 1.00, ARNT, BAG3, BCL2L13, C11orf95, CABP1, CACNA1C, CACNB1, CALCOCO2, CAMLG, CCL27, CDIPT, CDK2AP1, CELA2B, CILP, CLEC3B, CMPK1, CNIH4, COL10A1, COL14A1, COL5A3, COMP, COPA, COPB1, COPS8, CORIN, CTRB2, CUL7, CYP19A1, DMWD, DPT, DPY19L4, DVL1, EID1, ELN, EMILIN1, ENO1, ERC1, ERF, ETF1, FGF7, FNDC3A, FOXC2, FSHB, FTO, GANAB, GDF5, GLT8D1, GMPPA, GOLGA4, GORASP2, GRIA3, HAS1, HDAC11, HDLBP, HOXD3, HSPB7, IBSP, IDUA, IFNB1, IFT46, IGF1, IL17B, IL17RC, IRGC, ISLR, KDELR1, KIAA0368, KLF15, KLHL26, KRT10, LAPTM4A, LEP, LRRC15, LTBR, MAP3K6, MAP4K5, MAST2, MCHR1, MIER2, MIF, MIOS, MKRN2, MLLT1, MLN, MYH13, NAALADL1, NCKAP1, NCOR2, NEUROG2, NFATC4, NFKBIL1, NGRN, NPHP4, NRBF2, NXPH3, OCRL, OGN, OLAH, OMD, OS9, OTOF, OTUD7B, P4HB, PCDHGC3, PDE3A, PFN2, PGM1, PHLDB1, PJA2, PKNOX2, PODNL1, POFUT2, POMT2, PPIL6, PRDX4, PRELP, PRG4, PRKRA, PRL, PRRC1, PTH1R, PTPN11, PYY, RAB3A, RANBP9, RGR, RIC8A, RNF11, ROS1, RUNX1, RUNX2, S100A6, SCFD1, SCRG1, SEC61A1, SERINC3, SGCD, SLC13A4, SLC26A10, SLC41A3, SLC5A12, SMAD5, SNED1, SNTB2, SNX13, SPAG9, SPATA7, SPIN1, SSX1, SVEP1, TACR3, TBC1D16, TBC1D9B, TCEAL4, TF, TM2D1, TM9SF1, TMED10, TMED7, TMEM59, TMEM59L, TNNT3, TRIM3, TSSK1B, TTC23, TTC37, TUB, TUBG2, UBXN6, WDR41, WISP1, YAP1, YIF1A, YIPF2, ZBTB16, ZFAND3, 127 Chondrocytes ZFPL1, ZMYND11, ZNF358, ZNF471WSGR Docket No.50272-712.601 SEPTIN10, ABCC9, ACAN, ADM2, ADRA1D, AK1, ANGPTL7, APOC3, ARNT, BAG3, BCL2L13, C11orf95, CABP1, CACNA1C, CALCOCO2, CAMLG, CDIPT, CILP, CLEC3B, COL10A1, COL14A1, COL5A3, COMP, COPA, COPS8, CORIN, CUL7, CYP19A1, DPT, DVL1, EID1, ELN, EMILIN1, ERF, FGF7, FTO, GANAB, GDF5, GLT8D1, GRIA3, HAS1, HDLBP, HSPB7, IBSP, IFNB1, IFT46, IL17B, IL17RC, ISLR, KDELR1, KLHL26, LAPTM4A, LRRC15, MAP3K6, MAP4K5, MIF, MKRN2, MLLT1, NCKAP1, NFATC4, NFKBIL1, NPHP4, NXPH3, OGN, OLAH, OMD, P4HB, PCDHGC3, PHLDB1, PJA2, PKNOX2, PODNL1, POMT2, PRELP, PRG4, PRKRA, PRRC1, PTH1R, PYY, RIC8A, RUNX1, RUNX2, SCRG1, SNED1, SPATA7, SPIN1, SVEP1, TCEAL4, TM9SF1, TMED10, TMED7, TMEM59L, TTC23, TTC37, TUB, TUBG2, UBXN6, WDR41, WISP1, YIF1A, YIPF2, ZBTB16, ZFAND3, ZFPL1, ZMYND11, ZNF358, 128 Chondrocytes ZNF471 SEPTIN10, ABCC9, ACAN, ADRA1D, AK1, ANGPTL7, ARL 1.00, BAG3, BCL2L13, C11orf95, CACNA1C, CALCOCO2, CAMLG, CDIPT, CDK2AP1, CILP, CLEC3B, COL10A1, COL14A1, COL5A3, COMP, COPS8, CORIN, CUL7, CYP19A1, DPT, DVL1, EID1, ELN, EMILIN1, ENO1, ERF, FGF7, FNDC3A, FSHB, GANAB, GDF5, GLT8D1, GOLGA4, GRIA3, HAS1, HDAC11, HDLBP, HSPB7, IBSP, IDUA, IGF1, IL17RC, ISLR, KDELR1, KLF15, KLHL26, KRT10, LAPTM4A, LEP, LRRC15, MAP3K6, MAP4K5, MCHR1, MKRN2, MLLT1, NCKAP1, NFATC4, NFKBIL1, NPHP4, NRBF2, NXPH3, OGN, OLAH, OMD, OS9, P4HB, PDE3A, PGM1, PHLDB1, PJA2, PKNOX2, PODNL1, POMT2, PRELP, PRG4, PRRC1, PYY, RNF11, RUNX1, SCRG1, SEC61A1, SERINC3, SGCD, SNED1, SNTB2, SNX13, SPATA7, TCEAL4, TF, TMED10, TMEM59L, TNNT3, TTC37, TUBG2, UBXN6, WDR41, WISP1, YAP1, YIPF2, ZBTB16, 129 Chondrocytes ZFPL1, ZNF358 ACAN, COL14A1, COMP, CSNK1A1, DSTN, ERG, HSPB7, ISLR, MORF4L2, MPHOSPH6, NFATC4, PODNL1, PRELP, 130 Chondrocytes REM1, SCRG1, TBX4 ACAN, COL14A1, COMP, ERG, HSPB7, ISLR, LBP, 131 Chondrocytes MPHOSPH6, PRELP, REM1, SCRG1, TBX4 ACAN, COL14A1, COMP, ERG, ISLR, MPHOSPH6, PRELP, 132 Chondrocytes REM1, RGS11, SCRG1, TBX4 Class-switched BAIAP3, BLK, CR1, DEPDC5, EPS15, PTPN6, RAD17, 133 memory B-cells RAPGEF1, TNFRSF17, TRAF3, UBE2G1, UBE2I ABI1, ADAMDEC1, AFTPH, BAIAP3, BLK, CPSF4, CR1, DEPDC5, EPS15, NARFL, NDUFA9, PIKFYVE, PRDM10, PTPN6, RAD17, RAPGEF1, SCRN1, SLC12A3, SP140, SUB1, Class-switched TAF6, TNFRSF13B, TNFRSF17, TRAF3, TRRAP, UBE2G1, 134 memory B-cells UBE2I, UBE2N ABI1, AFTPH, BAIAP3, BLK, CR1, DEPDC5, EPS15, NDUFA9, NGLY1, PIKFYVE, PRDM10, PTPN6, RAD17, Class-switched RAPGEF1, SEC24A, TNFRSF13B, TNFRSF17, TRAF3, 135 memory B-cells UBE2G1, UBE2I BAIAP3, BLK, CD80, COL19A1, CXCR5, FCRL2, GPR25, Class-switched HLA-DQB2, KHDRBS2, MS4A1, PAX5, SNED1, SPIB, 136 memory B-cells TNFRSF13B, TNFRSF17, ZBTB32WSGR Docket No.50272-712.601 BAIAP3, BLK, CD80, COL19A1, CXCR5, FCRL2, GPR25, Class-switched HLA-DQB2, MS4A1, PAX5, SNED1, SPIB, TNFRSF13B, 137 memory B-cells ZBTB32 BAIAP3, BLK, CD80, COL19A1, CXCR5, FCRL2, GPR25, Class-switched HLA-DQB2, MS4A1, PAX5, SNED1, SPIB, TNFRSF13B, 138 memory B-cells ZBTB32 AIMP1, COX6C, DNTT, GPN3, HIVEP3, IGLL1, MYL12B, 139 CLP PLP2, PSMA6, TOMM20, VPREB1 ADNP, AIMP1, C11orf57, C19orf53, COX6C, DNTT, FAM76A, GPN3, H3F3B, HIVEP3, IDH3A, IGLL1, OXA1L, PLP2, 140 CLP PSMA6, SNRPD1, VPREB1 ADNP, AIMP1, ASCC1, ATP6V1G1, C11orf57, C19orf53, CALM1, COX6C, DNTT, DSTN, FAM76A, GAPDH, GPN3, H3F3B, HIVEP3, IDH3A, IGBP1, IGLL1, METAP2, MYL12B, NGLY1, OXA1L, PLP2, PSMA6, PSMB3, RFC4, RPL8, 141 CLP SNRPD1, TOMM20, VPREB1, WDR33 AZU1, CPA3, CRHBP, CRYGD, CTSG, ELANE, EPX, IGLL1, MPO, MS4A2, MS4A3, NAALADL1, PRG2, PRTN3, RNASE2, 142 CMP RNASE3, SERPINB10, STAR AZU1, CLC, CPA3, CRHBP, CTSG, ELANE, EPX, FLT3, HDC, HPGDS, MPO, MS4A2, MS4A3, NAALADL1, PRG2, PRTN3, 143 CMP RNASE2, RNASE3, SERPINB10 AZU1, CLC, CPA3, CRHBP, CTSG, ELANE, EPX, FLT3, 144 CMP HPGDS, MPO, MS4A3, PRG2, PRTN3, RNASE2, RNASE3 ACTL7A, AFF2, ALOX15, ALX4, ANKRD34C, ANXA3, ATXN7L1, AZU1, BFSP2, BRSK2, BYSL, C1QB, C21orf62, C22orf46, CACNA1E, CACNG3, CADM3, CAMK2A, CCL18, CCL23, CCNB2, CCNC, CD1E, CD63, CDSN, CENPN, CHAF1A, CLC, CLIC1, CLPP, CNGA3, CNTN6, COG7, COL14A1, COMMD4, CPA3, CPB1, CPN1, CRTAC1, CRYGA, CSF2RB, CTSG, CYP3A43, CYSLTR2, DAB1, DAZAP1, DEFA6, DHX32, DLEC1, DNAI1, DNASE2B, DPEP3, EDN3, ERLIN1, EYA3, FAIM2, FAM76A, FHL5, FMO2, FOXB1, FRMPD4, G6PC, GALR1, GDF5, GDF9, GLYAT, GNAT1, GNMT, GPD1, GPR135, GRIA4, GRM2, GUCA2B, H2AFX, HDC, HIST1H2BO, HPGDS, HSF4, IDUA, IL13, IL5RA, IMPG1, IRF2BP1, IRS4, ITIH3, ITIH4, KCNJ10, KCNN1, KLHL11, L2HGDH, LCT, LIN28A, LIPF, LONP1, LRRC19, LRRC3, MADCAM1, MEA1, MED18, MKI67, MPL, MPO, MS4A2, MS4A3, MYF5, MYH11, MYLPF, NAALADL1, NDOR1, NDUFB4, NEU3, NRIP2, NTN1, OXT, P2RY4, PAX1, PAX3, PDIA2, PDX1, PGR, PLA2G6, POLA2, PRG2, PRG3, PRKG2, PRODH2, PTPRS, PTPRT, PYY, RAB40A, RASL10A, RASL12, RCVRN, RERGL, RHO, RORB, RUNX1, RUVBL2, SCN1A, SERPINB10, SERPINI2, SIM1, SLC13A2, SLC18A2, SLC4A5, SLC5A2, SMARCAL1, SNAPC4, SNW1, SNX5, SPAG5, SPATA7, STAR, TAB1, TBC1D16, TIMM50, TNR, TOLLIP, TP73, TRAIP, TRPC3, TSGA10, TUBA1B, TYR, UBQLN3, UNG, VN1R1, VPREB1, XPNPEP3, ZBBX, ZMAT5, 145 CMP ZNF174, ZNF428 ADSS, ARHGAP33, ATP8B4, BAHCC1, CCDC121, CDT1, CENPJ, EHMT2, ERLIN1, EXD3, FAM111A, FANCG, GPR27, HDAC6, HIST1H2BL, HIST1H2BO, HIST1H3C, HIST1H4C, 146 CMP HMGXB3, HRH4, JRK, KIAA1033, KLHL9, LPAR4, LTC4S,WSGR Docket No.50272-712.601 LYL1, MAP2K5, MAPK14, MGA, MPO, MS4A2, MS4A3, MTHFSD, MYO9A, NARFL, NSMAF, PGM1, PHF7, PIKFYVE, POLE, REV1, RHOT1, RNASE2, RREB1, S100PBP, SCAMP1, SETD5, SMC5, SPAG8, SPN, STAR, TAF6L, TFCP2, TGM5, TNPO3, TOP2B, TOR1A, TPSAB1, TRIM27, TSGA10, USP19, USP48, VPS54, WHSC1, XPO1, ZKSCAN3, ZKSCAN4, ZKSCAN5, ZMYM4, ZNF197, ZNF221, ZNF324B, ZNF471, ZNF629, ZNF701, ZNF747, ZNF768, ZNF780B, ZZZ3 BAHCC1, CENPJ, GPR27, HIST1H2BO, HIST1H3C, LTC4S, 147 CMP LYL1, MPO, MS4A2, MS4A3, NAT6, STAR, TPSAB1, ZNF221 ATP8B3, CD33, CDT1, CENPJ, ERLIN1, FBXL4, GPR27, HDAC6, HIST1H4C, LPAR4, MPO, MS4A3, NAT6, S100PBP, SPAG8, TFCP2, TGM5, TNPO3, TOR1A, TPSAB1, TSGA10, ZKSCAN3, ZMYM4, ZNF197, ZNF324B, ZNF471, ZNF701, 148 CMP ZZZ3 ERLIN1, GPR27, HDAC6, LPAR4, MPO, SPAG8, TGM5, TNPO3, TOR1A, ZKSCAN3, ZMYM4, ZNF197, ZNF471, 149 CMP ZNF701 ATP8B4, BAHCC1, CENPJ, GPR27, HIST1H2BO, HIST1H3C, LPAR4, LTC4S, LYL1, MPO, MS4A2, PHF7, SPAG8, STAR, 150 CMP TPSAB1, TRIM27, ZKSCAN3, ZNF197, ZNF221, ZNF629 ALDH1A2, ALOX15, CCL13, CCL17, CD1A, CD1B, CD1E, 151 DC CD209 ALDH1A2, ALOX15, CCL13, CCL17, CD1A, CD1B, CD1E, 152 DC CD209, HLA-DQA1 153 DC ALOX15, CCL13, CCL17, CD1A, CD1B, CD1E, CD209, FPR3 C1QA, C1QB, CCL13, CCL17, CCL22, CD1A, CD1B, CD1E, 154 DC CD9, CLEC10A, FPR3, SLAMF8, TFEC, TREM2 ACHE, ALOX15B, ARL8B, BCL2L11, BCL2L13, CAMK1G, CCDC81, CCL13, CCL17, CCL18, CCL22, CCL23, CCR7, CD1A, CD1B, CD1E, CD80, CD86, CEP350, CUL1, DPYS, ETV3, FBXL4, GRIN1, GRSF1, HCRTR2, HPS5, IL12B, IL21R, IRF4, KCNC3, KCNN1, LOR, MAP3K13, MAP3K6, MCF 2.00, MPHOSPH6, NECAP2, NFKB1, NXPH3, PLD2, PRRG2, PTGES2, PTGIR, RAB8A, RNF2, SAMSN1, SIGLEC1, SLAMF1, SLC30A4, SLCO5A1, SNX11, SPINT2, SUZ12, TBC1D13, TDRD7, TMEM131, TMSB10, TNFRSF4, TRAF1, 155 DC TXN, UBE2Z, VAV2 ALCAM, C1QA, C1QB, CCL13, CCL17, CCL22, CD1A, CD1B, CD1C, CD1E, CD209, CLEC10A, F13A1, FCER2, FPR3, 156 DC MS4A6A, SLAMF8, SPINT2, STAB1, TACSTD2, TREM2 ALOX15, CCL13, CCL17, CCL18, CCL19, CCL22, CD1B, 157 DC CD1E, CD209, CD80, SLCO5A1 ALOX15, CCL13, CCL17, CCL18, CCL19, CCL22, CD1B, 158 DC CD1E, CD209, CD80, SLCO5A1 ALOX15, CCL13, CCL17, CCL18, CCL19, CD1B, CD1E, 159 DC SLCO5A1 ALDH1A2, CCL17, CD1B, CD1E, CD86, FCER2, KCNK13, 160 DC RRP1B ALDH1A2, ALOX15, C1QB, CCL13, CCL17, CCL18, CCL22, CCL23, CCL24, CCL8, CD1A, CD1B, CD1E, CD209, CD80, CD86, CLEC10A, DNASE1L3, F13A1, FCER2, FGL2, FPR3, GUCA1A, HK3, HLA-DQA1, HS3ST2, KCNK13, MS4A4A, 161 DC MS4A6A, NAGPA, SPINT2WSGR Docket No.50272-712.601 ALDH1A2, ALOX15, C1QB, CCL13, CCL17, CCL18, CCL22, CCL23, CD1A, CD1B, CD1E, CD209, CD86, CLEC10A, FPR3, 162 DC HLA-DQA1, HS3ST2 Endothelial ACVRL1, ARHGEF15, FAM124B, HYAL2, MMRN2, ROBO4, 163 cells TIE1, VWF Endothelial ACVRL1, ARHGEF15, FAM124B, HYAL2, MMRN2, ROBO4, 164 cells TIE1, VWF ACTR1A, ACVRL1, ADSS, ANGPT2, ANO2, ANXA2, ANXA3, AP2A2, AP2S1, ARHGEF15, ARL2BP, ARPC1A, ATF6, BCL10, BFAR, BMX, BTBD1, CANX, CARM1, CAV1, CCT6A, CD9, CD93, CDC123, CDC27, CDC37, CISD1, CLDN5, CLEC1A, CLTA, COPS6, CXorf36, DAD1, DCTN5, DDX10, DEF8, DYNLL1, ECD, EDC3, EI24, EIF2B2, EIF4G2, EMCN, ERG, FAM107A, FAM124B, FEZ2, FLT1, FLT4, FOXC2, FXR1, G6PC3, GDF3, GJA4, GNB1, GOT2, GPR4, HSP90B1, HSPA4, HTR2B, HYAL2, IGF2BP3, IPO7, ITGB1BP1, KANK3, KDR, KIF20A, KLHL9, LRRC59, LYVE1, MAPK12, MMRN1, MMRN2, MNAT1, MRPL17, MTCH1, MTMR2, MYCT1, MYL12B, MYL6, NCBP2, NEDD8, NETO2, NNAT, NOTCH4, NPLOC4, PCDH12, PCGF3, PDE3A, PDIA6, PIK3C2A, PITRM1, PLS3, PLVAP, PNP, PPM1F, PPP2R2A, PSMB7, PSMD1, PSMD10, PSMD2, PTTG1IP, PWP1, RALA, RANGAP1, RARS, RASIP1, RCN2, RHOC, ROBO4, S100A6, SAE1, SCARF1, SELE, SEMA6B, SH3GL1, SLC16A1, SNTB2, SOX18, SPATS2, SPTBN5, SSBP1, STAB1, TAOK2, TARBP2, TEK, TIE1, TIMM17A, TJP1, TMED9, TMEM184B, Endothelial TMEM39B, TNFSF18, TNPO1, TPD52L2, TTLL5, TUSC2, 165 cells TXNDC9, UFD1L, VWF, YKT6 ACTR1A, ACVRL1, ANGPT2, ANXA2, ARHGEF15, BMX, CAV1, CD93, CLDN5, CLEC1A, CLTA, COPS6, CXorf36, DAD1, ECD, EMCN, ERG, FAM107A, FEZ2, FOXC2, GPR4, HYAL2, KANK3, KDR, LRRC59, MAPK12, MMRN1, MMRN2, MRPL17, MTCH1, MYCT1, MYL6, PCDH12, PCGF3, PIK3C2A, PLS3, PLVAP, PSMD10, PTTG1IP, PWP1, Endothelial RALA, RASIP1, RHOC, ROBO4, SLC16A1, SOX18, TEK, 166 cells TIE1, TTLL5, TXNDC9, VWF ACTR1A, ACVRL1, ANGPT2, ANXA2, ARHGEF15, BMX, CAV1, CD93, CLDN5, CLEC1A, CLTA, COPS6, CXorf36, EMCN, ERG, FAM107A, GPR4, HYAL2, KANK3, KDR, MAPK12, MMRN1, MMRN2, MRPL17, MTCH1, MYCT1, PCDH12, PCGF3, PLS3, PLVAP, PSMD10, PTTG1IP, PWP1, Endothelial RALA, RASIP1, RHOC, ROBO4, SLC16A1, SOX18, TIE1, 167 cells TTLL5, VWF ACTR1A, ACVRL1, ANGPT2, ARHGEF15, BMX, CLEC1A, CLTA, CXorf36, EMCN, GPR4, HYAL2, KANK3, KDR, Endothelial MAPK12, MMRN2, MYCT1, PLS3, PSMD10, RALA, RASIP1, 168 cells RHOC, ROBO4, SOX18, TIE1, VWF Endothelial ACVRL1, ANGPT2, ARHGEF15, EMCN, HYAL2, KDR, 169 cells MMRN2, MYCT1, ROBO4, TIE1 Endothelial ACVRL1, ANGPT2, ARHGEF15, BMX, EMCN, HYAL2, KDR, 170 cells MMRN2, MYCT1, ROBO4, TIE1 ACVRL1, ANGPT2, ANXA3, ARHGEF15, ART4, BMX, Endothelial CAV1, CD93, CLDN5, CLEC1A, CXorf36, EIF2B2, EMCN, 171 cells ERG, FAM124B, FLT1, GDF3, GJA4, GPR4, HTR2B, HYAL2,WSGR Docket No.50272-712.601 IGF2BP3, KANK3, KDR, KRT19, MAPK12, MMRN1, MMRN2, MYCT1, PDE3A, RALA, RASIP1, ROBO4, SELE, SNTB2, SOX18, TACSTD2, TIE1, TNFSF18, VWF Endothelial ANGPT2, BMX, CD93, CLDN5, EMCN, GIMAP4, KDR, 172 cells MMRN1, ROBO4, STAB1, TIE1, VWF Endothelial 173 cells ANGPT2, BMX, FLT4, KDR, MMRN1, SEMA6B, TIE1, VWF ACTR1A, ACVRL1, AP2S1, ARHGEF15, BCL10, BUB3, CD93, CDC27, CEP55, CISD1, CLDN5, CLEC1A, CLPP, DLGAP5, EDC4, EIF2B2, ERCC1, FAM124B, FAM65A, FBL, FDPS, FLT4, FOXC2, GIMAP6, GIT1, GJA4, GPR4, HTR1B, HYAL2, KANK3, LYL1, LYPLA1, LYVE1, MAPK12, METTL3, MMRN2, MTG1, MTMR2, MYCT1, N4BP3, NOTCH4, NOVA2, NPLOC4, PNP, PPP2R2A, PRKD2, PRPF19, PSMC5, PTTG2, RALA, RELA, ROBO4, RPS14, SCARF1, SEMA6B, SEMA6C, SPATS2, STAB1, STK25, Endothelial STRAP, TAOK2, TIE1, TMEM39B, TNFSF18, TTLL5, TUSC2, 174 cells TUT1, VWF, WDR4 ADAM18, C3AR1, CCR3, CDH19, CLC, CUX2, DRP2, HRH4, IL5RA, KCNA5, KIF5A, NPY2R, NYX, PLXNB3, RASL12, 175 Eosinophils RGS13, TACSTD2, TNFSF11 AP4E1, CCR3, DRP2, FBXO40, GIPR, HRH4, LMTK2, MMRN2, NXPH3, PHLDA2, RGS13, SYCP1, TACSTD2, 176 Eosinophils TNFSF11, TRIM48 ADAM18, ADAM21, AGTR2, ASPA, ATOH1, BMX, C3AR1, CCR1, CCR3, CDH19, CLC, CUX2, DRP2, FBXO40, FCER1A, FEV, FMO3, GAST, GIPR, HDC, HRH4, HS3ST2, IL5RA, KCNA5, KIF5A, LECT2, LMTK2, LRRC36, MMP26, MOG, MS4A3, MYO3A, NPY2R, NXPH3, NYX, PLXNB3, POLQ, PRL, PURG, RASL12, RGS13, STATH, TACSTD2, TNFSF11, 177 Eosinophils TRIM48, ZNF197 ABTB2, ADORA3, ALOX15, ARHGAP33, CA4, CCR3, CD101, CEACAM8, CEBPE, CLC, CORO7, CYSLTR2, DEFA4, DPEP2, ENTPD2, EPN2, GIPR, GMIP, HIC1, HRH4, IL5RA, KCNK7, KDM6B, KSR1, LTF, MBOAT7, MKL1, MMP25, MNT, OLIG2, P2RY14, PGLYRP1, PTTG2, RARA, SETD1B, 178 Eosinophils SIGLEC8, SLC19A1, TRPM6 ABTB2, ADORA3, ALOX15, ARHGAP33, CA4, CCR3, CEBPE, CLC, CORO7, CYSLTR2, DEFA4, DPEP2, ENTPD2, EPN2, GIPR, GMIP, HRH4, IL5RA, KCNK7, KDM6B, KSR1, LTF, MBOAT7, MKL1, MMP25, MNT, OLIG2, PGLYRP1, 179 Eosinophils PTTG2, RARA, SETD1B, SIGLEC8, SLC19A1, TRPM6 ABTB2, ADORA3, ALOX15, ARHGAP33, BPI, CA4, CAMP, CCL23, CCR3, CD101, CEACAM8, CEBPE, CLC, CORO7, CSF2RB, CYSLTR2, DEFA4, DPEP2, DPEP3, ENTPD2, EPN2, GIPR, GMIP, HIC1, HRH4, IL5RA, KCNK7, KDM6B, KSR1, LTF, MBOAT7, MKL1, MMP25, MNT, OLIG2, P2RY14, PGLYRP1, PTTG2, RARA, SETD1B, SIGLEC8, SLC19A1, 180 Eosinophils SRRM2, TRPM6 ABTB2, ALOX15, CCR3, CEBPE, CLC, DEFA4, DPEP2, EPN2, HRH4, IL5RA, KCNK7, KDM6B, MMP25, RARA, 181 Eosinophils SIGLEC8, TRPM6 ABTB2, ALOX15, CCR3, CEBPE, CLC, DEFA4, DPEP2, 182 Eosinophils HRH4, IL5RA, KSR1, MMP25, SIGLEC8, TRPM6WSGR Docket No.50272-712.601 ARHGAP33, CCR3, CEBPE, CLC, CORO7, DPEP2, ENTPD2, 183 Eosinophils GIPR, HIC1, HRH4, IL5RA, KSR1, LTF, OLIG2, SIGLEC8 AP1M2, B3GNT3, CBLC, HES2, PRSS8, RAB25, S100A14, 184 Epithelial cells SFN, TACSTD2 AP1M2, B3GNT3, CNKSR1, FLNB, GRB7, HES2, IER3, LAD1, LAMA5, PRSS8, RAB25, RHOD, S100A14, SEMA3F, SFN, 185 Epithelial cells SH2D3A, SLC10A3, SOX15, TBC1D2, TMEM40, TUFT1 A4GALT, ADAM15, AP1M2, AP1S1, ARHGEF18, AXIN1, B3GNT3, BIK, BTG2, C1orf116, CDC42EP2, CELSR1, CHAC1, CNKSR1, CORO2A, DFNA5, DOK4, DSG3, EFNB1, EPPK1, ETHE1, EVPL, EXT2, F3, FBXL18, FGD6, FGFBP1, FLNB, FST, GJB3, GJB5, GLTP, GRB7, HBEGF, HES2, IER3, IRF6, JUP, LAD1, LAMA5, LIMK2, LRRC8E, MST1R, NR2F6, PDLIM1, PI3, PIP4K2C, PLAGL2, PORCN, PPP1R13L, PRRG2, PRSS8, RAB25, RELB, RHBDF2, RHOD, RHOF, RPS6KA4, S100A14, SEMA3F, SFN, SH2D3A, SHC1, SLC10A3, SLC12A4, SMAGP, SOX15, SPINT1, SSH3, ST14, STXBP2, TACSTD2, TAPBP, TBC1D2, TFCP2L1, TMEM40, TMPRSS4, TNFRSF10B, TNFRSF21, TNFSF9, TNK1, TUFT1, 186 Epithelial cells XYLT2, ZBED2, ZFYVE21 AP1M2, CLDN4, DSC2, G0S2, ITGB4, ITGB6, KRT7, LAD1, LAMA3, LAMB3, LSR, MPZL2, RAB25, SFN, SH2D3A, 187 Epithelial cells SPINT1, THBD ADM, ALS2CL, ANGPTL4, AP1M2, C1orf116, CLDN4, DAPP1, DSC2, EFNA1, EGFR, EPS8L1, EVPL, FERMT1, G0S2, HMGA2, ITGB4, ITGB6, KRT17, KRT7, LAD1, LAMA3, LAMB3, LLGL2, LSR, MPZL2, PCDH1, PDGFB, PPL, PPP1R13L, PRSS8, PTGES, RAB11FIP1, RAB25, RAB3D, RAP1GAP2, S100A2, SCNN1A, SFN, SH2D3A, SLC35F2, 188 Epithelial cells SPINT1, ST14, TACSTD2, THBD, TSPAN1 AP1M2, CLDN4, DSC2, EHF, ELF3, G0S2, ITGB4, ITGB6, KRT6A, KRT7, LAD1, LAMA3, LAMB3, LSR, MPZL2, 189 Epithelial cells PTGES, S100A14, SCEL, SFN, SH2D3A, SLPI, ST14, TGFA AP1M2, CBLC, F3, PRSS8, RAB25, S100A14, SFN, SLPI, 190 Epithelial cells TACSTD2 AP1M2, CBLC, F3, PRSS8, RAB25, S100A14, SFN, SLPI, 191 Epithelial cells STXBP2 AP1M2, APOBEC3C, B3GNT3, CBLC, F3, FLNB, HES2, NQO1, PRSS8, RAB25, RASSF7, RIPK4, S100A14, SDC4, 192 Epithelial cells SFN, SH3BP1, SLPI, STXBP2, TACSTD2 AHSP, ALAS2, CA1, EPB42, EPX, GYPE, HBD, HMBS, KLF1, 193 Erythrocytes MYL4, RHAG, XPO7 AHSP, ALAS2, CA1, EPB42, EPX, GYPE, HBD, HMBS, KLF1, 194 Erythrocytes MYL4, RHAG, XPO7 AHSP, ALAS2, CA1, EPB42, EPX, GYPE, HBD, HMBS, KLF1, 195 Erythrocytes MYL4, RHAG, XPO7 AHSP, ALAS2, CA1, EPB42, GYPA, GYPE, KRT1, MYL4, 196 Erythrocytes SLC4A1 AHSP, ALAS2, EPB42, GYPA, GYPB, GYPE, HBB, HBD, KLF1, MYL4, PKLR, PRG2, RHAG, RHCE, RHD, SLC4A1, 197 Erythrocytes TSPO2 AHSP, ALAS2, ART4, EPB42, GYPA, GYPB, GYPE, HBB, 198 Erythrocytes HBD, HMBS, KLF1, MYL4, PKLR, RHAG, RHDWSGR Docket No.50272-712.601 AHSP, AURKA, CENPA, GATA1, GLRX5, KLF1, RHAG, 199 Erythrocytes SPTA1 AHSP, ALAS2, CA1, EPB42, EPX, GYPE, HBD, HMBS, KLF1, 200 Erythrocytes MYL4, RHAG, XPO7 AHSP, ALAS2, CA1, EPB42, EPX, GYPE, HBD, HMBS, KLF1, 201 Erythrocytes MYL4, RHAG, XPO7 AHSP, ALAS2, APLNR, ATP1B2, AURKA, BRCA1, BUB1B, C1orf112, CCNB2, CDC20, CDCA3, CDKN3, CENPF, CENPN, CENPO, CHEK1, DES, DLGAP5, DTL, EPB42, EPCAM, FANCI, FEN1, FKBPL, GINS1, GINS3, GLRX5, GMNN, GYPA, GYPB, GYPE, HBB, HIST1H4C, HJURP, HMBS, HMGB2, HTRA2, KEL, KIF15, KIF22, KIF2C, KIF4A, KLF1, MCM10, MCM4, MELK, MINPP1, MYL4, NEK2, NUSAP1, OAT, OIP5, PAXIP1, PCNA, PKLR, POLE2, PRG2, PRMT3, RAD54L, RCL1, RHD, RRM1, RRM2, SLC2A4, SLC4A1, SPC25, ST7, STIL, TOP2A, TRIM10, TROAP, TSPO2, TUBG1, 202 Erythrocytes UBAC1, ZWINT AHSP, ALAS2, BRCA1, CCNB2, CDCA3, CENPF, CENPN, DES, DLGAP5, DTL, EPB42, FANCI, FEN1, FKBPL, GINS1, GMNN, GYPA, GYPB, GYPE, HBB, HMBS, HMGB2, KIF22, KLF1, MCM10, MELK, MINPP1, MYL4, NEK2, NUSAP1, PCNA, PKLR, POLE2, PRMT3, RHD, RRM1, TOP2A, TROAP, 203 Erythrocytes TUBG1, UBAC1 AHSP, ALAS2, AMHR2, ART4, ATP1B2, BIRC5, BRCA1, BUB1, CA1, CAST, CCNA2, CCNB1, CCNB2, CDC20, CDCA3, CDCA8, CDK1, CDKN3, CENPE, CENPF, CENPN, CHAF1B, CSE1L, DBF4, DCLRE1A, DES, DLGAP5, DPM2, DTL, EIF2S2, EPB42, EPCAM, EPRS, FANCI, FEN1, FKBPL, GATA1, GFI1B, GINS1, GMNN, GYPA, GYPB, GYPE, HBB, HBD, HIST1H4C, HJURP, HMBS, HMGB2, HPS1, KEL, KIF22, KIF2C, KLF1, MCM10, MELK, METAP2, MINPP1, MYL4, NCAPG2, NEK2, NUP37, NUSAP1, PBK, PCNA, PKLR, PNMT, POLE2, PRG2, PRMT3, PTTG1, RACGAP1, RAD51, RBX1, RCL1, RFC4, RGS6, RHAG, RHD, RPA3, RRM1, RRM2, SLC16A1, SLC2A4, SMC2, SMC4, SPC25, SPTA1, ST7, STIL, TAL1, TARS, TOP2A, TROAP, TUBG1, 204 Erythrocytes UBAC1, UMPS, WBP4, ZWINT SEPTIN10, SEPTIN2, ACBD3, ACTR10, ADAMTS12, ADH5, AHNAK, AMOTL2, ANKRD40, AP2M1, ARF4, ARL 1.00, ASPA, ASPN, ATP2A2, ATXN2, B4GALT2, BAD, BAG2, BMPR1A, C7orf25, CACNA1C, CAND1, CCDC90B, CLEC3B, CORIN, CSNK1A1, CSNK1G3, DCTD, DNAJC13, DPT, DPY19L4, DSTN, ECT2, ELN, EMILIN1, ETF1, FGF7, FKTN, FRS2, FTO, GANAB, GARS, GGPS1, GOLGA4, GPATCH2, GPR85, GRIA1, GRIA3, HIF1A, HLCS, HSPB6, HSPB7, HTR2A, HTR2B, ICMT, IMPACT, IPO5, ITIH3, KDELR2, KIF26B, KRT19, LGALS1, LMOD1, LRRC42, MAGEF1, MAP4K5, MARS, MBTPS2, MPZL1, MYH1, MYH2, MYL12B, MYOF, NFATC4, NGRN, NPTN, OCRL, PODNL1, POMGNT1, PPIB, PRDX6, PRKG1, PRPH2, PTGIR, RAB11FIP2, RAB3GAP1, RAD23B, RCN2, RNASEH1, RNF14, RNF41, RRAS2, RYK, SAR1A, SCAMP1, SCRN1, SEC22A, SEC61A1, SGCD, SGCG, SHMT2, SIM1, SLC25A32, SLC35A2, SLC35A5, SMAD5, SNTB2, SNX19, SPIN1, SPTLC1, ST7L, 205 Fibroblasts SVEP1, TBL2, TBX5, TCEAL4, TFG, THAP10, TM2D1,WSGR Docket No.50272-712.601 TMED10, TMED9, TNFRSF12A, TOR1AIP2, TPD52L2, TRIM32, TSPYL1, TTC26, TXLNA, VCL, WNT2, XPOT, YAP1, ZC3H14, ZFPL1, ZFYVE9, ZMYM4 SEPTIN10, SEPTIN2, ACBD3, AHNAK, AMOTL2, ARF4, ASPN, ATP2A2, BAG2, BMPR1A, CACNA1C, CAND1, CCDC90B, CORIN, CSNK1A1, CSNK1G3, DPT, DPY19L4, ELN, FGF7, FKTN, FRS2, GPATCH2, GPR85, GRIA1, GRIA3, HIF1A, HSPB7, HTR2B, IPO5, ITIH3, KDELR2, KIF26B, LGALS1, LRRC42, MAP4K5, MBTPS2, MYH2, MYL12B, MYOF, NFATC4, PODNL1, PRDX6, PRKG1, PRPH2, RAB11FIP2, RAD23B, RNASEH1, RNF41, RRAS2, RYK, SCAMP1, SCRN1, SEC22A, SGCD, SGCG, SIM1, SLC35A2, SLC35A5, SPIN1, SPTLC1, SVEP1, TBX5, THAP10, TM2D1, 206 Fibroblasts TRIM32, TXLNA, WNT2, XPOT, YAP1, ZFPL1 AMOTL2, ASPN, BAG2, BMPR1A, CORIN, DPT, DPY19L4, ELN, FGF7, FKTN, GRIA3, HSPB6, HTR2B, ISLR, ITIH3, KIF26B, KRT19, LMOD1, MYH2, MYOF, PRKG1, SGCD, SGCG, SIM1, SNTB2, TBX5, THAP10, TNFRSF12A, TRIM32, 207 Fibroblasts WNT2, YAP1 ABCA6, ADD1, ADH1B, ADI1, ALDH9A1, AMOTL2, ANXA11, ARF4, ARHGAP6, ASPN, ATG9A, ATP13A1, BAG2, C11orf95, C6orf62, C7, C7orf25, CACNA1C, CAMLG, CASS4, CECR5, CGGBP1, CIRBP, CLEC3B, CSF1, CSTF2T, CYBA, DDX19A, DEK, DPT, DPY19L4, DUT, EDA2R, EMILIN1, EML3, EXOC1, FAIM2, FBXL8, FGF7, FMO2, FTL, GANAB, GARS, GOLGA1, GPR137, GSTM5, HAND2, HDAC5, HDAC7, HEXA, HIC1, HPS1, HTR2B, IDUA, IPO5, ISLR, JAK3, KDELR1, KDELR2, KIAA1614, LAPTM4A, LSM 6.00, LTBR, LTC4S, MASP1, MFSD5, MGMT, MGST3, MMP17, MMP19, MOSPD3, MPHOSPH10, MTHFD2, NDST2, NFATC4, NFE2L2, P4HB, PAPPA2, PCDHGA11, PDE4A, PFDN5, PGK 1.00, PIK3R2, PRDX4, PRKG2, PTGIR, RASA2, RASL12, RNF4, RNH1, ROM1, RPL23, RPL37A, S1PR2, SEC24A, SH3BP2, SHQ1, SLC25A32, SLC35A5, SLC35E1, SMAD5, SNAPC2, SNED1, SPATA7, SVEP1, TADA2A, TBC1D17, TBX5, TCF21, TFDP1, TFG, TMEM165, TNXB, TOR1AIP1, TXNL4B, UBE2D4, VIM, WDR73, WISP1, XPOT, ZBTB1, ZNF32, ZNF358, ZNF444, 208 Fibroblasts ZNF446, ZNF771 C6orf120, CAMLG, FTL, GPR137, GSTM5, HAND2, ISLR, JAK3, KIAA1614, MMP19, NFATC4, PRKG2, PTGIR, S1PR2, 209 Fibroblasts SPAG16, SVEP1, ZNF771 ABCA6, ADH1B, ADI1, ADM2, ARHGAP6, ASPN, C19orf24, C7, COL14A1, DPT, EMILIN1, FGF7, FMO2, FTL, GDF5, GPR137, HAND2, HIC1, HTR2B, ISLR, JAK3, KDELR1, KIAA1614, MASP1, MGMT, MIF, MMP17, MMP19, MRPL12, PODNL1, POLR2E, PRDX4, PRKG2, PTGIR, ROM1, S1PR2, 210 Fibroblasts SIX5, SVEP1, TBX5, TCF21, TNXB, WISP1, ZNF358 ADH1B, ADI1, BAG2, C7, C7orf25, CACNA1C, CAMLG, CSTF2T, FBXL8, FTL, GPR137, HAND2, HEXA, IDUA, IPO5, ISLR, JAK3, KIAA1614, MASP1, MGST3, MTHFD2, NFATC4, NFE2L2, PAPPA2, PCDHGA11, PRDX4, PRKG2, PTGIR, 211 Fibroblasts RNH1, TBX5, TCF21, TFDP1, XPOT, ZNF771 ADH1B, ADI1, ANXA11, ARF4, ATP13A1, BAG2, C6orf120, 212 Fibroblasts C7, C7orf25, CACNA1C, CAMLG, CASS4, CGGBP1, CSTF2T,WSGR Docket No.50272-712.601 CYP20A1, DNPEP, EML3, FBXL8, FTL, GOLGA1, GPR137, HAND2, HDAC5, HEXA, IDUA, IPO5, ISLR, JAK3, KDELR1, KIAA1614, LTBR, MASP1, MGST3, MKRN2, MOSPD3, MTHFD2, NFATC4, NFE2L2, PAPPA2, PCDHGA11, PFDN5, PRDX4, PRKG2, PTGIR, RASL12, RNH1, SMAD5, TADA2A, TBX5, TCF21, TEX261, TFDP1, TMEM115, TOR1AIP1, UBE3B, VIM, XPOT, ZCCHC4, ZNF358, ZNF471, ZNF771 ABCA6, ADD1, ADH1B, ADI1, ALDH9A1, AMOTL2, ANXA11, ARF4, ARHGAP6, ASPN, ATG9A, ATP13A1, BAG2, C11orf95, C6orf62, C7, C7orf25, CACNA1C, CAMLG, CASS4, CECR5, CGGBP1, CIRBP, CLEC3B, CSF1, CSTF2T, CYBA, DDX19A, DEK, DPT, DPY19L4, DUT, EDA2R, EMILIN1, EML3, EXOC1, FAIM2, FBXL8, FGF7, FMO2, FTL, GANAB, GARS, GOLGA1, GPR137, GSTM5, HAND2, HDAC5, HDAC7, HEXA, HIC1, HPS1, HTR2B, IDUA, IPO5, ISLR, JAK3, KDELR1, KDELR2, KIAA1614, LAPTM4A, LSM 6.00, LTBR, LTC4S, MASP1, MFSD5, MGMT, MGST3, MMP17, MMP19, MOSPD3, MPHOSPH10, MTHFD2, NDST2, NFATC4, NFE2L2, P4HB, PAPPA2, PCDHGA11, PDE4A, PFDN5, PGK 1.00, PIK3R2, PRDX4, PRKG2, PTGIR, RASA2, RASL12, RNF4, RNH1, ROM1, RPL23, RPL37A, S1PR2, SEC24A, SH3BP2, SHQ1, SLC25A32, SLC35A5, SLC35E1, SMAD5, SNAPC2, SNED1, SPATA7, SVEP1, TADA2A, TBC1D17, TBX5, TCF21, TFDP1, TFG, TMEM165, TNXB, TOR1AIP1, TXNL4B, UBE2D4, VIM, WDR73, WISP1, XPOT, ZBTB1, ZNF32, ZNF358, ZNF444, 213 Fibroblasts ZNF446, ZNF771 ARHGAP6, ARPP21, ATP5J, CBX3, CD5L, CDH9, CLEC1B, CPA3, CRHBP, CRYGD, CTSG, CXorf21, DEK, DNTT, FLT3, GABPA, GAR1, GNL2, GTPBP4, H2AFZ, HDC, HMGB2, HNRNPA1, HNRNPM, HPGDS, ITGA9, KCNJ14, KIF17, LDHB, LPAR4, MAP7D3, MPO, MRPL20, MRPL3, MS4A3, MTIF2, NCOA4, NOL7, NPM1, PARK7, PNN, PRG2, PRKG2, PSMA4, RAG2, RNASE2, RNASE3, RPL5, RYR3, SELP, SERPINB10, SMC5, SMNDC1, SRP9, SSB, STIL, STXBP3, TEC, TOP2B, TRH, TTC27, TTF2, UBA2, UBA3, UBB, UFC1, 214 GMP UMPS, VPREB1, VPS54, WDR12, XPO1 ANP32A, APLNR, ARHGAP6, ARPP21, ATP5J, CBX3, CD5L, CDH9, CLDN17, CLEC1B, COMMD8, COX7A2L, CPA3, CRHBP, CRYGD, CTSG, CXorf21, DDX21, DEK, DHX9, DNTT, EIF4E, ELANE, EWSR1, FLT3, FOXI1, GABPA, GAR1, GLRA2, GNL2, GTPBP4, GUCY2D, H2AFZ, HDC, HMGB2, HNRNPA1, HNRNPD, HNRNPM, HPGDS, ITGA9, KARS, KCNJ14, KIF17, KPNA2, LDHB, LPAR4, LUC7L3, MAP7D3, MC4R, METAP1, MPO, MRPL20, MRPL3, MS4A3, MTIF2, NCOA4, NDUFC2, NOL7, NOP16, NPM1, PARK7, PNN, PRG2, PRKG2, PSMA4, RAG2, RNASE2, RNASE3, RPL15, RPL5, RWDD1, RYR3, SELP, SERPINB10, SLN, SMC5, SMNDC1, SRP9, SSB, STIL, STXBP3, TAF15, TEC, TOP2B, TRH, TTC27, TTF2, UBA2, UBA3, UBB, UFC1, 215 GMP UMPS, VPREB1, VPS54, WDR12, XPO1 ARPP21, ATP5J, CBX3, CD5L, CDH9, CPA3, CRHBP, CRYGD, CTSG, DEK, DNTT, FLT3, FOXI1, GAR1, GNL2, GTPBP4, H2AFZ, HDC, HMGB2, HNRNPA1, HNRNPM, 216 GMP KIF17, LDHB, LPAR4, MPO, MRPL20, MRPL3, MS4A3,WSGR Docket No.50272-712.601 MTIF2, NOL7, NPM1, PARK7, PNN, PRG2, PSMA4, RAG2, RNASE2, RNASE3, RPL15, RPL5, RYR3, SERPINB10, SMC5, SRP9, SSB, STIL, TEC, TOP2B, TRH, TTC27, TTF2, UBA2, UBA3, UBB, UFC1, UMPS, VPREB1, VPS54, WDR12 ALOX12, ARPP21, B3GNT4, BMS1, C21orf59, CD33, CDK5RAP1, CEACAM4, CEP63, CHD9, CLP 1, CNOT8, CTSG, DDX27, DNAJC7, DNTT, ENOSF1, ERCC3, GLMN, GPATCH8, GPR3, GSTM5, GTPBP4, H3F3B, HNRNPA2B1, INSL3, IQCC, IQSEC3, KARS, KCNJ14, KIF17, LAMB4, LAS1L, LPO, LUZP1, MPO, MRPL9, MUL1, MYOZ3, NCL, NCOR1, NGLY1, OMD, PARK2, PEX2, POLE3, POU2F1, PRPF40A, PRTN3, PUM1, RBM25, RBM4B, RNF220, RPRD2, SERPINI2, SETD4, SLC25A14, SLC5A5, SMG7, SUPV3L1, SUV39H1, TCTN2, TRH, TRIP11, TTC26, VPREB1, WDR43, 217 GMP WDR60, ZC3H15, ZNF207, ZNF35, ZNF593 CEACAM8, CFP, CLC, CLEC1B, CSF1R, CXCL9, DEFA4, 218 GMP FEN1, LPAR4, MEFV, MPO, PRG2, RNASE2, ZNF710 ANAPC5, AP2M1, APPL1, ARID1A, ARPP21, ATXN10, B3GNT4, BMS1, CALCOCO2, CCNT2, CD33, CHD4, CLP 1, CNOT8, COG4, CPSF7, CSPP1, DDOST, DDX27, DHX9, DNAJA2, DNAJC2, DNAJC7, DNMT1, DUSP12, EIF3A, ENO1, ENOSF1, ERCC3, ERP29, FLT3, G6PC3, GAS8, GGCX, GNL2, GOLGA7, GPN1, GPR3, GTPBP4, H3F3B, HDAC3, HEATR1, HMGXB4, HNRNPA1, HNRNPA2B1, HNRNPA3, HNRNPH3, HNRNPR, HPS4, IK, IL3RA, IMPDH2, INSL3, IPO5, IQSEC3, IRGQ, KARS, KCNJ14, KIF17, LAMB4, LAS1L, LPO, LUC7L3, LUZP1, MAP7D3, MED9, MPO, MRPL9, MTIF2, MUL1, NCL, NCOR1, NGLY1, NIT2, NR2C1, OMD, PAFAH1B2, POU2F1, PPIP5K2, PRRC1, PRTN3, RBM19, RBM25, RBM39, RFC1, RNF220, RPRD2, SERPINI2, SF3B2, SLC24A1, SLC25A36, SLC5A5, SMG7, SUPV3L1, SUV39H1, TCP1, TCTN2, THRAP3, TRH, TTF2, TXNL4B, UBE2G2, USP36, WDR41, WDR43, WDR60, ZBTB39, 219 GMP ZC3H15, ZNF207, ZNF35 220 GMP CLC, CPA3, CRHBP, DNTT, FLT3, MPO, PRG2, RNASE2 221 GMP CLC, CPA3, CRHBP, DNTT, FLT3, MPO, PRG2, RNASE2 222 GMP CLC, CPA3, CRHBP, DNTT, FLT3, MPO, PRG2, RNASE2 A1CF, ABAT, ABCA8, ABCB1, ABCB4, ABCC2, ABCG5, ACADL, ACOX2, ACSM5, ADH1A, ADH6, AFM, AGMAT, AGT, AGXT, AHSG, AKR1C4, AKR1D1, ALB, ALDH8A1, ALDOB, AMBP, ANGPTL3, ANPEP, ANXA10, AOX1, APCS, APOA1, APOA2, APOC1, APOC3, APOE, APOH, APOM, ARSE, ASGR1, ATF5, BAAT, BHMT, BHMT2, C1R, C1S, C2, C4BPA, C4BPB, C5, C8A, C8B, C8G, C9, CD14, CDO1, CPB2, CPN2, CPS1, CUX2, CYP2C19, CYP2C8, CYP2C9, CYP 20.00, CYP3A4, CYP3A5, CYP7A1, DAO, DIO1, DPYS, ECHDC3, EHHADH, ENPEP, EPHX2, F13B, F2, F5, F9, FABP1, FAM134B, FERMT2, FGA, FGB, FGG, FGL1, FXYD1, GAS2, GATM, GBA3, GC, GHR, GLDC, GNMT, GRB14, GYS2, HABP2, HAO1, HAO2, HGD, HHEX, HLF, HPD, HPN, HPX, HRG, HSD17B11, HSD17B6, IGFBP1, INHBE, IQGAP2, ITIH2, ITIH3, KCNJ8, KMO, KNG1, LECT2, LGALS4, LIPC, MAN1A1, MAOB, MAT1A, MBL2, MT1G, MT1M, MTTP, 223 Hepatocytes MYLK, MYRIP, NAT8, NNMT, NR1H4, NR1I3, ORM1, OTC,WSGR Docket No.50272-712.601 PAH, PAPSS2, PBLD, PCK1, PCOLCE2, PIPOX, PLA2G4C, PON1, PPP1R3C, PROX1, PXMP2, QPRT, RARRES2, RBP4, RGN, RUNDC3B, SALL1, SDC2, SERPINA1, SERPINA10, SERPINA4, SERPINA5, SERPINA6, SERPINA7, SERPINC1, SERPIND1, SERPINF2, SERPING1, SKAP1, SLC10A1, SLC17A2, SLC17A3, SLC22A1, SLC2A2, SLC30A10, SLC38A4, SLC47A1, SLC6A1, SLC7A9, SLCO1B1, SLCO1B3, SNX10, SPINK1, SPP1, SULT2A1, TAT, TDO2, TFPI, TFR2, TM4SF4, TM4SF5, TMEM176A, TMEM176B, TRPM8, TTR, UBD, UGT2A3, UGT2B15, UGT2B4, VIL1, VNN 1.00, VTN A1CF, ABAT, ABCA8, ABCB1, ABCB4, ABCG5, ACOX2, ACSM5, ADH1A, ADH1C, ADH6, AFM, AGMAT, AGT, AGXT, AHSG, AKR1C4, AKR1D1, ALB, ALDH1A1, ALDH8A1, ALDOB, AMBP, ANGPTL3, ANPEP, ANXA10, AOX1, APCS, APOA1, APOA2, APOC1, APOC3, APOE, APOH, APOM, ARSE, ASGR1, BAAT, BHMT, BHMT2, C1R, C1S, C4BPA, C4BPB, C5, C8A, C8B, C8G, C9, CDO1, CPB2, CPS1, CRYM, CYP2C8, CYP2C9, CYP 20.00, CYP3A4, CYP3A5, CYP7A1, DIO1, DPYS, EFHD1, EHHADH, ENPEP, F13B, F2, F5, F9, FABP1, FGA, FGB, FGG, FGL1, FXYD1, GAS2, GATM, GBA3, GC, GHR, GNMT, GRB14, GSTA1, GYS2, HABP2, HAO1, HGD, HHEX, HPD, HPN, HPX, HRG, HSD17B6, IGFBP1, INHBE, IQGAP2, ITIH2, ITIH3, KNG1, LECT2, LGALS4, LIPC, MAOB, MAT1A, MBL2, MT1G, MT1M, MTTP, MYLK, MYRIP, NAT8, NNMT, NR1H4, NR1I3, ORM1, OTC, PAH, PAPSS2, PCK1, PCOLCE2, PIPOX, PLA2G4C, PLSCR4, PON1, QPRT, RARRES2, RBP4, RUNDC3B, SALL1, SDC2, SEPP1, SERPINA1, SERPINA4, SERPINA5, SERPINA6, SERPINA7, SERPINC1, SERPIND1, SERPINF2, SERPING1, SKAP1, SLC10A1, SLC17A3, SLC22A1, SLC2A2, SLC30A10, SLC38A4, SLC6A1, SLCO1B1, SLCO1B3, SNX10, SPINK1, SPP1, SULT2A1, TAT, TDO2, TM4SF4, TMEM176A, TMEM176B, TRPM8, TTR, UBD, UGT2A3, UGT2B15, UGT2B4, VIL1, VNN 1.00, 224 Hepatocytes VTN A1CF, ABCA8, ABCB1, ABCB4, ABCC2, ABCG5, ACADL, ACOX2, ACSM5, ADH1A, ADH6, AFM, AGMAT, AGT, AGXT, AHSG, AKR1C4, AKR1D1, ALB, ALDH8A1, ALDOB, AMBP, ANGPTL3, ANPEP, ANXA10, AOX1, APCS, APOA1, APOA2, APOC1, APOC3, APOE, APOH, APOM, ARSE, ASGR1, BAAT, BHMT, BHMT2, C1R, C1S, C2, C4BPA, C4BPB, C5, C8A, C8G, C9, CDO1, CPB2, CPS1, CYP2C8, CYP2C9, CYP 20.00, CYP3A4, CYP3A5, DIO1, DPYS, EHHADH, ENPEP, F13B, F2, F5, F9, FABP1, FGA, FGB, FGG, FGL1, FXYD1, GAS2, GATM, GBA3, GC, GHR, GLDC, GNMT, GRB14, GYS2, HABP2, HAO1, HAO2, HGD, HHEX, HLF, HPD, HPN, HPX, HRG, HSD17B6, IGFBP1, INHBE, IQGAP2, ITIH2, ITIH3, KMO, KNG1, LGALS4, LIPC, MAOB, MAT1A, MBL2, MT1G, MT1M, MTTP, MYLK, MYRIP, NAT8, NNMT, NR1H4, NR1I3, ORM1, OTC, PAH, PAPSS2, PCK1, PCOLCE2, PIPOX, PLA2G4C, PON1, PXMP2, RARRES2, RBP4, RUNDC3B, SALL1, SDC2, SERPINA1, SERPINA10, SERPINA4, SERPINA5, SERPINA6, SERPINA7, 225 Hepatocytes SERPINC1, SERPIND1, SERPINF2, SERPING1, SKAP1,WSGR Docket No.50272-712.601 SLC10A1, SLC17A3, SLC22A1, SLC2A2, SLC38A4, SLC6A1, SLCO1B1, SPINK1, SPP1, SULT2A1, TAT, TDO2, TFPI, TM4SF4, TMEM176A, TMEM176B, TRPM8, TTR, UGT2A3, UGT2B15, UGT2B4, VIL1, VNN 1.00, VTN A1CF, AADAC, ABCB11, ACADS, ACAT1, ACSM5, ADH1A, ADH1B, ADH1C, AFM, AGMAT, AHSG, AKR1C4, ALDH2, ALDH3A2, ALDH6A1, AMBP, ANGPTL3, APCS, APOA1, APOA2, APOC3, APOE, APOF, APOL1, ARG1, ATF7IP2, BCHE, BHMT, C14orf105, C1R, C4BPA, C4orf19, C5, C6, C8A, C8B, C8G, CCDC69, CCL16, CDO1, CEACAM1, CEBPA, CFH, CPB2, CPN2, CRP, CXCL2, CYB5A, CYP2A6, CYP2A7, CYP2C8, CYP2C9, CYP2J2, CYP3A5, CYP4F12, DDC, DEFB1, DIO1, DPYS, EHHADH, ELF3, EPHX1, EPHX2, F10, F11, F13B, F2, F5, F7, F9, FABP1, FCN2, FGA, FGB, FGG, FGL1, G6PC, GADD45G, GC, GCGR, GCH1, GCKR, GSTA1, HAAO, HAL, HAO1, HGFAC, HMGCS2, HP, HPX, HRG, HSD17B6, HYAL1, IGFALS, IL17RB, ITIH3, KHK, KLKB1, KNG1, KYNU, LBP, LCAT, LGALS4, MAN1A1, MIA2, MLXIPL, MT1F, MT1G, MT1X, NPC1L1, NR1H4, OGDHL, ORM1, OSGIN1, PCCA, PCK1, PCSK6, PID1, PLA2G2A, PON1, PROZ, PZP, QPRT, SDS, SEC14L4, SELENBP1, SEMA4G, SERPINA1, SERPINA10, SERPINA6, SERPINA7, SERPINC1, SERPIND1, SERPINF1, SERPINF2, SERPING1, SHC2, SLC17A2, SLC17A4, SLC22A1, SLC22A7, SLC27A2, SLC27A5, SLC2A2, SLC30A10, SLC38A3, SLC38A4, SLC47A1, SLC6A1, SLC7A2, SLCO1B1, SLCO1B3, SPINK1, 226 Hepatocytes TAT, TM4SF5, UGT2B4, UPB1 A1CF, AADAC, ABCB11, ACAT1, ACSM5, ADH1A, ADH1C, AFM, AGMAT, AGT, AHSG, AKR1C4, ALDH2, ALDH6A1, ALDH8A1, AMBP, ANGPTL3, APCS, APOA1, APOA2, APOB, APOC3, APOE, APOF, APOH, APOL1, ASGR2, ATF7IP2, BAAT, BDH1, BHMT, BHMT2, C2, C4BPA, C4BPB, C4orf19, C5, C6, C8A, C8B, C8G, CAT, CCL16, CEBPA, CFB, CFH, CFHR5, COL18A1, COLEC11, CPB2, CPN2, CREG1, CTSO, CYP2A6, CYP2B6, CYP2C8, CYP2C9, CYP2J2, CYP3A5, CYP4F11, CYP4F12, DAO, DCXR, DDC, DDO, DIO1, DPYS, EHHADH, ENPP1, ENTPD5, EPHX1, EPHX2, F10, F11, F13B, F2, F5, F7, F9, FABP1, FCN2, FCN3, FGA, FGL1, G6PC, GC, GCGR, GCH1, GCKR, GHR, GNMT, GREM2, GRTP1, GYS2, HAAO, HABP2, HAL, HAO1, HGFAC, HMGCS2, HRG, HSD11B1, HSD17B2, HSD17B6, HYAL1, IGFALS, IL17RB, INHBC, INHBE, INSR, ITIH2, ITIH3, KCNK5, KHK, KLKB1, KNG1, LBP, LCAT, LECT2, LGALS4, LIPC, LPIN2, MAN1A1, MASP1, MAT1A, MIA2, MLXIPL, MT1F, MT1G, NPC1L1, NR1H4, NR1I3, NR5A2, OGDHL, PC, PCCA, PCK1, PID1, PON1, PROZ, PZP, RAB20, RET, RNASE4, SEC14L4, SEC23A, SEL1L, SEPHS2, SERPINA1, SERPINA10, SERPINA6, SERPINA7, SERPIND1, SERPINF2, SERPING1, SLC10A1, SLC17A4, SLC1A1, SLC22A1, SLC22A7, SLC25A13, SLC26A1, SLC27A2, SLC27A5, SLC2A2, SLC30A10, SLC35C1, SLC35D1, SLC38A4, SLC47A1, SLC7A9, SLCO1B1, SLCO1B3, SPINK1, SPP2, SULT2A1, TAT, TFR2, THPO, TM4SF5, TMPRSS2, TTPA, UBD, 227 Hepatocytes UGT2B4, UPB1WSGR Docket No.50272-712.601 A1CF, AADAC, ABCB11, ACAT1, ACSM5, ADH1A, ADH1B, ADH1C, AFM, AGMAT, AHSG, AKR1C4, ALDH2, ALDH3A2, AMBP, ANGPTL3, APCS, APOA1, APOA2, APOC3, APOE, APOF, APOL1, ARG1, BHMT, C14orf105, C1R, C4BPA, C4orf19, C5, C6, C8A, C8B, C8G, CCDC69, CCL16, CDO1, CEACAM1, CEBPA, CFH, CPB2, CPN2, CRP, CXCL2, CYB5A, CYP2A6, CYP2A7, CYP2C8, CYP2C9, CYP2J2, CYP3A5, CYP4F12, DDC, DEFB1, DIO1, DPYS, EHHADH, ELF3, EPHX1, EPHX2, F10, F11, F13B, F2, F5, F7, F9, FABP1, FCN2, FGA, FGB, FGG, FGL1, G6PC, GADD45G, GC, GCGR, GCH1, GCKR, GSTA1, HAAO, HAL, HAO1, HGFAC, HMGCS2, HP, HPX, HRG, HSD17B6, HYAL1, IGFALS, IL17RB, ITIH3, KHK, KLKB1, KNG1, KYNU, LBP, LCAT, LGALS4, MAN1A1, MIA2, MLXIPL, MT1F, MT1G, MT1X, NPC1L1, NR1H4, OGDHL, ORM1, OSGIN1, PCK1, PCSK6, PLA2G2A, PON1, PROZ, PZP, SEC14L4, SERPINA1, SERPINA10, SERPINA6, SERPINA7, SERPINC1, SERPIND1, SERPINF2, SERPING1, SHC2, SLC17A2, SLC17A4, SLC22A1, SLC22A7, SLC27A2, SLC27A5, SLC2A2, SLC30A10, SLC38A3, SLC38A4, SLC47A1, SLC6A1, SLC7A2, SLCO1B1, 228 Hepatocytes SLCO1B3, SPINK1, TAT, TM4SF5, UGT2B4, UPB1 CD34, CRHBP, ERG, FAM124B, GSTM5, LAPTM4B, 229 HSC MMRN1, PLS3 ATP8B4, CCDC121, CD34, CRHBP, ERG, EXD2, FAM124B, FLT3, GSTM5, KLHL9, LAPTM4B, LSM 2.00, MMRN1, 230 HSC MYCT1, PLS3, SCARF1, SLC4A1, TCEAL4, XPO7, ZMYM3 AHSP, ALAS2, ATP8B4, CA1, CD34, CRHBP, ERG, EXD2, FAM124B, FLT3, GSTM5, GYPA, GYPE, KLHL9, LAPTM4B, 231 HSC MMRN1, PLS3, SCARF1, SLC4A1, TCEAL4 CRHBP, CRYGD, CTNNA3, ELN, EMCN, ERG, KCNJ13, LECT1, MPL, MYCT1, PDE6G, PHF20, PLA2G1B, RNF7, 232 HSC SYPL1, ZNF3 CRHBP, CTNNA3, ELN, EMCN, GSTM5, LECT1, PLA2G1B, 233 HSC SYPL1 CLEC3B, CRHBP, CTNNA3, ELN, EMCN, GSTM5, LECT1, 234 HSC SYPL1 CRHBP, CTNNA3, ELN, EMCN, GSTM5, LECT1, PDE6G, 235 HSC SYPL1, VN1R1 CLEC3B, CRHBP, CTNNA3, ELN, EMCN, GSTM5, LECT1, 236 HSC SYPL1 CRHBP, CRYGD, CTNNA3, ELN, EMCN, LECT1, PDE6G, 237 HSC PHF20, SYPL1 ALDH1A2, ALOX15, CCL13, CCL17, CCL18, CCL23, CCL24, 238 iDC CD209, F13A1, FCER2, IL3RA ALDH1A2, ALOX15, CCL13, CCL17, CCL24, CD86, 239 iDC CLEC10A, FCER2, IL3RA, SPINT2 ALOX15, CCL13, CCL17, CCL18, CCL23, CCL24, CD209, 240 iDC F13A1 AP1M2, CALML3, CSTA, DSG3, F3, FGFBP1, FST, GJB5, HES2, IL1A, JAG2, KRT5, KRT6B, KRT6C, LAD1, LY6D, MMP28, PI3, RAB25, S100A14, SERPINB5, SFN, SOX15, 241 Keratinocytes SULT2B1, TMEM40, ZBED2WSGR Docket No.50272-712.601 AP1M2, CALML3, CSTA, DSG3, FGFBP1, FST, GJB5, HES2, IL1A, KRT6B, KRT6C, PI3, RAB25, S100A14, SFN, SOX15, 242 Keratinocytes TMEM40, ZBED2 AP1M2, DSG3, FGFBP1, GJB5, IL1A, KRT6B, S100A14, SFN, 243 Keratinocytes SOX15, TMEM40 ADAM8, AIM1, ALDH1A3, ALS2CL, ANGPTL4, AP1M2, ARTN, C1orf116, CALML3, CBLC, CDH3, CLCA2, COL17A1, CSTA, CXCL3, CYP27B1, DEF6, DLK2, DSC3, DSG3, ELMO3, EPHA1, EPN3, EREG, ESRP1, F3, FAT2, FERMT1, FGFBP1, FGFR3, FST, FXYD3, G0S2, GJB3, GJB5, GNA15, GPR87, HES2, IL1A, IL1B, IRX4, ITGA6, ITGB4, ITGB6, JAG2, KCNJ15, KIAA0040, KLK5, KLK8, KRT14, KRT16, KRT17, KRT5, KRT6A, KRT6B, LAD1, LAMA3, LAMB3, LAMC2, LPAR3, LSR, MMP28, MMP9, MPZL2, NDRG1, NGFR, PGF, PKP1, PKP3, PLCH2, PPL, PPP1R13L, PRSS8, PTHLH, RAB25, S100A14, S100A2, S100A8, S100A9, SDC1, SERPINB2, SERPINB5, SFN, SH2D3A, SLC2A9, SORL1, SOX15, SPRR1B, ST14, ST6GALNAC2, TGFA, TMEM40, 244 Keratinocytes TNS4, TP63, TRIM29, TSPAN1, XDH, ZBED2 ADAM8, ANGPTL4, AP1M2, ARTN, C1orf116, CBLC, CLCA2, COL17A1, CSTA, CYP27B1, DSC3, DSG3, ELMO3, EPN3, EREG, ESRP1, FAT2, FERMT1, FGFBP1, FST, FXYD3, G0S2, GJB3, GJB5, GNA15, GPR87, IL1A, IL1B, ITGB4, KLK5, KLK8, KRT14, KRT16, KRT17, KRT5, KRT6A, KRT6B, LAD1, LAMA3, LAMB3, LSR, MMP9, MPZL2, PKP1, PKP3, PLCH2, PPP1R13L, PRSS8, RAB25, S100A14, S100A2, S100A8, S100A9, SERPINB5, SFN, SH2D3A, SLC2A9, SOX15, 245 Keratinocytes ST14, TNS4, TP63, TRIM29, ZBED2 ADAM8, ANGPTL4, AP1M2, ARTN, C1orf116, CALML3, CBLC, CLCA2, COL17A1, CSTA, CYP27B1, DSC3, DSG3, ELMO3, EPN3, EREG, ESRP1, FAT2, FERMT1, FGFBP1, FST, FXYD3, G0S2, GJB3, GJB5, GNA15, GPR87, IL1A, IL1B, ITGB4, KLK5, KLK8, KRT14, KRT16, KRT17, KRT5, KRT6A, KRT6B, LAD1, LAMA3, LAMB3, LAMC2, LSR, MMP9, MPZL2, PKP1, PKP3, PLCH2, PPP1R13L, PRSS8, RAB25, S100A14, S100A2, S100A8, S100A9, SERPINB5, SFN, SH2D3A, SLC2A9, SOX15, ST14, TNS4, TP63, TRIM29, 246 Keratinocytes ZBED2 CLCA2, DLK2, DSG3, FGFBP1, FLRT3, GJB4, IL1A, KRT14, KRT5, KRT6C, LY6D, MMP28, PKP3, PRMT5, PTK6, 247 Keratinocytes SERPINB5, SULT2B1, TP53AIP1 AP1M2, B3GNT3, BDKRB2, C1orf116, CALML3, CLCA2, CNKSR1, CORO2A, CSTA, DLK2, DSC3, DSG3, DUSP14, F3, FGD6, FGFBP1, FLRT3, FST, GJB3, GJB4, GJB5, HES2, IL1A, IRF6, JAG2, KRT14, KRT5, KRT6B, KRT6C, LAD1, LPAR3, LTB4R2, LY6D, MMP28, PGF, PI3, PKP1, PLA2G4A, PRMT5, PRSS8, PTK6, RAB25, S100A14, SERPINB5, SFN, SLC12A4, SLC6A11, SOX15, SULT2B1, TFCP2L1, TMEM40, ZBED2, 248 Keratinocytes ZNF750 AP1M2, B3GNT3, BDKRB2, C1orf116, CALML3, CLCA2, CNKSR1, CORO2A, CSTA, DSC3, DSG3, F3, FGD6, FGFBP1, FST, GJB3, GJB4, GJB5, HES2, IL1A, IRF6, JAG2, KRT14, KRT5, KRT6B, KRT6C, LAD1, LPAR3, LTB4R2, LY6D, 249 Keratinocytes MMP28, MST1R, PI3, PKP1, PRMT5, PTK6, RAB25, S100A14,WSGR Docket No.50272-712.601 SERPINB5, SFN, SOX15, SULT2B1, TFCP2L1, TMEM40, ZBED2, ZNF750 ly Endothelial CLEC1A, CXorf36, FLT4, HYAL2, KANK3, MYCT1, ROBO4, 250 cells SOX18 ACVRL1, ANGPT2, ARHGEF15, BMX, CD93, CETP, CLDN5, CLEC1A, CXorf36, EMCN, ERG, FEZ2, FLT4, FUS, GPR4, HYAL2, KALRN, KANK3, KDR, LYVE1, MAPK12, MMRN1, MMRN2, MRPL17, MYCT1, MYL2, N4BP3, NETO2, ly Endothelial NOTCH4, PLVAP, POMGNT1, RALA, RASIP1, RHOC, 251 cells ROBO4, SEMA6B, SOX18, TEK, TIE1, TMEM39B, TNFSF18 ly Endothelial ANGPT2, CLEC1A, CXorf36, FEZ2, FLT4, HYAL2, KALRN, 252 cells KANK3, MAPK12, MYCT1, ROBO4, SOX18, TEK ly Endothelial ARHGEF15, CLDN5, CLEC1A, GJA4, HYAL2, KANK3, 253 cells MMRN2, ROBO4, SELE, TIE1, TNFSF18, VWF ly Endothelial CLDN5, GJA4, KANK3, MMRN2, ROBO4, SELE, TIE1, 254 cells TNFSF18, VWF ly Endothelial ARHGEF15, CLDN5, CLEC1A, FLT4, GJA4, HYAL2, KANK3, 255 cells MMRN2, ROBO4, SELE, TIE1, TNFSF18, TPM3, VWF ACP2, ADAMDEC1, ARHGEF11, ARL8B, ARPC4, ATOX1, ATP6AP2, ATP6V0C, ATP6V0E1, ATP6V1E1, ATP6V1F, BAIAP2, C12orf4, C1QA, CCL22, CCL8, CCR1, CD163, CD48, CD84, CD9, CIAO1, CLCN7, COMMD9, CRYBB1, CYBB, ERP29, FCER1G, FDX1, FKBP15, FOLR2, FPR3, FTL, GUF1, HAMP, HEXA, HEXB, HS3ST2, KCNJ5, LAIR1, LILRB4, LILRB5, LY86, M6PR, MARCO, MDH1, MS4A4A, NUBP1, ORMDL2, P2RX7, PRDX3, RAC1, SDS, SIGLEC7, SIGLEC9, SLAMF8, SPG21, STX4, TCEB1, TMEM126B, TMEM70, TRAPPC2L, TREM2, TYROBP, UBE2D4, VAMP8, VSIG4, 256 Macrophages YIF1B, ZZZ3 ACP2, ADAMDEC1, ADCK2, ARHGEF11, ARL8B, ARPC4, ATOX1, ATP6AP2, ATP6V0C, ATP6V0E1, ATP6V1E1, ATP6V1F, BAIAP2, C12orf4, C1QA, CCL22, CCL8, CCR1, CD163, CD48, CD63, CD84, CD9, CIAO1, CLCN7, CLEC5A, CMKLR1, COMMD8, COMMD9, COX5A, COX5B, COX7B, COX8A, CRYBB1, CYBA, CYBB, DNAJC13, DNASE2B, ERP29, FCER1G, FDX1, FKBP15, FOLR2, FPR3, FTL, GLB1, GUF1, HAMP, HEXA, HEXB, HIGD2A, HS3ST2, ITGB1BP1, KCNJ5, KCTD5, LAIR1, LAMP1, LILRB4, LILRB5, LY86, MAPK13, MARCO, MRPL40, MRS2, MS4A4A, MS4A6A, NDUFB3, NDUFS3, NDUFS6, NOP10, NUBP1, ORMDL2, P2RX7, PCMT1, PMFBP1, PPCS, PQLC2, PRDX3, PSME1, RB1, S1PR2, SCAMP2, SDHD, SDS, SIGLEC7, SIGLEC9, SLAMF8, SLC31A1, SLC38A7, SNX2, SNX3, SPG21, STAB1, SUMO3, TCEB1, TMEM126B, TMEM70, TMEM9B, TPP1, TRAPPC2L, TREM2, TYROBP, UBE2D4, UQCR10, UQCR11, 257 Macrophages UQCRC2, VAMP8, VSIG4, WDR11, YIF1B, ZZZ3 ACP2, ADAMDEC1, ADCK2, ARHGEF11, ARL8B, ARPC4, ATOX1, ATP6AP2, ATP6V0C, ATP6V0E1, ATP6V1E1, ATP6V1F, C12orf4, C1QA, CCL22, CCL8, CCR1, CD163, CD48, CD63, CD84, CD9, CIAO1, CLCN7, CLEC5A, COMMD9, COX5A, COX8A, CRYBB1, CYBB, DNAJC13, ERP29, FCER1G, FDX1, FKBP15, FOLR2, FPR3, FTL, GUF1, HAMP, HEXA, HEXB, HS3ST2, ITGB1BP1, KCNJ5, KCTD5, 258 Macrophages LAIR1, LAMP1, LILRB4, LILRB5, LY86, MAPK13, MARCO,WSGR Docket No.50272-712.601 MRPL40, MS4A4A, MS4A6A, NDUFB3, NDUFS6, NOP10, ORMDL2, P2RX7, PMFBP1, PPCS, SCAMP2, SDS, SIGLEC7, SIGLEC9, SLAMF8, SNX2, SNX3, SPG21, STAB1, TCEB1, TMEM126B, TMEM70, TREM2, TYROBP, UBE2D4, UQCR11, UQCRC2, VAMP8, VSIG4, WDR11, YIF1B, ZZZ3 ACP2, ADAMDEC1, CCL22, CD84, CHIT1, CLEC5A, CSF1, CYP19A1, DNASE2B, FDX1, HAMP, HK3, MSR1, MYOZ1, 259 Macrophages SDS, SLC6A12, VSIG4 ABCD1, ACP2, ADAMDEC1, ADCY3, ALCAM, ATP6V1H, BPI, CCL1, CCL22, CCL7, CD163, CD84, CHIT1, CLEC5A, CXCL9, CYP19A1, DNASE2B, FKBP15, HAMP, HK3, KCNJ1, KCNMB1, LILRB4, LONRF3, MFSD7, MMP8, MSR1, MYOZ1, NCAPH, PQLC2, SDS, SLAMF8, SLC1A2, SLC6A12, 260 Macrophages VSIG4 ABCD1, ACP2, ADAMDEC1, ADCY3, ALCAM, ARHGEF11, ATP6AP2, ATP6V0A1, ATP6V1A, ATP6V1H, BCAP31, BPI, CCDC88A, CCL1, CCL22, CCL7, CD163, CD164, CD84, CHIT1, CIR1, CLEC5A, CSF1, CXCL9, CYBB, CYP19A1, DNASE2B, FDX1, FGR, FKBP15, HAMP, HK3, ITGAX, KCNJ1, KCNMB1, LILRB4, LONRF3, MAPK13, MARCO, MFSD7, MMP19, MMP8, MSR1, MYOZ1, NCAPH, NUMB, P2RX7, PCDHB11, PQLC2, SDS, SLAMF8, SLC11A1, 261 Macrophages SLC1A2, SLC31A1, SLC6A12, STX4, VSIG4 SEPTIN10, ACADVL, ACP2, ACTR10, ADAMDEC1, AGPS, ARSB, ATP2C1, ATP6V0C, ATP6V1A, ATP6V1C1, ATP6V1D, ATP6V1F, ATP6V1H, BAG3, BCAP31, BTBD1, C12orf4, CCL18, CCL7, CCR1, CD63, CETN2, CHD9, CIR1, CLEC5A, CLIP1, CNIH4, COX15, COX5B, CYBB, DBI, DERA, DNASE2B, ECHS1, EFR3A, ELOVL1, EMILIN1, FDX1, FEZ2, FKBP15, G6PC3, GLRX2, GRB2, GSTO1, HADHB, HAMP, HEXA, HEXB, HK3, HMGCL, HSD17B12, IBTK, IGSF6, ITGAX, KCMF1, KIFC3, KLHL12, LAIR1, LAMP1, LILRB4, LONP1, LONRF3, MAPK13, MAPKAP1, MARCO, MFSD7, MGST3, MKL2, MLX, MS4A4A, MSR1, NARS, NDUFA8, NDUFAF1, NDUFB6, NDUFS8, NOP10, NPTN, NSMAF, NSUN3, NUDT9, PDCD6IP, PEX14, PEX19, PRDX1, PSMD10, PTPN12, QDPR, RAB1A, RAB5C, RALA, RENBP, RTN4, SDHB, SIGLEC9, SLC30A5, SLC31A1, SNUPN, SNX4, SPIN1, SRC, STAM2, STX4, STYXL1, TBC1D16, TCEAL4, TCEB1, TGOLN2, TM2D1, TMBIM4, TMEM127, TMEM147, TMEM33, TNFRSF12A, TNPO1, TRAF3, TULP4, VSIG4, 262 Macrophages VTI1B, ZDHHC24, ZDHHC3, ZMPSTE24 SEPTIN10, ACP2, ADAMDEC1, AGPS, ARSB, ATP2C1, ATP6V0C, ATP6V1A, ATP6V1C1, ATP6V1D, ATP6V1F, ATP6V1H, BCAP31, C12orf4, CCL7, CCR1, CD63, CETN2, CLEC5A, CNIH4, COX15, COX5B, CYBB, DBI, DERA, DNASE2B, ECHS1, EFR3A, ELOVL1, EMILIN1, FDX1, G6PC3, GLRX2, GSTO1, HAMP, HEXB, IGSF6, LAIR1, LAMP1, LILRB4, LONRF3, MAPK13, MAPKAP1, MARCO, MFSD7, MGST3, MLX, MS4A4A, MSR1, NDUFA8, NDUFAF1, NDUFS8, NPTN, NSMAF, NUDT9, PEX19, PRDX1, PSMD10, PTPN12, RAB1A, RALA, RENBP, RTN4, SDHB, SIGLEC9, SLC30A5, SLC31A1, STX4, TBC1D16, TCEB1, TMEM147, TMEM33, TNFRSF12A, TRAF3, VSIG4, 263 Macrophages VTI1B, ZDHHC24, ZDHHC3WSGR Docket No.50272-712.601 SEPTIN10, ACADVL, ACP2, ACTR10, ADAMDEC1, AGPS, ARSB, ATP2C1, ATP6V0C, ATP6V1A, ATP6V1C1, ATP6V1D, ATP6V1F, ATP6V1H, BCAP31, BTBD1, C12orf4, C7orf25, CCL7, CCR1, CD63, CEPT1, CETN2, CLEC5A, CLIP1, CNIH4, COX15, COX5B, CYBB, DBI, DERA, DNASE2B, ECHS1, EFR3A, ELOVL1, EMILIN1, FDX1, FKBP15, G6PC3, GLRX2, GRB2, GSTO1, HADHB, HAMP, HCCS, HEXA, HEXB, HK3, HMGCL, IGSF6, ITGAX, KCMF1, LAIR1, LAMP1, LILRB4, LONRF3, MAPK13, MAPKAP1, MARCO, MFSD7, MGST3, MKL2, MLX, MS4A4A, MSR1, NDUFA8, NDUFAF1, NDUFS8, NPTN, NSMAF, NSUN3, NUDT9, PANK3, PDCD6IP, PEX19, PRDX1, PSMD10, PTPN12, QDPR, RAB1A, RALA, RENBP, RTN4, SDHB, SETD3, SIGLEC9, SLC25A24, SLC30A5, SLC31A1, SNX4, SPIN1, SRC, STAM2, STX4, TBC1D16, TCEB1, TGOLN2, TM2D1, TMEM115, TMEM127, TMEM147, TMEM33, TNFRSF12A, TNPO1, TRAF3, TRAPPC3, TULP4, UQCRC2, USF2, VSIG4, VTI1B, 264 Macrophages ZDHHC24, ZDHHC3, ZMPSTE24 ABCD1, ACP2, ADAMDEC1, ADCY3, ATP6AP2, ATP6V1A, ATP6V1H, CD164, CD84, CHIT1, CLCN7, CLEC5A, COX5B, CYP19A1, DNASE2B, FDX1, FKBP15, HEXA, HK3, KCNJ1, LDHAL6B, LILRB4, LONP1, LONRF3, M6PR, MFSD7, MSR1, MYOZ1, PQLC2, SDS, SLC30A5, SLC31A1, SLC6A12, STX18, 265 Macrophages TGOLN2, TPP1, VSIG4 ABCD1, ACP2, ADAMDEC1, ADCY3, ALCAM, ARHGEF11, ARSB, ATOX1, ATP6AP2, ATP6V0A1, ATP6V0C, ATP6V1A, ATP6V1E1, ATP6V1H, BCAP31, BPI, C12orf49, CCDC88A, CD164, CD63, CD84, CHIT1, CIR1, CLCN7, CLEC5A, COX5B, CPNE6, CSF1, CYBB, CYP19A1, DNASE2B, FDX1, FKBP15, HAMP, HEXA, HK3, HTT, IL17RA, ITGAX, KCNJ1, LAMP1, LDHAL6B, LILRB4, LONP1, LONRF3, M6PR, MAPK13, MFSD7, MMP19, MSR1, MTHFR, MTMR14, MUL1, MYOZ1, NRBP1, NUMB, P2RX7, PQLC2, PTPRA, RABGGTA, SDS, SH3GLB1, SLAMF8, SLC1A2, SLC30A5, SLC31A1, SLC38A7, SLC6A12, STX18, STX4, TCEB1, TGOLN2, TMEM33, TPP1, 266 Macrophages TSPO, TTLL4, VPS53, VSIG4, ZDHHC24 ACP2, ADAMDEC1, CCL22, CD84, CHIT1, CLEC5A, CSF1, CYP19A1, DNASE2B, FDX1, HAMP, HK3, MSR1, MYOZ1, 267 Macrophages SDS, SLC6A12, VSIG4 Macrophages ABCD1, ACP2, ADAMDEC1, ARL8B, C1QA, CCL22, CD163, 268 M1 FDX1, HAMP, SCAMP2 Macrophages ABCD1, ACP2, ADAMDEC1, CCL22, CCL8, CD163, FDX1, 269 M1 HAMP, TREM2 ABCD1, ABI1, ABTB2, ACP2, ACTR2, ADAMDEC1, ADCK2, ADO, ADRA2B, AGPS, ALCAM, ARHGEF11, ARL8B, ATOX1, ATP6V0C, ATP6V1E1, ATP6V1F, BCAP31, BCKDK, BLVRA, C1QA, CCDC47, CCL19, CCL22, CCL7, CCL8, CCR1, CD163, CD48, CD63, CD84, CECR5, CIAO1, CLCN7, CLTC, CMKLR1, CORO7, COX5B, CXCL9, DNAJC13, DOT1L, EXOC5, FAM32A, FCER1G, FDX1, FKBP15, FOLR2, FPR3, FTL, HAMP, HAUS2, HEXB, HK3, IL10, IL12B, IL17RA, ITGAE, ITGB1BP1, LAIR1, LILRB4, LIMD2, MAPK13, MARCO, MMP19, MRPL40, MRS2, MS4A4A, Macrophages NARS, NDUFS2, NECAP2, NRBP1, OGFR, OTUD4, P2RX7, 270 M1 PDCL, PQLC2, PTGIR, PTPRA, RELA, SCAMP2, SDS,WSGR Docket No.50272-712.601 SIGLEC7, SIGLEC9, SLC25A24, SLC31A1, SNX3, SRC, STIP1, STX12, STX4, TCEB1, TDRD7, TFEC, TFRC, TMX1, TPP1, TREM2, TRIP4, UQCR11, USP14, UTP3, VPS33A, WDR11, WTAP, ZC3H15, ZMPSTE24 ACP2, ACTR3, ADAMDEC1, ADCK2, ADRA2B, AFG3L2, ALCAM, AP1M2, C3AR1, CALR, CCL1, CCL24, CCL7, CCL8, CECR5, CHIT1, CLPB, COQ2, CYBB, CYC1, CYP19A1, DAGLA, DLAT, DNASE2B, EMILIN1, FCER1G, GP1BA, GPD1, HSPB7, IFNAR1, IL10, KCNJ5, KIFC3, LILRB1, LILRB4, MARCO, MFSD7, MRPL12, MT2A, MYBPH, Macrophages MYH11, MYO7A, P2RX7, PRDX1, RAB3IL1, RNH1, RRP1, 271 M1 S1PR2, SDS, SLAMF8, SRC, TSPO, VIM, VSIG4, WSB2 ACP2, ADAMDEC1, ADCK2, ADCY3, ADRA2B, ALCAM, AP1M2, ATP6V1D, ATP6V1H, C1QA, C1QB, C3AR1, CCL1, CCL18, CCL19, CCL24, CCL7, CCL8, CD163, CD63, CHIT1, CLPB, CMKLR1, CSF1, CSF1R, CYBB, CYC1, CYFIP1, CYP19A1, DAGLA, DNASE2B, EMILIN1, FANCE, FCER1G, FDX1, FKBP15, FPR3, FTL, GLRX2, GP1BA, GPD1, HAMP, HEXB, HSPB7, IGSF6, IL10, KCNJ1, KCNJ5, KIFC3, LAMP1, LILRB1, LILRB4, LONP1, LONRF3, MARCO, MFSD7, MMP19, MRPL12, MS4A4A, MSR1, MT2A, MYBPH, MYO7A, MYOF, MYOZ1, NCAPH, NDUFAF1, P2RX7, PHLDB1, PKD2L1, PLEKHB2, PQLC2, PRDX1, RAB3IL1, RRP1, S1PR2, Macrophages SDS, SLAMF8, SLC31A1, SLC6A12, SPG21, SPR, SRC, TFEC, 272 M1 TMEM33, TMEM70, TREM2, TSPO, VSIG4 ABCD1, ACP2, ADAMDEC1, ADCK2, ADCY3, ADO, ADRA2B, ALCAM, ANXA2, ATP6V1A, ATP6V1H, C1QA, C1QB, C3AR1, CCL1, CCL18, CCL19, CCL22, CCL24, CCL7, CCL8, CD163, CD300C, CD63, CD80, CHIT1, CLEC4E, CMKLR1, CSF1, CSF1R, CYBB, CYC1, CYP19A1, DAGLA, DNASE2B, EMILIN1, FANCE, FDX1, FPR2, FPR3, GLRX2, GPD1, HAMP, HEXB, HSPB7, HYAL2, IGSF6, KCNJ1, KCNJ5, KCNK13, KIFC3, LILRB1, LILRB4, LONP1, MAPK13, MARCO, MFSD7, MMP19, MS4A4A, MSR1, MT2A, MYBPH, MYOF, MYOZ1, NCAPH, P2RX7, PHLDB1, S100A11, SDS, Macrophages SIGLEC1, SLAMF8, SLC11A1, SLC1A2, SLC31A1, SLC6A12, 273 M1 SRC, TBC1D16, TIE1, TMEM33, TMEM70, TREM2, VSIG4 ABCD1, ACP2, ACSM5, ADAMDEC1, ADCY3, AGGF1, ALK, ANKFY1, ARHGEF11, ARSB, ATP2A2, ATP6V0A1, ATP6V0D1, ATP6V1C1, BTBD1, C10orf76, C16orf62, CAMP, CANX, CCDC88A, CD63, CD81, CDS2, CLCN7, COL4A3BP, COMMD9, CYFIP1, DNASE2B, EXOC1, FDX1, FGR, FKBP15, FTL, GABARAP, GGA1, GLB1, GORASP1, HADHB, HAMP, HEXA, HEXB, HPS1, HS3ST2, HSPH1, IARS2, IFNAR1, ITGAX, KCNJ5, LAIR1, LAMP1, LILRB4, LONRF3, MARCO, MS4A4A, MSR1, MTMR14, MYO9B, NCKAP1L, NOP10, OS9, P2RX7, PABPC4, PDCD6IP, PICK1, PLEKHM2, POGK, PQLC2, RIN2, SCAMP2, SDCBP, SDS, SLAMF8, SLC25A24, SLC31A1, SLC38A7, SLC39A1, SMG5, SNX1, SNX2, SNX5, SPG21, STX18, STX4, TBC1D9B, TFEC, TMEM184C, Macrophages TMEM70, TPP1, TSPO, UBXN6, UNC50, VPS35, VPS53, 274 M2 VSIG4, WDFY3, ZC3H3 Macrophages CLCN7, FGR, FKBP15, GLB1, HEXA, HEXB, HS3ST2, 275 M2 PQLC2, SLC38A7, TMEM70WSGR Docket No.50272-712.601 ACP2, ALK, ARSB, ATP6V0A1, ATP6V0D1, CD63, CLCN7, COL4A3BP, COMMD9, CYFIP1, FGR, FKBP15, GLB1, HADHB, HAMP, HEXA, HEXB, HS3ST2, IFNAR1, KCNJ5, LONRF3, MARCO, MS4A4A, MYO9B, NCKAP1L, P2RX7, Macrophages PDCD6IP, PLEKHM2, PQLC2, SDCBP, SLC31A1, SLC38A7, 276 M2 SLC39A1, SMG5, STX18, TMEM70, TPP1, VPS53 ADRA2B, AGGF1, AKR7A2, ALDH9A1, ALG9, ANGPT4, ANXA11, AP1B1, AQP8, ARFGEF2, ATP6V1D, BAIAP2, BCAP31, CARD14, CCDC85C, CCDC88A, CD52, CEPT1, DHX57, EFR3A, ELK1, FH, FKBP15, FLT1, GPD1, GSTO1, HEXB, HS3ST2, HSPH1, IARS2, IPPK, KCNJ1, KCNK13, KCTD5, KIAA0196, LAMP1, LILRA2, MFN1, MMP19, MRM1, MS4A4A, MSR1, MYO15A, MYOZ1, NAGPA, NCAPH, NCKAP1L, NDUFB1, NFS1, NPR 1.00, OSBPL11, PDE1B, PEX19, POGK, PQLC2, S100A6, SLC25A46, SLC6A12, SLC6A7, SLC9A6, SNAPC2, SNX1, SNX3, TAF10, TMED5, Macrophages TMEM9B, TNFSF14, TREM2, UCP3, UGP2, USF2, VTI1B, 277 M2 XPNPEP2, ZCCHC4, ZNF219 ADRA2B, DNASE1L3, DNASE2B, FDX1, GPD1, GUCA1A, Macrophages HS3ST2, KCNJ1, MS4A4A, MSR1, MYOZ1, PDE1B, SDS, 278 M2 TREM2, UCP3 Macrophages ADRA2B, CD52, GPD1, HS3ST2, MSR1, MYO15A, 279 M2 NPR 1.00, UCP3 ACBD3, AGGF1, AGPS, AMHR2, ANXA1, ANXA11, ARHGEF12, ATP6V1C1, ATXN10, ATXN2L, BAHD1, BAIAP2, BET1L, BMPR1A, BTBD7, BTK, C3AR1, C6orf25, C8G, CASP10, CD22, CD33, CD84, CDC16, CENPJ, CHD9, CMA1, CPA3, CPSF1, CTDSP1, CTR9, CTSG, DCLRE1B, DEPDC5, DIAPH1, DNAJC13, DNAJC28, DR1, ESYT1, FAM120A, FBXO11, FBXO38, GATA1, HDC, HIBCH, HPGDS, HRH4, HSPH1, IFT27, IL5, IL5RA, ITGA2B, KCNJ5, KLRG1, KRT1, LAX1, LCP2, LRIG2, LTC4S, LYL1, MAGT1, MAP3K7, MBOAT7, MRPS28, MS4A2, MTR, NBAS, NDST2, NTRK1, OSBP, OSBPL9, P2RX1, PABPC4, PAK2, PDE4A, PIK3R2, POLR2A, PPP3R1, PRG2, PRKAR1A, PTGDR, PTGER3, RAD23B, REM1, RENBP, RGS11, RGS13, RNF103, RXRB, SERINC3, SIGLEC6, SIGLEC7, SIGLEC8, SLC18A2, SNRNP70, SNUPN, SNX5, SOS 2, SPAG8, STAM, STAP1, SYPL1, TADA2A, TAL1, TEC, THRAP3, TIPRL, TPSAB1, TRIM32, TTC17, U2AF2, UPF2, USP10, USP48, WDR46, ZBTB1, ZBTB7A, ZKSCAN3, ZMYM1, ZMYM4, ZNF212, 280 Mast cells ZNF264, ZNF426, ZNF471, ZNF549 ACBD3, AGPS, AMHR2, ANXA1, ANXA11, ARHGEF12, ATP6V1C1, ATXN10, ATXN2L, BAIAP2, BET1L, BMPR1A, BTBD7, BTK, C3AR1, C6orf25, C8G, CASP10, CD22, CD84, CDC16, CENPJ, CHD9, CPA3, CPSF1, CTDSP1, CTR9, CTSG, DCLRE1B, DEPDC5, DIAPH1, DNAJC13, DR1, ESYT1, FAM120A, GATA1, HDC, HPGDS, HRH4, HSPH1, IFT27, IL5, ITGA2B, KCNJ5, KLRG1, KRT1, LAX1, LCP2, LTC4S, LYL1, MAP3K7, MBOAT7, MS4A2, MTR, NBAS, NDST2, NTRK1, OSBP, OSBPL9, P2RX1, PABPC4, PAK2, PIK3R2, POLR2A, PPP3R1, PRG2, PRKAR1A, PTGER3, REM1, RENBP, RGS11, RGS13, RNF103, RXRB, SERINC3, SIGLEC6, SIGLEC7, SIGLEC8, SLC18A2, SNRNP70, SNUPN, SNX5, STAM, 281 Mast cells STAP1, SYPL1, TADA2A, TAL1, TEC, TIPRL, TPSAB1,WSGR Docket No.50272-712.601 U2AF2, UPF2, USP10, USP48, WDR46, ZBTB1, ZKSCAN3, ZNF264, ZNF549 ACBD3, AGPS, AMHR2, ANXA1, ANXA11, ATXN2L, BET1L, BMPR1A, BTK, C3AR1, C6orf25, C8G, CASP10, CD22, CPA3, CPSF1, CTR9, CTSG, DCLRE1B, DEPDC5, DIAPH1, FAM120A, GATA1, HDC, HPGDS, HRH4, HSPH1, IFT27, IL5, ITGA2B, KLRG1, KRT1, LAX1, LTC4S, LYL1, MBOAT7, MS4A2, NBAS, NDST2, NTRK1, OSBP, OSBPL9, P2RX1, PABPC4, PAK2, PIK3R2, PRG2, PTGER3, REM1, RENBP, RGS11, RGS13, RXRB, SIGLEC6, SIGLEC8, SLC18A2, SNRNP70, SNX5, STAP1, TADA2A, TAL1, TIPRL, 282 Mast cells TPSAB1, U2AF2, UPF2, USP10, WDR46, ZKSCAN3 ARHGAP6, CLEC1B, GP1BA, GP6, HTR2A, MPL, PF4V1, 283 Megakaryocytes RUFY1, SELP ANXA3, ARHGAP6, CLEC1B, GP1BA, MPL, NCKAP1, 284 Megakaryocytes PF4V1, SELP, TUBB1 ANXA3, ARHGAP6, CLEC1B, GP1BA, LRP2BP, MPL, SELP, 285 Megakaryocytes TUBB1 CLEC1B, GP1BA, GP6, HBD, KALRN, PF4V1, RGS6, SELP, 286 Megakaryocytes VWF CLEC1B, GP1BA, GP6, HBD, KALRN, PF4V1, RGS6, SELP, 287 Megakaryocytes TUBB1, VWF 288 Megakaryocytes CLEC1B, GP1BA, GP6, KALRN, PF4V1, RGS6, SELP, VWF CA8, FARP2, GJA3, GPR137B, KCNAB2, KCNE4, MLANA, 289 Melanocytes OCA2, PIR, QPCT, S100B, SLC45A2, SOX10, TBC1D16, TYR APH1B, CA8, CDH3, CLEC11A, FARP2, GJA3, GPR137B, KCNAB2, MCOLN1, MLANA, MMP17, OCA2, PIR, QPCT, SLC45A2, SOX10, TBC1D16, TNFRSF14, TYR, TYRP1, 290 Melanocytes UAP1L1, WFDC1, WIPI1 CA8, CBR3, FARP2, NOV, OCA2, PIR, QPCT, SLC45A2, 291 Melanocytes SOX10, TBC1D16, TYR, UAP1L1 DCT, MLANA, OCA2, PLXNC1, SLC16A6, SLC45A2, SOX10, 292 Melanocytes TRPM1, TYR, TYRP1 AATK, ABCC2, ACACB, ACP5, ALX1, ANKRD28, ARHGAP24, ASB9, ASPA, ATP10B, ATP6V0A4, AVPI1, BCL2A1, BIN3, BNC2, C21orf91, CA14, CAPN3, CDH19, CDK2, CDON, CEACAM1, CLCN5, CYP27A1, CYSLTR2, DAAM2, DAB2, DCT, DDX58, DDX60, DOCK10, EDNRB, ERBB3, ESR2, FARP2, FOXD3, GAPDHS, GAS7, GCNT2, GJB1, GK, GMPR, GNAL, GNPTAB, GPNMB, GPR143, GREB1, GYG2, HERC5, HERC6, HLA-DRA, HLA-DRB1, HLA-F, HMG20B, IFI16, IFI27, IFI35, IFI44, IFI44L, IFI6, IFIH1, IFIT1, IFIT2, IFIT3, IFIT5, INPP4B, IQGAP2, IRF4, ISG15, ISG20, ITGB8, ITM2A, ITPKB, KCNAB2, KCNJ13, KIT, KYNU, L1CAM, LAP3, LEF1, LYST, LZTS1, MBP, MCF2L, MCOLN3, MITF, MLANA, MMP8, MOCOS, MX1, MX2, MYO5A, NEDD4L, NLGN1, NOV, OAS1, OAS2, OASL, OCA2, OSTM1, PAEP, PAX3, PDE3A, PDE3B, PDE4B, PHACTR1, PI15, PIR, PLA1A, PLEKHA5, PLP1, PLSCR1, PLXNC1, PRKD3, PROS1, PRUNE2, QPCT, RAB17, RAB27A, RNF144A, RSAD2, RTP4, RUNX3, SAMD9, SEMA6A, SGK1, SLC16A6, SLC1A4, SLC39A6, SLC43A3, SLC45A2, SLC7A11, SOX10, SOX13, SP110, STAT1, TAP1, TBC1D16, TCN1, 293 Melanocytes TDRD7, TESK2, TLR1, TMEM140, TMPRSS5, TNFRSF14,WSGR Docket No.50272-712.601 TRPM1, TRPV2, TYR, TYRP1, UBA7, WARS, WIPI1, XAF1, XYLB, ZEB2, ZFYVE16, ZNF749 AATK, ABCC2, ABL2, ACACB, ACP5, ACSL3, ALX1, ANKRD28, APOD, APOL6, APOLD1, ARHGAP24, ASB9, ASPA, ATP10B, ATP6V0A4, AVPI1, BCAN, BCL2A1, BIN3, BNC2, C21orf91, CA14, CAPN3, CBR3, CD36, CDH19, CDK2, CDON, CEACAM1, CIT, CLCN5, CLCN7, CTSK, CYP27A1, CYSLTR2, DAAM2, DAB2, DCT, DDX58, DDX60, DOCK10, EDNRB, EGLN1, ERBB3, ESR2, ETV5, FARP2, FOXD3, GAPDHS, GAS7, GCNT2, GJB1, GK, GMPR, GNAL, GNPTAB, GPNMB, GPR143, GREB1, GYG2, HERC5, HERC6, HLA-DPA1, HLA-DRA, HLA-DRB1, HLA-F, HMG20B, IFI16, IFI27, IFI35, IFI44, IFI44L, IFI6, IFIH1, IFIT1, IFIT2, IFIT3, IFIT5, IFITM1, INPP4B, IQGAP2, IRAK3, IRF4, IRF7, ISG15, ISG20, ITGB8, ITM2A, ITPKB, KCNAB2, KCNJ13, KIT, KYNU, L1CAM, LAP3, LEF1, LGALS3, LYST, LZTS1, MBP, MCF2L, MCOLN3, MITF, MLANA, MME, MMP8, MOCOS, MX1, MX2, MYO5A, NAMPT, NEDD4L, NLGN1, NOV, NQO1, OAS1, OAS2, OASL, OCA2, OSTM1, PAEP, PAX3, PDE3A, PDE3B, PDE4B, PDE4D, PDK4, PDZRN3, PHACTR1, PI15, PIR, PLA1A, PLEKHA5, PLP1, PLSCR1, PLXNC1, POPDC3, PPFIBP2, PRKCE, PRKD3, PROS1, PRUNE2, PYHIN1, QPCT, RAB17, RAB27A, RNF144A, RPP25, RSAD2, RTP4, RUNX3, SAMD9, SAMHD1, SEMA6A, SGK1, SLC12A2, SLC16A6, SLC1A4, SLC27A3, SLC39A6, SLC43A3, SLC45A2, SLC7A11, SORBS2, SOX10, SOX13, SP100, SP110, SPATA6, STAT1, STAT5A, STX3, TAP1, TBC1D16, TBX2, TCN1, TDRD7, TESK2, TLR1, TMEM140, TMPRSS5, TNFRSF14, TRIB2, TRIM14, TRPM1, TRPV2, TYR, TYRP1, UBA7, UBE2L6, VGF, WARS, WIPI1, XAF1, XYLB, ZEB2, 294 Melanocytes ZFYVE16, ZFYVE26, ZNF749 ACRV1, ADAM20, ADAM30, ADAMTS12, ADCY2, AIPL1, ANKRD34C, AP1M2, AQP8, ART1, BAIAP3, BLK, C4BPA, CASQ2, CCR6, CCR9, CD180, CD19, CD1C, CD22, CD37, CD3EAP, CD72, CD79A, CD79B, CER1, CETP, CHP2, CHRM2, CHRNA2, CHST5, CLDN17, CNR2, COLEC10, COX6A2, CPA2, CPB1, CRB1, CSN1S1, CYLC2, CYP2A7, CYP2C19, DCC, DDX4, DPP6, DSCR4, DSP, FCRL2, FMO1, FMO6P, FSCN3, FSHR, GABRA4, GAD2, GGA2, GK2, GLYAT, GNAT2, GNRHR, GPRC5D, GPX5, GRIN2B, GRM6, HCRTR2, HECW1, HLA-DPB1, HRH4, HSD3B2, HTN3, INHBC, KALRN, KCNA5, KCNJ10, KHDRBS2, KIAA0125, KIF5A, KRT2, KRT75, LECT2, LY86, MAP3K9, MBL2, MC4R, MEFV, MEP1B, MOGAT2, MS4A1, MS4A5, MYOZ3, NCAN, NPHS2, NPY5R, NTRK3, OBSCN, OTC, PAX5, PGLYRP4, PLIN1, PNLIPRP1, PNOC, POU4F2, PRKCB, PROZ, PRPH2, PTH1R, QRSL1, RIC3, RRH, S100G, SELP, SERPINA4, SHISA6, SIGLEC6, SLC12A3, SLC17A1, SLC17A7, SLC24A2, SLC30A10, SLC5A7, SLCO1C1, SLN, SP140, SPIB, SSX3, STAP1, SYN2, SYPL1, TAS2R14, TCTN2, TMPRSS11D, TNFRSF13B, TNFRSF17, TRPM3, TSHB, TSPAN13, ULK4, UNC5C, VN1R1, VPREB3, WNT16, WNT2, 295 Memory B-cells ZIC3, ZNF548, ZNF747 ADAM21, AIPL1, ANKRD34C, BLK, CAPN3, CCR6, CCR9, 296 Memory B-cells CD180, CD19, CD22, CD37, CD3EAP, CD72, CD79A, CD79B,WSGR Docket No.50272-712.601 CNR2, CPA2, CPB1, CSN1S1, DDX4, DPP6, DSP, FCRL2, FMO1, FMO6P, GGA2, GK2, GRM6, HNRNPL, HRH4, INHBC, KCNA5, KIAA0125, LY86, MC4R, MOGAT2, MS4A1, MS4A5, OBSCN, PAX5, PGLYRP4, PIKFYVE, PNOC, POU4F2, PRKCB, QRSL1, RRH, SCRN1, SHISA6, SLC17A1, SLC30A10, SLC5A7, SLCO1C1, SP140, SPIB, STAP1, SYPL1, TCTN2, TNFRSF13B, TNFRSF17, TRPM3, TSHB, ULK4, VPREB3, WNT16, ZIC3, ZNF548 AIPL1, ART1, BLK, CASQ2, CCR6, CD180, CD19, CD1C, CD22, CD37, CD3EAP, CD72, CD79A, CD79B, CER1, CHRM2, CHST5, CNR2, CPB1, CSN1S1, CYP2A7, DDX4, FCRL2, FMO1, GABRA4, GAD2, GGA2, GK2, GNAT2, GRM6, HECW1, INHBC, KALRN, KCNJ10, KIAA0125, KRT2, LY86, MAP3K9, MEFV, MOGAT2, MS4A1, NPY5R, PAX5, PGLYRP4, PNLIPRP1, PNOC, POU4F2, PRKCB, QRSL1, RRH, S100G, SELP, SHISA6, SIGLEC6, SLC24A2, SLC5A7, SLCO1C1, SP140, SPIB, SSX3, STAP1, TAS2R14, TNFRSF13B, TRPM3, TSHB, TSPAN13, VN1R1, VPREB3, 297 Memory B-cells ZNF548, ZNF747 BLK, CD19, CD22, CD3EAP, CD79A, CD79B, FCRL2, GRM6, INHBC, KIAA0125, MOGAT2, MS4A1, PNOC, QRSL1, SPIB, 298 Memory B-cells TNFRSF13B, TSHB, TSPAN13, VPREB3 ANKRD34C, BLK, CCR9, CD180, CD19, CD22, CD37, CD3EAP, CD72, CD79A, CD79B, CSN1S1, DSP, FCRL2, FMO1, FMO6P, GGA2, GK2, GRM6, HRH4, INHBC, KIAA0125, LY86, MS4A1, PNOC, POU4F2, PRKCB, QRSL1, RRH, SHISA6, SLC5A7, SP140, SPIB, STAP1, TNFRSF13B, 299 Memory B-cells TNFRSF17, TSHB, VPREB3, ZNF548 BLK, CD19, CD22, CD79A, CD79B, FCRL2, INHBC, 300 Memory B-cells KIAA0125, MS4A1, PNOC, QRSL1, SPIB, TSPAN13, VPREB3 BLK, CD1C, CD79B, FSCN2, LY86, MBD4, NT5C, ODC1, 301 Memory B-cells SP140, SPIB, TNFRSF13B, TRMT61A, WNT16, ZBTB32 AICDA, BLK, CCR6, CD19, CD22, CXCL13, CXCR5, FCRL2, HTR3A, LY86, MBD4, MGAT5, MIOS, MS4A1, RNGTT, 302 Memory B-cells SP140, SPIB, TNFRSF13B, ZBTB32 BLK, CD1C, CD79B, FSCN2, MBD4, NT5C, ODC1, SP140, 303 Memory B-cells SPIB, TNFRSF13B, WNT16, ZBTB32 AHCY, CDK4, CPA3, CRHBP, EPCAM, ERG, GSTM5, HDC, 304 MEP HPGDS, LAPTM4B, MINPP1, PAICS, PRG2, RYR3 AHCY, CDK4, CPA3, CRHBP, EPCAM, GSTM5, HDC, 305 MEP HPGDS, LAPTM4B, POLE2, TCEAL4 BUB1B, C11orf95, CPA3, CRHBP, ERG, FAM124B, HDC, 306 MEP HPGDS, KIAA0101, MRPL15, POLE2, RYR3, SNX5, STIL ACD, AIMP2, CHAF1B, CTNNBL1, DCTPP1, ERAL1, EXOSC5, FXN, GATA1, HBD, HPGDS, KEL, MRPS2, MTG1, MYL4, NAT6, PCCB, PNMT, PRG2, RHAG, RUVBL1, SURF2, 307 MEP TPSAB1, UNG ACD, AIMP2, C21orf59, CHAF1B, CTNNBL1, DCTPP1, DDX41, ERAL1, EXOSC5, FBXO7, FCER1A, FMO1, FXN, HPGDS, IFRD2, KRT1, MECR, MRPL28, MRPS2, MTG1, 308 MEP NOL12, NTRK1, PCCB, PNMT, RUVBL1, TMED1, UNG ACD, CTNNBL1, FXN, HPGDS, MRPS2, NOL12, PCCB, 309 MEP PNMT, TMED1WSGR Docket No.50272-712.601 ATIC, BCCIP, BCS1L, CARS2, CCT6A, CDKN2AIP, CTNNBL1, DDX1, DDX10, ERAL1, FASTKD2, FN3KRP, FXN, GNL3, GUF1, HNRNPAB, IFRD2, ITGA2B, KEAP1, LDB1, LRPPRC, MED25, MKS1, MLC1, MRM1, MRPS2, MRTO4, MYO16, NAA15, NOL10, NOP16, NUDC, NUFIP1, OGFOD2, PAK1IP1, PCCB, PDCD11, POLE2, PPRC1, PRMT3, PSMG1, RPUSD2, RRP15, RUVBL1, RYR3, SERBP1, TIMM50, TRAP1, TSR1, TXNL1, URB1, VAPA, WDR12, 310 MEP WDR43, ZKSCAN3 ACD, CA1, COQ3, CTNNBL1, FMO1, FXN, HDC, HPGDS, 311 MEP MRPS2, NTRK1, PNMT, PRG2 ACD, AIMP2, C21orf59, CHAF1B, CTNNBL1, DCTPP1, DDX41, ERAL1, EXOSC5, FBXO7, FCER1A, FMO1, FXN, HPGDS, IFRD2, KRT1, MECR, MRPL28, MRPS2, MTG1, 312 MEP NOL12, NTRK1, PCCB, PNMT, RUVBL1, TMED1, UNG CDH6, CDKN1C, CLSTN2, CRISPLD2, DOC2B, EDN2, FOXD1, FOXF1, HOXA11, HOXD1, KIRREL, LHX1, MICB, 313 Mesangial cells PADI2, RHOF, TLL2 ARL4C, CDKN1C, CLSTN2, DOC2B, EDN2, FOXD1, 314 Mesangial cells HOXA11, HOXD1, KIRREL, LHX1, MICB, TLL2 APOBEC3F, ARID3A, ARL4C, ASPHD1, BICC1, CDH16, CDH6, CDKN1C, CLDN6, CLSTN2, COL4A1, CORO1B, CPA4, CRISPLD2, CSDC2, DHRS3, DOC2B, EDN2, EPHB2, FGF18, FOXD1, GALNT14, GPI, HNF1B, HOXA11, HOXA3, HOXB2, HOXB3, HOXC10, HOXD1, HOXD11, HS3ST3A1, HSPA12A, ILK, INPP5A, ISYNA1, ITGA3, ITGB3, KIRREL, LAMA5, LAPTM4B, LHX1, MGAT4B, MICB, NME3, NR2F2, NTHL1, ORAI2, PAX2, PCYOX1L, PDCD2, PDZD7, PFDN1, PLA2G16, PLEC, PNPLA6, PROCR, PVR, PXDN, RBM38, RBPMS, REC8, RHOF, RNASET2, SMOX, THAP7, TLL2, 315 Mesangial cells UBA1, UCP2, UROD, WNT7B ABCB1, ALPK3, ANKRD1, BGN, BST2, CD70, CLDN1, CLDN4, CLDN6, CNN1, COL1A1, COL3A1, COL4A1, COL5A1, COL6A3, CREB3L1, CRISPLD2, CTGF, DCN, DIRAS3, DLC1, DOC2B, DPYSL3, DYSF, ELF3, EPHB2, F2RL2, FGB, FLNC, FN1, FOXC1, FOXD1, GATA6, GLI2, GREM1, HKDC1, HMGA2, HNF1B, HOXA10, HOXA11, HOXA5, HOXA7, HOXB2, HOXB3, HOXB6, HOXB9, HOXD11, HS3ST3A1, IGFBP2, IGFBP4, IGFBP5, IL32, ITGB3, KCNJ15, LAD1, LAMA5, LHX1, LIF, LMCD1, LRRC17, LSR, LYPD1, MITF, MMP24, MMP7, MXRA8, MYL9, MYLK, NID2, NUAK2, NUP210, OLFML2A, PAX8, PDGFB, PDGFRB, PLAT, PODXL, POSTN, PPARG, PPP1R13L, PTGER2, PTX3, RAB3B, RAP1GAP2, RCN3, REC8, RGS4, RHOF, SFN, SLC29A1, SMAD6, SPOCK2, TAGLN, TFPI, TGFB2, TGM2, 316 Mesangial cells UCP2, VCAM1, VCAN, WNT7B ABCB1, B3GALT5, C14orf105, CD70, CDHR1, CLDN6, DOC2B, F2RL2, FGB, FOXC2, HNF1B, HOXA10, HOXA11, HOXA3, HOXA7, HOXB8, HOXB9, HOXD10, HOXD11, IFITM2, NUP210, PAX2, PAX8, PODXL, RAB3B, REC8, 317 Mesangial cells RHOF, SALL1, UCP2 ABCB1, ACTA2, ADAM19, ADAMTS3, ADORA1, AEBP1, ALPK3, ARHGAP4, ARL4C, ATP6V0E2, ATP7B, B3GALT5, BGN, BICC1, BMP4, C14orf105, CACNA1H, CADM4, CCR10, 318 Mesangial cells CD70, CDH6, CDHR1, CLDN1, CLDN3, CLDN4, CLDN6,WSGR Docket No.50272-712.601 CLIC3, CNN1, COL18A1, COL1A1, COL1A2, COL3A1, COL5A1, COL6A1, COL6A2, COL6A3, CPA4, CPNE7, CREB3L1, CRIP1, CRISPLD2, CYBA, DCN, DENND3, DIRAS3, DLC1, DNAJC22, DNM1, DOC2B, DPYSL3, DYSF, EDN2, EFEMP2, ELF3, ENPEP, F2RL2, FGB, FN1, FOXC1, FOXC2, FOXD1, FXYD6, FZD2, GAL3ST1, GALNT14, GATA6, GLI2, GPRC5C, GRB14, GREM1, HCFC1R1, HIPK2, HKDC1, HNF1B, HOXA10, HOXA11, HOXA3, HOXA4, HOXA6, HOXA7, HOXB6, HOXB8, HOXB9, HOXC10, HOXD10, HOXD11, HOXD9, HPGD, HS3ST3A1, HSPA2, IFITM2, IGFBP4, IL32, ITGB3, KBTBD11, KIRREL, KRT19, LBH, LFNG, LHX1, LRRC20, MEIS2, MGP, MITF, MMP24, MMP7, MSLN, MYL9, NCAM1, NID2, NOV, NPPB, NR2F1, NUP210, OLFML2B, OLFML3, OXTR, PAPPA, PAX2, PAX8, PCOLCE, PDGFB, PDGFRB, PDLIM7, PLA2G16, PLAT, PLK2, PLXND1, PODXL, POSTN, PPARG, PROM1, PTGER2, PTPRJ, QPRT, RAB11FIP1, RAB3B, RBPMS, RCN3, REC8, RGS4, RHOF, RPH3AL, RRAD, SALL1, SDC2, SDR39U1, SHC3, SIX1, SIX2, SLC22A3, SLC29A1, SMAD6, SPOCK2, SPP1, SST, SULF1, SYT13, TAGLN, TBL1X, TBXAS1, TGFB1I1, TGFB2, TNFSF10, TRHDE, TRPC4, UCP2, WT1, ZNF580 ASGR2, CCR2, CD300C, CFP, FCN1, LILRA1, LILRA5, 319 Monocytes MS4A6A, RNASE2, TLR7 AIF1, ASGR2, CCR2, CD101, CD163, CFP, CLEC5A, CYBB, FBXL5, FCAR, FCN1, KCNMB1, MEFV, MNDA, RETN, 320 Monocytes S100A12, TREM1 ASGR2, CD93, CFP, CSF3R, F13A1, FCN1, LILRB2, MS4A6A, 321 Monocytes P2RY13, PADI4, RNASE2, S100A12 ACAP2, AIF1, APAF1, ARNT, ASGR2, ATP6V0D1, BEST1, C3AR1, CALCOCO2, CAMKK2, CASP5, CCR2, CD101, CD163, CD300C, CD33, CFP, CLEC4E, CLEC5A, COMMD9, CSF1R, CSF3R, CYBB, DENND1A, DPEP2, EIF4E2, FBXL5, FCAR, FCN1, FGL2, FPR2, GABARAP, HCK, HK3, HRH2, HSPA6, IL17RA, IQGAP1, KCNMB1, LILRA1, LILRA2, LILRA5, LILRB1, LILRB2, LILRB3, LST1, MAP3K3, MEFV, METTL9, MNDA, MS4A6A, MYO1F, NUP214, OSBPL11, P2RY13, PHKG2, PILRA, PTGIR, RARA, RETN, RHOA, RHOG, RHOT1, RIN2, RPGRIP1, RPH3A, S100A12, TGOLN2, TLR7, TLR8, TMEM127, TMEM9B, TNFRSF8, TREM1, 322 Monocytes TREX1, TYROBP, UBE2D1, UPK3A, VASP, VENTX ABCB7, ACAP2, AGFG1, AHNAK, AIF1, AKAP13, ANKS1A, ANXA1, ARF5, ASGR2, ATG3, BEST1, BPI, BTAF1, BTK, C3AR1, CALCOCO2, CAMKK2, CAPN2, CAPN3, CAPNS1, CARS2, CAST, CCR2, CD101, CD163, CD300C, CD33, CD4, CD93, CEACAM4, CECR5, CEPT1, CFP, CLEC10A, CLEC3B, CLEC4A, CLEC4E, COL4A3BP, COMMD9, COQ2, CSF1R, CSF3R, CTBP2, CX3CR1, CXCR2, CXorf21, CYBB, DENND1A, DHX57, DHX8, DNAJC13, DOK2, DOK3, DPEP2, EIF4E2, F13A1, FAM32A, FBXL5, FBXO11, FCAR, FCER1G, FCN1, FGR, FOLR2, FPR2, GABARAP, GIT2, H2AFY, HADHA, HCK, HIPK1, HK3, HSPA6, IL10RA, IL17RA, IMPDH1, KCNMB1, KDM6B, KIAA1033, KLHL18, LILRA1, LILRA2, LILRA5, LILRB1, LILRB2, LILRB3, LST1, LTBR, 323 Monocytes LY86, LYL1, MAN2C1, MAP3K11, MAP3K3, MAPK14,WSGR Docket No.50272-712.601 MARCO, MARK3, MBOAT7, MED13L, METTL9, MNDA, MNT, MS4A6A, MTMR14, MTMR3, MYO1F, NCF4, NCOA4, NDST2, NEK4, NKIRAS2, NPLOC4, NSFL1C, NUBP1, NUP214, OGFR, P2RY13, PADI4, PANK2, PCTP, PGLS, PIAS1, PILRA, PLP2, PPM1F, PPP1CB, PRKACA, PSTPIP1, PTEN, PTGIR, PTPN18, QKI, RARA, RBM41, RETN, RGS19, RHOT1, RIN2, RNASE2, RPH3A, RTN3, S100A10, S100A12, S100A6, SEC11A, SETX, SH3BP2, SIGLEC9, SIK3, SIN3B, SLC11A1, SLC38A10, SNX17, SPEN, SPG11, STAB1, STRN4, STX12, SUN1, TLR7, TLR8, TMBIM4, TMEM11, TPD52L2, TREM1, TREX1, TSEN34, TSPO, UBE2D1, UBR2, UBXN2B, USP15, USP4, VENTX, WAS, WDR11, WWP2, YTHDF3, ZDHHC3 ABCB7, ACAP2, AGFG1, AHNAK, AIF1, AKAP13, ANKS1A, ANXA1, ARF5, ASGR2, ATG3, BEST1, BPI, BTAF1, BTK, C3AR1, CALCOCO2, CAMKK2, CAPN2, CAPN3, CAPNS1, CARS2, CAST, CCR2, CD101, CD163, CD300C, CD33, CD4, CD93, CEACAM4, CECR5, CFP, CLEC10A, CLEC3B, CLEC4A, COMMD9, COQ2, CSF1R, CSF3R, CTBP2, CUL5, CX3CR1, CYBB, DENND1A, DHX57, DHX8, DNAJC13, DOK2, DOK3, DPEP2, EIF4E2, F13A1, FAM32A, FBXL5, FBXO11, FCAR, FCER1G, FCN1, FGR, FPR2, GABARAP, GIT2, H2AFY, HCK, HIPK1, HK3, HSPA6, IL10RA, IMPDH1, JMJD1C, KCNMB1, KDM6B, KIAA1033, KLHL18, LILRA1, LILRA2, LILRA5, LILRB1, LILRB2, LILRB3, LST1, LTBR, LY86, LYL1, MAN2C1, MAP3K11, MAP3K3, MAPK14, MARCO, MARK3, MBOAT7, METTL9, MNDA, MNT, MS4A6A, MTMR14, MTMR3, MYO1F, NCOA4, NDST2, NKIRAS2, NPLOC4, NSFL1C, NUP214, OGFR, P2RY13, PADI4, PANK2, PCTP, PGLS, PIAS1, PIKFYVE, PILRA, PLP2, PPM1F, PPP1CB, PRKACA, PSTPIP1, PTEN, PTPN18, RARA, RBM41, RETN, RGS19, RHOT1, RIN2, RNASE2, RPH3A, RTN3, S100A10, S100A12, S100A6, SEC11A, SH3BP2, SIGLEC9, SIK3, SIN3B, SLC38A10, SNX17, SPEN, SPG11, STAB1, STRN4, STX12, SUN1, TLR7, TLR8, TMEM11, TPD52L2, TREM1, TREX1, TSEN34, TSPO, UBE2D1, USP15, 324 Monocytes USP4, USP48, VENTX, WAS, WDR11, WWP2, ZMYM4 ARL8B, CD244, CFP, CLEC5A, CSNK1A1, EIF1B, FCN1, FGR, FKBP15, FNDC3A, HUS1, LILRB2, LRRFIP1, NUBP1, 325 Monocytes PNP, RETN, RPS6KC1, SH3BP2, SRC ARL8B, CASP5, CD48, CFP, CLEC5A, CSNK1A1, FCN1, FGR, 326 Monocytes FKBP15, FPR2, LILRA1, MEFV, PNP, RETN, SIGLEC9 AATK, ADIPOR1, AGFG1, AKAP13, AKAP8, AP1G1, APBB3, ASGR2, AZIN1, BCL10, BCL2L11, BNIP2, CASP5, CBX6, CD101, CD1E, CD300C, CD33, CD93, CDC40, CDK9, CFP, CLEC10A, CLEC1A, CLEC4A, CLEC4E, CLEC5A, CLIP1, CSF1R, CYSLTR2, DCTN4, DDX21, DDX3X, DENND1A, DHX8, DLG4, DNAH17, ELL, ETF1, ETV3, EWSR1, FBXL5, FCAR, FCN1, FGL2, FOLR2, FOLR3, FPR2, GALNT3, GGA1, GNA13, GNMT, GPR162, GPR183, GRPEL1, HIF1A, HNRNPU, IQSEC2, JMJD6, KCNC3, KCNMB1, KSR1, LILRA1, LILRA5, LILRB1, LILRB2, MAP2K1, MAP3K2, MAPK6, MEFV, METTL9, MIOS, MMP17, MPHOSPH6, MS4A6A, MTF1, MTHFR, MTMR14, NDST2, OGFR, OSM, 327 Monocytes P2RY13, PDE6H, PGGT1B, PGLS, PHLDA2, PLAA, PLD2,WSGR Docket No.50272-712.601 PLEK, POU2F2, PPM1A, PRKACA, PTCH2, PTGIR, PTP4A2, QKI, RAB5A, RABGEF1, REEP4, RELA, RIOK3, RLIM, S100A10, S100A12, SAMSN1, SAR1A, SERP1, SH3BP2, SLC11A1, SOCS3, STAB1, STRN4, STX5, SUPT6H, TBK1, TMEM104, TMEM110, TNFRSF8, TREM1, TREX1, TSPO, TTLL4, UBE2D1, UPK3A, USP8, VENTX, VNN 3.00, WTAP, YTHDF3, ZBTB7A, ZFC3H1, ZNF668, ZNF710, ZNF787 ASGR2, CFP, FBXL5, FCAR, FCN1, FOLR2, HIC1, LILRA5, MEFV, MS4A6A, RABGEF1, S100A12, SAMSN1, SLC11A1, 328 Monocytes TNFRSF8, TREM1, VENTX, VNN 3.00 ASGR2, FCAR, FCN1, LILRA5, MS4A6A, S100A12, TREM1, 329 Monocytes UPK3A, VENTX, VNN 3.00 ASGR2, CASP5, CFP, CLEC5A, DENND1A, FBXL5, FCAR, FCN1, FGL2, FOLR2, GNA13, HIC1, HIF1A, LILRA5, MEFV, METTL9, MMP17, MS4A6A, OSM, P2RY13, PADI4, PTGIR, RABGEF1, RIOK3, S100A12, SAMSN1, SEMA6B, SLC11A1, SOCS3, STAB1, TNFRSF8, TREM1, UPK3A, VENTX, 330 Monocytes VNN 3.00 ACAP2, AHNAK, AIF1, ANKS1A, ANXA1, ASGR2, ATG3, BPI, BTK, C3AR1, CAPN3, CARS2, CAST, CCR2, CD101, CD163, CD300C, CD33, CD4, CD93, CEACAM4, CFP, CLEC10A, CLEC3B, CLEC4A, COMMD9, COQ2, CSF3R, CTBP2, CX3CR1, CXCR2, CYBB, DOK2, DPEP2, F13A1, FBXL5, FCAR, FCER1A, FCN1, FGR, FPR2, GIT2, HCK, HK3, HSPA6, KCNMB1, LILRA1, LILRA2, LILRA5, LILRB1, LILRB2, LILRB3, LST1, LTBR, LY86, LYL1, MAPK14, MARCO, METTL9, MNDA, MS4A4A, MS4A6A, MYO1F, NCF4, NEK4, NUP214, P2RY13, PADI4, PCTP, PGLS, PILRA, PLP2, PPM1F, PSTPIP1, PTEN, PTPN18, RETN, RGS19, RHOT1, RNASE2, RTN3, S100A12, STAB1, TLR7, TLR8, 331 Monocytes TREM1, TSEN34, TSPO, UBE2D1, UBXN2B, USP15 ASGR2, CFP, FBXL5, FCAR, FCN1, FOLR2, MEFV, MS4A6A, 332 Monocytes S100A12, SLC11A1, TREM1, UPK3A, VENTX, VNN 3.00 ASGR2, CFP, FCAR, FCN1, FOLR2, MEFV, MS4A6A, 333 Monocytes S100A12, SLC11A1, TREM1, UPK3A, VENTX, VNN 3.00 ADCY3, AGK, ALG8, ATP2C1, ATP5SL, ATXN10, BAHCC1, CALCOCO2, CAPRIN1, CENPO, CRHBP, CSTF2T, DPPA4, DPY19L4, ERG, FAM136A, G6PC3, H2AFY, HIBCH, HNRNPA3, LSM 5.00, MED7, MKL2, MLF 2.00, MRPS31, NSMAF, P2RX1, PARP2, PPM1F, PTRH2, SERPINI2, SF3A3, SFXN3, SPAG16, TAOK3, TBC1D13, TEC, TIE1, TMEM147, TMEM70, UBA5, UMPS, USP6, WDR60, 334 MPP ZNF219, ZNF282 ABO, AIF1, AKAP13, AMD 1.00, ANP32B, ATP2C1, ATP5J, AVP, BTF3, BZW2, CD164, CDC123, CLNS1A, CPA3, CRHBP, CRYGD, DNTT, DUT, EIF1B, EIF3E, ERCC8, ERLIN1, ESYT1, FANCA, GALR2, GGCT, GLTSCR2, HAUS5, HINT1, IGLL1, IMP4, INVS, ITGA9, KIAA0125, LRCH4, LSM 1.00, MAP7D3, MED7, MPL, MPO, MTDH, NACA, NAT6, NFYA, NHP2, NPM1, NSFL1C, NUP98, PCID2, PIGF, PMPCB, PPIH, PRTN3, PSMA2, PSMA6, RAG2, RBBP7, REV1, RFC2, RNASE2, RNASE3, RPS24, RPS3, RSL24D1, 335 MPP SMARCC2, SNRPF, SRBD1, TATDN2, TEC, TERT, TEX10,WSGR Docket No.50272-712.601 TFB2M, TMEM9B, TRH, TSSC1, TTF1, ZNF134, ZNF32, ZNF35, ZNF639 ABO, AKAP13, AMD 1.00, ATP5J, AVP, BTF3, BZW2, CLNS1A, CRHBP, CRYGD, CSPP1, DUT, EIF3E, ERCC8, ESYT1, FAM136A, FANCA, GALR2, GGCT, HAUS5, HINT1, IGBP1, IGLL1, IMP4, IMPDH2, INVS, ITGA9, KIAA0125, LRCH4, LSM 1.00, MAP7D3, MED7, MPL, MPO, MTDH, NACA, NHP2, NSFL1C, NUBP2, NUP98, PCID2, PIGF, PPIH, PSMA2, PSMA6, RBBP7, RFC2, RNASE2, RPS24, RPS3, RSL24D1, SNRPF, SRBD1, TATDN2, TERT, TEX10, TFB2M, 336 MPP TMEM9B, ZDHHC6, ZNF134, ZNF32, ZNF639 AVP, AZU1, CD164, CRHBP, DNTT, FLT3, IGLL1, ITGA9, KIAA0125, LSM 1.00, MPL, MPO, NUP98, RNASE2, SPN, 337 MPP VPREB3 338 MPP AVP, CRHBP, FLT3, IGLL1, KIAA0125, MPL, MPO, VPREB3 339 MPP AVP, CRHBP, FLT3, IGLL1, KIAA0125, MPL, MPO, VPREB3 ABO, ACAP1, AGAP2, ARHGAP15, AVP, BAIAP3, CD244, CD37, CD52, CIITA, CPA3, CRHBP, CSF2RB, CSF3R, CTSW, DOK3, DPEP2, DPPA4, FCER1A, FLT3, GATA1, GFI1B, HBB, HBD, HDC, HLA-DOA, IGLL1, IKZF1, ITGA2B, ITGA9, ITGAL, JAK3, KIAA0125, KLF1, MLC1, MPL, MPO, NKG7, OSM, P2RX1, PIK3CG, PLCB2, PRKCB, PRTN3, PTPN6, PTPN7, PTPRC, PTPRCAP, S1PR4, SELPLG, SPN, TRAF3IP3, 340 MPP TSPAN32 ABO, ACAP1, AGAP2, ARHGAP15, ATP8B4, AVP, BAIAP3, CD244, CD300A, CD37, CD52, CIITA, CPA3, CRHBP, CSF2RB, CSF3R, CTSW, DOK3, DPEP2, DPPA4, FCER1A, FLT3, FMNL1, GATA1, GFI1B, HBB, HBD, HDC, HLA-DOA, IGLL1, IKZF1, IRF5, ITGA2B, ITGA9, ITGAL, JAK3, KIAA0125, KLF1, LST1, MLC1, MPL, MPO, NKG7, OSM, P2RX1, PIK3CG, PLCB2, PRKCB, PRTN3, PTPN6, PTPN7, PTPRC, PTPRCAP, S1PR4, SASH3, SELPLG, SPN, TRAF3IP3, 341 MPP TSPAN32 AGAP2, AVP, CD37, CD53, CPA3, CRHBP, CSF3R, CTSW, FCER1A, GFI1B, HBD, HLA-DOA, IGLL1, IKZF1, ITGA2B, KIAA0125, LAIR1, MLC1, MPO, MYO1F, NCF4, NCKAP1L, OSM, P2RX1, PTPN7, PTPRC, PTPRCAP, S1PR4, SASH3, 342 MPP SPI1, SPN, TSPAN32 AVP, CRHBP, ESYT1, FLT3, IGLL1, KIAA0125, MPL, MPO, 343 MPP NUP98, VPREB3 344 MPP AVP, CRHBP, FLT3, IGLL1, KIAA0125, MPL, MPO, VPREB3 345 MPP AVP, CRHBP, FLT3, IGLL1, KIAA0125, MPL, MPO, VPREB3 ADAMTS12, CDKL5, CTRB2, HTR7, MMP17, PKD2L1, 346 MSC PLA2G5, ZNRF4 ADAMTS12, CCL21, CDKL5, CTRB2, HTR7, MMP17, 347 MSC PKD2L1, PRB3, TNP2, ZNF408, ZNRF4 ADAMTS12, CDKL5, COL10A1, COPS8, CTRB2, DVL1, HAS1, HTR7, MMP17, NPAS1, PKD2L1, PLA2G5, PRB3, 348 MSC TNP2, TRIM3, TSSK1B, WISP1, ZNRF4 CABIN1, CUEDC2, CUL7, DCTD, DDOST, EEF1D, EIF4E2, EMILIN1, HAND2, KIF22, LAMP1, MKRN2, MRPL24, MRPS11, NDUFA8, NHEJ1, PARN, PODNL1, POLR2G, PRX, 349 MSC RER1, SCAMP3, SF3B5, SIX5, SLC35A2, SLC38A10, SNF8,WSGR Docket No.50272-712.601 SPAG16, SPCS1, STUB1, TBC1D17, THAP3, TMEM147, TRMT112, YIF1A, ZNF446 CABIN1, CUL7, DCTD, EEF1D, EIF4E2, EMILIN1, HAND2, KIF22, MRPL24, NHEJ1, PODNL1, POLR2G, PRX, RER1, 350 MSC SCAMP3, SF3B5, THAP3, TRMT112, YIF1A AAAS, ARL6IP4, ATP6V1C1, BAG3, C7orf26, CABIN1, CLUAP1, COMMD4, COPZ1, CPSF3L, CUEDC2, CUL7, CUTA, CYC1, CYHR1, DCTD, DDOST, EEF1D, EIF4E2, EMILIN1, ERCC1, GMPPA, GSTM5, GTF2H5, HAND2, HIC1, HMGXB3, IMPDH1, INTS5, IQSEC2, KDELR1, KIF22, LAMP1, LMAN2, LMOD1, LRRC59, MDH2, MFSD5, MKRN2, MRPL24, MRPS11, MRPS17, MRPS18B, NDUFA8, NDUFB4, NFATC4, NHEJ1, OGFOD1, PARN, PMPCA, PODNL1, POLR2G, PRX, PTPN11, RER1, RNF41, RNF7, RPL8, RPN1, SCAMP3, SF3B5, SIX5, SLC35A2, SLC38A10, SNF8, SNX17, SPAG16, SPCS1, STOML2, STUB1, TBC1D17, TCTN3, THAP3, TIMM10, TIMM44, TMEM11, TMEM147, TMEM161A, TMEM184B, TMEM223, TRMT112, VTI1B, 351 MSC YIF1A, ZNF446, ZNF771 ABCB9, ACTR1A, ACVRL1, ANGPT2, ARHGEF15, ATXN2L, BANF1, BCL10, BFAR, CAV2, CCNG1, CD34, CECR5, CETP, CHST12, CIAPIN1, CISD1, CLDN5, CLEC1A, CLPP, CNIH4, COPS6, COX4I1, COX7A2L, CPSF3L, CSNK2A2, CXorf36, DCTN2, DCTPP1, DDA1, DEF8, DHX38, DNPEP, DYNC1LI2, DYRK1B, ECHS1, EDC3, EDC4, EI24, EIF2B2, EMCN, EXD2, F11R, FAM124B, FAM65A, FAM96B, FDPS, FLT4, FN3K, FRMD8, FTSJ3, GEMIN7, GFOD2, GIPC1, GIT1, GJA4, GMPR2, GOLGA3, GOT2, GPKOW, GTF3C5, HADHA, HCFC1, HCRTR1, HOXD3, HSPB11, HTATIP2, HYAL2, ILF2, INPP5E, ITGA9, KANK3, KCTD2, KDR, KEAP1, LGALS1, LMBR1L, LYPLA1, LYPLA2, LYVE1, MAPK12, MAPK3, MBTPS1, MED16, METTL3, MMRN2, MMS19, MRPL15, MRPL9, MRPS16, MRPS28, MTCH1, MTCH2, MTG1, MTMR2, MYCT1, N4BP3, NDUFB11, NOTCH4, NOVA2, NUBP2, PABPC4, PCDH12, PCGF3, PIAS4, PLXNB3, PPM1F, PRKD2, PRPSAP1, PSMB7, PSMC5, PSMD8, RAB35, RALA, RALY, RAMP3, RANGAP1, RGS11, RHOC, RNF25, RNPS1, ROBO4, RRAS2, SAMD14, SCARF1, SELE, SEMA6B, SH3GL1, SLC24A1, SLC25A6, SMARCD1, SMARCE1, SNAPC4, SNTB2, SOX18, SPATS2, STAB1, STK25, STRAP, SUMO3, SUN1, TAOK2, TARBP2, TBX1, TEK, TIE1, TMEM115, TNFSF18, TPM3, TSPO, TTLL5, TUSC2, TUT1, mv Endothelial TXNDC9, UBAP2, UBE2E1, UFD1L, URM1, VAMP3, VWF, 352 cells WDR13, YWHAE, ZC3H7B, ZDHHC24, ZNF205, ZNF282 ABCB9, ABCD4, ACTR1A, ACVRL1, AHDC1, ANGPT2, ARHGEF15, ATF7, ATP1B3, BFAR, BYSL, C19orf24, CAV2, CCDC90B, CD34, CDC27, CECR5, CETP, CHST12, CIAPIN1, CISD1, CLDN5, CLEC1A, CLIC1, CLPP, CNIH4, COPA, COPS6, COX4I1, COX7A2L, CPSF3L, CSNK2A2, CXorf36, DCAF7, DCTN2, DEF8, DHX38, DYNC1LI2, DYRK1B, EDC3, EDC4, EIF2B2, EIF3K, EIF4E2, ELOVL1, EMCN, ENOPH1, EXD2, EXOC7, F11R, FAM124B, FAM65A, FAM96B, FBXO22, FDPS, FLT4, FN3K, FRMD8, FRS3, FTSJ3, GDI2, mv Endothelial GEMIN7, GIPC1, GIT1, GMPR2, GOT2, GPKOW, GPN2, 353 cells GSDMD, GYG1, HCFC1, HCRTR1, HOXD3, HSPB11,WSGR Docket No.50272-712.601 HTATIP2, HYAL2, ILF2, INPP5E, INPP5K, KANK3, KAT5, KCTD2, KDR, KEAP1, LAMP1, LGALS1, LMBR1L, LYPLA1, LYVE1, MAPK12, MAPK3, MBTPS1, METTL3, MMRN2, MOSPD3, MRPL15, MRPS16, MRPS28, MTCH1, MTCH2, MTHFR, MTMR2, MUL1, MYCT1, MYL6, N4BP3, NAP1L4, NDUFB11, NDUFC2, NOTCH4, NOVA2, PABPC4, PCDH12, PCGF3, PGLS, PIAS4, PLD2, PLXNB3, POLR2F, PRKD2, PRPSAP1, PSMB7, PSMC5, RAB35, RALA, RAMP3, RANGAP1, RHOC, ROBO4, RPL4, RPN2, RRAS2, SAMD14, SCARF1, SELE, SENP5, SH3GL1, SIN3B, SLC24A1, SLC25A6, SMARCD1, SMARCE1, SNAPC4, SNTB2, SOX18, SPATS2, SSBP1, STK25, STRAP, STX12, TACO1, TAOK2, TARBP2, TBC1D10B, TBX1, TEK, THAP4, TIAL1, TIE1, TIPRL, TMEM115, TMEM39B, TNFSF18, TOR1AIP2, TPM3, TSPO, TTLL5, TUSC2, TXNDC9, TXNL1, UBAP2, UBIAD1, UBXN1, UFD1L, URM1, WDR13, YWHAE, ZC3H7B, ZDHHC24, ZFPL1, ZNF282, ZWILCH ABCB9, ACVRL1, ANGPT2, ARHGEF15, ATXN2L, BCL10, BFAR, CAV2, CD34, CECR5, CETP, CHST12, CIAPIN1, CISD1, CLDN5, CLEC1A, CLPP, COPS6, COX4I1, CPSF3L, CSNK2A2, CXorf36, DCTPP1, DDA1, DYNC1LI2, DYRK1B, EDC3, EDC4, EI24, EIF2B2, EMCN, F11R, FAM124B, FAM65A, FDPS, FLT4, FN3K, FRMD8, FTSJ3, GEMIN7, GFOD2, GIPC1, GIT1, GJA4, GMPR2, GOLGA3, GOT2, GTF3C5, HADHA, HCFC1, HCRTR1, HOXD3, HSPB11, HTATIP2, HYAL2, ILF2, ITGA9, KANK3, KCTD2, KDR, KEAP1, LGALS1, LMBR1L, LYPLA1, LYPLA2, LYVE1, MAPK12, MAPK3, MED16, METTL3, MMRN2, MRPL15, MRPL9, MRPS16, MRPS28, MTCH1, MTCH2, MTG1, MYCT1, N4BP3, NDUFB11, NOTCH4, NOVA2, PCDH12, PCGF3, PLXNB3, PPM1F, PRKD2, PRPSAP1, PSMB7, PSMC5, RAB35, RALA, RALY, RAMP3, RANGAP1, RGS11, RHOC, ROBO4, RRAS2, SAMD14, SELE, SEMA6B, SH3GL1, SLC24A1, SLC25A6, SMARCD1, SMARCE1, SNAPC4, SNTB2, SOX18, SPATS2, STAB1, STK25, STRAP, SUMO3, TAOK2, TARBP2, TBX1, TEK, TIE1, TMEM115, TNFSF18, mv Endothelial TPM3, TTLL5, TUSC2, TUT1, UFD1L, URM1, VWF, WDR13, 354 cells ZC3H7B, ZNF205, ZNF282 ACE, ACVRL1, ANGPT2, ANO2, ARHGEF15, ART4, ARVCF, ATF6, ATP6V0E1, BCL10, BMX, CALM1, CASP10, CAV1, CD34, CD9, CD93, CDK9, CEACAM21, CETP, CLDN5, CLEC1A, CSF2RB, CTNNA1, CXorf36, ELK4, EMCN, ENTPD1, ERG, ERH, FAM124B, FLOT2, FLT1, FLT4, GABPB1, GFOD2, GIMAP4, GIMAP6, HDAC1, HERC1, HOXD3, HTATIP2, HTR1B, HYAL2, IL3RA, KANK3, KDR, KIF17, LMBR1L, LRRFIP1, LSG1, LYL1, LYVE1, MAP3K3, MAT2B, MGAT5, MMRN1, MMRN2, MYCT1, MYL12A, N4BP3, NCK1, NECAP2, NOTCH4, NOVA2, PCDH12, PDCL, PIK3CG, PLCG1, PLVAP, PNP, PPM1F, PRPSAP1, RALA, RALB, RAMP3, RASIP1, RNF34, ROBO4, SCARF1, SELE, SEMA6B, SEMA6C, SOX18, SPTBN5, STAB1, TAL1, TAOK2, mv Endothelial TBX1, TDRD7, TEK, TFEC, TIE1, TMEM109, TMEM39B, 355 cells TNFSF18, TSPAN13, UFD1L, VWF, ZNF22 mv Endothelial ACE, ACVRL1, ANGPT2, ANO2, ARHGEF15, ARVCF, ATF6, 356 cells BMX, CASP10, CAV1, CD34, CD9, CD93, CETP, CLDN5,WSGR Docket No.50272-712.601 CLEC1A, CSF2RB, CXorf36, EMCN, ENTPD1, ERG, FAM124B, FLT1, FLT4, GIMAP4, GIMAP6, HTR1B, HYAL2, IL3RA, KANK3, KDR, KIF17, LYL1, LYVE1, MAP3K3, MMRN1, MMRN2, MYCT1, N4BP3, NOTCH4, NPR 1.00, PCDH12, PNP, PPM1F, RAMP3, RASIP1, ROBO4, SCARF1, SEMA6B, SOX18, STAB1, TAL1, TAOK2, TBX1, TEK, TFEC, TIE1, TNFSF18, VWF SEPTIN2, ABCF2, ACTG1, ACTL7A, ACTR1A, ADRA1B, AKAP4, ANXA2, AP2M1, ARF1, ARL2BP, ARPC1A, ATP2B3, AURKAIP1, B3GALT5, BMX, C16orf62, C1orf123, CAPN11, CAV1, CCKAR, CD34, CD93, CDC37, CHCHD2, CLDN14, CLDN5, CLEC4M, CLPP, CLTA, COMMD4, COPS6, CPA1, CTNNA1, DAD1, DCTN5, DDX56, DLST, DNAJC7, DYNC1H1, ECD, EIF2B2, EIF4G1, FAM107A, FAM124B, FNDC8, FOXC2, FOXD3, GANAB, GJA4, GLRX3, GPR4, HSP90AB1, HSPA4, KCNAB1, LRRC3, LYPLA2, LYZL6, MAGEB1, MAPK3, MED20, MIF, MIP, MMRN2, MRPL17, MTCH1, MYL6, NCBP2, NEDD8, NOC2L, NOTCH4, NUP188, OXA1L, P4HB, PCDH12, PCDHA6, PCGF3, PITPNB, PLS3, PMPCA, POLR2J, POM121L2, PPP2R1A, PPP2R2A, PRND, PSMB7, PSMD1, PSMD10, PTTG1IP, PWP1, RALA, RCN2, RHOA, RHOC, SAE1, SEC61A1, SEMA6B, SLC6A7, SNTG2, SPTBN5, STRAP, TAF12, TAOK2, TJP1, TMED9, TMEM115, mv Endothelial TRAPPC3, TRPC4AP, TUSC2, UFD1L, UNC45A, USP5, VWF, 357 cells YIF1B, YKT6 ACVRL1, ARHGEF15, CETP, CLDN5, CLEC1A, CXorf36, mv Endothelial EIF2B2, HYAL2, KANK3, LYVE1, MMRN2, RANGAP1, 358 cells ROBO4, SELE, TIE1, TNFSF18, VWF ACVRL1, ARHGEF15, CETP, CLDN5, CLEC1A, CXorf36, EIF2B2, FAM65A, FLT4, HCRTR1, HYAL2, KANK3, KDR, LYVE1, MMRN2, MYCT1, NOVA2, PCDH12, RALA, mv Endothelial RANGAP1, ROBO4, SELE, SOX18, TEK, TIE1, TNFSF18, 359 cells TPM3, TUT1, VWF ACVRL1, ARHGEF15, CETP, CLDN5, CLEC1A, CXorf36, EIF2B2, FLT4, HYAL2, KANK3, LYVE1, MMRN2, mv Endothelial RANGAP1, ROBO4, SELE, SOX18, TIE1, TNFSF18, TUT1, 360 cells VWF BAG2, EVC, EXOC1, IMPACT, KRTAP1-1, MUSK, SGCA, 361 Myocytes SHQ1, SIM1, SMAD5 BAG2, COPB1, DENR, EVC, EXOC1, IMPACT, KRTAP1-1, MAP3K7, MUSK, MYF5, PRRC1, SGCA, SHQ1, SIM1, 362 Myocytes SMAD5, XPNPEP3 SEPTIN2, ACTG1, ACTR10, ACTR8, ADI1, AFG3L2, ALDOA, AMOTL2, ANXA5, ARF4, ARL2BP, BAG2, BAG5, BCKDK, C10orf88, C2orf47, CAD, CAST, CAV1, CAV3, CCDC90B, CCNG1, CCT6A, CDH15, CDIPT, CHCHD3, CHRNG, CLTC, CLUAP1, CMPK1, COL14A1, COPB1, COPS8, COX8A, CPSF4, CSNK1G3, DCLRE1B, DCTD, DDOST, DENR, DNAH7, DNAJC16, EDA2R, EIF2S2, EIF4EBP2, EPN1, EPRS, ERCC4, ERGIC3, ERP29, EVC, EXOC1, FAM120C, FAM160B2, FBXL4, FBXW4, FRS2, GDI2, GLE1, GNAS, GPR173, GRSF1, GTF2H1, GUF1, HARS2, HIF1A, HIF1AN, HMGXB3, HNRNPA0, HRC, HSPA8, HSPB7, IARS2, IBTK, IDUA, IFT52, IMPACT, INTS5, IPO7, KIF2A, KRTAP1-1, 363 Myocytes LAMP1, LGALS1, LRPPRC, MAP3K7, MBTPS2, MCTS1,WSGR Docket No.50272-712.601 METAP2, METTL8, METTL9, MIF, MKLN1, MKRN2, MMP11, MPHOSPH6, MRPS11, MRPS16, MSH3, MUSK, MYBPH, MYF5, MYL6B, MYLPF, MYO19, MYOD1, MYOF, NDUFA10, NFU1, NONO, NOP10, NPTN, NUCKS1, NUDT9, NUP54, PAFAH1B1, PALB2, PARN, PCDHGC3, PEX26, PKNOX2, PLXNB3, POFUT1, POFUT2, POLR2J, PREPL, PRRC1, PRX, PSMB4, PTGES3, PTPN11, QRSL1, RAB7A, RAD50, RAPSN, RC3H2, RCN2, RNF11, RPN2, RPS6KC1, RUFY1, RWDD1, SEC61A1, SGCA, SHQ1, SIM1, SLC25A3, SLC38A10, SLC38A7, SMAD2, SMAD5, SND1, SNX17, SPAG16, SPATA7, SPG7, SPIN1, SPPL2B, SS18, SSR4, STAU1, TBC1D17, TCTN3, TIAL1, TM2D1, TMEM147, TNPO1, TOR1A, TUBG2, TXN2, UBA2, UBAP2L, UBE2D4, UBE3A, UCHL5, USP14, UTP20, VPS4A, WISP1, XPNPEP3, XPOT, YAP1, YTHDF3, ZDHHC4, ZMPSTE24, ZNF214, ZNF221, ZNF37A, ZNF446, ZNF668 ALPL, CASQ2, CDH15, DES, HAS1, MYBPH, MYF5, MYH7, MYL1, MYL4, MYLPF, MYOG, RAPSN, SGCA, TNNI1, 364 Myocytes TNNT2, TTN ACTA1, ACTN2, ALPL, ATP1B4, CDH15, CKM, GSG1, HAS1, HEYL, HRC, ITGB1BP2, MSTN, MUSK, MYBPH, MYF5, MYH1, MYH2, MYH7, MYH8, MYL1, MYL4, MYLPF, MYOD1, MYOG, RAPSN, ROS1, SGCA, SGCG, SLN, TNNC2, 365 Myocytes TNNI1, TNNT2 CDH15, HAS1, MYF5, MYH7, MYL1, MYL4, MYOG, RAPSN, 366 Myocytes TNNI1, TNNT2 367 naive B-cells BLK, CD19, CD72, CXCR5, FCRL2, MS4A1, SPIB, TCL1A BLK, CCR6, CD180, CD19, CD22, CD72, CD79B, CXCR5, FCRL2, GPR18, MBD4, MGAT5, MS4A1, SPIB, TCL1A, 368 naive B-cells TREML2, TSPAN13, UTP6 BLK, CD180, CD19, CD72, CXCR5, FCRL2, MS4A1, SPIB, 369 naive B-cells TCL1A AP3B1, BLK, CD19, CD22, CD37, CD72, CD79A, CSNK1G3, DEF8, DSP, EGOT, FCER2, FCRL2, GCM1, GGA2, GMFB, MBD4, MFN1, MS4A1, P2RY10, PNOC, PRDM4, PWP1, 370 naive B-cells RRAS2, SIPA1L3, SNX2, STAG3, STAP1 AP3B1, CD19, CD1A, CD22, CD37, CD72, CD79A, CSNK1G3, DEF8, DSP, EGOT, FCER2, FCRL2, GGA2, GMFB, MBD4, MFN1, MGAT5, MS4A1, P2RY10, PNOC, PRDM4, PWP1, 371 naive B-cells RRAS2, SIPA1L3, SNX2, STAG3, STAP1, VPREB3 AP3B1, BLK, C10orf76, CAPN3, CD19, CD1A, CD22, CD37, CD72, CD79A, CD79B, CSNK1G3, DEF8, DSP, EGOT, FCER2, FCRL2, GCM1, GGA2, GMFB, HSPA4, MBD4, MCM9, MFN1, MGAT5, MS4A1, P2RY10, PNOC, PRDM4, PWP1, RRAS2, 372 naive B-cells SIPA1L3, SMC6, SNX2, SP140, STAG3, STAP1, VPREB3 ADAM20, AKAP6, BCL2L10, BMP3, CACNA1F, CAPN3, CD19, CD22, CD72, COL19A1, CSNK1G3, CXCR5, DAZL, DSP, FCER2, FRS2, GGA2, GNG3, GPR18, KHDRBS2, MAP3K9, MATN1, MMP17, MS4A1, MYBPC2, MYO3A, P2RY10, PAX5, PHKG1, PRDM2, PRDM4, PYGM, RBM15, RRAS2, SDK2, SIPA1L3, SMC6, SYN3, TCL1A, TCL1B, 373 naive B-cells TCL6, TSPAN13, UBE2O, USP6, USP7, WDR74, ZNF154 ADAM20, AKAP6, BMP3, CACNA1F, CAPN3, CD19, CD22, 374 naive B-cells CD72, COL19A1, CSNK1G3, CXCR5, DAZL, FCER2, FRS2,WSGR Docket No.50272-712.601 GGA2, GNG3, KHDRBS2, MAP3K9, MMP17, MS4A1, MYBPC2, P2RY10, PAX5, PHKG1, PRDM2, PRDM4, RRAS2, SIPA1L3, SMC6, SYN3, TCL1A, TCL1B, TCL6, TSPAN13, UBE2O, USP6, USP7, WDR74 8-Mar, ADAM20, AKAP6, BCL2L11, BMP3, CACNA1F, CAPN3, CD19, CD1A, CD22, CD72, CDK13, CIITA, COL19A1, CSNK1G3, CUBN, CXCR5, DAZL, DSP, FCER2, FCRL2, FRS2, GGA2, GH1, GNG3, HLA-DOA, KHDRBS2, LY9, MAP3K9, MATN1, MMP17, MS4A1, MYBPC2, N4BP3, NOC3L, P2RY10, PAX5, PGAM2, PHKG1, PIKFYVE, POU2F1, PRDM2, PRDM4, PRKCB, PTCH2, PYGM, RB1, RBM15, RERE, RRAS2, SDK2, SIPA1L3, SLC30A4, SMC6, SNTG2, STAG3, SYN3, TBC1D5, TCL1A, TCL1B, TCL6, TRA2B, TRAPPC9, TSPAN13, UBE2O, USP6, USP7, WDR74, 375 naive B-cells ZNF154 ACTL6B, CAMKV, EPHA3, GNG3, INSM1, KCNQ2, 376 Neurons NEUROD2, PCDH8, STMN4 ABCA3, ACTL6B, ACVR2B, ADRA2A, AGPAT5, AGTPBP1, ANKRD10, AP1AR, ARC, B3GALT2, BEX1, BSN, C14orf1, C16orf45, C1orf216, C21orf62, CACNA1B, CACNB3, CACNG4, CACNG5, CAMKV, CD200, CDK5R1, CELSR3, CFDP1, CHRNB2, CPE, CRABP1, CRMP1, CTNNA1, CUX2, CXCL12, CYTH2, DCHS1, DPYSL4, EFNB3, ELAVL3, EMID1, ENOX1, EPHA3, FAM105A, FNBP1L, FOXG1, GAD2, GAP43, GDAP1L1, GEM, GNG3, GPR173, GRIK3, GRM2, HAP1, HIST1H3D, HUNK, ID1, IFI44, INSM1, IPO13, IREB2, KCNK12, KCNMB4, KCNN1, KCNQ2, KCNQ3, KCTD13, KIAA1107, KIF21B, KLC1, LHX2, LRRN3, LRRTM2, MAPK8, MEIS2, MLF 1.00, MLLT11, MLLT3, MN1, MUM1, MVD, NCAN, NEUROD2, NEUROD6, NEUROG2, NKAIN1, NNAT, NPTX1, NPY, NTRK3, NUP93, PAQR3, PARD6A, PCDH8, PCDHB11, PCSK1N, PDHX, PDLIM3, PGAP1, PKIA, PLXNA2, PNMA2, PODXL2, POU3F1, POU3F2, POU3F3, PTBP2, PTPRZ1, PTX3, RANBP6, RASL11B, REEP1, RNF219, RPE65, SCG3, SCGN, SCN3A, SEMA6C, SETBP1, SEZ6L, SFRP4, SH3GL2, SH3GL3, SOX11, SOX4, SPAST, ST8SIA2, ST8SIA4, STMN2, STMN4, STX16, SULT4A1, TET3, THRA, TMSB15A, TSPAN2, TUBA1A, VASH2, WDR47, YIPF4, ZBED5, ZBTB6, ZFP30, ZFP37, ZMAT4, ZNF117, ZNF14, ZNF195, ZNF211, ZNF223, ZNF253, ZNF34, ZNF354A, ZNF415, ZNF43, ZNF430, ZNF484, ZNF493, ZNF510, ZNF529, ZNF606, ZNF614, ZNF669, ZNF675, ZNF682, ZNF711, 377 Neurons ZNF821, ZSCAN16 ABCA3, ACTL6B, ADRA2A, ARC, B3GALT2, BEX1, BSN, CACNA1B, CAMKV, CD200, CELSR3, CHRNB2, CPE, CRABP1, CRMP1, CTNNA1, CUX2, CYTH2, EFNB3, ELAVL3, EMID1, ENOX1, EPHA3, FAM105A, FOXG1, GAD2, GAP43, GDAP1L1, GEM, GNG3, GRIK3, GRM2, HAP1, HUNK, INSM1, KCNK12, KCNQ2, KCNQ3, KIAA1107, KIF21B, KLC1, MAPK8, MLLT11, MLLT3, NCAN, NEUROD2, NEUROD6, NEUROG2, NKAIN1, NNAT, NPTX1, NPY, PCDH8, PCSK1N, PDHX, PGAP1, PKIA, PLXNA2, PODXL2, POU3F1, POU3F3, PTBP2, PTPRZ1, RASL11B, REEP1, RNF219, SCG3, SCGN, SEZ6L, SH3GL2, SH3GL3, 378 Neurons SOX11, ST8SIA4, STMN2, STMN4, SULT4A1, TMSB15A,WSGR Docket No.50272-712.601 TSPAN2, TUBA1A, WDR47, YIPF4, ZBED5, ZBTB6, ZFP37, ZMAT4, ZNF14, ZNF34, ZNF415, ZNF614, ZNF675, ZNF821 ACSL6, ALDOC, ANKS1B, AP3B2, ARHGAP26, ATP1A3, ATP1B1, ATP2B2, ATP6V1G2, BEX1, C1orf61, CA8, CACNA1G, CACNB4, CALB1, CAMK2B, CDH18, CNKSR2, COX7A1, COX7B, DDX25, DEFB1, DGKB, DLG2, DNAJC12, DPP6, ELMO1, FABP6, FAIM2, FGF12, FGF9, FSTL4, FXYD7, GABBR1, GABRA1, GABRB2, GABRG2, GAD1, GNAO1, GNG13, GNG3, GPM6A, GPR63, GPRC5B, GRIA2, GRID2, GRIK1, GRM1, GRM7, ID2, IL20RA, INA, ITPR1, KCNC1, KIAA1107, KIF5C, KLHL1, LPL, LRRN3, MT3, NDUFA5, NEFH, NEFL, NEFM, NELL1, NPPC, NPTX1, NRXN1, OMG, PCDH9, PCP4, PLXDC1, PRKCG, PRMT8, PTPRR, PVALB, REEP1, RGS16, RORA, RTN1, SCG3, SEC62, SERPINI1, SEZ6L, SH3GL2, SHISA6, SLC12A5, SLC24A2, SLC6A1, SMPX, SNAP25, SNAP91, SNCG, SPARCL1, SPOCK3, ST8SIA3, STMN2, STMN4, SUSD4, SYP, TAC1, TCEAL2, 379 Neurons TM6SF1, TRIM9, TRPC3, TSPAN7, ZNF208 ABCC8, ACSL6, ACYP2, AGTR2, AKAP7, ALDOC, ANKS1B, AP3B2, ARHGAP26, ATP1A3, ATP1B1, ATP1B2, ATP2A3, ATP2B2, ATP5L, ATP6V1G2, BCL11A, BEX1, C1orf61, CA7, CA8, CACNA1A, CACNA1G, CACNA2D2, CACNB2, CACNB4, CALB1, CAMK2B, CDH18, CEP76, CHGB, CISD1, CLUL1, CNKSR2, CORO2B, COX7A1, COX7B, CPNE6, DAB1, DACH1, DDX25, DEFB1, DGKB, DGKG, DLG2, DNAJC12, DNM3, DPP6, DYNC1I1, ELMO1, FABP3, FABP6, FAIM2, FAM134B, FAM184A, FAM21A, FGF12, FGF14, FGF9, FSTL4, FXYD7, GABARAPL2, GABBR1, GABBR2, GABRA1, GABRB2, GABRB3, GABRG2, GAD1, GAD2, GALNT8, GNAO1, GNG13, GNG3, GNG4, GOLIM4, GPM6A, GPR63, GPRASP1, GPRC5B, GRIA2, GRIA3, GRID2, GRIK1, GRM1, GRM7, HHLA3, HOPX, HSBP1, HTR5A, ID2, IL20RA, INA, IQCK, ITPR1, JAKMIP2, KCNA2, KCNAB1, KCNC1, KCNK1, KCNMB4, KIAA1107, KIF3A, KIF5A, KIF5C, KIFAP3, KLHL1, KPNA5, LPCAT4, LPL, LRRC49, LRRN3, MAP9, MAPK10, MGAT4A, MT3, MYT1L, NAP1L2, NCAM1, NDRG4, NDUFA3, NDUFA4, NDUFA5, NDUFAF4, NEFH, NEFL, NEFM, NELL1, NELL2, NME7, NPPC, NPTX1, NRCAM, NRXN1, NRXN3, OMG, PCDH17, PCDH9, PCLO, PCP4, PDE9A, PEG3, PIGP, PIP5K1B, PLXDC1, PPFIA2, PPP3CA, PRKCG, PRMT8, PTPRR, PVALB, RAB33A, RAB3A, RALYL, REEP1, RGS16, RGS7, RORA, RTN1, RYR2, SAP18, SCG3, SCN1A, SCN2A, SEC62, SERPINI1, SEZ6L, SH3GL2, SHISA6, SLC12A5, SLC1A6, SLC24A2, SLC6A1, SLC8A1, SMPX, SNAP25, SNAP91, SNCG, SPARCL1, SPOCK3, ST8SIA3, STMN2, STMN4, SULT4A1, SUSD4, SV2C, SYNJ1, SYP, TAC1, TCEAL2, THSD7A, TM6SF1, TRIM9, TRPC3, TSPAN7, TTYH1, VAMP2, ZNF208, 380 Neurons ZNF385D ACSL6, ACYP2, ALDOC, ANK2, ANKS1B, AP3B2, ARHGAP26, ATP1A3, ATP1B1, ATP2A3, ATP2B2, ATP6V1G2, BCL11A, BEX1, C1orf61, CA8, CACNA1A, CACNA1G, CACNB2, CACNB4, CALB1, CAMK2B, CDH18, CEP76, CHGB, CHN1, CNKSR2, COX7A1, COX7B, CRMP1, 381 Neurons DDX25, DEFB1, DGKB, DGKG, DLG2, DNAJC12, DNM3,WSGR Docket No.50272-712.601 DPP6, DYNC1I1, ELMO1, FABP3, FABP6, FABP7, FAIM2, FAM134B, FGF12, FGF14, FGF9, FSTL4, FXYD7, GABARAPL2, GABBR1, GABBR2, GABRA1, GABRB2, GABRG2, GAD1, GNAO1, GNG13, GNG3, GPM6A, GPM6B, GPR63, GPRC5B, GRIA2, GRID2, GRIK1, GRM1, GRM7, HOPX, ID2, IL20RA, INA, ITPR1, JAKMIP2, KCNAB1, KCNC1, KIAA1107, KIF5C, KLHL1, KLK8, LHX1, LPL, LRRC49, LRRN3, MGAT4A, MT3, NAP1L2, NCAM1, NDRG4, NDUFA3, NDUFA5, NDUFAF4, NEFH, NEFL, NEFM, NELL1, NELL2, NME5, NME7, NOVA1, NPPC, NPTX1, NRXN1, NRXN3, OMG, PCDH17, PCDH9, PCP4, PDE9A, PEG3, PIGP, PLXDC1, PRKCG, PRMT8, PTPRN, PTPRR, PVALB, RAB33A, RAB3A, RCAN2, REEP1, RGS16, RORA, RTN1, SCG3, SCN1A, SEC62, SERPINI1, SEZ6L, SH3GL2, SHISA6, SLC12A5, SLC1A6, SLC24A2, SLC6A1, SLC8A1, SMPX, SNAP25, SNAP91, SNCG, SNX10, SORL1, SOSTDC1, SPARCL1, SPOCK2, SPOCK3, ST8SIA3, STMN2, STMN4, SUSD4, SYP, TAC1, TCEAL2, TM6SF1, TRIM9, TRPC3, TSPAN7, ZNF208, ZNF385D CA4, CEACAM3, CXCR1, CXCR2, FCGR3B, MMP25, 382 Neutrophils PGLYRP1, TRPM6, ZDHHC18 CA4, CEACAM3, CXCR1, FCGR3B, MMP25, PGLYRP1, 383 Neutrophils VNN 3.00, ZDHHC18 CA4, CEACAM3, CXCR1, CXCR2, MMP25, P2RY13, 384 Neutrophils PGLYRP1, ZDHHC18 ACAP2, APAF1, BEST1, BTNL8, CBL, CEACAM3, CLEC4E, CSF2RB, CXCR2, DDX3X, DHX34, ELL, FCAR, FCGR3B, FPR2, GCC1, HERC3, HRH4, LMTK2, MED13L, MTMR3, NDEL1, NFYA, NRBF2, PAK2, PGLYRP1, PTEN, RMND5A, SLC19A1, SLC25A44, TECPR2, TMEM185B, TMUB2, TOX4, TREM1, TREML2, UBE2B, UBN1, UBXN2B, WDFY3, WWP2, 385 Neutrophils ZDHHC18 ACAP2, APAF1, BEST1, BTN2A1, BTNL8, CA4, CAMKK2, CASP5, CBL, CEACAM3, CEACAM8, CIR1, CLEC4E, CSF2RB, CXCR2, DDX3X, DHX34, ELL, FBXO38, FCAR, FCGR3B, FPR2, GCC1, HERC3, HRH4, IP6K1, KSR1, LILRA1, LMTK2, MAK, MED13L, MTMR3, NDEL1, NFYA, NMI, NRBF2, PADI4, PGLYRP1, PTEN, SDF2, SLC19A1, SLC25A44, SPAG9, TECPR2, TGM3, TMUB2, TOP 1.00, TOX4, TREM1, TREML2, TRIM25, TTLL4, UBE2B, UBE2D1, 386 Neutrophils UBN1, UBXN2B, USP15, WDFY3, WWP2, ZDHHC18 ACAP2, APAF1, BEST1, BTNL8, CBL, CEACAM3, CLEC4E, CSF2RB, CXCR2, DDX3X, DHX34, ELL, FCGR3B, FPR2, GCC1, HERC3, HRH4, LMTK2, MED13L, MTMR3, NDEL1, NFYA, NRBF2, PGLYRP1, PTEN, SLC19A1, TECPR2, TMUB2, TREM1, TREML2, UBE2B, UBN1, UBXN2B, 387 Neutrophils WDFY3, WWP2, ZDHHC18 CLC, CSF3R, CXCR2, FCGR3B, FPR2, HBB, P2RY13, 388 Neutrophils S100A12, TREM1 CLC, CSF3R, CXCR2, FCGR3B, FPR2, HSPA6, P2RY13, 389 Neutrophils S100A12, TREM1 CLC, CSF3R, CXCR2, FCGR3B, FPR2, HBB, HSPA6, LILRA2, 390 Neutrophils LILRB2, P2RY13, S100A12, TREM1WSGR Docket No.50272-712.601 BTNL8, CEACAM3, CXCR1, CXCR2, FCGR3B, MEFV, 391 Neutrophils MMP25, VNN 3.00 AATK, BMX, BTNL8, CA4, CEACAM3, CXCR1, CXCR2, FCGR3B, FPR2, MEFV, MMP25, P2RY13, TREM1, TRPM6, 392 Neutrophils VNN 3.00 AATK, BMX, BTNL8, CA4, CEACAM3, CXCR1, CXCR2, FCGR3B, FPR2, GPR27, IL18RAP, MEFV, MMP25, P2RY13, 393 Neutrophils TREM1, TRPM6, VNN 3.00 AGK, CD244, DNAJB14, FASLG, IL18RAP, KLRD1, NCR1, 394 NK cells PTGDR, PTPN4, SACM1L, TKTL1, XCL1, ZMYND11 AGK, ARPC5L, BRD2, CD244, CX3CR1, DNAJB14, FASLG, FIP1L1, GNLY, GZMB, GZMM, HELZ, HIPK1, IL18RAP, IL2RB, KLRD1, KPNB1, MAP3K7, MAPK1, MED1, NCR1, PJA2, PRF1, PTGDR, PTPN4, RAB14, SACM1L, STAG2, STX8, TBX21, TKTL1, TNFSF11, XCL1, ZBTB1, ZMYND11, 395 NK cells ZNF264 AGK, CD244, CD247, CTSW, CX3CR1, DNAJB14, FASLG, GNLY, GZMB, HIPK1, IL18RAP, IL2RB, KLRD1, LTA, NCR1, PRF1, PTGDR, PTPN4, SACM1L, TBX21, TKTL1, TNFSF11, 396 NK cells XCL1, ZMYND11, ZNF426 CD160, CD247, CX3CR1, FASLG, GNLY, GRIK4, GZMB, 397 NK cells IL2RB, LIM2, NCR1, NMUR1, PRF1, PTGDR, TBX21, TKTL1 ARPC5L, DNAJB14, IL18RAP, IL2RB, NCR1, PTGDR, 398 NK cells SACM1L, XCL1 399 NK cells CD160, GNLY, GZMB, IL2RB, LIM2, NCR1, NMUR1, PTGDR SEPTIN7, AMZ2, ANKRD11, ASTE1, BAD, C1orf174, CD247, CDKN2AIP, CHRNE, DNAJB14, DNAJC2, DR1, GIPR, GNA13, GOLGA4, GPATCH8, GZMB, GZMH, GZMM, HIPK1, HIST1H3A, HNRNPL, IFNG, KLRG1, LAG3, MGAT2, MLH1, NCR1, NCR3, NEK1, NFE2L2, NKG7, NMUR1, OSBPL7, PPP2CA, PRDM2, PRDX6, PRF1, PRKAG1, PTGDR, RBM25, SF3B4, SON, SUPV3L1, TBCC, TBX21, THAP1, 400 NK cells TSTD2, UBE2Q1, XCL1, YAF2 ALG13, AMZ2, ASTE1, BAD, CHRNE, COQ10B, CTSW, DR1, FBXW4, GIPR, GNLY, GOLGA4, GTF3C1, GZMH, HIPK1, HIST1H3A, IFNG, IL18RAP, IL21R, KLRD1, LAG3, LEMD3, MGAT2, NCR1, NKG7, NMUR1, PPP2CA, PTGDR, RBM39, RGS9, RSRC2, SUPV3L1, TBX21, TSPYL1, WBP11, XCL1, 401 NK cells ZCCHC11 SEPTIN7, AMZ2, ASTE1, BAD, CCL4, CD247, CDKN2AIP, CHRNE, DR1, GGPS1, GIPR, GPATCH8, HIST1H3A, HNRNPL, IFNG, IL21R, IL2RB, MLH1, NCR1, NCR3, NEK1, NKG7, NMUR1, OSBPL7, PRDX6, PRF1, PRKAG1, PTGDR, SBF1, SF3B4, TBCC, TBX21, THAP1, TSTD2, UBE2Q1, 402 NK cells WDR45, XCL1, YAF2, ZBTB39 AGK, CD244, DNAJB14, FASLG, IL18RAP, KLRD1, NCR1, 403 NK cells PTGDR, PTPN4, SACM1L, TKTL1, XCL1, ZMYND11 AGK, CD244, CTSW, CX3CR1, DNAJB14, FASLG, GZMM, IL18RAP, KLRD1, MED1, NCR1, PTGDR, PTPN4, SACM1L, 404 NK cells TKTL1, TNFSF11, XCL1, ZMYND11, ZNF264 DNAJB14, IL18RAP, KLRD1, NCR1, PTGDR, PTPN4, TKTL1, 405 NK cells XCL1 ASTE1, CHRNE, GIPR, HIST1H3A, IFNG, NCR1, NMUR1, 406 NK cells TBX21WSGR Docket No.50272-712.601 AMZ2, ASTE1, CHRNE, GIPR, HIST1H3A, IFNG, NCR1, 407 NK cells NMUR1, PRKAG1 CD160, GNLY, GZMB, GZMH, KLRD1, NCR1, NMUR1, 408 NK cells PRF1, TBX21 BEST1, CASP5, DOLK, GMIP, GSG1, PHKG1, RARA, S100B, 409 NKT TP53TG5 AMBN, BEST1, CASP5, GMIP, IL17B, L1TD1, PHKG1, 410 NKT RARA, S100B, SGCA, TCOF1, TP53TG5 ARPC1A, BEST1, GEMIN7, GSG1, KLHL26, L1TD1, PHKG1, 411 NKT PMFBP1, RARA, TP53TG5 APPBP2, ATG9A, BMPR1A, C5orf15, CBY1, COL14A1, COMP, COPS7A, DCTD, DHX32, DOLK, DYNC1LI2, EBF2, EIF2S2, EPYC, GRSF1, IBSP, ICMT, IL26, ILVBL, ITGA8, KIF3B, LAPTM4A, LIN7C, LRRC15, MAB21L2, MFAP3, MUL1, MYOZ2, NUDT9, PAPPA2, PRELP, RNF121, RPS6KC1, SGCG, STARD7, TM2D3, TMEM50A, UBE2L3, 412 Osteoblast UNC119B, XPOT APPBP2, ARCN1, BMPR1A, CBY1, COL14A1, DYNC1LI2, EBF2, EIF4E2, EPYC, FAM168B, FRMPD4, FTO, GRSF1, HTR2A, IBSP, ICMT, IL26, ILVBL, ITGA8, KIF3B, LIN7C, LRRC15, MAB21L2, MFAP3, MYOZ2, NPLOC4, PAPPA2, PSMB5, RNF121, RPS6KC1, SGCG, STARD7, TM2D3, 413 Osteoblast UBE2L3, UNC119B, XPOT, YIPF6, YME1L1 AMOTL2, APPBP2, ARCN1, ATG9A, BMPR1A, C5orf15, CBY1, CCDC47, COL14A1, COMP, COPS7A, DCTD, DHX32, DOLK, DPY19L4, DYNC1LI2, EBF2, EIF2S2, EPYC, FAM168B, FRMPD4, FTO, GRSF1, HLCS, IBSP, ICMT, IL26, ILVBL, ITGA8, KCMF1, KIF3B, LAPTM4A, LIN7C, LRRC15, MAB21L2, MFAP3, MKRN2, MUL1, MYOZ2, NPLOC4, NUDT9, PAPPA2, PRELP, PSMB5, RNF121, RPS6KC1, SGCD, SGCG, STARD7, TM2D3, TMEM185B, TMEM50A, TRAF3IP1, TRIM32, UBE2L3, UNC119B, XPOT, YAP1, 414 Osteoblast YME1L1, ZNF629 ABCF2, ACTG1, ARL6IP4, ARPC1A, CBY1, CCKAR, CHMP2A, CUEDC2, EIF3G, EXTL1, FIBP, FIP1L1, FOXC2, GDF5, GGCX, GLT8D1, GLUD1, HIST1H1A, IFT46, LONP1, MAB21L2, NFATC4, PCDHGA11, PGK 1.00, RHOA, RNF41, SFXN3, SLC9A5, SSNA1, TMED9, TMEM222, 415 Osteoblast TRPC4AP, TUB, TXNL4A, ZNF16 ABCF2, ARL6IP4, CHMP2A, CUEDC2, EXTL1, FIBP, GDF5, GGCX, LONP1, MAB21L2, NFATC4, PCDHGA11, RHOA, 416 Osteoblast TMEM222, TXNL4A, ZNF16 ABCB9, ABCF2, ACTG1, AKR7A2, AP2S1, ARL6IP4, ARPC1A, ATF6B, BRIP1, C12orf43, CBY1, CCKAR, CHMP2A, COX7A2L, CPSF7, CUEDC2, DPAGT1, DRG2, EIF3G, EIF6, EPN1, EXOC7, EXTL1, FIBP, FIP1L1, FOXC2, GDF5, GGCX, GLT8D1, GLUD1, HARS, HIST1H1A, HSPB7, IARS, IFT46, KIF22, LMOD1, LONP1, MAB21L2, MTCH1, NFATC4, PARK7, PCDHGA11, PGK 1.00, PRDM11, RHOA, RNF41, SFXN3, SLC9A5, SNUPN, SNX17, SSNA1, SSR1, TMED9, TMEM222, TRPC4AP, TUB, TXNL4A, ZFPL1, 417 Osteoblast ZNF16, ZNF768 APOC3, CSHL1, CUX2, DNASE1L3, GZMB, HIST1H2BB, 418 pDC HPD, KCNA5, LILRB4, LRRC36, P2RY14, SCTWSGR Docket No.50272-712.601 CSHL1, CUX2, DNASE1L3, GZMB, HIST1H2BB, KCNA5, 419 pDC LILRB4, LRRC36, P2RY14, RPL3L, SCT CACNB1, CCR2, CELA2A, CUX2, CXCL13, DNASE1L3, FKBP2, GZMB, IL3RA, KCNA5, KCNK10, KCTD5, LILRB4, LRRC36, MAPKAPK2, MYBPC1, P2RY14, SCT, SLC12A3, 420 pDC SLITRK3, SPCS1, SPIB, TLR7, TSPAN13, ZNF221 CD2AP, FLT3, FUT7, GZMB, IDH3A, PTCRA, SCT, SPIB, 421 pDC TLR7 CD2AP, FLT3, FUT7, GZMB, IDH3A, KCNK10, PTCRA, SCT, 422 pDC SPIB, TLR7 CD2AP, CXCR3, FLT3, FUT7, GZMB, IDH3A, KCNK10, 423 pDC PTCRA, RUNX2, SCT, SPIB, TACR1, TLR7 ADCY3, ASH2L, BCKDK, CBX6, CDK4, COPS6, DYNLL1, ERGIC3, GGCX, HIC1, MAPK3, MIER2, MLLT1, MYBPC2, P4HB, PRDM11, PTGER1, S1PR2, SLC6A13, TAF15, TMED1, 424 Pericytes TMEM184B, TXN2, ZBTB22, ZNF444 ADAMTS12, ADCY3, AHDC1, AKR7A2, AP3D1, ARF5, ARFRP1, ARNT, ASH2L, ATP5G1, ATP5J, ATP5SL, BCKDK, C11orf95, C19orf53, C7orf26, CALR, CBX6, CDK4, CIC, COPS6, COPZ1, CTBP1, CTDSP2, DMWD, DYNLL1, ELK1, ERAL1, ERGIC3, GAR1, GGCX, GIPC1, GMPPA, HIC1, HPS6, IL17RC, IRF2BP1, ISLR, KCNAB3, KDELR1, KIF22, LMAN2, LMOD1, LZTR1, LZTS1, MAPK3, MED16, MIER2, MLLT1, MLXIP, MMP11, MYBPC2, MYBPH, MYLPF, NDUFB1, NDUFB7, NNAT, P4HB, PCDHGA9, PFN1, PKNOX2, PPP2CB, PPP2R5D, PRDM11, PSMD8, PTGER1, RAB35, RALY, RNF41, S1PR2, SLC35A2, SLC6A13, TAB1, TAF15, TIMM44, TM9SF1, TMED1, TMEM115, TMEM161A, TMEM184B, TMEM185B, TNXB, TOM1L2, TRIM27, TRIM3, TSEN34, TXLNA, TXN2, USP22, VPS4A, WDR6, YIF1A, 425 Pericytes ZBTB22, ZFPL1, ZNF444, ZNF500, ZNF768 SEPTIN9, AKR7A2, AP3D1, ARFRP1, ASH2L, ATP5J, ATP5SL, BCS1L, C19orf53, CALR, CBX6, CBY1, CDK10, CHMP2A, COPZ1, CTDSP2, CUTA, DDX49, DNAJC4, DNPEP, DPAGT1, DPF2, DYNLL1, EDF1, EHMT2, ELK1, ERAL1, ERGIC3, GANAB, GMPPA, GNAS, GPR173, HIC1, HPS6, IRF2BP1, KCNAB3, KDELR1, KHSRP, LMAN2, LZTR1, MED16, MMP11, MYBPC2, MYLPF, NDUFA8, NDUFB1, NDUFB7, NDUFS5, OPA3, P4HB, PACS1, PCDHGB5, PES 1.00, PKNOX2, POFUT1, PPIB, PPP2R5D, PRDM11, PSKH1, PSMD8, PTGER1, RAB5B, RALY, REM1, RNF41, RPN1, RXRB, SEC16A, SEC24C, SGSM3, SH3GL1, SIRT3, SLC35A2, SLC6A13, SLC6A4, SMG5, SNX17, SSR2, TAB1, TCF25, TM9SF1, TMEM115, TMEM161A, TMEM184B, TOM1L2, TRIM27, TSEN34, UBXN6, USP5, WDR6, ZBTB22, 426 Pericytes ZFPL1, ZNF500, ZNF771 ADAMTS12, ADAMTSL2, BTN2A1, COLEC10, FLT1, GDNF, HAND2, HTR2B, KIF26B, KSR1, MASP1, P2RX1, POU2F2, 427 Pericytes S1PR2 ABTB2, ADAMTS12, ADAMTSL2, COL10A1, COLEC10, DSCR4, FLT1, GDNF, HAND2, HTR2B, IGF2BP3, MASP1, 428 Pericytes SLC31A1 ADAMTS12, BTN2A1, COLEC10, FLT1, GDNF, HTR2B, 429 Pericytes KIF26B, MASP1WSGR Docket No.50272-712.601 AMPD1, FKBP2, PNOC, RNGTT, SPATS2, SSR4, TNFRSF17, 430 Plasma cells UBA5, ZBP1 ADM2, AIPL1, ALG5, ALPI, AMPD1, ARHGEF16, ATF6, AVIL, AVP, B4GALT3, BMP8B, C21orf2, CAMP, CASP10, CCDC121, CCDC33, CCDC40, CCDC88A, CCL25, CCNC, CD27, CD79A, CDH15, CELA2B, CHST8, CLINT1, CNKSR1, CNTD2, COX6A2, CRYBB1, CRYBB3, CRYGC, CUX2, CYP11A1, DKKL1, DMTF1, EBAG9, ENTPD1, EPO, FKBP2, FNDC3A, GDF2, GMPPA, GOLGA3, GOLGA4, GORASP2, GRM4, GRWD1, GUCA1A, HAND2, HIST1H2BB, HSF4, HSP90B1, HSPA6, IDE, IMP4, IRF4, ITGA8, KCNN3, KIAA0125, KNTC1, LAX1, LBX1, LTB4R2, MAGEF1, MANF, MAPK8IP3, MARS, MAST1, MATN4, MBTPS1, MCTS1, MGAT2, MIS12, MRPS31, MTDH, NDOR1, NEUROG1, NOS2, NPAS1, NPPA, NPPC, PDIA2, PNOC, PRDM14, PRDX4, PREB, PRM1, PRX, PTPRS, RAB3A, RAD17, RGS1, RGS13, RNF103, RNF113A, RNGTT, RPN2, SCFD1, SEC24A, SEC62, SEMA6C, SERP1, SHANK1, SIX5, SLC35B1, SLC5A2, SLCO5A1, SPATS2, SRP54, SRPRB, SS18, SSR4, STMN4, SYT5, TBL2, TBX4, TG, TMED10, TMEM39A, TNFRSF17, TNNT3, TRABD, TREH, UBA5, UBE2G1, UBXN4, UFSP2, 431 Plasma cells USP48, WDR45, YIPF1, ZBP1, ZNF133, ZPBP ABCB9, ACBD3, ALG5, ALG9, AMPD1, APOA1, APOC3, ARHGEF16, ARL 1.00, ARSA, AUP1, B4GALT3, BFSP2, BMP8B, C19orf73, C6orf25, CA7, CACNA1S, CASP10, CAV1, CCNC, CD180, CD19, CD79A, CD79B, CEACAM21, CHRNA4, CHRNG, CLCNKB, CNKSR1, CNPY2, CNTD2, CRYBA4, CRYBB1, CSHL1, CSPP1, CUL7, CYBA, CYP11A1, DAD1, DDN, DDOST, DEF8, DNAJC4, DNASE1L2, DOK3, DPAGT1, DRD4, DRD5, ELL, ERGIC3, FBP2, FCRL2, FGF6, FKBP2, FNDC3A, FTCD, GH2, GLT8D1, GMPPA, GNB3, GOLGB1, GORASP2, GP9, GPR37L1, GPRC5D, GRIN1, HDLBP, HEYL, HSP90B1, IFT52, IGF1, IRGC, ISCU, ITGA8, KCNN3, KCNQ4, KDELR2, KIAA0125, KLC2, KRT10, LBX1, LEFTY2, LMAN1L, LMAN2, LTB4R2, MAGEF1, MANF, MAST1, MIA3, MTDH, MYH13, MYL2, MYL7, NDOR1, NGLY1, NTRK1, OGFOD2, P2RY4, PABPC4, PCDHA5, PDE6A, PDIA6, PHOX2A, PICK1, PNOC, POMC, POU3F3, PPIB, PPIL2, PRDX4, PREB, PTGER1, R3HCC1, RAD17, RALY, RASIP1, RAX, RFX2, RGS13, RNF208, RPN1, RPN2, SAP30BP, SEC24A, SEC61A1, SEC61B, SEC61G, SEC62, SEC63, SERP1, SHBG, SIPA1L3, SIX5, SLC13A2, SLC35B1, SLC35C2, SLC38A10, SLC6A13, SMPD2, SNAPC4, SPATS2, SPCS1, SRPRB, SSR1, SSR4, SURF1, SYT5, T, TBL2, TERT, THAP4, TIMM17B, TM9SF1, TMED10, TMED9, TMEM59, TNFRSF13B, TNFRSF17, TNNT3, TP73, TSHR, TSSK2, UBA5, UFSP2, UGGT1, UTF1, VPREB1, VPREB3, VSX1, 432 Plasma cells WDR45, YIPF1, YIPF2, ZDHHC4, ZNF133, ZNF142, ZNF37A ADM2, CCL25, CD79A, CNKSR1, FN3K, GABRR2, GPLD1, KCNN3, KLF15, NPAS1, PNOC, PREB, RGS13, SLC5A2, SSR4, TMEM39A, TNFRSF17, TP73, TSHR, UBA5, VPREB3, 433 Plasma cells ZBP1 ACBD4, ADM2, BMP8B, C16orf58, C21orf2, CCL25, CD27, CD79A, CNKSR1, CNR1, FKBP2, FN3K, GABRR2, GPLD1, 434 Plasma cells HOOK2, HSF4, HSP90B1, KCNN3, KLF15, NPAS1, PNOC,WSGR Docket No.50272-712.601 PREB, RGS13, SEC61A1, SERP1, SIX5, SLC5A2, SPATS2, SSR4, TCF3, TMEM39A, TNFRSF17, TP73, TSHR, UBA5, VPREB3, WNT1, ZBP1 ADM2, CCL25, CD79A, CNKSR1, GABRR2, GPLD1, KCNN3, KLF15, NPAS1, PNOC, RGS13, SLC5A2, SSR4, TNFRSF17, 435 Plasma cells TP73, VPREB3, ZBP1 AMPD1, KCNN3, PNOC, RGS13, RNGTT, SEC24A, SSR4, 436 Plasma cells TNFRSF17, ZBP1 ADM2, ALG5, AMPD1, ATF6, CCNC, CD79A, EBAG9, FKBP2, FNDC3A, GMPPA, GOLGA3, HSF4, HSP90B1, IMP4, KCNN3, LAX1, LTB4R2, MANF, MAST1, MGAT2, MRPS31, MTDH, PNOC, PREB, RGS13, RNF113A, RNGTT, SEC24A, SERP1, SPATS2, SRP54, SSR4, TBL2, TMEM39A, TNFRSF17, 437 Plasma cells UBA5, UBE2G1, UBXN4, USP48, YIPF1, ZBP1 AMPD1, CCNC, CD79A, FKBP2, KCNN3, MTDH, PNOC, PREB, RGS13, RNGTT, SEC24A, SPATS2, SSR4, TMEM39A, 438 Plasma cells TNFRSF17, TSHR, UBA5, UBE2G1, ZBP1 ACSBG1, ACTR10, ADCY8, ADI1, AKAP3, ALOX12, ANO2, APOA1, ARHGAP6, ARHGEF12, ARMC7, ASAP2, ASB8, AVPR1A, BEST2, BIN2, BMP8B, BNIP2, CABP5, CASQ1, CCDC40, CD160, CDK2AP1, CETP, CHD1L, CHST8, CIITA, CLEC1B, CMPK1, COL10A1, CRISP2, DERA, DNAI1, EIF2AK1, ENDOU, FBXO9, GNAS, GNB3, GP1BA, GP5, GP6, GPR17, GPR3, HIST1H2BO, HPCA, HSD17B3, HTATIP2, IGF2BP3, IL5, ITGA2B, KIF2A, KLK14, KSR1, L1TD1, LCN1, LEFTY1, LIMS1, LPAR4, LRTM1, LSM 1.00, MAN2A2, MAX, MEA1, MLH3, MORC1, MPL, MUC6, MYOM1, NCKIPSD, NCOA4, NEK4, NEUROD4, NPPA, NXF3, PANK2, PARK7, PCDHGA1, PCDHGB5, PCTP, PDE6A, PDE6H, PF4V1, PHF20L1, PHKB, PITX3, PLXNB3, PPM1A, PPY, PRKCG, PTCRA, PTPRA, RAB8A, RABGEF1, RAX, RDH11, RGS6, RNF10, RNF11, RNF186, RNF2, RNF8, RPA1, RUFY1, SEC14L5, SELP, SIX6, SLC6A4, SMAD2, SNPH, SNX3, ST7, SYNPO2L, TACR2, TAL1, TAOK3, TECPR2, TG, TLK1, TMEM50A, TNNC2, TRIM10, TUBA8, TUBB1, TXNL4B, UIMC1, ULBP1, VCL, VDAC3, WDR11, XPNPEP1, ZNF214, 439 Platelets ZNF37A 8-Mar, ACSBG1, ACTR10, ADCY8, ADI1, ADIPOQ, ADIPOR2, AKAP3, ALOX12, AMD 1.00, ANO2, ANXA7, ARHGAP6, ARHGEF12, ARMC7, ASAP2, ASB8, AVPR1A, BEST2, BIN2, BMP8B, BNIP2, C7orf43, CABP5, CASQ1, CCDC40, CD160, CDK2AP1, CEND1, CETN2, CHD1L, CLEC1B, CMPK1, COL10A1, CRISP2, DERA, DNAI1, DPYS, EGR4, EIF2AK1, ENDOU, F13A1, FAM32A, FBXO9, FOXO4, GNAS, GNB3, GP1BA, GP5, GP6, GP9, GPR17, GPR3, GRIK1, HIST1H2BO, HPCA, HSD17B3, HTATIP2, IGF2BP3, IL5, IPCEF1, ITGA2B, KCMF1, KIF2A, KLK14, KSR1, L1TD1, LCN1, LEFTY1, LIMS1, LPAR4, LRTM1, LSM 1.00, LSM14A, MAN2A2, MAX, MEA1, MLH3, MORC1, MPL, MTR, MYL12A, MYOM1, NCK2, NCKIPSD, NCOA4, NEK4, NOS2, NPFFR1, NPPA, NPTN, NPVF, NR2E3, NXF3, ODC1, PANK2, PARK7, PCDHGA1, PCDHGB5, PCTP, PDCD10, PDE6A, PDE6H, PEX11B, PF4V1, PGRMC1, PHKB, PITX3, PPM1A, PPY, PRKCG, PRX, PTCRA, PTGDR, PTPRA, 440 Platelets RAB8A, RABGEF1, RAX, RDH11, RGS6, RHCE, RIT2,WSGR Docket No.50272-712.601 RNF11, RNF186, RNF2, RNF8, RPA1, RUFY1, SEC14L5, SELP, SIX6, SLC16A8, SLC18A2, SLC6A4, SMAD2, SNPH, SNW1, SNX3, ST7, SYNPO2L, TACR2, TAL1, TAOK3, TCAP, TECPR2, TG, TLK1, TMEM50A, TNNC2, TRAPPC9, TRIM10, TUBA8, TUBB1, TXNL4B, UIMC1, ULBP1, USP12, VCL, VDAC3, WDR11, XPNPEP1, ZMYM1, ZNF214, ZNF37A, ZNF592 ACSBG1, ALOX12, ARHGAP6, CABP5, CLEC1B, GP1BA, 441 Platelets MLH3, PDE6H, PF4V1, SEC14L5, SELP, TUBB1 ALG8, ARCN1, ARL 1.00, BAG3, BUB3, CDK7, FAM98A, FGF7, GLG1, GLT8D1, HAND2, HAS1, HDLBP, KDELR2, MMADHC, MORF4L2, NCKAP1, SEC63, SLC25A32, SRPRB, 442 Preadipocytes SSR1, TCTN3, TESK1, TMEM11, TUBA1B ALG8, BAG3, BUB3, FGF7, GLT8D1, HAND2, HAS1, 443 Preadipocytes KDELR2, MORF4L2, NCKAP1, SLC25A32, SSR1, TMEM11 ACBD3, ADAMTS12, ADH5, ADM2, ADO, ALCAM, ALG8, ANXA1, ANXA5, APPBP2, ARCN1, ARL 1.00, ARSB, ASCC3, ATF6B, ATP5C1, ATP5G1, ATP6V1D, BAG2, BAG3, BUB3, C14orf166, C7orf25, C7orf26, CANX, CAST, CCDC90B, CDK7, CNPY2, COPA, COPB1, COPB2, COPS4, COPS8, COQ6, DCTN6, DDX47, DHRS7B, DOLK, DPAGT1, DSTN, EBF2, ECT2, EDA2R, EIF2S2, EIF3H, ELK1, EPRS, ERGIC3, FAM98A, FCF1, FGF7, FXR2, GLG1, GLT8D1, GNPAT, GRSF1, GTF2H5, HAND2, HAS1, HDLBP, HIF1A, HNRNPA0, HSP90B1, HSPA4, IARS, ICMT, IFT46, IMPACT, ISLR, KDELR2, KIF3B, LAPTM4A, LMOD1, LRRC15, MAGT1, MBTPS1, MBTPS2, METAP2, METTL8, MINPP1, MKRN2, MMADHC, MOCS2, MORF4L2, MPZL1, MRPL20, MRPL3, MTMR2, MYL12A, MYOF, NACA, NCKAP1, NDUFB4, NPTN, OCRL, P4HB, PGRMC1, PODNL1, PPIB, PRRC1, PTGIR, PTP4A2, PTPN11, PURA, PWP1, RAD50, RANBP9, RARS, RHOA, RIC8A, RIOK2, RNF2, RNF25, RPN2, RRM1, RWDD1, SCRG1, SEC61A1, SEC61B, SEC63, SENP5, SLC25A32, SLC25A6, SLC38A10, SNAPC2, SRPRB, SSR1, SYNCRIP, TBC1D17, TCTN3, TESK1, TIPRL, TMED7, TMEM11, TMEM147, TMEM87A, TNXB, TOR1A, TUBA1B, 444 Preadipocytes UCHL5, UFC1, WASL, WISP1, XPOT, YAP1, YIF1A, YKT6 ABCA6, ADAMTS8, ADCYAP1R1, AQP9, CYP19A1, EBF2, EDA2R, FAM98A, HIST1H2BB, IL17RC, IPO5, IPO7, IRF4, 445 Preadipocytes PIWIL2, SLC1A2, SNED1, TBC1D16, TNXB, TRPM3 ABCA6, ACE, ADAMTS8, ARHGAP6, EBF2, HAS1, IRF4, 446 Preadipocytes SNED1, TNXB ABCA6, ACE, ADAMTS8, ADCYAP1R1, AQP9, ARHGAP6, CENPE, CYP19A1, EBF2, EDA2R, FAM98A, GLUD1, HAS1, HIST1H2BB, HIST1H2BM, IL17RC, IPO5, IPO7, IRF4, LSP1, MDN1, MRPS27, PIWIL2, S1PR2, SLC1A2, SMC5, SNED1, 447 Preadipocytes TBC1D16, TNXB, TREM1, TRPM3, XPNPEP2 AFM, AKAP8L, ALPL, ARPP21, BTBD7, C2orf49, CACNA1G, CCR8, CLDN14, CNKSR1, CRX, CSRP3, CXorf36, DCC, DNTT, FIP1L1, FRS3, GABRA6, GCK, GNL2, GPR3, GPR4, GREB1, GRIK3, GYS2, HAMP, HIST1H2BM, HSPB6, IMP4, KNG1, LARP7, LILRB1, LRRTM4, MEN1, MUSK, OMD, OR7A5, PARK2, PLA2G2D, POU3F1, RAG2, RPE65, SCRT1, SLC12A1, SLC25A31, SLC2A2, SOX14, SPATA7, SRY, 448 pro B-cells STRN4, TLX2, TNFRSF12A, VPREB1, ZNF674, ZNF81WSGR Docket No.50272-712.601 ADAM21, ADAMTS8, ADARB2, ADCY8, AGXT, AHDC1, AKAP8L, ALOX15B, ANAPC2, ANXA3, APOC3, ARG1, ARPP21, ART4, ASPM, ATP1B4, BMP10, BMX, BTBD7, CA1, CA14, CALY, CCKAR, CCL17, CETP, CLCA4, CLCN1, CLDN14, CLEC1A, CNGB3, CNKSR1, CNTFR, COQ3, CRH, CSHL1, CSRP3, CTSG, CXorf36, CYP2A7, DCC, DKKL1, DLX4, DNTT, DPYS, DSP, EDF1, EFNA2, FBRS, FBXO24, FCAR, FCN2, FGF8, FRS3, FSTL4, GDF10, GMIP, GPR3, GPR4, GRIK3, GYS2, HAMP, HCRTR2, HIST1H2BL, HIST1H2BM, HP1BP3, HSPB6, HTR1B, HTR5A, IFNA1, IGLL1, IL12B, IMP4, IQCC, KCNJ13, KCNJ9, KCNQ4, KCNV2, KERA, KIF23, KLHL12, KRI1, KRT12, KRT19, KRTAP1-3, LARP7, LILRB1, LILRB4, LLGL1, LTC4S, MAG, MKI67, MRPL12, MRPS15, MSTN, MUC6, MYH4, MYH8, MYL7, NACA2, NEUROG1, NMUR1, NOL12, NOTCH4, NXF3, OMG, ONECUT1, OR7A5, OTOF, PADI4, PAPOLB, PARK2, PCDH11Y, PDE11A, PLK4, PMP2, PMPCA, POU1F1, PPEF2, PPP4R2, PRB4, PRMT1, PSG11, PSORS1C2, R3HCC1, RAD23A, RAG2, RAPSN, RBP3, RNF25, RPGRIP1, RRM2, SIGLEC6, SLC13A4, SLURP1, SOX14, SPATA7, SPC25, SPTBN5, STIP1, TACR3, TCF3, TESK1, TGM3, THPO, TNFRSF12A, TNNT2, TRPM1, TSSK2, TUT1, UCP3, VPREB1, 449 pro B-cells VPREB3, VSX1, ZMYND10, ZNF155, ZNF407 ADAM21, ADAMTS8, AHDC1, AKAP8L, ARG1, ARPP21, BMP10, C2orf49, CLCN1, CLDN14, CNTFR, CSHL1, CXorf36, DCC, DNTT, DPEP3, FBXO24, FCAR, FGF8, FRS3, GPR3, HAMP, HIST1H2BL, HSPB6, HTR1B, IGLL1, IMP4, KCNQ4, LAMB4, LILRB1, LTC4S, MEP1B, MRPL12, MUC6, MYL7, OTUD7B, PCDHA5, PRB4, PSG11, RAG2, SGCA, SOX14, SPC25, TACR3, TCOF1, TESK1, TGM3, TRPM1, VPREB1, 450 pro B-cells VSX1, ZNF155 AHSP, ARPP21, AZU1, BLK, C16orf59, CCDC81, CD72, CD79B, CENPA, CEP55, CETP, DNTT, ESPL1, FLT3, FOXM1, GTSE1, H2AFX, HNRNPA0, HPS4, IGLL1, KIF11, KIF14, KIF4A, LSM 2.00, LY6H, MKI67, MRTO4, MYBL2, NOP56, NUSAP1, OR7A5, P2RY14, PDE6D, POLA1, PRTN3, PTTG1, QRSL1, RAG2, RFC2, RFC5, RRM2, SAC3D1, SHCBP1, SIVA1, SMARCA4, SMC4, SNRPD1, SPC25, SPTA1, TACC3, TCF3, TCL1B, TERT, TOP2B, TRA2A, TSSC1, 451 pro B-cells UBE2C, VPREB1, VPREB3, XPNPEP2 ARPP21, BLK, CCDC81, CD72, CD79B, DNTT, FLT3, IGLL1, 452 pro B-cells PRTN3, QRSL1, RAG2, VPREB1, VPREB3 ARPP21, BLK, CCDC81, CD72, CD79B, DNTT, FLT3, IGLL1, 453 pro B-cells LY6H, MYBL2, PRTN3, QRSL1, RAG2, VPREB1, VPREB3 AP1M2, C1orf116, CALML3, CSF2, CSTA, CTSK, DSG3, FGFBP1, FST, GJB5, HES2, IL1A, IL24, IRF6, KRT6B, KRT6C, KRT75, LAD1, LPAR3, LTB4R2, MMP3, PDZK1IP1, PI3, PKP1, RAB25, S100A14, SFN, SOX15, SULT2B1, 454 Sebocytes TFCP2L1, TMEM40, ZNF750 AP1M2, CALML3, CSF2, CSTA, CTSK, DSG3, FGFBP1, GJB5, HES2, IL1A, KRT6B, KRT6C, KRT75, MMP3, PDZK1IP1, PI3, PKP1, RAB25, S100A14, SFN, SOX15, SULT2B1, TMEM40, 455 Sebocytes ZNF750 AP1M2, C1orf116, CALML3, CSF2, CSTA, CTSK, DSG3, 456 Sebocytes ENTPD3, FGFBP1, FST, GJB3, GJB5, HES2, IL1A, IL24, IRF6,WSGR Docket No.50272-712.601 KRT6B, KRT6C, KRT75, LAD1, LPAR3, LTB4R2, MMP3, PDZK1IP1, PI3, PKP1, PTK6, RAB25, S100A14, SFN, SOX15, SULT2B1, TFCP2L1, TMEM40, ZNF750 CAV3, CDH15, CHRNG, EBF2, MYBPH, MYF5, MYLPF, 457 Skeletal muscle RAPSN CAV3, CDH15, CHRNG, EBF2, LSM 2.00, MYBPH, 458 Skeletal muscle MYF5, MYLPF, MYOD1, RAPSN CDK1, CENPE, CHRNG, EBF2, MYBPH, MYLPF, MYOG, 459 Skeletal muscle RAPSN ABCC9, ACHE, ACTA1, ACTN2, AMPD1, APOBEC2, ART1, ART3, ASB4, ATP1A2, ATP2A1, BAG3, CA3, CACNA1S, CACNB1, CACNG1, CASQ1, CASZ1, CDH15, CHRND, CHRNG, CKM, CLIP1, CNTFR, COX6A2, CSRP3, CTNNA3, DDN, DES, FBP2, FBXO40, HADHB, HRC, HSPB7, IQSEC2, KCNN3, KIAA0368, LBX1, LDB3, LONRF3, MAPK12, MAST2, MIOS, MUSK, MYBPC1, MYBPC2, MYBPH, MYF6, MYH1, MYH2, MYH6, MYH7, MYL1, MYL2, MYL3, MYLPF, MYOD1, MYOG, MYOM1, MYOT, MYOZ1, MYOZ2, NRAP, OBSCN, OSBPL11, PCNT, PGAM2, PGM1, PHKG1, POPDC2, PPP1R3A, PYGM, RAD23A, RAPGEF1, RAPSN, RPL3L, RYR3, SEMA6C, SGCA, SGCG, SIRT2, SLC2A4, SLN, SPTB, SYNPO2L, TCAP, TNNC2, TNNI1, TNNT3, TRDN, TTN, 460 Skeletal muscle UBAC1, UBE2D4, UCP3 ACTA1, ACTN2, AMPD1, APOBEC2, ART1, ART3, ATP1A2, ATP2A1, CA3, CACNA1S, CACNB1, CACNG1, CASQ1, CDH15, CHRND, CHRNG, CKM, COX6A2, CSRP3, CTNNA3, DDN, DES, FBP2, FBXO40, HRC, IQSEC2, KCNN3, LDB3, MYBPC1, MYBPC2, MYBPH, MYF6, MYH1, MYH2, MYH6, MYH7, MYL1, MYL2, MYL3, MYLPF, MYOG, MYOM1, MYOT, MYOZ1, MYOZ2, NRAP, OBSCN, OSBPL11, PGAM2, PHKG1, POPDC2, PPP1R3A, PYGM, RAPSN, RPL3L, SEMA6C, SGCA, SGCG, SLC2A4, SLN, SPTB, SYNPO2L, 461 Skeletal muscle TCAP, TNNC2, TNNI1, TNNT3, TRDN, TTN, UCP3 ACTA1, ACTN2, AMPD1, APOBEC2, ART1, ART3, ASB4, ATP1A2, ATP2A1, CA3, CACNA1S, CACNB1, CACNG1, CASQ1, CASZ1, CDH15, CHRND, CHRNG, CKM, CLIP1, COX6A2, CSRP3, CTNNA3, DDN, DES, FBP2, FBXO40, HADHB, HRC, HSPB7, IQSEC2, KCNN3, LBX1, LDB3, MUSK, MYBPC1, MYBPC2, MYBPH, MYF6, MYH1, MYH2, MYH6, MYH7, MYL1, MYL2, MYL3, MYLPF, MYOG, MYOM1, MYOT, MYOZ1, MYOZ2, NRAP, OBSCN, OSBPL11, PCNT, PGAM2, PHKG1, POPDC2, PPP1R3A, PYGM, RAPSN, RPL3L, SEMA6C, SGCA, SGCG, SLC2A4, SLN, SPTB, SYNPO2L, TCAP, TNNC2, TNNI1, TNNT3, 462 Skeletal muscle TRDN, TTN, UCP3 ASAP2, BAG2, COPA, COPB1, FKTN, HDLBP, HSPB6, PFN2, 463 Smooth muscle PREPL, PRKAG1, PRKG1, USO1, VTI1B ACBD3, AMZ2, ANAPC13, ARL 1.00, ASAP2, COPA, COPB1, COPS2, DCTN1, DYNC1LI2, DYNLRB1, FKTN, GDF5, HDLBP, HOXA3, HSPB6, KLHL9, LGALS1, MBTPS1, 464 Smooth muscle MYOF, PFN2, PRKAG1, PRKG1, TCF21, USO1, VTI1B ACBD3, ADH1B, ALCAM, AMZ2, ANAPC13, ARCN1, ARL 1.00, ASAP2, BAG2, COPA, COPB1, COPS2, 465 Smooth muscle COPS7A, CRYZL1, DCTN1, DCTN4, DYNC1LI2, DYNLRB1,WSGR Docket No.50272-712.601 EDA2R, EID1, EIF4G2, ERLIN1, FAF2, FKTN, GDF5, HDLBP, HOXA3, HPD, HSPB6, IFT46, INVS, IPO5, ISLR, KIF3B, KLHL9, LGALS1, MBTPS1, MRPL40, MYOF, NBR1, OCRL, OGN, PFN2, PKNOX2, PREPL, PRKAG1, PRKG1, SCAMP1, SDF2, STAM2, TCF21, THAP10, TTC37, UFSP2, USO1, VTI1B SEPTIN2, ABI2, ACBD3, ADD1, ADH1B, ADH5, ALCAM, AMZ2, ANAPC13, ANKFY1, ANXA5, ARCN1, ARF4, ARHGAP6, ARL 1.00, ASAP2, ASCC3, ASPN, ATXN2, BAD, BAG2, C11orf95, C2orf49, C6orf120, CACNA1C, CAPNS1, CCDC102B, CCDC53, CCDC90B, CETN2, CIZ1, CLEC3B, COG7, COPA, COPB1, COPS2, COPS7A, CRYZL1, CSNK1G3, CSNK2A2, CTNNA1, CUEDC2, CUL7, DALRD3, DCTN1, DCTN4, DDA1, DDB1, DHX29, DKKL1, DNAJC13, DPT, DPY19L4, DYNC1LI2, DYNLRB1, EDA2R, EID1, EIF4G2, ELN, EMILIN1, EPN2, ERC1, ERGIC3, ERLIN1, EXOC1, EXOC7, FAM189B, FAM98A, FBXL12, FBXO21, FGF7, FKTN, FSHB, FTO, GANAB, GDF10, GDF5, GLT8D1, GOLGA3, GORASP2, GRSF1, GTF2H5, HDLBP, HLCS, HOXA3, HPD, HSP90AB1, HSPB6, HSPB7, IFT46, IGF2BP3, INVS, IPO5, ISLR, KDELR1, KDELR2, KIF3B, KLHL9, KRT19, KTN1, LGALS1, LMOD1, LTC4S, MAP3K7, MBTPS1, MBTPS2, MEA1, METTL5, MLH3, MRPL17, MRPL40, MYL6B, MYOF, MYOZ2, NBR1, NETO2, NFATC4, NGRN, OCRL, OGN, PCDHGC3, PEX12, PFN2, PHKG1, PKNOX2, PPP2R1A, PREPL, PRKAG1, PRKG1, PRRC1, PSMB7, PTPN11, RARS, RBMS1, RER1, RFX2, RING1, S100A10, S100A6, SCAMP1, SCFD1, SCRG1, SDF2, SGCD, SIX5, SLC35E1, SMAD5, SNTB2, SNUPN, SNX13, SPAG16, SPAG7, SPATA7, SPATS2, SPEG, SPIN1, SRPRB, SSR1, STAM2, SVEP1, TBC1D17, TBL2, TCF21, TEX261, TFG, THAP10, TM2D1, TMED9, TMEM165, TMEM184B, TMEM59L, TNPO2, TOMM20, TRIM32, TSEN34, TTC37, TUBG2, TXNL1, UFSP2, USO1, VAMP3, VCL, VIM, VTI1B, WDFY3, YAP1, YIPF2, ZMPSTE24, ZMYM4, ZNF358, ZNF426, 466 Smooth muscle ZNF471 ACBD3, AMZ2, ANAPC13, ARL 1.00, ASAP2, COPA, COPB1, COPS2, DCTN1, DYNC1LI2, DYNLRB1, FKTN, GDF5, HDLBP, HOXA3, HSPB6, KLHL9, LGALS1, MBTPS1, 467 Smooth muscle MYOF, PFN2, PRKAG1, PRKG1, TCF21, USO1, VTI1B ABI2, ACBD3, ADD1, ALCAM, AMZ2, ARF4, ASPN, ATXN2, BAG2, C11orf95, CCDC102B, CCDC90B, CIZ1, CLEC3B, COPA, COPS2, CUL7, DCTN1, DPT, DYNLRB1, EDA2R, EID1, EMILIN1, EXOC7, FAM98A, FGF7, FKTN, FTO, GDF10, GDF5, HDLBP, HOXA3, HPD, HSPB6, HSPB7, ISLR, KDELR1, KLHL9, LGALS1, MYL6B, NBR1, NFATC4, OCRL, OGN, PCDHGC3, PEX12, PFN2, PRKG1, S100A10, S100A6, SCAMP1, SMAD5, SPAG16, SPATA7, SPIN1, SSR1, SVEP1, TCF21, THAP10, TMED9, TMEM59L, TTC37, TXNL1, 468 Smooth muscle ZMYM4, ZNF358, ZNF471 469 Smooth muscle CDK4, CISD1, DDX47, LGALS1, PLS3, SOD1, TMED7, UFC1 ADAMTS12, CCNG1, CETN2, DDX47, GTF2H5, MCTS1, 470 Smooth muscle NDUFS4, PLS3, POMP, RNF7, RPL10, RPS19, S1PR2, TMED9 SEPTIN2, ACTG1, ADAMTS12, ADH5, AHSA1, ALDOA, 471 Smooth muscle ANXA1, ANXA5, ATP5G1, ATP5J, ATP6V0E1, ATP6V1D,WSGR Docket No.50272-712.601 ATXN10, BAG3, BUB3, BYSL, C12orf10, C14orf2, C19orf24, CALR, CANX, CAPN2, CCL8, CCNG1, CDIPT, CDK4, CDK7, CETN2, CISD1, CNIH4, CNN2, COPA, COPB1, COPB2, COX8A, CUEDC2, CUTA, DDX47, DYNLRB1, ECT2, EDA2R, EEF1D, EIF3I, EIF6, EMILIN1, ERCC1, ERGIC3, FAM160B2, FAM98A, FBXO22, FKTN, GMPR2, GOLGA3, GTF2H5, HADHA, HDLBP, HIC1, HSP90B1, HSPA8, ISCU, KIF3B, LAMP1, LAPTM4A, LGALS1, LMOD1, LRRC15, MANF, MBTPS1, MCTS1, MIF, MINPP1, MMADHC, MORF4L2, MPZL1, MRPL15, MRPS11, MRPS33, MTMR2, MYCT1, MYL6, NCKAP1, NDUFA8, NDUFB4, NDUFS4, NDUFS5, NFATC4, NOP10, P4HB, PCDHGB5, PEX2, PGRMC1, PLS3, POFUT2, POLR2F, POMP, PRRC1, PSMB1, PSMB4, PSMB5, PSMC5, PSMD10, PSMD14, PTGIR, PTP4A2, PTPN11, PTTG1IP, RARS, RHOC, RIN2, RNF7, RPL10, RPL35A, RPL4, RPN2, RPS19, RWDD2B, S1PR2, SEC61B, SEC61G, SENP5, SHFM1, SOD1, SPATS2, SRPRB, SSBP1, ST13, STX12, TBC1D17, TIMM17A, TM2D1, TMED1, TMED7, TMED9, TMEM59, TMEM87A, TPI1, TPM3, TRAPPC4, UFC1, UFD1L, UQCRQ, VCP, ZNF771, ZNHIT1 ABCD2, ACD, ACTR8, AK2, ARMC1, ARPC2, ARPC5L, ASXL2, ATG5, ATP8B4, AURKA, BUB1, CCNF, CCR2, CCR3, CCR5, CD101, CD2, CD244, CD247, CD300A, CD40LG, CD96, CDC25C, CENPA, CENPJ, CENPN, CEP55, CHEK1, CHMP4A, CHST12, CLIC1, CLPP, CXCR3, CXCR6, DAXX, DBF4, DGCR14, DLGAP5, DR1, ECT2, FAM120A, FAM96B, FASLG, GLE1, GLMN, GLO1, GMEB1, GNLY, GPI, GPKOW, GPR15, GPR171, GPR68, GRAP2, GYG1, GZMA, GZMB, GZMH, GZMK, H2AFX, HAT1, HJURP, HMGN4, HMOX2, HNRNPUL1, HSPB11, IKZF4, IL12RB1, IL13, IL18RAP, IL26, IL2RA, IL2RB, IL4, IL5, ITGAL, ITGB7, KIF11, KIF14, KIF22, KIF2A, KIF2C, KLHL7, KLRG1, LAG3, LAIR2, LCP2, LIM2, LTA, MKI67, MMP25, MSH3, MTDH, NCAPD3, NCDN, NCR3, NEK2, NKG7, NUSAP1, PDE4A, PDE6D, PEX2, PFN1, PLK4, POP4, PPID, PPP1CA, PRF1, PSMA3, PSMB2, PSMC4, PSMD13, PSTPIP1, PTP4A2, PTPN4, PTPN7, PTPN9, PVRIG, RACGAP1, RAD50, RALY, RANBP3, RBL1, RGS9, RNF167, RPA1, RRM1, RRM2, SAC3D1, SF3B4, SFXN1, SLC26A4, SMC2, SOS 1, SPC25, STIP1, TAF1B, TBX21, TINF2, TMPO, TOP3B, TOR1AIP1, TPX2, UCHL5, UEVLD, VANGL1, WBP11, ZBTB16, ZBTB32, ZMAT5, ZNF174, 472 Tgd cells ZNF668 ABCD2, ACD, ACTR8, ARMC1, ARPC2, ARPC5L, ARPP19, ASCC1, ASXL2, ATG5, ATP8B4, AURKA, BUB1, CCNA2, CCNF, CCR2, CCR3, CCR5, CD101, CD2, CD247, CD96, CDC123, CDC25C, CDC5L, CDK1, CENPA, CENPN, CEP55, CHMP4A, CIAO1, CLIC1, CLPP, COMMD8, COX8A, CSTF1, CXCR3, CXCR6, DAXX, DBF4, DBR1, DCAF7, DGCR14, DLGAP5, DR1, DRG2, ECT2, FAF1, FAM120A, FAM96B, FASLG, GLE1, GLMN, GLO1, GMEB1, GMIP, GNLY, GPI, GPKOW, GPR15, GPR171, GPR68, GRAP2, GYG1, GZMA, GZMB, GZMH, GZMK, H2AFX, HIC1, HJURP, HMOX2, HSPB11, IFNG, IKZF4, IL12RB1, IL13, IL26, IL2RA, IL2RB, IL4, IL5, INPP4A, ITGAL, ITGB7, KIF2C, KLHL7, KLRG1, 473 Tgd cells LAG3, LAIR2, LCP2, LIM2, LTA, MKI67, MMP25, MNAT1,WSGR Docket No.50272-712.601 MRPS15, MSH3, MTDH, NCDN, NCR3, NEK2, NFKBIB, NMT1, PDE4A, PDE6D, PEX2, PIAS4, PLK4, POP4, PPID, PPP1CA, PRF1, PSMA1, PSMA3, PSMB2, PSMC4, PSMD13, PSMD14, PSTPIP1, PTP4A2, PTPN4, PTPN7, PTPN9, PUF60, PVRIG, RANBP3, RBL1, RGS9, RNF167, RNF6, RRM1, RRM2, SASH3, SF3B4, SFXN1, SLC25A32, SLC26A4, SMC2, SOS 1, SPC25, SRF, STIP1, STX8, TAF1B, TBX21, TDP1, TIPRL, TMEM110, TMPO, TOP3B, TOR1A, TOR1AIP1, TPX2, TTK, UCHL5, UEVLD, VANGL1, WBP11, ZBTB16, ZBTB32, ZCCHC4, ZNF174, ZNF668 ABCD2, ACD, ACTR8, AGGF1, AMZ2, ARPC2, ARPP19, ASCC1, ASXL2, ATG5, ATP8B4, AURKA, BARD1, BUB1, CCL1, CCNA2, CCNF, CCR2, CCR3, CCR5, CD101, CD2, CD244, CD247, CD300A, CD40LG, CD96, CDC123, CDC25C, CDK1, CENPA, CENPN, CEP55, CHMP4A, CIAO1, CLIC1, CLPP, COLQ, COMMD8, COX8A, CSNK1G1, CTLA4, CXCR3, CXCR6, DAXX, DBF4, DBR1, DCLRE1A, DGCR14, DLGAP5, DR1, DRG2, ECT2, FAM120A, FAM96B, FASLG, FZR1, G3BP2, GLE1, GLMN, GLO1, GMEB1, GMIP, GNLY, GPI, GPKOW, GPR15, GPR171, GPR68, GRAP2, GYG1, GZMA, GZMB, GZMH, GZMK, H2AFX, HIC1, HIVEP3, HJURP, HMOX2, HNRNPF, HSPB11, IFNG, IKZF4, IL12RB1, IL13, IL26, IL2RA, IL2RB, IL4, IL5, INPP4A, ITGAL, ITGB7, KIF14, KIF22, KIF2A, KIF2C, KLHL7, KLRG1, LAG3, LAIR2, LCP2, LIM2, LTA, MELK, MFSD5, MKI67, MMP25, MNAT1, MSH3, MTDH, NCDN, NCR3, NEK2, NFKBIB, NKG7, NMT1, PBK, PDE4A, PDE6D, PEX2, PLK4, POP4, PPID, PPP1CA, PRF1, PSMA3, PSMB2, PSMC4, PSMD13, PSMD14, PSMD4, PSMD7, PSTPIP1, PTP4A2, PTPN4, PTPN7, PTPN9, PVRIG, RAD21, RAD50, RANBP3, RB1, RBL1, RC3H2, RGS9, RNF167, RRM1, SASH3, SCAMP2, SF3B4, SFXN1, SH2D1A, SHMT2, SLAMF1, SLC25A32, SLC26A4, SMC2, SOS 1, SPC25, STIP1, STX8, TAB2, TACC3, TAF1B, TBX21, TIPRL, TMEM110, TMPO, TOP3B, TOR1AIP1, TPX2, UCHL5, UEVLD, USP1, VANGL1, WBP11, ZBTB16, ZBTB32, 474 Tgd cells ZCCHC4, ZNF174, ZNF668 CDC123, CHD1L, CHD4, CSTF1, CUEDC2, EIF2B2, FIBP, GNLY, IFNG, LAG3, MDC 1.00, MNAT1, NCAPD3, POLD2, PPM1G, R3HDM1, RUVBL2, SLAMF1, SNRPC, 475 Th1 cells TACO1, THOP1, TMEM39B, TRIM28, UBAP2 CUEDC2, GNLY, IFNG, LAG3, NCAPD3, RUVBL2, SNRPC, 476 Th1 cells TACO1, TMEM39B, UBAP2 COX10, CUEDC2, EIF2B2, HTRA2, IFNG, KIF20A, LAG3, NUP205, PKMYT1, PSMD3, PTTG1, RNPS1, SNRPC, TTLL5, 477 Th1 cells WDR18, WRAP53, ZBTB32 478 Th2 cells BAG2, CEP55, CXCR6, GZMK, IL13, IL5, MAD2L1, RRM2 BAG2, CEP55, CXCR6, IL13, IL5, MAD2L1, NPHP4, NUP37, 479 Th2 cells RRAS2 BAG2, CDK2AP1, CXCR6, GPR15, GZMA, IL13, IL5, NPHP4, RAD50, RGS9, RNF34, SLC25A44, SMAD2, THADA, 480 Th2 cells TMEM39B, UBAP2 ATG2B, BANP, CCR3, CTLA4, CXCR6, ICOS, IKZF4, IL2RA, IPCEF1, LAX1, MCM9, PLCL1, PPM1B, STAM, TTN, TULP4, 481 Tregs UBE4A, VPS54, ZCCHC8, ZFC3H1, ZMYM1, ZNF236WSGR Docket No.50272-712.601 BANP, CTLA4, ICOS, IKZF4, IL2RA, IPCEF1, PLCL1, STAM, 482 Tregs ZNF236 ATG2B, BANP, CTLA4, ICOS, IKZF4, IL2RA, IPCEF1, PLCL1, PPM1B, STAM, TULP4, VPS54, ZCCHC8, ZFC3H1, 483 Tregs ZNF236 BANP, CTLA4, ICOS, IKZF4, IL2RA, IPCEF1, PLCL1, STAM, 484 Tregs ZNF236 ATG2B, BANP, CTLA4, ICOS, IKZF4, IL2RA, IPCEF1, PLCL1, PPM1B, STAM, TULP4, VPS54, ZCCHC8, ZFC3H1, 485 Tregs ZNF236 CCR4, CCR8, CD28, CD5, CTLA4, FOXP3, GALNT8, GPR25, HS3ST3B1, ICOS, IL10RA, IL2RA, ITGB7, KCNA2, LRP2BP, 486 Tregs MCF2L2, PLCL1, RGS1, SIT 1.00, SPTAN1, TULP4 CCR4, CCR8, CTLA4, FOXP3, GPR25, HS3ST3B1, ICOS, 487 Tregs IL2RA, KCNA2, LAIR2, MCF2L2, RGS1 CCR4, CCR8, CTLA4, FOXP3, GPR25, HS3ST3B1, IL2RA, 488 Tregs KCNA2, LAIR2, MCF2L2 CCR4, CCR8, CTLA4, FOXP3, GPR25, HS3ST3B1, IL2RA, 489 Tregs KCNA2, LAIR2, MCF2L2, RGS1 1Full names for the cell types and their groupings are provided in Table 2B. Table 2B: Cell Type Names and Groupings Cell type Full name Group aDC Activated dendritic cells Myeloid Adipocytes Adipocytes Stroma Astrocytes Astrocytes Epithelial B-cells B-cells Lymphoid Basophils Basophils Myeloid CD4+ memory T-cells CD4+ memory T-cells Lymphoid CD4+ naive T-cells CD4+ naive T-cells Lymphoid CD4+ T-cells CD4+ T-cells Lymphoid CD4+ Tcm CD4+ central memory T-cells Lymphoid CD4+ Tem CD4+ effector memory T-cells Lymphoid CD8+ naive T-cells CD8+ naive T-cells Lymphoid CD8+ T-cells CD8+ T-cells Lymphoid CD8+ Tcm CD8+ central memory T-cells Lymphoid CD8+ Tem CD8+ effector memory T-cells Lymphoid cDC Conventional dendritic cells Myeloid Chondrocytes Chondrocytes Stroma Class-switched memory B-cells Class-switched memory B-cells Lymphoid CLP Common lymphoid progenitors HSC CMP Common myeloid progenitors HSC DC Dendritic cells Myeloid Endothelial cells Endothelial cells Stroma Eosinophils Eosinophils Myeloid Epithelial cells Epithelial cells Epithelial Erythrocytes Erythrocytes HSC Fibroblasts Fibroblasts Stroma GMP Granulocyte-macrophage progenitors HSC Hepatocytes Hepatocytes EpithelialWSGR Docket No.50272-712.601 HSC Hematopoietic stem cells HSC iDC Immature dendritic cells Myeloid Keratinocytes Keratinocytes Epithelial ly Endothelial cells Lymphatic endothelial cells Stroma Macrophages Macrophages Myeloid Macrophages M1 Macrophages M1 Myeloid Macrophages M2 Macrophages M2 Myeloid Mast cells Mast cells Myeloid Megakaryocytes Megakaryocytes HSC Melanocytes Melanocytes Epithelial Memory B-cells Memory B-cells Lymphoid MEP Megakaryocyte–erythroid progenitors HSC Mesangial cells Mesangial cells Stroma Monocytes Monocytes Myeloid MPP Multipotent progenitors HSC MSC Mesenchymal stem cells Stroma mv Endothelial cells Microvascular endothelial cells Stroma Myocytes Myocytes Stroma naive B-cells naive B-cells Lymphoid Neurons Neurons Epithelial Neutrophils Neutrophils Myeloid NK cells NK cells Lymphoid NKT Natural killer T-cells Lymphoid Osteoblast Osteoblasts Stroma pDC Plasmacytoid dendritic cells Myeloid Pericytes Pericytes Stroma Plasma cells Plasma cells Lymphoid Platelets Platelets HSC Preadipocytes Preadipocytes Stroma pro B-cells pro B-cells Lymphoid Sebocytes Sebocytes Epithelial Skeletal muscle cells Skeletal muscle cells Stroma Smooth muscle cells Smooth muscle cells Stroma Tgd cells Gamma delta T-cells Lymphoid Th1 cells Type 1 T-helper cells Lymphoid Th2 cells Type 2 T-helper cells Lymphoid Tregs Regulatory T-cells Lymphoid Table 3: Cell fractions switch model Cluster Day 1 Day 2 Day 3 Day 4 Day 5 0 0.003294427 0.005347591 0.012081654 0.01231173 0.00082438 1 0.5160007 0.51787037 0.4829121 0.52297074 0.46897933 2 0.003665262 0.013208234 0.014365748 0.00041886 5.76E-05 3 0.006981805 0.005082286 0.002157072 0.000540785 5.67E-05 4 0.12846752 0.1263922 0.15588246 0.1417947 0.12584612 5 0.001931314 0.002371719 0.002445096 0.000721555 0.000110169 6 0.3005145 0.29759157 0.3023676 0.31605008 0.34344494 7 0.02269922 0.000745356 0.000489529 0.000171006 4.56E-05 8 0.001981802 0.003469942 0.002998484 0.000580525 8.7E-05 9 0.003104583 0.010330803 0.009462568 0.000438006 6.17E-05 10 0.002057982 0.002985753 0.002782023 0.000666256 9.79E-05WSGR Docket No.50272-712.601 11 0.002473356 0.001803802 0.000858457 0.001482215 0.06012887 12 0.002246196 0.004499522 0.003670252 0.000560753 8.21E-05 13 0.001909173 0.002708688 0.002785438 0.000614411 9.62E-05 14 0.002672115 0.005592173 0.004741537 0.000678433 8.13E-05

[0172] The cell fractions across 5 days in a menstrual cycle were consistently dominated by three populations – cluster 1, 4, and 6. In this example, there were no prior biological properties (e.g. cell types, cell functions) known for the 15 UMAP clusters in the reference set. The switch profiles were calculated for these 1905 single cells using the RNA sequencing output for each cell. To explore the biological properties for each cluster, the switch expression values were averaged for the 1905 single cells across all 489 switches (Table 4).WSGR Docket No.50272-712.601 IDSw17161 it514131211109 8 7 6 5 4 3 2 1chA s A A A A A A A A A Aaw tr so tr d d d d d d d d dDac o ip i CDaCDaCDaCDaCDswTi h isycyo p ic o p i i i i i i Cc o pc o pc o pc o p p p tc ec oc oc oc hresuetes te ys t yes t yes t y y y y y y ie ss tes tes tes tes tes tes on9.0 6.8 7.5 4.5 5.2 3. 2. 2. 5. 5. 4. 11 11 8. 3. 3. 2. -0clu4168669929 7 0 11 85 85 91 20 57 .2 .9 91 10 10 55st831 4682051582 239076007606212 1582 820521 94162034208721 8721 1526 er7. 7 5 4 4 3 - c7 .4 .5 .5 . 3 3 5 4 4 9 1 5 3 3 27 . . . . . . . 0 . . . .1lu4362350554 3 6 25 02 02 03 76 53 45 .6 95 38 38 65st82387343843860672 91 7891 78828843863438926368601692 126812684922 er5. 5 3 3 3 - c8 .7 .8 .4 . 2 2 3 3 3 7 8 4 3 3 25 .8 . . . . . . . . . .2lu8121172486630072296 0 80 56 56 42 07 08 19 01 01 45st343423557355734342 4342 72934582 976869865255256919 er9. 6 7 3 4 2 2 5 4 3 1 1 7 2 - c9 .2 .1 .8 .0 .8 .8 .1 .0 .8 0. 1 .8 . 29 . 29 .33lu063649763810682 64145075753505735057351557 4 9 .8 9 0 0 2st3057561 45853768153500411 0411 2819 er12 11 1 1 1 3 3 3 1 1 1 1 1 1 3 3 3 -4cl7.64954.2 1861 9. 062862. 06 . .8 317061033 .1 .1 154695410954107. 01 . 089836102. 461705. 423144. 09 . .3 8 .8 .1 u732 921 6978069789st05463 erT a6. 6 5 3 3 2 2 4 3 3 8 9 4 3 3 2 -0 . . . . . . . . . . . . . . .5cl ble36 0 7 4 9 8 8 4 9 4 7 8 9 0 0 4 u68305709844 507507474449868 2299 6 6 6ste 4382763 5 43353534343 5 9 1 9765381 0981 09 26 r10 8. 9. 4. 5. 2. 2. 8. 5. 4. 14 15 12 10 1 7. -6cl7.0 33 72 89 99 80 80 55 99 89 .1 .7 .5 .9 0.9 16 ust504272771 42 960357513557355730871 5751 9603979688477759624562457815 er10 7.5 8.3 4. - c7 5. 3. 2. 2. 7. 5. 4. 11 12 8. 3. 3. 2.7lu5.2949 3 8 42 16 95 95 07 42 78 .6 .6 28 36 36 43st7385331 2961 06572421 66545654561385572461 06249034369304548954890809 er7.1 6.7 4. 4. 4. 3. 2. 2. 4. 4. 4. 8. 10 4. 2. 2. 2. -8clu62369 64 20 38 16 80 80 44 38 20 13 .6 71 87 87 32st3843022459301 8678605573557392341 86593011 01 5822 61 52 952695262819 er7. 6 4 4 4 - c2 .9 .7 . 3 3 3 4 4 4 7 9 4 3 3 22 . . . . . . . . . . . . .9lu8942528 9 8 44 77 43 43 48 44 28 93 28 68 14 14 53st7233074244247444670221 0221 93824744244577346729545342 5342 8934 er6. 5 4 3 3 2 2-c0 .9 .3 .5 .6 .8 . 3 3 3 7 8 4 3 3 28 . . . . . . . . .10lu26901 6581651222 33276 0 0 66 66 53 61 85 25 12 12 66s3t031 85 5573557331 8531 8532704981 0552 299431 4331 43208 er9.2 7.5 7. 4 5 2 2 6 5 4 1 1 6 - c5 .9 .4 .8 .8 . .4 .9 0 1 .6 4.8 4.8 2.8 11lu371 481124702 95068645429735057350 357376592 54 92978649.76280.9411846 779237792315st479 er6. 68 . 4. 3 4 3 3 3 4 4 3 7 9 4 3 3 2- 1cl12 7 . . . . . . . . . . . . . .8 4 8 0 3 0 0 1 0 8 9 2 6 0 0 42us6670378999581886776562 433845766757 26678792 76562 1886737781 022650141822999229992525 t7 er6. 65 . 6 5 5 2 2 5 5 5 8 8 4 3 3 2 -1 cl11 8 . . . . . . . . . . . . . .3 43 0 1 8 8 2 1 0 1 9 3 0 0 33us752 085497387531594445350573505723245944457531542527 3 4 4 2t2 2772831 366936692819 er5. 5513 . 339 . 370 . 332 . 245 . 280 .8 30 . 345 . 3 74 8 4 2 2 2- 1cl5 .32 .1 .0 .8 .8 .8 .34us592 1723404281932943557355733294329428192956996277264595057350 2t5732819 erWSGR Docket No.50272-712.601 353433323130292827262524232221201918B a B a B a B a B a B - B - B - B - B -...

Claims

1. WSGR Docket No.50272-712.601 CLAIMS What is claimed is:

1. A method of analyzing a compound, comprising: identifying a first expression level of a switch from a first RNA sequencing dataset obtained from a first vaginally derived sample; identifying a second expression level of the switch from a second RNA sequencing dataset from a second vaginally derived sample; wherein the switch comprises a set of genes selected from genes co-expressed within a biological relevance parameter; wherein the first and second vaginally derived samples are derived from a same mixed population of cells, and wherein the compound is administered ex vivo to the first vaginally derived sample and / or the second vaginally derived sample prior to the generation of the first RNA sequencing dataset and / or the second RNA sequencing dataset; generating a first score for the switch based on the first expression level of the switch; and generating a second score for the switch based on the second expression level of the switch.

2. The method of claim 1, wherein the second RNA sequencing dataset is obtained at a time period subsequent to administration of the compound to the second vaginally derived sample.

3. The method of claim 2, wherein the first RNA sequencing dataset is obtained without administration of the compound to the first vaginally derived sample.

4. The method of claim 2, wherein the first RNA sequencing dataset is obtained at a time period subsequent to administration of the compound to the first vaginally derived sample.

5. The method of claim 4, wherein the amount of the compound administered to the first vaginally derived sample is different from the amount of the compound administered to the second vaginally derived sample.

6. The method of claim 4, wherein the amount of the compound administered to the first vaginally derived sample is the same as the amount of the compound administered to the second vaginally derived sample.

7. The method of claim 6, wherein the amount of time of incubation with the compound isdifferent between the first vaginally derived sample and the second vaginally derived sample.

8. The method of any one of claims 1-7, wherein the mixed population of cells are not WSGR Docket No.50272-712.601 cultured or expanded prior to administration of the compound.

9. The method of any one of claims 1-8, wherein the compound is a therapeutic agent or a candidate therapeutic agent.

10. The method of any one of claims 1-9, wherein the first vaginally derived sample and the second vaginally derived sample comprise menstrual fluid, cervical-vaginal fluid or a combination thereof.

11. The method of any one of claims 1-10, wherein the first vaginally derived sample and the second vaginally derived sample are derived from a healthy or putatively healthy subject.

12. The method of any one of claims 1-10, wherein the first vaginally derived sample and the second vaginally derived sample are derived from one or more individuals having a diseased state or putative diseased state.

13. The method of claim 12, wherein diseased state or putative diseased state is selected from the group consisting of endometriosis, cervical cancer, infertility, uterine cancer, ovarian cancer, autoimmune disease, inflammatory disease, and fibroids.

14. The method of claim 12, wherein diseased state or putative diseased state is a bacterial infection, a fungal infection or a viral infection.

15. The method of claim 12, wherein the one or more individuals exhibits one or more ofinflammation, bleeding, heavy bleeding, anemia, change in regular menstruation cycle, pain and a change in microbiome diversity, microbial species or microbial abundance.

16. A method of analyzing RNA sequencing data, comprising: identifying an expression level of a first switch from a first RNA sequencing dataset obtained from a first vaginally derived sample, wherein the first vaginally derived sample comprises a mixed population of cells, wherein the first switch comprises a set of genes selected from genes co- expressed within a biological relevance parameter; and generating a first score for the switch based on the first expression level of the first switch.

17. The method of claim 16, wherein the mixed population of cells are not cultured orexpanded prior to obtaining the first RNA sequencing dataset.

18. The method of claim 16 or claim 17, further comprising: identifying an expression level of a second switch from the first RNA sequencing dataset, wherein the second switch comprises a second set of genes selected from genes co- expressed within a second biological relevance parameter; and generating a second score based on the expression level of the second switch.

19. The method of claim 18, further comprising comparing the first score with the second WSGR Docket No.50272-712.601 score.

20. The method of claim 16 or claim 17, comprising identifying an expression level of a plurality of switches from the first RNA sequencing dataset, wherein each switch of the plurality of switches comprises a set of genes selected from genes co-expressed within a biological relevance parameter and generating a score for each of the plurality of switches.

21. The method of any one of claims 16-20, wherein the first vaginally derived sample comprises menstrual fluid, cervical-vaginal fluid or a combination thereof.

22. The method of any one of claims 16-21, wherein the first vaginally derived samplecomprises a mixture of cell types or tissue types.

23. The method of any one of claim 16 or claim 17, further comprising identifying a second expression level of the first switch from a second RNA sequencing dataset from a second vaginally derived sample and generating a second score for the first switch based on the second expression level of the first switch.

24. The method of claim 23, wherein the first vaginally derived sample and the second vaginally derived sample comprise menstrual fluid, cervical-vaginal fluid or a combination thereof.

25. The method of claim 23 or claim 24, wherein the first vaginally derived sample and the second vaginally derived sample comprises a mixture of cell types or tissue types.

26. The method of claim 25, further comprising comparing the first score with the second score.

27. The method of any one of claims 23-26, wherein the first RNA sequencing dataset is derived from one or more healthy or putative healthy individuals.

28. The method of any one of 23-27, wherein the second RNA sequencing dataset is derived from one or more individuals having a diseased state or putative diseased state.

29. The method of any one of claims 23-27, wherein the second RNA sequencing dataset is derived from one or more healthy or putative healthy individuals.

30. The method of any one of 23-26, wherein the first RNA sequencing dataset and the second RNA sequencing dataset are derived from a same individual.

31. The method of claim 30, wherein the first RNA sequencing dataset and the second RNA sequencing dataset are derived from different time points or different sample types.

32. The method of claim 30, wherein the first vaginally derived sample is obtained under a first condition and the second vaginally derived sample is obtained under a second condition.

33. The method of claim 32, wherein the first condition is prior to a diagnosis of a disease or infection, exhibition, progression or recurrence of one or more symptoms, reduction or WSGR Docket No.50272-712.601 disappearance of one or more symptoms, or a medical treatment.

34. The method of claim 32, wherein the first condition is prior to a diagnosis of a disease orinfection, exhibition, progression or recurrence of one or more symptoms, reduction or disappearance of one or more symptoms, or a medical treatment, and the second condition is subsequent to the diagnosis of a disease, exhibition, progression or recurrence of one or more symptoms, reduction or disappearance of one or more symptoms, or a medical treatment.

35. The method of claim 32, wherein the first condition is subsequent to a diagnosis of a diseaseor infection, exhibition, progression or recurrence of one or more symptoms, reduction or disappearance of one or more symptoms, or a medical treatment.

36. The method of any one of claims 33-35, wherein the disease is selected from the groupconsisting of endometriosis, cervical cancer, infertility, uterine cancer, ovarian cancer, autoimmune disease, inflammatory disease and fibroids.

37. The method of any one of claims 33-35, wherein the infection comprises a bacterial infection,a fungal infection or a viral infection.

38. The method of any one of claims 33-35, wherein the one or more symptoms are selected fromthe group consisting of inflammation, bleeding, heavy bleeding, anemia, change in regular menstruation cycle, pain and a change in microbiome diversity, microbial species or microbial abundance.

39. The method of any one of claims 33-35, wherein the medical treatment is selected from thegroup consisting of surgery and administration of a therapeutic agent.

40. The method of any one of claims 33-35, wherein the first condition is a time period within amenstrual window.

41. The method of claim 32, wherein the first condition is a time period outside a menstrualwindow.

42. The method of claim 32, wherein the first condition is a time period within a menstrualwindow and the second condition is a time period outside the menstrual window.

43. The method of claim 32, wherein the first condition and the second condition are a timeperiod within a menstrual window, and wherein the first condition and the second condition are different days of menstruation.

44. The method of claim 16, further comprising identifying a differential for the first switch,wherein the differential is identified for the first switch when the first score differs by more than the standard deviation of a reference score of the first switch in a reference score set.

45. The method of claim 44, wherein the reference score set is derived from optimal RNAsamples. WSGR Docket No.50272-712.601 46. The method of claim 44, wherein the reference score set is derived from an RNA sequencing dataset from one or more healthy or putative healthy individuals.

47. The method of claim 44, wherein the reference score set is derived from an RNAsequencing dataset from one or more individuals having a diseased state or putative diseased state.

48. The method of any one of claims 1-47, wherein the biological relevance parameter is selected from a group consisting of cell type, tissue type, biological pathway, and biological function.

49. The method of any one of claims 1-48, wherein the first RNA sequencing dataset and / or the second RNA sequencing dataset comprises bulk RNA sequencing data.

50. The method of any one of claims 1-15, 23-43 and 48, wherein the second RNA sequencing dataset comprises bulk RNA sequencing data.

51. The method of any one of claims l-49, wherein the first score is calculated using a number of reads within the RNA sequencing dataset of the genes within the switch.

52. The method of any one of claims l-15, 18-43, 48 and 50, wherein the second score is calculated using the number of reads within the RNA sequencing dataset of the set of genes within the switch.

53. The method of any one of claims 1-52, wherein the score, first score and / or second score is normalized by the level of one or more housekeeping genes in the RNA sequencing dataset.

54. The method of any one of claims 1-53, wherein the score, first score and / or second scoreindicates the presence or absence of the activity of one or more cell types in the first vaginally derived sample.

55. The method of any one of claims 1-54, wherein the first switch comprises at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 genes.

56. The method of any one of claims 1-54, wherein the first switch comprises at least 5, at least 10, at least 15, at least 20, or at least 25 genes.

57. The method of any one of claims 1-15 and 18-43, wherein the second switch comprises at least 2, 3, 4, 5, 6, 7, 8, 9, or l 0 genes.

58. The method of any one of claims 1-15 and 18-43, wherein the second switch comprises at least 5, at least 10, at least 15, at least 20, or at least 25 genes.

59. The method of any one of claims 1-54, wherein the first switch comprises at least one set of genes selected from Table 2.

60. The method of claims 1-59, wherein the biological relevance parameter is a cell type and the cell type is one or more of stromal cells, monocytes, neutrophils, dendritic cells, WSGR Docket No.50272-712.601 macrophages, eosinophils, B cells, megakaryocytes, epithelial cells. progenitor cells, stem cells, lymphoid cells, non-lymphoid cells, hemopoietic cells, and non-hemopoietic cells.

61. The method of any one of claims 1-59, wherein the biological relevance parameter is a tissue type and the tissue type is one or more of cervical, endometrial, vaginal, uterine, blood, placental, muscle, ovarian, fetal, and maternal.

62. The method of any one of claims 1-59, wherein the biological relevance parameter is a biological pathway.

Images