Method for screening for resetting gene or therapeutic gene

Abstract

The present application relates to a method for screening for a gene. Some embodiments of the present application provide a method for screening for a cell resetting gene. Some embodiments of the present application provide a method for screening for a therapeutic gene.

Description

Methods for screening for resetting genes or therapeutic genes

[0001] Some embodiments of the present disclosure relate to a method for screening genes. Some embodiments of the present disclosure relate to a method for screening cell resetting genes. Some embodiments of the present disclosure relate to a method for screening therapeutic genes.

[0002] Conventional candidate gene screening methods for functional genes, such as therapeutic genes, primarily screen for genes exhibiting abnormal expression levels in disease models. However, as previously mentioned, these methods (1) screen for genes based on gene expression levels in previously induced disease models, thus failing to reflect information related to the disease pathogenesis, cell or tissue damage, or functional decline. Furthermore, (2) the sheer number of genes exhibiting abnormal expression levels in disease models makes efficient screening difficult. Due to these limitations, conventional candidate gene screening methods are resource-intensive.

[0003] Conventional genetic screening methods, which select genes based on their expression levels in pre-induced disease models, are problematic in that they fail to capture information related to disease pathogenesis, cell or tissue damage, or functional decline. Furthermore, they often result in a significant number of genes exhibiting abnormal expression levels in disease models. Therefore, a novel approach to genetic screening is urgently needed.

[0004] The present disclosure provides a novel genetic screening method. Some embodiments of the present disclosure provide a method for screening cell resetting genes. Some embodiments of the present disclosure provide a method for screening therapeutic genes.

[0005] A genetic screening method according to some embodiments of the present disclosure comprises selecting first candidate genes by utilizing patterns of time-series gene expression levels for genes acquired during two-dimensional differentiation into specific target cells and three-dimensional differentiation into specific target cells. The screening method according to some embodiments of the present disclosure may be significant in that it screens genes using a novel approach not used in conventional screening methods.

[0006] Some embodiments of the present disclosure provide methods for screening for cell resetting genes.

[0007] Some embodiments of the present disclosure provide a method of screening for one or more cell resetting genes for repair of damage or dysfunction in a specific target cell or a specific target tissue associated with the specific target cell, comprising:

[0008] (i) Obtain vectors for multiple genes;

[0009] At this time, the vectors for the above multiple genes

[0010] Obtained from a two-dimensional gene expression profile data set for two-dimensional differentiation of differentiable cells capable of differentiating into the above-mentioned specific cells and a three-dimensional gene expression profile data set for three-dimensional differentiation,

[0011] At this time, the two-dimensional gene expression profile data set for the two-dimensional differentiation is composed of two-dimensional gene expression profiles for multiple genes,

[0012] At this time, the two-dimensional gene expression profile for each of the above multiple genes is

[0013] It consists of the first to Nth gene expression levels of the two-dimensional differentiation for each of the plurality of genes measured at each of the first to Nth time points of the two-dimensional differentiation process,

[0014] At this time, the 3D gene expression profile data set for the 3D differentiation is composed of 3D gene expression profiles for multiple genes,

[0015] At this time, the 3D gene expression profile for each of the above multiple genes is

[0016] Consists of the first to Mth gene expression levels of the three-dimensional differentiation for each of the plurality of genes measured at each of the first to Mth time points of the three-dimensional differentiation process;

[0017] (ii) Classifying the gene vectors for the above plurality of genes into two or more clusters using a clustering algorithm;

[0018] (iii) based on predetermined cluster selection criteria, selecting one of the two or more clusters and determining the first candidate genes as genes belonging to the selected cluster; and

[0019] (iv) Among the first candidate genes, genes related to at least one of metabolic, catabolic, and wound healing are determined as the cell resetting genes.

[0020] In some embodiments, the plurality of genes may be greater than 2000.

[0021] In some embodiments, N can be an integer from 2 to 6.

[0022] In some embodiments, M can be an integer from 2 to 6.

[0023] In some embodiments, information about genes associated with the metabolism can be obtained from a knowledge base on gene ontology.

[0024] In some embodiments, the functions of the genes associated with metabolism may be related to metabolism. In some embodiments, the processes involved in the genes associated with metabolism may be associated with metabolism. In some embodiments, the gene ontology (GO) of the genes associated with metabolism may be associated with metabolism.

[0025] In some embodiments, information about genes associated with (or linked to) catabolism can be obtained from a knowledge base on gene ontology.

[0026] In some embodiments, the functions of the genes associated with catabolism may be related to catabolism. In some embodiments, the processes involved in the genes associated with catabolism may be related to catabolism. In some embodiments, the gene ontology of the genes associated with catabolism may be related to catabolism.

[0027] In some embodiments, information about genes associated with wound healing can be obtained from a knowledge base on gene ontology.

[0028] In some embodiments, the functions of the genes associated with wound healing may be related to wound healing. In some embodiments, the processes involved in the genes associated with wound healing may be related to wound healing. In some embodiments, the gene ontology of the genes associated with wound healing may be related to wound healing.

[0029] In some embodiments, (iv) may be determining, among the first candidate genes, two or more genes associated with metabolic, catabolic and wound healing as the cell resetting genes.

[0030] In some embodiments, (iv) may be determining, among the first candidate genes, genes associated with metabolic, catabolic and wound healing as the cell resetting genes.

[0031] In some embodiments, the predetermined cluster selection criteria may be determined based on the number of genes associated with the particular target cell belonging to each of the two or more clusters.

[0032] In some embodiments, the predetermined cluster selection criterion may be to select a cluster among the two or more clusters having the highest cluster score calculated by one of the following formulas:

[0033] Cluster score = ;

[0034] Cluster score = x / S g ; and

[0035] Cluster score = x / N a ,

[0036] Here, x is the number of genes associated with a specific target cell belonging to each of two or more clusters,

[0037] S g is the sum of the number of genes associated with the specific target cell in all clusters,

[0038] N a is the number of genes belonging to each of two or more clusters.

[0039] In some embodiments, the particular target cell is a neuron, and wherein the genes associated with the particular target cell may be neuron-related factors (e.g., neuronal factors).

[0040] In some embodiments, the neuron-related factors may be factors belonging to factor set 1.

[0041] In some embodiments, the specific target cell is a hepatocyte, wherein the genes associated with the specific target cell may be hepatocyte-related factors (e.g., liver factors).

[0042] In some embodiments, the hepatocyte-related factors may be factors belonging to factor set 2.

[0043] In some embodiments, the specific target cell is a pancreatic cell, and wherein the genes associated with the specific target cell may be pancreatic cell-related factors.

[0044] In some embodiments, the pancreatic cell-related factors may be factors belonging to factor set 3.

[0045] In some embodiments, the (ii) classifying the gene vectors for the plurality of genes into two or more clusters using a clustering algorithm.

[0046] Determine the optimal number of clusters, and

[0047] It may include classifying the gene vectors for the above plurality of genes into the optimal number of clusters.

[0048] In some embodiments,

[0049] The above optimal number of clusters can be determined through a calculation formula for the score for the number of clusters to determine the optimal number of clusters,

[0050] At this time, the calculation formula for the score for the number of clusters can be as follows:

[0051] Score for number of clusters = ,

[0052] Here, x is the number of genes associated with a specific target cell belonging to each of the two or more clusters,

[0053] μ is the mean of the x values ​​of each cluster,

[0054] Nc is the number of clusters,

[0055] S g is the sum of the number of genes associated with the specific target cell in all clusters,

[0056] N g is the number of genes related to the specific target cell belonging to the above plurality of genes.

[0057] In some embodiments, the optimal number of clusters may not be 2.

[0058] In some embodiments, the two or more clusters may be first to Rth clusters (i.e., R clusters).

[0059] In some embodiments, R can be an integer from 2 to 8.

[0060] Some embodiments of the present disclosure provide methods for screening therapeutic genes for treating a disease or disorder.

[0061] Some embodiments of the present disclosure provide a method of screening for one or more therapeutic genes for treating a specific disease or disorder, comprising:

[0062] In a method for screening one or more cell resetting genes according to some embodiments of the present disclosure, among one or more cell resetting genes for repairing damage or dysfunction of a specific target cell or a specific target tissue associated with the specific target cell, genes having an abnormal expression level in the specific disease or disorder are identified.

[0063] In some embodiments, the specific disease or condition may be caused by damage or dysfunction of the specific target cell or specific target tissue.

[0064] In some embodiments, genes having abnormal expression levels in the particular disease or condition may be genes having lower expression levels in a model or individual of the particular disease or condition, compared to a healthy model or individual.

[0065] The method of genetic screening according to some embodiments of the present disclosure may have the advantage of reflecting information on patterns of time-series gene expression levels for genes obtained during two-dimensional differentiation and three-dimensional differentiation into specific target cells, and / or information on abnormal developmental processes (e.g., disease development processes) and / or information on normal developmental processes.

[0066] The genetic screening method according to some embodiments of the present disclosure may have the advantage of being able to screen genes more associated with damage or dysfunction of cells or tissues by including a process of additionally selecting genes related to metabolism and / or recovery of cells.

[0067] The genetic screening method according to some embodiments of the present disclosure may have the advantage of being able to select candidate genes more related to the disease or condition by including a process of additionally identifying genes related to the disease or condition.

[0068] According to a genetic screening method according to some embodiments of the present disclosure, one or more genes having a cell resetting effect and / or a disease therapeutic effect can be selected. A genetic screening method according to some embodiments of the present disclosure may have the advantage of screening fewer genes than conventional screening methods.

[0069] FIG. 01 illustrates a server or device (100) according to one embodiment of the present disclosure.

[0070] FIG. 02 is a schematic diagram of a resetting gene screening method according to some embodiments of the present disclosure.

[0071] FIG. 03 is a schematic diagram of a therapeutic gene screening method according to some embodiments of the present disclosure.

[0072] Figure 04 is a schematic diagram of a method for screening first candidate genes according to some embodiments of the present disclosure. Specifically, Figure 04 illustrates an example of a method for screening first candidate genes that includes clustering.

[0073] Figure 05 is a schematic diagram of a method for screening first candidate genes according to some embodiments of the present disclosure. Specifically, Figure 05 illustrates an example of a method for screening first candidate genes that includes grouping.

[0074] Figure 6 illustrates the selection of reset genes according to some embodiments of the present disclosure. Specifically, Figure 6 shows that genes associated with metabolic, catabolic, and wound-healing functions among the first candidate genes can be selected as reset genes.

[0075] Figure 07 is a bright field photograph and an immunofluorescence staining photograph taken after differentiating neural stem cells into neural cells in two dimensions and three dimensions.

[0076] Figure 08 shows the results of a heatmap analysis of the expression levels of genes belonging to clusters at each differentiation time point. In Figure 08, C1 represents cluster 1, C2 represents cluster 2, C3 represents cluster 3, and C4 represents cluster 4. The expression levels of genes were confirmed on day 0 (X2D_day0), day 3 (X2D_day3), day 8 (X2D_day8), and day 18 (X2D_day18) of two-dimensional differentiation, and on day 0 (X3D_day0), day 3 (X3D_day3), day 8 (X3D_day8), and day 18 (X3D_day18) of three-dimensional differentiation. The expression levels of genes were expressed as Log (Fold Change) values.

[0077] Figure 09 is a table showing the average Log (Fold Change) values ​​of the expression levels of genes used in the heatmap analysis results of Figure 08. In Figure 09, C1 represents cluster 1, C2 represents cluster 2, C3 represents cluster 3, and C4 represents cluster 4. The expression levels of the genes were confirmed on day 0 (X2D_day0), day 3 (X2D_day3), day 8 (X2D_day8), and day 18 (X2D_day18) of two-dimensional differentiation, and on day 0 (X3D_day0), day 3 (X3D_day3), day 8 (X3D_day8), and day 18 (X3D_day18) of three-dimensional differentiation.

[0078] Figure 10 shows the results of confirming dopaminergic neuron markers (Tuj1, Map2) in the control group, the 6-OHDA treatment group (Ndst3 untreated group), and the Ndst3 treatment group (6-OHDA and Ndst3 treated group). Specifically, photographs confirming the expression of neuron markers in each group through immunofluorescence staining are disclosed in Figure 10.

[0079] Figure 11 shows the results of confirming dopaminergic neuron markers (Tuj1, Map2) in the control group, the 6-OHDA treatment group (Ndst3 untreated group), and the Ndst3 treatment group (6-OHDA and Ndst3 treated group). Specifically, Figure 11 quantifies the results of the immunofluorescence staining analysis of Figure 10.

[0080] Hereinafter, the invention provided by this disclosure will be described in more detail through embodiments and examples. The invention provided by this disclosure can be implemented in various ways and is not limited to the specific embodiments described herein.

[0081] Those skilled in the art will readily recognize various modifications and alternative embodiments of the invention disclosed herein. Therefore, it should be understood that the invention disclosed herein is not limited to the specific embodiments or examples described herein, and that modifications and alternative embodiments are also encompassed within the scope of the invention disclosed herein.

[0082] Explanation of Terms

[0083] Unless otherwise stated, all technical and scientific terms used in this disclosure have the meanings commonly understood by one of ordinary skill in the art to which this disclosure pertains. All publications, patents, and other references mentioned in this disclosure are incorporated by reference in their entirety.

[0084] specific target cell

[0085] In the present disclosure, a specific target cell is used to refer to a differentiated (or completely differentiated) cell that constitutes a tissue or organ. For example, the specific target cell may be referred to as a somatic cell. The specific target cell may be, for example, a nervous system cell, a central nervous system cell, a peripheral nervous system cell, a hepatocyte, a pancreatic cell, a kidney cell, a heart cell, an osteocyte, a chondrocyte, a pancreatic cell, a muscle cell, an intestinal cell, a spleen cell, a blood cell, a thyroid cell, a parathyroid cell, a skin cell, or a lung cell. For example, a central nervous system cell may be a neuron, an astrocyte, an oligodendrocyte, a microglia, or an ependymal cell. For example, a pancreatic cell may be an alpha cell, a beta cell, a PP cell, a delta cell, or an epsilon cell. For example, liver cells can be hepatocytes, hepatic stellate cells (HSCs), Kupffer cells (KCs), or liver sinusoidal endothelial cells (LSECs).

[0086] group

[0087] In this disclosure, the term "tissue" refers to a portion composed of cells and possessing one or more functions. In some embodiments of the present disclosure, the term "tissue" may be used to encompass not only tissues classified by biological organization, such as cells, tissues, organs, and organ systems, but also organs. For example, the term "tissue" may refer not only to liver tissue but also to the liver.

[0088] specific target tissue related to the specific target cell

[0089] In the present disclosure, the specific target tissue associated with a specific target cell is used to refer to the tissue in which the specific target cell resides. For example, if the specific target cell is a neuron, the specific target tissue may refer to the tissue in which the neuron resides, i.e., central nervous system tissue and / or the central nervous system. As another example, if the specific target cell is a hepatocyte, the specific target tissue may refer to liver tissue and / or the liver.

[0090] specific disorder or disease

[0091] In the present disclosure, a specific disease or condition is used to refer to a disease or condition caused by damage or dysfunction of a specific target cell or specific target tissue. For example, if the specific target cell is a neuron, the specific disease may be a neurological disease. For example, if the specific target cell is a neuron, the specific disease may be a neurodegenerative disease. For example, if the specific target cell is a neuron, the specific disease may be Parkinson's disease, Alzheimer's disease, Huntington's disease, tauopathies, amyotrophic lateral sclerosis, autism spectrum disorder, spinal muscular atrophy, or prion diseases. For example, if the specific target cell is a pancreatic cell, the specific disease may be diabetes, pancreatitis, or diabetic ketoacidosis. For example, if the specific target cell is a hepatocyte, the specific disease or condition may be liver failure, liver fibrosis, Wilson disease, or cirrhosis.

[0092] therapeutic gene

[0093] In this disclosure, the term "therapeutic gene" refers to a gene that has the potential to be used or is used for the purpose of treating a certain disease or condition. A therapeutic gene may be a gene that can have a preventive or therapeutic effect on a certain disease or condition. A therapeutic gene may be a gene that can produce a genetic product that has a preventive or therapeutic effect on a certain disease or condition. Gene therapy broadly refers to an approach that uses genetic material to prevent and treat a disease or condition. For example, gene therapy may refer to a technology that aims to achieve a therapeutic effect by regulating gene expression or altering a living biological characteristic. A therapeutic gene can be used in such gene therapy. For example, a therapeutic gene can be administered to a subject (e.g., a human) with a certain disease or condition and used to treat said disease or condition. Typically, but not limited to, a therapeutic gene is delivered to a target site in the form of an exogenous gene (transgene) contained in a vector. The delivered therapeutic gene may act directly at the target site, or RNA transcribed from the therapeutic gene or expressed protein may act at the target site. If a gene, RNA transcribed therefrom, or protein expressed therefrom has a preventive or therapeutic effect on a disease or disorder, said gene may be referred to as a therapeutic gene in the present disclosure.

[0094] Resetting factor and resetting gene

[0095] In the present disclosure, the term "resetting factor" or "cell resetting factor (CRF)" is used to refer to a biologically active compound or genetic element that initiates cellular processes leading to the restoration of cellular homeostasis, rejuvenation of cellular functions, and repair of cellular damage without altering the original cell identity. A cell resetting factor may include, but is not limited to, a protein, peptide, RNA, DNA, gene, small molecule, virus, or a combination thereof, characterized by having any one or more of the following capabilities:

[0096] - Restore Homeostasis: The process of restoring the balance of cellular processes that have been disrupted or interrupted by stress, disease, or aging, to return the cells to their fundamental physiological state;

[0097] - Promote Cellular Longevity and Viability: Extend the functional lifespan of cells and improve their resilience to future stressors, contributing to overall organismal health;

[0098] - Enhance Repair Mechanisms: Activate or enhance cellular repair mechanisms, including DNA repair, protein homeostasis, and removal of damaged organelles through processes such as autophagy;

[0099] - Maintain Cellular Identity: Induce rejuvenation and repair without altering the fundamental identity or differentiation state of the cell, preserving its original functionality and role within the tissue context; and

[0100] - Selective Targeting and Activation: Demonstrates specificity in targeting and activating pathways involved in cellular repair and rejuvenation, with minimal off-target effects or toxicity.

[0101] In the present disclosure, a "resetting gene" or "cell resetting gene" refers to a gene that can be classified as a cell resetting factor. For example, a "resetting gene" may refer to a gene that has one or more of the following abilities: restoring homeostasis, enhancing cell life and viability, strengthening repair mechanisms, and maintaining cell identity, and / or may refer to a gene that can produce a genetic product that has one or more of the following abilities: restoring homeostasis, enhancing cell life and viability, strengthening repair mechanisms, and maintaining cell identity. For example, a resetting gene may have the ability to normalize damage or abnormal function of a cell comprising a specific cell or a specific tissue. For example, a resetting gene may produce a genetic product that has the ability to normalize damage or abnormal function of a cell comprising a specific cell or a specific tissue. In some embodiments of the present disclosure, the term "resetting gene" may be used interchangeably with the terms "specific cell resetting gene" or "specific tissue resetting gene." Here, "resetting a cell to normal" may refer to restoring a cell whose function has been impaired. For example, introducing a neuronal resetting gene into a neuron with impaired function can restore the function of the neuron to normal. For another example, introducing a hepatocyte resetting gene into a hepatocyte with impaired function can restore the function of the hepatocyte to normal.

[0102] differentiable cell

[0103] In this disclosure, the term "differentiable cell" refers to a cell with the potential for differentiation or differentiation potential. For example, a differentiable cell may be referred to as an undifferentiated cell. In some embodiments of the present disclosure, the term "differentiable cell" refers to a cell capable of differentiating into the specific target cell.

[0104] For example, a differentiating cell may be a stem cell. The stem cell may be, for example, a totipotent stem cell, a pluripotent stem cell (PSC), a multipotent stem cell (MSC), an oligopotent stem cell, a unipotent stem cell, or an adult stem cell. The stem cell may be, for example, a mesenchymal stem cell. A totipotent stem cell is known to refer to a cell that can divide and differentiate into cells of any organism. A totipotent stem cell is known to be a stem cell that can form cells of all germ layers but does not form extraembryonic structures such as the placenta. A totipotent stem cell (PSC) may be, but is not limited to, an induced pluripotent stem cell (iPSC) or an embryonic stem cell (ESC). Multipotent stem cells (MSCs) are known as stem cells that have a narrower differentiation spectrum than PSCs, but can specialize into multiple types of discrete cells of specific cell lineages. An example of a multipotent stem cell is a hematopoietic stem cell, which can develop into multiple types of blood cells (reviewed in [Zakrzewski, W., Dobrzynski, M., Szymonowicz, M., & Rybak, Z. (2019). Stem cells: past, present, and future. Stem cell research & therapy, 10(1), 1-22.]).

[0105] In some embodiments, the stem cells may be, but are not limited to, neural stem cells, embryonic stem cells, induced pluripotent stem cells, mesenchymal stem cells, and hematopoietic stem cells.

[0106] For example, a cell capable of differentiation may be a progenitor cell or precursor cell.

[0107] In some embodiments of the present disclosure, a differentiable cell may be described together with a specific target cell. For example, a differentiable cell may be referred to as a differentiable cell capable of differentiating into a specific target cell, wherein a differentiable cell capable of differentiating into a specific target cell refers to cells capable of differentiating into a specific target cell based on knowledge known in the art. For example, if the specific target cell is a neuron, the differentiable cell may be a neural stem cell (NSC), a neural progenitor cell, an embryonic stem cell, an induced pluripotent stem cell, or a mesenchymal stem cell. In another example, if the specific target cell is a hepatocyte, the differentiable cell may be a hepatic stem cell, a hepatic progenitor cell, an embryonic stem cell, an induced pluripotent stem cell, or a mesenchymal stem cell. As another example, if the specific target cell is a pancreatic cell, the differentiating cell may be a pancreatic progenitor cell, an embryonic stem cell, an induced pluripotent stem cell, or a mesenchymal stem cell. As another example, if the specific target cell is a cardiomyocyte, the differentiating cell may be a cardiac progenitor cell, an embryonic stem cell, an induced pluripotent stem cell, or a mesenchymal stem cell. Various methods known in the art can be used to differentiate a differentiating cell into a specific target cell.For example, differentiation from a differentiating cell into a specific target cell can be achieved by, but is not limited to, three-dimensional differentiation and two-dimensional differentiation as described below.

[0108] 3-dimensional differentiation, 2-dimensional differentiation

[0109] In this disclosure, "three-dimensional differentiation" is used to refer to a differentiation method based on three-dimensional cell culture. That is, three-dimensional differentiation is used to refer to a process or method for differentiating a differentiable cell into a specific target cell through a three-dimensional cell culture method. In this disclosure, two-dimensional differentiation is used to refer to a differentiation method based on two-dimensional cell culture. That is, two-dimensional differentiation is used to refer to a process or method for differentiating a differentiable cell into a specific target cell through a two-dimensional cell culture method.

[0110] Hereinafter, 3D cell culture used for 3D differentiation and 2D cell culture used for 2D differentiation are described based on knowledge known in the art. 3D cell culture and 2D cell culture are described in the following references [Edmondson, R., Broglie, JJ, Adcock, AF, & Yang, L. (2014). Three-dimensional cell culture systems and their applications in drug discovery and cell-based biosensors. Assay and drug development technologies, 12(4), 207-218.; Ravi, M., Paramesh, V., Kaviya, SR, Anuradha, E., & Solomon, FP (2015). 3D cell culture systems: advantages and applications. Journal of cellular physiology, 230(1), 16-26.; Haycock, JW (2011). 3D cell culture: a review of current approaches and techniques(pp. 1-15). Humana Press.; and Lee, J., Cuddihy, MJ, & Kotov, NA (2008). Three-dimensional cell culture matrices: state of the art. Tissue engineering part B: reviews, 14(1), 61-86.], the contents of each of which are incorporated herein by reference.

[0111] Cell cultures have proven essential for a variety of applications, both from a research and industrial perspective. For example, the introduction of appropriate models and cell culture processes allows for testing of cellular changes (e.g., signal transduction, differentiation, changes in gene expression patterns, etc.) and drug sensitivity. These test results can then be used to establish cell differentiation processes, screen for disease-related genes, or conduct drug screening. Thus, cell culture systems represent one of the most compelling in vitro scientific models and have undergone rapid development over the past several decades.

[0112] These cell culture systems can be divided into two-dimensional (2D) culture and three-dimensional (3D) culture depending on the form in which the cells are cultured or the form in which the cells are to be cultured. Since numerous studies on 2D and 3D culture have already been conducted, the methods and advantages of 2D and 3D culture are widely known in the art. 2D culture refers to a traditional cell culture method (e.g., monolayer cell culture) in which cells are cultured on a flat, rigid substrate. Although 2D cell culture has been proven to be a useful method for cell-based studies, many limitations of this 2D cell culture method have been identified. Since almost all cells in the in vivo environment are surrounded by other cells and the extracellular matrix in a three-dimensional manner, 2D cell culture is known to not adequately consider the natural 3D environment of the cells.

[0113] Recently, 3D culture systems have been shown to more accurately represent the in vivo cell environment compared to 2D culture systems. Because 3D culture systems provide an excellent in vitro model, enabling the study of cellular responses in an environment similar to the in vivo environment, they are increasingly being recognized as a more suitable research model than 2D culture systems for drug discovery, cancer cell biology, stem cell research, tissue engineering for transplantation, and other cell-based assays.

[0114] While traditional two-dimensional (2D) culture typically grows cells as a monolayer in glass or polystyrene plastic flasks, three-dimensional (3D) cell culture is a cell culture method that aims to grow cells into 3D aggregates / spheroids using a scaffold / matrix, or in the absence of a scaffold. In 3D culture systems, cells form aggregates or spheroids within the matrix, on the matrix, or in a suspension medium. Because cell-cell interactions and cell-extracellular matrix (ECM) interactions in cell aggregates / spheroids more closely mimic the natural environment found in vivo, the morphology of the cells can more closely resemble their natural form in the body.

[0115] Scaffold / matrix-based 3D culture can be performed by seeding cells in an acellular 3D matrix or by dispersing cells in a liquid matrix and then solidifying or polymerizing them. Commonly used scaffold / matrix materials include biologically derived or biologically derived scaffold systems and synthetically based materials. BD Matrigel basement membrane matrix (BD sciences), Cultrex Materials or products such as basement membrane extract (BME; Trevigen), collagen, and hyaluronic acid are known to be usable as biologically derived matrices. Furthermore, polyethylene glycol (PEG), polyvinyl alcohol (PVA), polylactide-co-glycolide (PLG), and polycaprolactone (PLA) are known to be usable to form synthetic scaffolds, but are not limited thereto. Scaffold-free 3D cell spheroids can be generated from suspension using, but are not limited to, the forced floating method, the hanging drop method, or agitation-based approaches. 3D culture and scaffolds / matrices used in 3D culture are reviewed in the literature [Fischbach, C., Chen, R., Matsumoto, T., Schmelzle, T., Brugge, JS, Polverini, PJ, & Mooney, DJ (2007). Engineering tumors with 3D scaffolds. Nature methods, 4(10), 855-860.; Breslin, S., & O'Driscoll, L. (2013). Three-dimensional cell culture: the missing link in drug discovery. Drug discovery today, 18(5-6), 240-249.; Gurski, L. A., Petrelli, N. J., Jia, X., & Farach-Carson, M. C. (2010). 3D matrices for anti-cancer drug testing and development. Oncology issues, 25(1), 20-25.; Tibbitt, M. W., & Anseth, K. S. (2009).Hydrogels as extracellular matrix mimics for 3D cell culture. Biotechnology and bioengineering, 103(4), 655-663.; and Rimann, M., & Graf-Hausner, U. (2012). Synthetic 3D multicellular systems for drug development. Current opinion in biotechnology, 23(5), 803-809.], the contents of each of which are incorporated herein by reference.

[0116] gene expression amount or level

[0117] In the present disclosure, the amount or level of gene expression refers to the amount or level of a gene product produced from a gene. The gene product may be, for example, a protein or RNA (e.g., mRNA encoding a protein), but is not limited thereto. That is, the amount or level of gene expression may include information about the degree of transcription and / or translation of the corresponding gene, and may be used as an indicator of the activity of the gene. Preferably, the gene product may be the final product of a certain gene. The amount or level of expression of a certain gene may be obtained by measuring the amount of RNA (e.g., RNA transcribed from the certain gene) of the certain gene or the amount of protein (e.g., protein produced from the certain gene) of the certain gene in a sample, such as a cell or a cell population, using a method known in the art. The gene expression level may be information that has been modified or normalized from the gene expression level using a method well known in the art so that it can be easily confirmed and utilized by those skilled in the art (e.g., researchers, practitioners, or developers in the relevant field). In this disclosure, gene expression level and gene expression amount are used interchangeably, since both gene expression level and gene expression amount contain information about the amount of a gene product.

[0118] Gene expression profile

[0119] In the present disclosure, the term "gene expression profile" is used to refer to a set of measured gene expression levels for a certain gene. For example, the term "two-dimensional gene expression profile" as used in the present disclosure refers to a set of gene expression levels measured for a certain gene at multiple time points during a two-dimensional differentiation process. That is, a two-dimensional expression profile for a specific gene refers to a set of gene expression levels measured for a certain gene at multiple time points included in a two-dimensional differentiation process. As a more specific example, when a gene expression level for a certain gene is measured at a first time point and a second time point during a two-dimensional differentiation process, the two-dimensional gene expression profile for the certain gene includes information about the gene expression level for the certain gene measured at the first time point and the gene expression level for the certain gene measured at the second time point. For example, the term "three-dimensional expression profile" as used in the present disclosure refers to a set of gene expression levels measured for a certain gene at multiple time points during a three-dimensional differentiation process. That is, a three-dimensional expression profile for a specific gene refers to a set of gene expression levels measured at multiple time points included in a three-dimensional differentiation process for the specific gene. As a more specific example, if a gene expression level for a certain gene is measured at a first time point and a second time point of a three-dimensional differentiation process, the three-dimensional expression profile for the certain gene includes information about the gene expression level for the certain gene measured at the first time point and the gene expression level for the certain gene measured at the second time point.

[0120] Proteins, peptides, and polypeptides

[0121] The terms protein, peptide, and polypeptide as used herein are used interchangeably and encompass naturally occurring proteins and non-naturally occurring proteins.

[0122] Server or device

[0123] The steps or processes of a method according to some embodiments of the present disclosure may be performed by a human, or may be performed by a controller of a server or device. For example, one or more steps or processes of a screening method according to some embodiments of the present disclosure may be performed by a human. For example, one or more steps or processes of a screening method according to some embodiments of the present disclosure may be performed by a controller of a server or device.

[0124] The server or device includes at least a server or device control unit. In one embodiment, the server or device may further include a server or device storage unit and / or a server or device communication unit.

[0125] FIG. 01 is a drawing illustrating a server or device (100) according to one embodiment. Referring to FIG. 01, the server or device (100) may include a server or device control unit (110), a server or device storage unit (120), and a server or device communication unit (130).

[0126] A server or device (100) can process and perform calculations on various types of information through a control unit (110). The control unit (110) can control other components constituting the server or device (100). The control unit (110) can be implemented as a computer or a similar device according to hardware, software, or a combination thereof. In terms of hardware, the server or device control unit (110) can be one or more processors. Alternatively, the control unit (110) can be provided as processors that are physically separated and cooperate through communication. The control unit (110) can be, for example, a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), a state machine, an application specific integrated circuit (ASIC), a radio-frequency integrated circuit (RFIC), or a combination thereof, but is not limited thereto. The software control unit (110) may be provided in the form of a program that drives the hardware control unit (110). The operation of the server or device (100) may be interpreted as being performed by the control unit (110) or under the control of the control unit (110).

[0127] In some embodiments, the storage (120) may be internal storage of the server or device (100). Alternatively, the storage (120) may be external storage, such as cloud storage. The storage (120) may be, by way of example only, a hard disk, flash memory, a solid state drive (SSD), random access memory (RAM), read only memory (ROM), electrically erasable programmable read-only memory (EEPROM), magnetic memory, magnetic disk, optical disk, or a combination thereof, but is not limited thereto.

[0128] The storage unit (120) can store information acquired by the server or device (100). Furthermore, the server or device (100) can store various data, programs, or applications necessary for operation in the storage unit (120). The programs or applications stored in the storage unit (120) can include one or more instructions. The programs or applications stored in the storage unit (120) can be executed by the control unit (110).

[0129] The communication unit (130) can enable the server or device (100) to communicate with the outside. The communication unit (130) can perform wired or wireless communication. The communication unit (130) can be, for example, a wired / wireless Local Area Network (LAN) module, a WAN module, an Ethernet module, a Bluetooth module, a Zigbee module, a USB (Universal Serial Bus) module, an IEEE 1394 module, a Wi-Fi module, a mobile communication module, a satellite communication module, or a combination thereof, but is not limited thereto.

[0130] About

[0131] The term about as used herein means an amount, level, value, number, frequency, percentage, dimension, size, amount, weight, or length that varies by about 30, 25, 20, 15, 10, 9, 8, 7, 6, 5, 4, 3, 2, or 1% relative to a reference amount, level, value, number, frequency, percentage, dimension, size, amount, weight, or length.

[0132] Introduction

[0133] Problems with conventional genetic screening methods

[0134] Conventional screening methods for functional genes (e.g., therapeutic genes) screen genes by identifying genes with abnormal expression levels in disease models. However, these methods suffer from the following drawbacks: (1) because they select genes based on expression levels in previously induced disease models, they fail to reflect information related to disease pathogenesis, cell or tissue damage, or functional decline; and (2) because the number of genes with abnormal expression levels in disease models is simply too high.

[0135] In reality, there are very few cases where genes with truly therapeutic effects have been identified through conventional functional gene screening methods. This is presumably because conventional screening methods have the problem described in (1) above. Conventional functional gene screening methods attempt to restore normal function by regulating genes that have been excessively increased or decreased due to disease. Furthermore, according to conventional functional gene screening methods, as described in (2) above, since so many genes are ultimately screened, it takes a lot of time and money to experimentally confirm the therapeutic effects of so many genes that have been ultimately screened, resulting in a loss of resources.

[0136] Development of a novel genetic screening method

[0137] The inventors of the present application have solved the problems of the above conventional screening methods and developed a novel genetic screening method that is more efficient than the conventional screening methods.

[0138] The screening method provided in the present disclosure is

[0139] The present disclosure is characterized by including selecting first candidate genes by using patterns of time-series gene expression levels obtained during two-dimensional differentiation into specific target cells and three-dimensional differentiation into specific target cells. That is, the screening method of the present disclosure is characterized by using both patterns of gene expression levels in two-dimensional differentiation and patterns of gene expression levels in three-dimensional differentiation to select first candidate genes.

[0140] The inventor of the present invention has given the meaning of a "normal differentiation model" to 3D differentiation based on 3D culture, which more closely mimics the in vivo environment, such as the cell growth environment in vivo. In the case of 3D differentiation, it can substantially mimic the differentiation process into specific target cells or tissues, and thus can be used as a normal model. In the case of 2D differentiation based on 2D culture, which has a lower degree of mimicry of the in vivo environment, such as the cell growth environment in vivo, it has given the meaning of an "abnormal differentiation model." In the case of 2D differentiation, it cannot substantially mimic the differentiation process into specific target cells or tissues in full, and thus can be used as a model for imbalances, such as diseases or disorders.

[0141] Furthermore, a screening method according to some embodiments of the present disclosure comprises:

[0142] In addition to selecting the first candidate genes by using the patterns of time-series gene expression levels for genes obtained during the two-dimensional and three-dimensional differentiation processes,

[0143] It is characterized by including selecting genes related to cellular metabolism (metabolic and catabolic) and / or recovery from the first candidate genes.

[0144] When a disease or illness occurs, or when damage or dysfunction occurs in cells or tissues, changes occur in genes related to cell metabolism and / or recovery in the body.

[0145] The inventor of the present application judged that by additionally setting up this process, it would be possible to screen genes more related to cell metabolism and / or recovery from the first candidate genes, and more related to diseases or disorders, or more related to damage or dysfunction of cells or tissues.

[0146] Furthermore, a screening method according to some embodiments of the present disclosure comprises:

[0147] By using the time-series gene expression patterns of genes obtained during the two-dimensional differentiation and three-dimensional differentiation processes, the first candidate genes are selected, and

[0148] In addition to selecting genes related to cell metabolism and / or recovery from the first candidate genes,

[0149] It is characterized by including identifying altered patterns in a disease database from genes related to cell metabolism and / or recovery selected from the first candidate genes.

[0150] The inventor of the present application judged that by additionally setting up this process, it would be possible to select more specific therapeutic genes by identifying genes among the first candidate genes that are more related to cell metabolism and / or recovery and that exhibit changes in disease.

[0151] Features of a novel genetic screening method - Use of gene expression profiles obtained from two-dimensional differentiation and three-dimensional differentiation.

[0152] A novel genetic screening method of the present disclosure is characterized in that it includes using gene expression profiles obtained during a two-dimensional differentiation process and gene expression profiles obtained during a three-dimensional differentiation process for screening.

[0153] More specifically, a novel method of genetic screening according to some embodiments of the present disclosure comprises:

[0154] Obtain two-dimensional gene expression profiles for multiple genes from the two-dimensional differentiation process, and obtain three-dimensional gene expression profiles for multiple genes from the three-dimensional differentiation process.

[0155] It is characterized by including the process.

[0156] Furthermore, a novel method of genetic screening according to some embodiments of the present disclosure is

[0157] A process of classifying (e.g., clustering or grouping) genes with similar gene expression patterns based on two-dimensional gene expression profiles for multiple genes obtained from a two-dimensional differentiation process and three-dimensional gene expression profiles for multiple genes obtained from a three-dimensional differentiation process, and selecting candidate clusters or candidate groups from among the classified clusters or groups.

[0158] It is characterized by including.

[0159] As mentioned above, although there have been various and numerous genetic screening methods in the past, there has been no genetic screening method characterized by using gene expression profiles obtained from a two-dimensional differentiation process and gene expression profiles obtained from a three-dimensional differentiation process together to classify genes through clustering or grouping.

[0160] Here, genes belonging to candidate clusters or candidate groups may be referred to as candidate genes (or first candidate genes).

[0161] Overview of Resetting Gene Screening Methods

[0162] Some embodiments of the present disclosure provide a method for screening for resetting genes. The inventors of the present application have introduced a process for additionally screening genes related to cellular metabolism and / or recovery among the selected candidate genes for screening for resetting gene(s).

[0163] A method for screening resetting genes according to some embodiments of the present disclosure,

[0164] For screening of resetting genes,

[0165] Selecting genes associated with at least one of metabolic, catabolic, and wound healing from candidate genes (first candidate genes) selected through grouping or clustering.

[0166] It is characterized by including.

[0167] The resetting gene may be referred to as, but is not limited to, a second candidate gene.

[0168] A schematic diagram of an example of a reset gene screening method to aid understanding is provided in Figure 02.

[0169] Overview of therapeutic gene screening methods

[0170] Some embodiments of the present disclosure disclose a method for screening therapeutic genes (candidate therapeutic genes). The inventors of the present disclosure have introduced a process for additionally identifying genes among selected candidate genes or selected resetting genes for screening therapeutic genes, which exhibit altered expression patterns compared to normal levels in patients with a disease or disorder or in models of the disease or disorder. For example, genes with abnormal expression levels in patients or models of the disease or disorder can be identified. Genes with abnormal expression levels in patients or models of the disease or disorder can be identified from a disease or disorder database.

[0171] Among the selected candidate genes or selected resettable genes, genes that show altered expression patterns in patients or models of the disease or disorder (compared to the normal state) may be genes for treating the disease.

[0172] A therapeutic gene screening method according to some embodiments of the present disclosure comprises: for screening therapeutic genes,

[0173] It is characterized by including identifying genes having abnormal expression levels in a patient or model having a disease or condition among candidate genes selected through grouping or clustering, or reset genes.

[0174] The therapeutic gene may be referred to as, but is not limited to, a third candidate gene.

[0175] A schematic diagram of an example of a therapeutic gene screening method to aid understanding is provided in Figure 03.

[0176] Since three-dimensional differentiation methods are closer to actual living organisms than two-dimensional methods and have biological processes similar to those of real organisms, the screening method of the present disclosure requires two-dimensional gene expression profiles obtained from two-dimensional differentiation and three-dimensional gene expression profiles obtained from three-dimensional differentiation. Below, the two-dimensional gene expression profiles and three-dimensional gene expression profiles, and their acquisition, are described in detail.

[0177] Acquisition of gene expression profile data sets

[0178] Overview of acquisition of gene expression profile datasets

[0179] The gene expression profiles referred to in this disclosure are divided into two types: two-dimensional gene expression profiles and three-dimensional gene expression profiles. Two-dimensional gene expression profiles include gene expression levels for certain genes measured or obtained during a two-dimensional differentiation process. Three-dimensional gene expression profiles include gene expression levels for certain genes measured or obtained during a three-dimensional differentiation process.

[0180] In relation to a two-dimensional gene expression profile, specifically, gene expression levels for a given gene are measured at multiple time points during a two-dimensional differentiation process. Furthermore, two-dimensional gene expression profiles are obtained for multiple genes. In the present disclosure, the two-dimensional gene expression profiles for multiple genes may be referred to as a "two-dimensional gene expression profile data set."

[0181] In relation to 3D gene expression profiles, specifically, gene expression levels for a given gene are measured at multiple time points during a 3D differentiation process. Furthermore, 3D gene expression profiles are obtained for multiple genes. In the present disclosure, 3D gene expression profiles for multiple genes may be referred to as a "3D gene expression profile data set."

[0182] Furthermore, a set of two-dimensional gene expression profiles for a plurality of genes (a two-dimensional gene expression profile data set) and three-dimensional gene expression profiles for a plurality of genes (a three-dimensional gene expression profile data set) may be referred to as a “gene expression profile data set.”

[0183] The genetic screening method of the present disclosure comprises obtaining two-dimensional gene expression profiles for a plurality of genes and three-dimensional gene profiles for a plurality of genes.

[0184] Below, two-dimensional gene expression profiles for multiple genes and three-dimensional gene expression profiles for multiple genes and methods for obtaining them are described.

[0185] Two-dimensional gene expression profiles for multiple genes

[0186] A two-dimensional gene expression profile refers to information composed of gene expression level data obtained during a two-dimensional differentiation process. During the two-dimensional differentiation process, two-dimensional gene expression profiles for multiple genes can be obtained. These two-dimensional gene expression profiles for multiple genes may be referred to as a two-dimensional gene expression profile data set, but are not limited thereto.

[0187] Below, we describe a two-dimensional gene expression profile of a single gene (for illustration, referred to as gene A).

[0188] The two-dimensional gene expression profile of gene A includes expression level data of gene A obtained or measured at multiple time points during the two-dimensional differentiation process (or two-dimensional differentiation period).

[0189] For example, the two-dimensional gene expression profile of gene A can be described by the following table.

[0190] [Table 01] Two-dimensional gene expression profile of gene A

[0191]

[0192] Here, N is an integer greater than or equal to 2.

[0193] For example, if N is 2, the two-dimensional gene expression profile of gene A is

[0194] Information on the expression level of gene A measured at the first point in the two-dimensional differentiation process, and

[0195] Contains information on the expression level of gene A measured at the second time point of the two-dimensional differentiation process.

[0196] For example, if N is 3, the two-dimensional gene expression profile of gene A is

[0197] Information on the expression level of gene A measured at the first point in the two-dimensional differentiation process,

[0198] Information on the expression level of gene A measured at the second point in the two-dimensional differentiation process, and

[0199] Contains information on the expression level of gene A measured at the third time point of the two-dimensional differentiation process.

[0200] For example, the expression levels of gene A at the first to Nth time points for gene A can be obtained by measuring the expression levels of gene A N times in a two-dimensional differentiation process.

[0201] In some embodiments, N may be an integer from 2 to 20, but is not limited thereto. In certain embodiments, N may be an integer from 2 to 6. In certain embodiments, N may be an integer from 3 to 5. In certain embodiments, N may be 4.

[0202] Here, each of the first to Nth time points of the two-dimensional differentiation process refers to any specific time point (or day) selected during the two-dimensional differentiation process (or the two-dimensional differentiation period). In some embodiments, each of the first to Nth time points of the two-dimensional differentiation process may be a predetermined time point during the two-dimensional differentiation process. In some embodiments, the first to Nth time points of the two-dimensional differentiation process are each independently 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, May be selected from, but is not limited to, 57, 58, 59, and 60 days.

[0203] For example, if the first time point of the two-dimensional differentiation process is three days (e.g., +3 day) from the start date of the two-dimensional differentiation (e.g., +0 day), the 'expression level of gene A measured at the first time point of the two-dimensional differentiation process' may mean the expression level of gene A measured or confirmed from a cell or cell population during the two-dimensional differentiation process on the third day from the start date of the two-dimensional differentiation.

[0204] In the present disclosure, the unit period for distinguishing a "point in time" within the differentiation process is exemplified as a day, but the unit period for distinguishing a "point in time" need not necessarily be limited to this. For example, the unit period for distinguishing a "point in time" within the differentiation process may be a shorter unit period than a "day," such as 6 hours, 12 hours, or 18 hours, or a longer unit period may be used.

[0205] In some embodiments of the present disclosure, a genetic screening method can obtain two-dimensional genetic profiles for multiple genes. Examples of two-dimensional genetic profiles for multiple genes are as follows.

[0206] Below, to help understanding, an example is given where the multiple genes are three genes (genes A, B, and C).

[0207] [Table 02] Two-dimensional gene expression profiles for multiple genes (e.g., genes A, B, and C)

[0208]

[0209] The above table is only an example to help understanding, and two-dimensional gene expression profiles can be obtained for numerous genes (i.e. numerous types of genes) as well as the three genes mentioned above, and are not otherwise limited. In some embodiments, the number of genes in the plurality of genes can be about 100 (i.e. 100 types of genes), 500, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, 5000, 6000, 7000, 8000, 9000, 10000, 20000, 30000, or 40000 or more, or within a range set by any two of the above values. For example, the genes belonging to the plurality of genes may be, but are not limited to, 1,000 to 20,000, 2,000 to 10,000, or 2,000 to 8,000 types of genes. For example, two-dimensional gene profiles for about 4,000 to 7,000 types of genes may be obtained, but are not limited to these.

[0210] Here, when explaining using the first point in time of the two-dimensional differentiation process as an example, the 'first point in time of the two-dimensional differentiation process' at which the expression levels of multiple genes are measured all refer to the same day (or the same point in time). That is, the first point in time of the two-dimensional differentiation process at which the expression level of gene A is measured, the first point in time of the two-dimensional differentiation process at which the expression level of gene B is measured, and the first point in time of the two-dimensional differentiation process at which the expression level of gene C is measured all refer to the same day.

[0211] 3D gene expression profiles for multiple genes

[0212] As mentioned above, a 3D gene expression profile refers to information composed of gene expression level data obtained during a 3D differentiation process. During the 3D differentiation process, 3D gene expression profiles for multiple genes can be obtained. The 3D gene expression profiles for multiple genes may be referred to as a 3D gene expression profile data set, but are not limited thereto.

[0213] Below, we describe a three-dimensional gene expression profile of a single gene (for illustration, referred to as gene A).

[0214] The three-dimensional gene expression profile of gene A includes expression level data of gene A obtained or measured at multiple time points during the three-dimensional differentiation process (or three-dimensional differentiation period).

[0215] For example, the three-dimensional gene expression profile of gene A can be described by the following table.

[0216] [Table 03] 3D gene expression profile of gene A

[0217]

[0218] Here, M is an integer greater than or equal to 2.

[0219] For example, if M is 2, the 3D gene expression profile of gene A is

[0220] Information on the expression level of gene A measured at the first point in the three-dimensional differentiation process, and

[0221] Contains information on the expression level of gene A measured at the second time point of the three-dimensional differentiation process.

[0222] For example, if M is 3, the 3D gene expression profile of gene A is

[0223] Information on the expression level of gene A measured at the first point in the three-dimensional differentiation process,

[0224] Information on the expression level of gene A measured at the second point in the three-dimensional differentiation process, and

[0225] Contains information on the expression level of gene A measured at the third time point of the three-dimensional differentiation process.

[0226] For example, the expression levels of gene A at the first to Mth time points for gene A can be obtained by measuring the expression levels of gene A at M times during the three-dimensional differentiation process.

[0227] In some embodiments, M may be an integer from 2 to 20, but is not limited thereto. In certain embodiments, M may be an integer from 2 to 6. In certain embodiments, M may be an integer from 3 to 5. In certain embodiments, M may be 4.

[0228] In some embodiments, 'N' described in relation to the time point of expression level measurement in the two-dimensional differentiation process and 'M' described in relation to the time point of expression level measurement in the three-dimensional differentiation process may be the same integer or different integers. For example, N may be 4 and M may be 4. In another example, N may be 3 and M may be 4, but is not limited thereto.

[0229] Here, each of the first to Mth time points of the 3D differentiation process refers to any specific time point (or day) selected during the 3D differentiation process (or 3D differentiation period). In some embodiments, each of the first to Mth time points of the 3D differentiation process may be a predetermined time point during the 3D differentiation process. In some embodiments, the first to Mth time points of the three-dimensional differentiation process are each independently 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, May be selected from, but is not limited to, 57, 58, 59, and 60 days.

[0230] For example, if the first point in time of the 3D differentiation process is 3 days (e.g., day 3) from the start date of the 3D differentiation (e.g., day 0), the 'expression level of gene A measured at the first point in time of the 3D differentiation process' may mean the expression level of gene A measured or confirmed from a cell or cell population during the 3D differentiation process on the 3rd day from the start date of the 3D differentiation.

[0231] In some embodiments, any one of the first to Nth time points of the two-dimensional differentiation process and any one of the first to Mth time points of the three-dimensional differentiation process expressed through the same ordinal restriction may represent the same time point on the differentiation timeline, or may represent different time points. For example, when comparing the 'first time point' of the two-dimensional differentiation process with the 'first time point' of the three-dimensional differentiation process, the first time point of the two-dimensional differentiation process may be three days after the start date of the two-dimensional differentiation, and the first time point of the three-dimensional differentiation process may also be three days after the start date of the three-dimensional differentiation. As another example, when comparing the 'first time point' of the two-dimensional differentiation process with the 'first time point' of the three-dimensional differentiation process, the first time point of the two-dimensional differentiation process may be three days after the start date of the two-dimensional differentiation, while the first time point of the three-dimensional differentiation process may be four days after the start date of the three-dimensional differentiation. In this way, if a point in a two-dimensional differentiation process and a point in a three-dimensional differentiation process are expressed through the same ordinal limitation, they may refer to the same day on the differentiation timeline or they may refer to different days.

[0232] In some embodiments of the present disclosure, a genetic screening method can obtain three-dimensional genetic profiles for multiple genes. Examples of two-dimensional genetic profiles for multiple genes include the following.

[0233] Below, to help understanding, an example is given where the multiple genes are three genes (genes A, B, and C).

[0234] [Table 04] 3D gene expression profiles for multiple genes (e.g., genes A, B, and C)

[0235]

[0236] The above table is only an example to help understanding, and 3D gene expression profiles can be obtained for numerous genes (i.e. numerous types of genes) as well as the above three genes, and are not otherwise limited. In some embodiments, the number of genes in the plurality of genes can be about 100 (i.e. 100 types of genes), 500, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, 5000, 6000, 7000, 8000, 9000, 10000, 20000, 30000, or 40000 or more, or within a range set by any two of the above values. For example, the genes belonging to the plurality of genes may be, but are not limited to, 1,000 to 20,000, 2,000 to 10,000, or 2,000 to 8,000 types of genes. For example, three-dimensional gene expression profiles for about 4,000 to 7,000 types of genes may be obtained, but are not limited to these.

[0237] Here, when explaining using the first point in time of the 3D differentiation process as an example, the 'first point in time of the 3D differentiation process' at which the expression levels of multiple genes are measured all refer to the same day (or the same point in time). That is, the first point in time of the 3D differentiation process at which the expression level of gene A is measured, the first point in time of the 3D differentiation process at which the expression level of gene B is measured, and the first point in time of the 3D differentiation process at which the expression level of gene C is measured all refer to the same day.

[0238] In some embodiments, a set of two-dimensional gene expression profiles for a plurality of genes (a two-dimensional gene expression profile set) and a set of three-dimensional gene expression profiles for a plurality of genes (a three-dimensional gene expression profile set) may be referred to as, but is not limited to, a gene expression profile set.

[0239] Methods for obtaining gene expression profiles

[0240] Gene expression profiles can be obtained through two-dimensional differentiation and three-dimensional differentiation, or can be obtained through known databases or known literature.

[0241] For example, two-dimensional gene expression profiles for multiple genes can be obtained from a known database or literature (e.g., research papers related to two-dimensional differentiation) that discloses information related to two-dimensional differentiation. As a specific example, information on two-dimensional gene expression profiles for multiple genes can be obtained from a research paper related to two-dimensional differentiation that discloses measurement data on the expression levels of genes during the two-dimensional differentiation process.

[0242] For example, 3D gene expression profiles for multiple genes can be obtained from a known database or literature (e.g., research papers related to 3D differentiation) that discloses information related to 3D differentiation. As a specific example, information on 3D gene expression profiles for multiple genes can be obtained from a research paper related to 3D differentiation that discloses measurement data on the expression levels of genes during the 3D differentiation process.

[0243] In another aspect, the two-dimensional gene expression profiles for the plurality of genes and the three-dimensional gene expression profiles for the plurality of genes may be obtained from two-dimensional differentiation and three-dimensional differentiation, respectively, performed directly by a researcher, developer, or practitioner of a screening method (e.g., a genetic screening method) according to some embodiments of the present disclosure, or may be obtained from two-dimensional differentiation and three-dimensional differentiation, respectively, performed by a third party other than the researcher, developer, or practitioner of a screening method according to some embodiments of the present disclosure.

[0244] The acquisition of two-dimensional gene expression profiles for multiple genes and three-dimensional gene expression profiles for multiple genes can be performed by a person or through a control unit of a server or device.

[0245] Below, two-dimensional differentiation and three-dimensional differentiation are described in detail.

[0246] Overview of differentiation

[0247] Differentiation refers to the transformation of a cell or cell population into another type of cell or cell population. Through differentiation, the starting cell is known to transform into another cell type capable of performing a specific role or possessing a specific function. Generally, but not exclusively, cells are known to transform into more specialized forms through differentiation.

[0248] In this disclosure, the term "differentiation" may be used interchangeably with "induction of differentiation," for convenience. Specifically, induction of differentiation refers to a process or method for differentiating a starting cell or a cell population containing the starting cell into another type of cell or cell population containing the starting cell, with a specific function. This induction of differentiation may be performed artificially for differentiation purposes. Induction of differentiation may be referred to as differentiation, a differentiation process, a differentiation process, and the like, without limitation.

[0249] In the present disclosure, differentiation (e.g., induction of differentiation) can be divided into two-dimensional differentiation and three-dimensional differentiation.

[0250] 2D differentiation refers to differentiation based on 2D cell culture (2D cell culture) method, and 3D differentiation refers to differentiation based on 3D cell culture (3D cell culture) method.

[0251] In some embodiments of the present disclosure, differentiation can convert a differentiable cell (e.g., an undifferentiated cell such as a stem cell) into a specific target cell (e.g., a differentiated cell such as a somatic cell). In some embodiments, differentiation can be, but is not limited to, terminal differentiation.

[0252] specific target cells

[0253] In the present disclosure, a specific target cell may be used to refer to a type of cell to be produced or obtained through differentiation. For example, a specific target cell may be a cell differentiated from a differentiation-capable cell.

[0254] In some embodiments, the particular target cell may be a somatic cell.

[0255] In some embodiments, the specific target cell may be, but is not limited to, a nervous system cell, a central nervous system cell, a peripheral nervous system cell, a hepatocyte, a pancreatic cell, a kidney cell, a heart cell, an osteocyte, a chondrocyte, a pancreatic cell, a muscle cell, an intestinal cell, a spleen cell, a blood cell, a thyroid cell, a parathyroid cell, a skin cell, or a lung cell.

[0256] In some embodiments, the particular target cell may be a central nervous system cell.

[0257] In some embodiments, the specific target cell can be a neuron, an astrocyte, an oligodendrocyte, a microglia, or an ependymal cell.

[0258] In some embodiments, the neurons may be, for example, but are not limited to, GABAergic neurons, glutamatergic neurons, cholinergic neurons, dopaminergic neurons, or serotonergic neurons.

[0259] In some embodiments, the specific target cell may be a pancreatic cell. The specific target cell may be, for example, an alpha cell, a beta cell, a PP cell, a delta cell, or an epsilon cell.

[0260] In some embodiments, the specific target cell may be a hepatocyte. The specific target cell may be, for example, hepatocytes, hepatic stellate cells (HSCs), Kupffer cells (KCs), or liver sinusoidal endothelial cells (LSECs).

[0261] Differentiable cells

[0262] In the present disclosure, a "differentiable cell" may be used to refer to a type of cell capable of transforming into a specific target cell. A differentiable cell is a cell that has the potential or ability to differentiate and can differentiate into a specific target cell.

[0263] In some embodiments, the differentiating cell may be a stem cell. In some embodiments, the differentiating cell may be a totipotent stem cell, a pluripotent stem cell (PSC), a multipotent stem cell (MSC), an oligopotent stem cell, or a unipotent stem cell. In a particular embodiment, the differentiating cell may be a pluripotent stem cell (MSC).

[0264] In some embodiments, the differentiating cell may be a progenitor or precursor cell of a particular target cell.

[0265] A differentiable cell can be described together with the specific target cell described above. For example, a differentiable cell can be a cell capable of differentiating into a specific target cell (e.g., a cell with the potential to differentiate into a specific target cell). Accordingly, as a specific target cell is specified or defined, a differentiable cell can also be specified or defined. For example, a specific target cell can be a neuron, and a differentiable cell can be a cell capable of differentiating into a neuron. For example, a specific target cell can be a hepatocyte, and a differentiable cell can be a cell capable of differentiating into a hepatocyte.

[0266] For example, if the specific target cell is a neural cell, the differentiating cell may be a neural stem cell (NSC) (e.g., a human neural stem cell), an induced pluripotent stem cell, or a neural progenitor cell / neural precursor cell. In certain embodiments, the differentiating cell may be a neural stem cell. If the differentiating cell is a neural stem cell capable of differentiating into a neural cell lineage, the specific target cell may be a neuron, an astrocyte, an oligodendrocyte, a microglia, or an ependymal cell.

[0267] For example, if the specific target cell is a hepatocyte, the differentiating cell may be an induced pluripotent stem cell (iPSC) or a hepatocyte precursor cell. If the differentiating cell is an induced pluripotent stem cell or a hepatic precursor cell capable of differentiating into a hepatocyte, the specific target cell may be a hepatocyte, hepatic stellate cell (HSC), Kupffer cell (KC), or liver sinusoidal endothelial cell (LSEC).

[0268] For example, if the specific target cell is a pancreatic cell, the differentiating cell may be an induced pluripotent stem cell (iPSC) or a pancreatic progenitor cell. If the differentiating cell is an induced pluripotent stem cell or a pancreatic progenitor cell capable of differentiating into a pancreatic cell, the specific target cell may be an alpha cell, a beta cell, a PP cell, a delta cell, or an epsilon cell.

[0269] The cells referred to in the present disclosure, including the specific target cells and differentiating cells described above, may be human cells (e.g., human-derived), but are not limited thereto. In some embodiments, the cells may be human-derived or non-human vertebrate-derived. In some embodiments, the cells may be human-derived, equine-derived, feline-derived, camel-derived, mouse-derived, rat-derived, porcine-derived, rabbit-derived, sheep-derived, monkey-derived, chimpanzee-derived, or bovine-derived, but are not limited thereto.

[0270] 2D differentiation

[0271] In the present disclosure, two-dimensional differentiation may refer to a process, procedure, or method for differentiating a differentiable cell into a specific target cell based on a two-dimensional culture method.

[0272] In some embodiments, two-dimensional differentiation may comprise a process of two-dimensionally culturing differentiable cells under conditions to differentiate them into specific target cells.

[0273] For example, a two-dimensional differentiation might include:

[0274] Prepare differentiation-capable cells;

[0275] Culturing differentiating cells in a cell culture vessel with a medium for culturing cells; and

[0276] Cells are cultured by replacing the cell culture medium in the cell culture vessel with a differentiation medium for differentiation.

[0277] At this time, the differentiation medium for differentiation can be appropriately determined depending on the type of differentiable cells and the specific target cell for differentiation. Differentiation medium suitable for differentiable cells and the purpose of differentiation is well known in the art. For example, differentiation media such as Neural basal medium, DMEM / F12 medium, Neural stem cell basal medium, Neural stem cell maintenance media, etc. can be used for differentiation of neural stem cells into neural cells, but are not limited thereto. For example, differentiation media such as hepatocyte differentiation and Endoderm Differentiation can be used for differentiation of induced pluripotent stem cells into hepatocytes, but are not limited thereto. For example, differentiation media such as Pancreatic Progenitor medium, Endoderm Differentiation, and Endoderm Basal Medium can be used for differentiation of induced pluripotent stem cells into pancreatic cells, but are not limited thereto.

[0278] Below, an example of two-dimensional differentiation is described.

[0279] In some embodiments, two-dimensional differentiation can be performed to differentiate differentiable cells into neurons. In some embodiments, the differentiable cells may be neural stem cells. In some embodiments, the specific target cells may be neurons. Below, an example of a two-dimensional differentiation process for differentiating neural stem cells into neurons is provided: Neural stem cells are seeded in cell culture dishes. After 24 hours, the medium is replaced with neural differentiation medium. The medium is replaced with differentiation medium during the differentiation process.

[0280] The two-dimensional differentiation process for differentiating neural stem cells into neurons is widely known in the art, and references to this publication include [MS Vieira et al. Biotechnology advances, 2018], [Pistollato et al. J Vis exp., 2017], and [Mukherjee et al., Chapter in Methods in molecular biology, 2020].

[0281] In some embodiments, two-dimensional differentiation can be performed to differentiate differentiated cells into hepatocytes (e.g., hepatocytes). In some embodiments, the differentiated cells can be induced pluripotent cells. Below, an example of a two-dimensional differentiation process for differentiating induced pluripotent stem cells into hepatocytes is provided: Induced pluripotent stem cells are seeded in cell culture dishes. After 48 hours, the medium is replaced once a day with hepatocyte differentiation medium. The medium is replaced with differentiation medium during the differentiation process.

[0282] The two-dimensional differentiation process for differentiating induced pluripotent stem cells into hepatocytes (e.g., hepatocytes) is widely known in the art, and reference can be made to literature [SK Mallanna et al., Curr Protoc Stem Cell Biol., 2014], [Chen et al., Hepatology., 2012], [Corbett et al., Frontier in Medicine, 2012], etc.

[0283] In some embodiments, two-dimensional differentiation can be performed to differentiate differentiated cells into pancreatic cells (e.g., beta cells). In some embodiments, the differentiated cells can be induced pluripotent cells. Below, an example of a two-dimensional differentiation process for differentiating induced pluripotent stem cells into beta cells is provided: Induced pluripotent stem cells are seeded into cell culture dishes. After 48 hours, the medium is replaced once a day with beta cell differentiation medium. The medium is replaced with differentiation medium during the differentiation process.

[0284] The two-dimensional differentiation process for differentiating induced pluripotent stem cells into pancreatic cells (e.g., beta cells) is widely known in the art, and reference can be made to literature [Hogrebe et al. Nature protocol, 2021].

[0285] 3D differentiation

[0286] In the present disclosure, three-dimensional differentiation may refer to a process, procedure, or method for differentiating a differentiable cell into a specific target cell based on a three-dimensional culture method.

[0287] In some embodiments, three-dimensional differentiation may involve culturing differentiable cells in three dimensions under conditions to differentiate them into specific target cells.

[0288] For example, 3D differentiation may include:

[0289] Preparing differentiation-capable cells; and

[0290] To form aggregates of differentiable cells or 3D spheroids, differentiable cells are embedded in a matrix / scaffold (e.g., Matrigel) to form a 3D structure, and then cultured in a cell culture vessel with a medium for differentiation.

[0291] At this time, the matrix / scaffold may include one or more selected from collagen, hyaluronic acid, polyethylene glycol, polyvinyl alcohol, polylactide-co-glycolide (PLG), and polycaprolactone (PLA), but is not otherwise limited. Furthermore, commercially available Matrigel or Cultrex may be used for three-dimensional culture (e.g., to form aggregates or spheroids of cells).

[0292] At this time, the differentiation medium for differentiation can be appropriately determined depending on the type of differentiable cells and the specific target cell for differentiation. Differentiation medium suitable for differentiable cells and the purpose of differentiation is well known in the art. For example, differentiation media such as Neural basal medium, DMEM / F12 medium, Neural stem cell basal medium, Neural stem cell maintenance media, etc. can be used for differentiation of neural stem cells into neural cells, but are not limited thereto. For example, differentiation media such as hepatocyte differentiation and Endoderm Differentiation can be used for differentiation of induced pluripotent stem cells into hepatocytes, but are not limited thereto. For example, differentiation media such as Pancreatic Progenitor medium, Endoderm Differentiation, and Endoderm Basal Medium can be used for differentiation of induced pluripotent stem cells into pancreatic cells, but are not limited thereto.

[0293] Furthermore, 3D differentiation can be performed through scaffold-free 3D culture methods such as the forced floating method, the hanging drop method, or agitation-based approaches.

[0294] Below, an example of three-dimensional differentiation is described.

[0295] In some embodiments, 3D differentiation can be performed to differentiate differentiable cells into neural cells. In some embodiments, the differentiable cells may be neural stem cells. In some embodiments, the specific target cells may be neural cells. Below, an example of a 3D differentiation process for differentiating neural stem cells into neural cells is provided: Neural stem cells are prepared. After forming a 3D structure by embedding the cells in matrigel to form aggregates or 3D spheroids, the cells are cultured in a cell culture vessel with medium. After forming the 3D structure, the cell culture medium is replaced with neural cell culture medium every two days to induce differentiation.

[0296] The three-dimensional differentiation process for differentiating neural stem cells into neurons is widely known in the art, and references to literature such as [Watanabe et al. current protocol, 2021] and [Ciarpella, Star protocol, 2023] can be made.

[0297] In some embodiments, three-dimensional differentiation can be performed to differentiate differentiated cells into hepatocytes (e.g., hepatocytes). In some embodiments, the differentiated cells can be induced pluripotent cells. Below, an example of a three-dimensional differentiation process for differentiating induced pluripotent stem cells into hepatocytes is provided: Induced pluripotent stem cells are prepared in cell culture dishes. After forming a three-dimensional structure by embedding the cells in matrigel to form aggregates or 3D spheroids, the cells are cultured in a cell culture vessel with medium. After 48 hours, the medium is replaced once a day with hepatocyte differentiation medium. The medium is replaced with differentiation medium during the differentiation process.

[0298] The three-dimensional differentiation process for differentiating induced pluripotent stem cells into hepatocytes (e.g., hepatocytes) is widely known in the art, and reference can be made to literature [Kim et al. Organoid, 2022], etc.

[0299] In some embodiments, three-dimensional differentiation can be performed to differentiate differentiated cells into pancreatic cells (e.g., beta cells). In some embodiments, the differentiated cells can be induced pluripotent cells. Below, an example of a three-dimensional differentiation process for differentiating induced pluripotent stem cells into beta cells is provided: induced pluripotent stem cells are prepared in cell culture dishes. After forming a three-dimensional structure by embedding the cells in matrigel to form aggregates or 3D spheroids, the cells are cultured in a cell culture vessel with medium. After 48 hours, the beta cell differentiation medium is replaced once a day. The medium is replaced with differentiation medium during the differentiation process.

[0300] The 3D differentiation process for differentiating induced pluripotent stem cells into pancreatic cells (e.g., beta cells) is widely known in the art, and reference can be made to literature [Hohwieler et al., Gut, 2017], etc.

[0301] Methods for obtaining protein and gene expression levels

[0302] Gene expression levels for a plurality of genes at each time point in a two-dimensional differentiation process can be measured using a method known in the art. For example, a sample for measuring gene expression levels can be obtained from a cell, a cell population, or a culture composition during a two-dimensional differentiation process, and gene expression levels for a plurality of genes can be measured using the sample for measuring gene expression levels. As described above, gene expression levels can be measured at a plurality of time points in a two-dimensional differentiation process. As a specific example, at a first time point in a two-dimensional differentiation process, a sample for measuring gene expression levels can be obtained from a cell, a cell population, or a culture composition, and gene expression levels for a plurality of genes can be measured from the sample. Furthermore, at a second time point in a two-dimensional differentiation process, a sample for measuring gene expression levels can be obtained from a cell, a cell population, or a culture composition, and gene expression levels for a plurality of genes can be measured from the sample. In this way, samples for measuring gene expression levels can be obtained from cells, cell populations, or culture compositions at each of the first to Nth time points of a two-dimensional differentiation process, and gene expression levels for a plurality of genes at each time point can be measured from each of the samples. The sample for measuring gene expression levels can be all or part of a composition including cells or cell populations, or can be obtained by disrupting a cell population, but is not limited thereto.

[0303] Gene expression levels for a plurality of genes at each time point in the three-dimensional differentiation process can be measured using a method known in the art. For example, a sample for measuring gene expression levels can be obtained from a cell, a cell population, or a culture composition during the three-dimensional differentiation process, and the gene expression levels for a plurality of genes can be measured using the sample for measuring gene expression levels. As described above, gene expression levels can be measured at a plurality of time points in the three-dimensional differentiation process. As a specific example, at a first time point in the three-dimensional differentiation process, a sample for measuring gene expression levels can be obtained from a cell, a cell population, or a culture composition, and the gene expression levels for a plurality of genes can be measured from the sample. Furthermore, at a second time point in the three-dimensional differentiation process, a sample for measuring gene expression levels can be obtained from a cell, a cell population, or a culture composition, and the gene expression levels for a plurality of genes can be measured from the sample. In this way, samples for measuring gene expression levels can be obtained from cells, cell populations, or culture compositions at each of the first to Mth time points of the three-dimensional differentiation process, and gene expression levels for a plurality of genes at each time point can be measured from each of the samples. The sample for measuring gene expression levels can be all or part of a composition including cells or cell populations, or can be obtained by disrupting a cell population, but is not limited thereto.

[0304] Gene expression levels can be obtained by measuring and / or quantifying the level of the gene product.

[0305] For example, gene expression levels can be obtained by measuring the amount of a protein, which is a product of the gene. That is, gene expression levels can be obtained by quantifying each of a plurality of proteins corresponding to each of a plurality of genes. For example, gene expression levels for a plurality of genes can be obtained through proteomics analysis. To measure the amount of protein, any one or a combination of one or more of ELISA (nzyme-linked immunosorbent assay), Western blot, liquid chromatography (e.g., high performance liquid chromatography), and mass spectrometry can be used, but are not limited thereto, and methods widely known in the art can be used. For example, LC-MS (Liquid chromatography-mass spectrometry) can be used to measure the amount of protein. LC-MS (e.g., HPLC-MS or LC-MS / MS), a combination of LC and MS systems, is known to be used as a major analytical technique in proteomics.

[0306] As another example, the level of gene expression can be obtained by measuring the amount of mRNA, which is the product of the corresponding gene. That is, the gene expression levels can be obtained by quantifying each of a plurality of mRNAs corresponding to each of a plurality of genes. To measure the amount of mRNA, any one or a combination of one or more of Northern blotting, reverse transcription polymerase chain reaction (RT-PCR) (e.g., reverse transcription-quantitative polymerase chain reaction (RT-qPCR)), microarray, and RNA sequencing (RNA-seq) can be used, but is not limited thereto, and any method widely known in the art can be used.

[0307] Furthermore, in some embodiments, the measurement of the amount of the protein and the measurement of the amount of mRNA described above may be used together to obtain the level of gene expression, but are not limited thereto.

[0308] In some embodiments, obtaining gene expression levels may further include, but is not limited to, normalizing the level of the quantified gene product or the amount of the measured gene product. For example, normalizing the amount of the measured protein and / or the amount of the measured mRNA can be performed using methods known in the art.

[0309] Time points for measuring gene expression levels during the differentiation process

[0310] When embryonic stem cells or induced pluripotent stem cells are differentiated using differentiation medium, differentiation into each lineage (mesoderm, endoderm, ectoderm) can proceed around the 7th day. It is known that differentiation into mesoderm (e.g., cardiac cells, muscle cells, blood cells, and kidney-related cells) can be confirmed through markers such as NCAM, SM22A, and CD114. In differentiation into endoderm (e.g., lung cells, beta cells, and thyroid cells), it is known that differentiation into endoderm lineage can be confirmed through markers such as SOX17, FOXA2, and CXCR4. In differentiation into ectoderm (e.g., dermal cells, neural cells, and pigment cells), it is known that differentiation into ectoderm lineage can be confirmed through markers such as PAX6, NESTIN, and SOX2.

[0311] For example, differentiation can be divided into early, mid, and late stages. In some embodiments, gene expression levels can be measured at one or more stages selected from before differentiation, early differentiation, mid differentiation, and late differentiation. For example, gene expression levels can be measured at each of before differentiation, early differentiation, mid differentiation, and late differentiation. For example, gene expression levels can be measured at three or more stages selected from before differentiation, early differentiation, mid differentiation, and late differentiation.

[0312] The pre-differentiation stage may refer to, for example, the stage before replacing the cell culture medium with a differentiation medium.

[0313] The initial differentiation may refer to, for example, the process of inducing differentiation of cells (e.g., induced pluripotent stem cells, embryonic stem cells, mesenchymal stem cells, and neural stem cells) into specific cells. For example, during the differentiation process, the cell culture medium is replaced with a differentiation medium, and the initial differentiation may occur after the replacement with the differentiation medium.

[0314] Metaphase, for example, can refer to the stage in which differentiation progresses toward a specific lineage, or the process of inducing cells to become progenitors / precursor cells of a specific lineage. For example, cells observed in metaphase exhibit characteristics of immature cells or precursor cells.

[0315] Terminal differentiation can refer to the process of inducing cells into a specific lineage at a mature stage, for example. For example, cells observed in terminal differentiation have characteristics of mature cells, such as neurons and hepatocytes.

[0316] For example, differentiation of neural stem cells into neurons can be divided into pre-differentiation, early differentiation, mid-differentiation, and late differentiation. Pre-differentiation refers to the stage before replacing the cell culture medium with differentiation medium. Early differentiation refers to the stage at which differentiation into neurons begins. In the early differentiation stage, expression of neural stem cell markers can be confirmed. In the mid-differentiation stage, expression of neural progenitor cell markers can be confirmed. In the late differentiation stage, expression of mature neural cell markers can be confirmed. In some embodiments, the measurement period can be determined by determining gene / protein expression levels in samples obtained from certain cells.

[0317] For example, differentiation of induced pluripotent stem cells into hepatocytes can be divided into pre-differentiation, early differentiation, mid-differentiation, and late differentiation. Pre-differentiation refers to the stage before the cell culture medium is replaced with differentiation medium. Early differentiation refers to the stage at which differentiation into hepatocytes begins, and in the early differentiation stage, marker expression of induced pluripotent stem cells can be confirmed. In the mid-differentiation stage, marker expression of hepatocyte progenitor cells can be confirmed. In the late differentiation stage, marker expression of mature hepatocytes can be confirmed. In some embodiments, the measurement period can be set by determining the level of gene / protein expression through samples obtained from some cells.

[0318] For example, differentiation of induced pluripotent stem cells into beta cells can be divided into pre-differentiation, early differentiation, mid-differentiation, and late differentiation. Pre-differentiation refers to the stage before the cell culture medium is replaced with differentiation medium. Early differentiation refers to the stage in which differentiation into beta cells begins, and in the early differentiation stage, marker expression of induced pluripotent stem cells can be confirmed. In the mid-differentiation stage, marker expression of beta cell progenitor cells can be confirmed. In the late differentiation stage, marker expression of mature beta cells can be confirmed. In some embodiments, the measurement period can be determined by determining gene / protein expression levels through samples obtained from certain cells.

[0319] In some embodiments, in differentiation, the time points at which gene expression levels are measured may be any one or more selected from before differentiation, early differentiation, mid-differentiation, and late differentiation. The time points at which gene expression levels are measured may be any two or three or more selected from before differentiation, early differentiation, mid-differentiation, and late differentiation. In some embodiments, gene expression levels may be measured at each of before differentiation, early differentiation, mid-differentiation, and late differentiation. For example, in two-dimensional differentiation, gene expression levels may be measured at any one or more of before differentiation, early differentiation, mid-differentiation, and late differentiation. For example, in three-dimensional differentiation, gene expression levels may be measured at any one or more of before differentiation, early differentiation, mid-differentiation, and late differentiation. For example, in both two-dimensional differentiation and three-dimensional differentiation, gene expression levels may be measured at each of before differentiation, early differentiation, mid-differentiation, and late differentiation. In some embodiments, the time point at which the gene expression level is measured during the two-dimensional differentiation process may be the same as or different from the time point at which the gene expression level is measured during the three-dimensional differentiation process. In some embodiments, in the two-dimensional differentiation, the gene expression level may be measured on any one or more days (or time points) selected from, but not limited to, day 0 (wherein day 0 is the start day of differentiation), 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, and 30. For example, in the two-dimensional differentiation, the gene expression level may be measured on any three days, four days, or five days selected from the aforementioned days.In some embodiments, in the three-dimensional differentiation, the gene expression level can be measured on any one or more days (or time points) selected from, but not limited to, day 0 (wherein day 0 is the start day of differentiation), 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, and 30. For example, in the three-dimensional differentiation, the gene expression level can be measured on any three days, four days, or five days selected from the aforementioned days.

[0320] In some embodiments, in the differentiation of neural stem cells into neurons, the time points at which gene expression levels are measured may be at least one selected from before differentiation, early differentiation, mid-differentiation, and late differentiation. In some embodiments, gene expression levels may be measured at each of before differentiation, early differentiation, mid-differentiation, and late differentiation. For example, based on the start date of differentiation (day 0), gene expression levels may be measured at before differentiation (day 0), early differentiation (day 3), mid-differentiation (day 8), and late differentiation (day 18).

[0321] In some embodiments, in the differentiation of induced pluripotent stem cells into hepatocytes, the time points at which gene expression levels are measured may be at least one selected from pre-differentiation, early differentiation, mid-differentiation, and late differentiation. In some embodiments, gene expression levels may be measured at each of pre-differentiation, early differentiation, mid-differentiation, and late differentiation.

[0322] In some embodiments, in the differentiation of induced pluripotent stem cells into beta cells, the time points at which gene expression levels are measured may be at least one selected from before differentiation, early differentiation, mid-differentiation, and late differentiation. In some embodiments, gene expression levels may be measured at each of before differentiation, early differentiation, mid-differentiation, and late differentiation.

[0323] Obtaining gene vectors for multiple genes

[0324] Overview of obtaining gene vectors for multiple genes

[0325] A screening method according to some embodiments of the present disclosure may include obtaining gene vectors for a plurality of genes. In this case, the gene vectors for the plurality of genes may be obtained from the two-dimensional gene expression profiles for the plurality of genes and the three-dimensional gene expression profiles for the plurality of genes described above. In some embodiments, the gene vectors for the plurality of genes may be obtained through vectorization of the two-dimensional gene expression profiles for the plurality of genes and the three-dimensional gene expression profiles for the plurality of genes, but are not limited thereto. Hereinafter, the vectorization of the two-dimensional gene expression profiles for the plurality of genes and the three-dimensional gene expression profiles for the plurality of genes, and the structure of the gene vectors for the plurality of genes will be described in detail.

[0326] Vectorization Overview

[0327] The process of obtaining two-dimensional gene expression profiles for multiple genes and three-dimensional gene expression profiles for multiple genes has been described in detail above. The obtained two-dimensional gene expression profiles for multiple genes and three-dimensional gene expression profiles for multiple genes can be processed or aligned for subsequent clustering or grouping. This processing or alignment process can be performed by a person, a server, or a control unit of a device, or can be performed automatically along with clustering or grouping by algorithm or formula during the subsequent clustering or grouping (selection process of the first candidate genes). Furthermore, even without a separate processing process, since the subsequent step relates to the step of clustering or grouping genes, aligning the obtained data or information by gene can be a process without difficulty for those skilled in the art.

[0328] The process of sorting or processing the obtained information for subsequent clustering or grouping may be referred to as, but is not limited to, “vectorization” in this disclosure.

[0329] The present disclosure relating to vectorization is intended to aid the understanding of those skilled in the art, and it will be appreciated that vectorization may optionally be included in screening methods according to some embodiments of the present disclosure.

[0330] In some embodiments, the screening method of the present disclosure may optionally include a step of generating gene vectors for the plurality of genes based on the two-dimensional gene expression profiles for the plurality of genes obtained (the two-dimensional gene expression profile data set) and the three-dimensional gene expression profiles for the plurality of genes obtained (the three-dimensional gene expression profile data set).

[0331] Example of a gene vector for a single gene

[0332] Gene vectors for multiple genes can be generated from two-dimensional gene expression profiles for multiple genes and three-dimensional gene expression profiles for multiple genes.

[0333] For example, gene vectors for multiple genes can be generated from two-dimensional gene expression profiles for multiple genes and three-dimensional gene expression profiles for multiple genes through vectorization.

[0334] Below, to help understand vectorization and gene vectors, an example of a gene vector for a single gene (for illustration, referred to as gene A) is described.

[0335] The two-dimensional gene expression profile for gene A (see Table 01) and the three-dimensional gene expression profile for gene A (see Table 03) have been described in detail in the previous paragraphs. The gene vector for gene A can be generated by processing or aligning the two-dimensional gene expression profile for gene A and the three-dimensional gene expression profile for gene A.

[0336] For example, the genetic vector of gene A can be described through the following table.

[0337] [Table 05] Example 1 of gene vector for gene A

[0338]

[0339] As a more specific example, the two-dimensional gene expression profile and three-dimensional gene expression profile for gene A can be arranged (either chronologically or irrespective of chronological order) according to the type of differentiation and the time point at which the expression level was measured. For example, the gene vector for gene A may have the following structure.

[0340] [Table 06] Example 2 of gene vector for gene A

[0341]

[0342] Furthermore, in some embodiments, for clustering or grouping, each piece of data (e.g., expression levels) associated with gene A may be labeled. The following examples illustrate the case where each piece of data is labeled.

[0343] [Table 07] Example 3 of gene vector for gene A

[0344]

[0345] Examples of multiple gene vectors for multiple genes

[0346] A gene vector for a single gene can be generated for multiple genes. A collection of gene vectors for multiple genes may be referred to as, but is not limited to, a data set of gene vectors or multiple gene vectors.

[0347] Below, to help understand vectorization and gene vectors for multiple genes, an example is provided where the multiple genes are three genes (e.g., genes A, B, and C).

[0348] Two-dimensional gene expression profiles for multiple genes and three-dimensional gene expression profiles for multiple genes have been described in detail in the previous paragraphs. Gene vectors for multiple genes can be generated by processing or aligning the two-dimensional gene expression profiles for multiple genes and the three-dimensional gene expression profiles for multiple genes.

[0349] For example, gene vectors for multiple genes can be described through the following table.

[0350] [Table 08] Example 1 of gene vectors for multiple genes

[0351]

[0352] As a more specific example, the two-dimensional and three-dimensional gene expression profiles for each of multiple genes can be arranged (either chronologically or irrespective of chronological order) according to the type of differentiation and the time point at which expression levels were measured. For example, multiple gene vectors for multiple genes may have the following structure.

[0353] [Table 09] Example 2 of gene vectors for multiple genes

[0354]

[0355] Furthermore, in some embodiments, for clustering or grouping, each piece of data (e.g., expression levels) associated with multiple genes may be labeled. The following examples illustrate cases where each piece of data is labeled.

[0356] [Table 10] Example 3 of gene vectors for multiple genes

[0357]

[0358] Selection of first candidate genes (selection of differentiation-related genes)

[0359] Overview of the selection of candidate genes

[0360] The genetic screening method of the present disclosure includes a process of selecting (or confirming) first candidate genes from among a plurality of genes.

[0361] Based on the aforementioned gene expression profile data set (two-dimensional gene expression profiles and three-dimensional gene expression profiles for multiple genes) or gene vector data set (gene vectors for multiple genes), multiple genes can be classified into two or more clusters, groups, or populations through clustering or grouping.

[0362] For example, by clustering a data set of gene vectors, multiple genes can be classified into two or more clusters. Furthermore, candidate clusters can be selected from the two or more clusters based on predetermined candidate cluster selection criteria. A schematic diagram of an example of a method for screening first candidate genes, including clustering for ease of understanding, is provided in Figure 04.

[0363] For example, a data set of genetic vectors can be sorted or grouped according to predetermined criteria, thereby classifying multiple genes into two or more groups (e.g., two groups). In this case, the groups or genes sorted or selected according to the predetermined criteria may be referred to as candidate groups or candidate genes. A schematic diagram of an example of a method for screening first candidate genes, including grouping to aid understanding, is provided in Figure 05.

[0364] Below, the process of dividing multiple genes into two or more clusters or groups and selecting candidate clusters or candidate groups from the two or more clusters or groups is described in detail. Genes belonging to the candidate clusters or candidate groups may be referred to as first candidate genes. In some embodiments, the first candidate genes may be referred to as candidate genes associated with differentiation, but are not limited thereto.

[0365] Clustering Overview

[0366] In some embodiments, clustering may be performed to select the first candidate genes. Clustering may be performed to group genes with similar expression patterns in two-dimensional differentiation and three-dimensional differentiation, or in both two-dimensional differentiation and three-dimensional differentiation. In some embodiments, clustering may group genes with similar expression patterns in two-dimensional differentiation and similar expression patterns in three-dimensional differentiation. In some embodiments, clustering may group genes with similar expression patterns in two-dimensional differentiation and three-dimensional differentiation. In some embodiments, clustering may be performed by, but is not limited to, a human, a server, or a control unit of a device.

[0367] Clustering uses both the two-dimensional gene expression profile in the two-dimensional differentiation and the three-dimensional gene expression profile in the three-dimensional differentiation for a gene, or uses a gene vector having information of the two-dimensional gene expression profile and the three-dimensional expression profile, so that clustering is performed by considering the expression levels in the two-dimensional differentiation and the expression levels in the three-dimensional differentiation as a whole.

[0368] For convenience, clustering is described in detail below based on a data set of gene vectors, but is not limited thereto. As described above, the data set of gene vectors refers to gene vectors for multiple genes, and the data set of gene vectors (gene vectors for multiple genes) may be referred to as multiple gene vectors for convenience.

[0369] Clustering algorithm

[0370] A screening method according to some embodiments of the present disclosure comprises clustering a plurality of genetic vectors. A clustering algorithm may be used for the clustering.

[0371] A screening method according to some embodiments of the present disclosure comprises clustering a plurality of genetic vectors using a clustering algorithm. Any known clustering algorithm may be used to cluster the plurality of genetic vectors.

[0372] As known, types of clustering include, but are not limited to, density-based, distribution-based, centroid-based, hierarchical-based, and grid-based. In some embodiments, a plurality of genetic vectors can be clustered through density-based, distribution-based, centroid-based, hierarchical-based, or grid-based clustering.

[0373] As a specific example, the clustering method or algorithm includes, but is not limited to, k-means clustering algorithm, DBSCAN clustering, Gaussian Mixture Model algorithm, BIRCH algorithm (Balance Iterative Reducing and Clustering using Hierarchies algorithm), Affinity Propagation clustering algorithm, Mean-Shift clustering algorithm, OPTICS algorithm, and / or Agglomerative Hierarchy clustering algorithm. In some embodiments, the plurality of genetic vectors can be clustered via k-means clustering algorithm, DBSCAN clustering, Gaussian Mixture Model algorithm, BIRCH algorithm (Balance Iterative Reducing and Clustering using Hierarchies algorithm), Affinity Propagation clustering algorithm, Mean-Shift clustering algorithm, OPTICS algorithm, and / or Agglomerative Hierarchy clustering algorithm. In certain embodiments, clustering of multiple gene vectors may use, but is not limited to, K-means clustering or DBSCAN clustering.

[0374] Determining the number of clusters

[0375] Through clustering, multiple gene vectors (or multiple genes) can be clustered into two or more clusters. For example, multiple gene vectors can be classified into clusters 1 through R through clustering.

[0376] For example, a genetic screening method according to some embodiments of the present disclosure may comprise clustering a plurality of genetic vectors into two or more clusters using a clustering algorithm. In some embodiments, a genetic screening method according to the present disclosure may comprise clustering a plurality of genetic vectors into three or more clusters using a clustering algorithm.

[0377] For example, a genetic screening method according to some embodiments of the present disclosure may include clustering a plurality of genetic vectors into first to Rth clusters using a clustering algorithm.

[0378] In some embodiments, R can be an integer greater than or equal to 2. In some embodiments, R can be an integer from 2 to 20. For example, when R is 3, the plurality of genetic vectors are classified into a first cluster, a second cluster, and a third cluster. In some embodiments, R can be an integer from 2 to 10, or from 2 to 6. In a particular embodiment, R can be an integer from 3 to 5. In a particular embodiment, R can be 4.

[0379] In some embodiments, the number of clusters classified through clustering can be optimized. For example, in k-means clustering, the optimized number of clusters is referred to as the optimal k value. For example, a genetic screening method may optionally include a step of optimizing the number of clusters.

[0380] In some embodiments, optimization of the number of clusters can be performed using a cluster number optimization method known in the art or a calculation formula for cluster number optimization. For example, in k-means clustering, the optimal k value can be determined using the elbow method, the silhouette method, the dendrogram method, or the gap statistic method.

[0381] In some embodiments, the optimal number of clusters may be determined based on predetermined criteria. In some embodiments, the optimal number of clusters may be determined based on the number of factors associated with a specific target cell or a specific target tissue.

[0382] For example, the optimal number of clusters is

[0383] Compute scores for the number of clusters based on predetermined criteria; and select the cluster with the highest score.

[0384] can be determined through a process that includes .

[0385] In some embodiments, the predetermined criteria for calculating a score for the number of clusters may be determined based on a number of factors associated with a particular target cell or a particular target tissue.

[0386] In some embodiments, but not limited to, the predetermined criteria for calculating the score for the number of clusters may be the following calculation formula:

[0387] [Calculating formula 1]

[0388] Score for number of clusters =

[0389] ,

[0390] At this time,

[0391] x is a value that is set differently depending on the cluster, and is the number of factors (e.g., genes) related to a specific target cell or a specific target tissue belonging to each cluster (e.g., each of the first to Rth clusters).

[0392] μ is the mean of the x values ​​in each cluster,

[0393] N c is the number of clusters (e.g., N c is R),

[0394] S g is the sum of the number of factors (e.g., genes) associated with the specific target cell or specific target tissue in all clusters, and

[0395] N g is the number of factors (e.g., genes) associated with the specific target cell or specific target tissue belonging to multiple genes.

[0396] For example, if the score for the number of clusters when clustering into 4 clusters is higher than the score for the number of clusters when clustering into 3 clusters, the optimal number of clusters for clustering may be 4.

[0397] In some embodiments, the optimal number of clusters may not be 2. For example, even if the score for the number of clusters is the highest when the number of clusters is 2, 2 may be excluded from the optimal number of clusters. For example, in k-means clustering, even if the score for the number of clusters when k=2 is higher than the scores for the number of clusters when k-means clustering has other values ​​of k, the optimal number of clusters may not be 2.

[0398] In some embodiments, clustering quality can be improved by optimizing the number of clusters based on the number of factors associated with a specific target cell or tissue. This allows for effective grouping of the resulting data, further facilitating interpretation of the results.

[0399] Select candidate clusters

[0400] After clustering multiple gene vectors (or multiple genes) into two or more clusters, for example, first to Rth clusters (e.g., clustering into R clusters), candidate clusters can be selected from the two or more clusters. At this time, to select candidate clusters from the two or more clusters, a predetermined candidate cluster selection criterion can be used. For example, each cluster can be scored according to the predetermined candidate cluster selection criterion, and the cluster with the highest score among the two or more clusters can be selected.

[0401] A genetic screening method according to some embodiments of the present disclosure may include selecting a candidate cluster from two or more clusters based on predetermined candidate cluster selection criteria. A genetic screening method according to some embodiments of the present disclosure may include selecting a candidate cluster from first to Rth clusters based on predetermined candidate cluster selection criteria. Genes belonging to the selected candidate clusters may be candidate genes (e.g., first candidate genes).

[0402] In some embodiments, selection of candidate clusters can be performed based on a number of factors associated with a particular target cell or a particular target tissue in each cluster.

[0403] In some embodiments, the predetermined candidate cluster selection criteria for selecting candidate clusters may be determined based on, but are not limited to, the number of factors associated with a specific target cell or a specific target tissue, the ratio of the number of factors associated with a specific target cell or a specific target tissue in each cluster, or the weight of factors associated with a specific target cell or a specific target tissue in each cluster. For example, a cluster score calculation formula based on the number or ratio of factors associated with a specific target cell or a specific target tissue may be used, and the cluster with the highest cluster score derived through the cluster score calculation formula may be selected as the candidate cluster.

[0404] In some embodiments, the calculation formula for the cluster score for selecting a candidate cluster may be any one selected from the following calculation formulas 2 to 4, but is not limited thereto:

[0405] [Calculation Formula 2]

[0406] Cluster score = ;

[0407] [Calculation Formula 3]

[0408] Cluster score = x / S g ; and

[0409] [Calculation Formula 4]

[0410] Cluster score = x / N a ,

[0411] Here, x is the number of factors (e.g., genes) related to a specific target cell or a specific target tissue belonging to each cluster,

[0412] Sg is the sum of the number of factors (e.g., genes) associated with the specific target cell or specific target tissue in all clusters,

[0413] N a is the number of genes belonging to each cluster.

[0414] In a specific embodiment, the calculation formula for cluster scores for selecting candidate clusters may be Equation 2.

[0415] Below, factors related to specific target cells or specific target tissues, and factors relatively related to or closely related to 3D differentiation are described in detail.

[0416] Factors associated with specific target cells or specific target tissues

[0417] In some embodiments, predetermined criteria for cluster selection may be determined based on factors (e.g., genes) associated with a specific target cell or a specific target tissue. For example, among two or more clusters (e.g., first to Rth clusters), the cluster containing the largest number of factors associated with a specific target cell or a specific target tissue may be selected as the candidate cluster. In another example, among two or more clusters, the cluster with the highest proportion of factors associated with a specific target cell or a specific target tissue may be selected as the candidate cluster. In another example, among two or more clusters, the cluster with the highest weight for factors associated with a specific target cell or a specific target tissue may be selected as the candidate cluster. In yet another example, among two or more clusters, the cluster with the highest cluster score based on factors associated with a specific target cell or a specific target tissue may be selected as the candidate cluster.

[0418] Factors associated with specific target cells or tissues may be factors or genes known to be associated with specific target cells or tissues. Information regarding these factors associated with specific target cells or tissues may be obtained from one or more known or publicly available literature sources or one or more databases. For example, information regarding which factors correspond to factors associated with specific target cells or tissues may be obtained from publicly available literature sources or databases. In some embodiments, the known database from which information about factors associated with a specific target cell or a specific target tissue can be obtained may be, for example, but is not limited to, one or more of The Database for Annotation, Visualization and Integrated Discovery (DAVID), National Center for Biotechnology Information (NCBI), ArrayXPath, BioLattice, GenBank, European Molecular Biology Laboratory (EMBL), DNA Data Bank of Japan (DDBJ), Protein Data Bank (PDB), Protein Information Resource (PIR), PROSITE, Pfam, Kyoto Encyclopedia of Genes and Genomes (KEGG), UniProt, Harmonizome, The Comparative Toxicogenomics Database (CTD), and Online Mendelian Inheritance in Man (OMIM). Information about the function, involved process, ontology, annotation, and / or other information of factors (genes or gene products) can be obtained from these databases.

[0419] For example, if the gene ontology (GO) of a factor (e.g., a gene) is associated with a specific target cell or a specific target tissue, the factor may be referred to as a factor associated with a specific target cell or a specific target tissue.

[0420] For example, if the function of a factor (e.g., a gene) is associated with a specific target cell or a specific target tissue, the factor may be referred to as a factor associated with a specific target cell or a specific target tissue.

[0421] Below, a description of gene ontology is provided. The description of gene ontology below is provided to aid understanding based on knowledge known in the art, and thus, the description of gene ontology is not limited by the description below.

[0422] The Gene Ontology Project is a major bioinformatics initiative to unify the representation of gene and gene product attributes (see [Gene Ontology Consortium. (2008). The gene ontology project in 2008. Nucleic acids research, 36(suppl_1), D440-D444.]). The goals of the Gene Ontology Project are to maintain and develop a controlled vocabulary for gene and gene product attributes; to generate annotations for genes and gene products, and to assimilate and disseminate annotation data; and to provide tools that provide easy access to all aspects of the data provided by the Project, and to enable functional interpretation of experimental data using GO. The Gene Ontology Project provides an ontology of defined terms that describe the properties of genes or gene products. While gene nomenclature focuses on genes and gene products, Gene Ontology focuses on the functions of genes and gene products. Gene ontology broadly encompasses the categories of molecular function, cellular component, and biological process. A gene's molecular function, cellular component, and biological process are expressed through one or more GO terms. Information about the gene ontology of genes can be obtained from various literature, accessible databases, knowledgebases, and / or programs.Known databases may include, but are not limited to, The Database for Annotation, Visualization and Integrated Discovery (DAVID), National Center for Biotechnology Information (NCBI), ArrayXPath, BioLattice, GenBank, European Molecular Biology Laboratory (EMBL), DNA Data Bank of Japan (DDBJ), Protein Data Bank (PDB), Protein Information Resource (PIR), PROSITE, Pfam, Kyoto Encyclopedia of Genes and Genomes (KEGG), UniProt, or Online Mendelian Inheritance in Man (OMIM). For example, examples of databases for obtaining gene GO information include, but are not limited to, Gene ontology resource (see https: / www.geneontology.org / ), Amigo (see https: / amigo.geneontology.org / amigo), DAVID (see https: / david.ncifcrf.gov / ), EMBL-EBI (see https: / www.ebi.ac.uk / QuickGO / annotations), and UniProt (see https: / www.uniprot.org / help / gene_ontology).

[0423] For gene ontology, references may be made, but are not limited to, [Ashburner, M., Ball, CA, Blake, JA, Botstein, D., Butler, H., Cherry, JM, ... & Sherlock, G. (2000). Gene ontology: tool for the unification of biology. Nature genetics, 25(1), 25-29.]; [Gene Ontology Consortium. (2004). The Gene Ontology(GO) database and informatics resource. Nucleic acids research, 32(suppl_1), D258-D261.]; [Du Plessis, L., Skunca, N., & Dessimoz, C. (2011). The what, where, how, and why of gene ontology—a primer for bioinformaticians. Briefings in bioinformatics, 12(6), 723-735.].

[0424] For example, if a factor (e.g., a gene) is associated with a specific target cell or a specific target tissue, one of the GOs (specifically, GO terms and / or GO annotations) of said factor may be associated with the specific target cell or the specific target tissue. For example, a factor associated with a specific target cell or a specific target tissue may be a factor in which one or more of the GO terms of the factor are associated with the specific target cell or the specific target tissue, and / or one or more of the GO annotations are associated with the specific target cell or the specific target tissue. For example, factors associated with a specific target cell or tissue may be factors searched for using keywords of the specific target cell or tissue in any of the databases described above (e.g., DAVID).

[0425] Among the factors searched through keywords of a specific target cell or tissue, one or more additional criteria may be introduced to extract factors related to the specific target cell or tissue, but are not limited thereto.

[0426] In some embodiments, if the specific target cell is a neuron, the neuron-related factors may be factors or portions thereof searched for using keywords related to neuron or neuron in one or more of the aforementioned databases (e.g., DAVID, Harmonizome, and The Comparative Toxicogenomics Database). The neuron-related factors may be referred to as neuronal factors. For example, the neuron or neuron-related keywords may be, but are not limited to, one or more selected from Neuron, Synapse, Dopaminergic neuron, Cholinergic neuron, Serotonergic neuron, and Glutamatergic neuron.

[0427] In some embodiments, if the specific target cell is a hepatocyte, the genes associated with the hepatocyte may be genes or portions thereof searched using keywords related to liver or liver in any one or more of the aforementioned databases (e.g., DAVID, Harmonizome, and The Comparative Toxicogenomics Database). Factors associated with the hepatocyte may be referred to as liver factors. For example, keywords related to liver or liver may be one or more selected from, but not limited to, hepatocyte, hepatic stellate cells, Kupffer cells, and liver sinusoidal endothelial cells.

[0428] In some embodiments, when the specific target cell is a pancreatic cell, the genes associated with pancreatic cells may be factors or a portion thereof searched through keywords related to pancreatic or pancreatic in any one or more of the aforementioned databases (e.g., DAVID, Harmonizome, and The Comparative Toxicogenomics Database). Factors associated with pancreatic cells may be referred to as pancreatic factors. For example, the keywords related to pancreatic or pancreatic may be, but are not limited to, any one or more selected from alpha cells, beta cells, delta cells, epsilon cells, and PP cells.

[0429] If the specific target cell is a neuron, the neuron may be a GABAergic neuron, a glutamatergic neuron, a cholinergic neuron, a dopaminergic neuron, or a serotonergic neuron. Factors associated with the neuron may be, for example, factors of GABAergic neurons, glutamatergic neurons, cholinergic neurons, dopaminergic neurons, and serotonergic neurons.

[0430] If the specific target cell is a hepatocyte, the hepatocyte may be a cell of hepatocytes (HCs), hepatic stellate cells (HSCs), Kupffer cells (KCs), and liver sinusoidal endothelial cells (LSECs). Factors related to hepatocytes may be, for example, factors of hepatocytes (HCs), hepatic stellate cells (HSCs), Kupffer cells (KCs), and liver sinusoidal endothelial cells (LSECs).

[0431] If the specific target cell is a pancreatic cell, the pancreatic cell may be an alpha cell, a beta cell, a delta cell, or a PP cell. Factors associated with pancreatic cells may be, for example, factors of alpha cells, beta cells, delta cells, and PP cells.

[0432] Below, information on each cell-related factor is provided.

[0433] Information on neuron-related factors

[0434] In some embodiments, the neuron-related factors (e.g., neuron-related genes) may be selected from factors belonging to factor set 1 below. In some embodiments, the neuron-related factors may be 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 700, 800, 900, 1000, or 1100 or more factors selected from factors belonging to factor set 1 below. In some embodiments, the neuron-related factors may include at least 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 700, 800, 900, 1000, or 1100 factors selected from factors belonging to factor set 1 below. The collection of neuron-related factors may be referred to as a set of neuron-related factors. The set of neuron-related factors may serve as a criterion or reference for selecting genes associated with neurons from among genes belonging to each cluster. In some embodiments, the neuron-related factors may be all factors belonging to factor set 1 below.

[0435] [Factor Set 1] - A total of 1,553 types of factors (genes)

[0436] PRPH; DISP2; TUBB3; RBFOX3; ISL1; NEFM; NRGN; SGIP1; ENO2; CHAT; TH; CALB1; EPO; CSF3; NEFH; DLG4; SLC12A5; IFIT3; SLC11A1; TFF3; SLC1A1; CXCL1; CXCR5; AIM2; MATN2; SLC36A4; MECP2; RIN1; OMP; PTPN11; GHSR; AGRP; RESP18; STX1A; SCN2B; CBARP; B3GAT2; FMO1; FBLN2; SHISA2; LUZP2; SCRG1; FAM181B; IGF1; CD59A; CTXN2; TMEM130; DZANK1; ACSL4; CLSTN3; ARHGDIG; HAP1; NRSN2; ABAT; HDAC11; SLC30A9; SCAMP1; MAGEE1; NCALD; COPG1; KCNC1; FBXW7; GABRG2; NMT1; ARID4A; ZRSR1; RGS17; TMEM91; CDK5; SAFB; RNF220; RASGRF1; TCEAL6; TMEM179; NICN1; PNMAL1; NAPG; CMIP; ZMYND11; RHBDD2; ST8SIA3; PQBP1; MATK; TRO; CKMT1; AKAP8L; DUSP26; SYT5; GAD1; FLYWCH1; RIT2; TCEAL5; PNCK; TMEM59L; TCEAL3; GPRASP2; MAP7D2; MIAT; RB1CC1; PABPN1; GRIA3; NBEA; SFSWAP; WDR60; MIRG; SCN1A; RNPC3; HOXC8; RERE; SCN8A; TRIP11; EML5; KMT2C; AAK1; SAFB2; SACS; RYR2; CSPP1; GRIA4; CHD6; ZFP445; ZCCHC7; GPATCH8; PCLO; SYNE1; PROX1; PPP1R14C; UBE2D2A; SYNPR; CCDC28B; KCNK1; CNRIP1; FRRS1L; FAM69B; GPR162; CACNG2; SPOCK3; RGS7BP; DNM3;TMEM191C; SLC6A17; DNER; GRM5; CNTNAP2; CPLX1; SLC32A1; DLGAP1; PPP1R9A; GNL3L; ZCCHC12; ATP6V1H; HPCA; CHN1; CPNE6; PDE1A; GNAI1; CCK; RIMS3; OGFOD1; CELF6; DTD1; PRMT2; AVP; RORA; VGF; PCSK2; CHODL; NECAB2; TAC2; KCNMA1; CACNB3; NWD2; ZIC1; TESC; NECAB3; NRN1; GABBR2; SLC17A7; CAR10; DGKB; CACNB4; CTTNBP2; PPP1R1B; PCP4; MEG3; BEX2; BEX1; NDN; CAMK2N1; PNISR; SNHG11; STMN2; PTK2B; DDN; ICAM5; GNG2; SNAP25; VSNL1; PACSIN1; CACNA2D1; ATP6V0A1; MYO5A; DYNC1I1; CACNA2D2; SLC17A6; ELAVL3; CALB2; GABRA1; AQP1; NPY; PRUNE2; PAX7; TSHB; GH1; GH; IGSF1; POMC; GNRHR; PCSK1; FSHB; POU1F1; PRL; TRIM30B; GNB3; HEPACAM2; ASB4; RAX; NKX2-1; FGF10; PITX1; LHB; GMPPA; AGA; TBRG1; SIX6; SEZ6L2; RCN3; RAB27A; PITX2; NUCB2; NKTR; MEST; DDX26B; CPEB4; CHGA; ANGPT1; NRG1; EDIL3; FLRT3; CDH4; SRGAP3; FAT3; LINGO2; LHX1; PNOC; FAM65B; WRAP73; CCM2; ACHE; GDF5; MAPK8; ZDBF2; SLC25A36; RELN; WNT7B; HAR1A; CNR1; EOMES; TRP73; DACT1; RGMB; TBCK; NANOS1; AATF; ZIC2; EMX2; LHX5; KCNH7; TBR1; SMAD1; FAM196A;RALGPS2; CIB2; FUT9; ABCC5; TMEM170B; RNF138; LNPK; LINGO1; NDNF; LHX1OS; TMEM163; CLSTN2; NHLH2; SMOC2; ISOC1; ST3GAL5; DIABLO; SLITRK5; B3GALT1; TRIM44; BARHL2; RAB11FIP2; TCEAL1; FSD1; LZTFL1; ERC2; MAP9; HOMER2; DACH1; NSMCE3; SLC25A46; NUDT2; ATP13A2; EBF3; SLC6A3; PITX3; SMAD3; NEUROD6; SLC18A2; DDC; ZIM3; TENM1; SCN2A; PRKCG; MAPK8IP2; CHRNA6; CADPS2; LMX1B; NR4A2; KCNJ6; FOXA2; NTN1; SLC18A3; FOXJ1; RABL2; CFAP54; CCDC153; PIFO; DYNLRB2; RSPH1; CFAP44; PCP4L1; AK8; RIIAD1; TM4SF1; TEKT4; CALML4; CRYGN; HDC; FAM183B; TMEM107; EFNB3; ZMYND10; TMEM212; RFX2; LRRC23; PVALB; ANGPTL2; MYB; STOML3; PLTP; SSPO; HSPA2; TRIM71; SIX3; USP18; CETN4; AQP4; CELSR2; GFAP; ENKUR; FAM216B; KRT15; CATIP; DNAH12; FAM166B; IQCG; SPEF2; MYO16; ODF3B; PLXNB2; TEKT1; CHEK2; CROCC; NME9; CFAP126; MEIG1; CFAP65; RARRES2; S100B; AK7; GAD2; SLC6A1; GADD45B; PAX2; VIP; DLX1; TNFAIP8L3; SEMA3C; MYBPC1; PARM1; SST; SLC17A8; GRIN2B; GLS; MEIS2; SLC1A6; GRIN1; SLC1A2; NEUROG2; NEUROD1; EPHA3; YY2; FOXD2;SLC6A5; BCL2; NF1; SYN1; CREB1; NOTCH1; NOTCH3; EPHA7; HIVEP2; CUX2; STIM2; CUL5; ROBO1; NTRK3; RND2; FEZF2; SOX5; NES; ASCL1; NTRK1; LHX6; PAX6; SSTR2; FGF12; NXPH1; KCNC2; VSX2; EN1; ETV1; EVX1; LHX3; GRM1; GRIN2D; NOS1; TACR1; CRHR1; SYT7; GNG4; SHISA8; PCBP3; GAL; TRH; ADCY8; CTGF; TMEM132D; BACE1; ISCA1; LPGAT1; NAT14; DEPTOR; SLC22A15; PDE4A; CACNA1I; FRYL; CAR11; SURF1; TRIM9; RAB3B; CDK17; RAB26; VLDLR; CACNG7; INAFM1; BRINP1; PIANP; SHISA6; DHRS3; CDHR1; ABR; NXPH4; CHST1; SHISA9; NMB; LY6G6E; CALN1; CASK; CACNA1E; FAM81A; CAMKV; ARHGAP21; SV2B; CAMK4; NPTXR; HPCAL4; LMO3; LYPD1; CAMK2A; PRKCB; RAB15; FBXW11; ADCY1; FBXO11; IGSF21; NTNG1; PSD3; DCAF7; EID2; RGMA; SCRT1; POU2F2; SRGAP1; FCHSD2; SLC16A1; SLC36A1; ATG12; AGAP1; ZDHHC22; ACBD5; ZDHHC17; KITL; ZIC4; L1CAM; IDS; DNAJC6; ADGRA1; HSPA12A; SYT13; RAMP3; LYNX1; KCNA2; OXR1; SHOX2; GABRA4; NRIP3; HLF; PRKCD; PTPN3; CDKL5; CCDC136; PTPN4; PDP1; PLCB4; ELMO1; ADARB1; ILDR2; SPTY2D1; PCF11; CLDN9; FIGF; KCNK9; RORB; GATA2;TIMELESS; CXCL14; APOC3; CES1D; CGA; ADORA2A; RPRML; CACNB1; KCNQ5; GABRA5; HTR3A; ADGRL2; KCNIP2; RGS10; NOV; CPNE5; SLC5A7; CRH; GDA; SEMA3E; DLX2; DLX5; ARX; OPRM1; PTPRO; GRID2; CAR8; HCN1; CNPY1; SLC24A3; SLC24A2; PENK; TSHZ1; GPSM1; PPM1E; PRKCA; HDAC6; PRKG1; ATP2B2; PDE5A; ITPR3; PDE1B; PDE9A; CBLN1; MRPS35; ADGRB1; SHANK2; NKAIN4; CERK; CD3G; SYT2; PKP4; FGF13; CREBRF; KCNAB2; ATL1; HSPB8; FKBP1B; TLN2; SYNM; GDAP1; DGKZ; PHF24; DGKH; CPNE3; CCDC92; SNCG; ADD2; PIEZO2; VAMP1; SCN10A; TAC1; PRKX; PRDM12; DHCR24; CALCA; CALCB; VWA5A; PRRXL1; PPP1R1C; AVIL; PIRT; AHNAK2; SCN9A; RUFY2; FAM189B; CLGN; GCNT2; ZDHHC2; SCN7A; ABCA1; ABCG1; ABL1; ACTG1; ACTN1; ADAM10; ADAP1; ADCYAP1; ADCYAP1R1; ADH5; ADRBK1; AGAP2; AGER; AGT; AGTR1; AGTR2; AIFM1; AK1; AKAP12; AKAP9; AKT1; AKT2; AKT3; ALDH7A1; ALK; ALOX5; ALS2; ANG; ANK3; ANKS1B; ANOS1; ANP32A; AP3M2; APAF1; APBA1; APBA2; APBB1; APC; APEX1; APH1A; APH1B; APLN; APOBEC3G; APOD; APOE; APP; ARAF; ARF6; ARHGEF28; ARHGEF4; ARNT2; ASIC1;ASIC3; ASPM; ATAD2B; ATCAY; ATF3; ATG5; ATM; ATN1; ATP1A1; ATP1B1; ATP1B2; ATXN3; AXL; B4GALT2; BACE2; BAG1; BAG3; BAX; BCAT1; BCL2L1; BCL2L11; BDNF; BECN1; BICD1; BIRC5; BLM; BRAF; BRI3; BTBD10; C10ORF2; C11ORF31; C3; C9ORF72; CA7; CA8; CACNA1A; CACNA1G; CACNA1H; CACYBP; CALHM1; CALM1; CALM2; CAMK1; CAMK1G; CAMK2D; CAMKK2; CAMTA1; CAPN1; CAPNS2; CAPRIN1; CARD9; CARTPT; CASP3; CASP4; CASP6; CASP9; CAST; CAV1; CAV2; CBL; CBS; CC2D1A; CCDC141; CCKBR; CCL2; CCL3L1; CCND1; CCND2; CCNT1; CCR1; CCR5; CDCA5; CDH1; CDH2; CDH8; CDK1; CDK4; CDK5R1; CDKN1A; CDKN1B; CEBPA; CEBPB; CEND1; CEP89; CFL1; CHMP2B; CHRM1; CHRNA3; CHRNA4; CHRNA7; CHRNB2; CHRNB4; CIB1; CLCN2; CLN3; CLN5; CLN6; CLN8; CLN9; CLP1; CNGA1; CNR2; CNTF; CNTN2; COIL; COL17A1; COL18A1; COMT; CPEB1; CREBBP; CREG2; CRLF3; CSF1; CSF1R; CSK; CST3; CTDSP1; CTSB; CTSF; CTSL; CXCL12; CXCL16; CXCR3; CXCR4; CYB5B; CYBB; CYGB; CYP19A1; CYP2D6; DAO; DAOA; DAPK1; DCDC2; DCHS1; DCN; DCTN1; DCX; DERL1; DFFA; DIAPH1; DISC1; DIXDC1; DKK1; DLD;DLG1; DLST; DNAJB2; DNAJC5; DNM1; DNM1L; DNMT1; DNMT3A; DNMT3B; DOCK3; DOCK7; DOK4; DPYSL2; DPYSL3; DPYSL5; DRD1; DRD2; DRD3; DRD4; DSCAM; DST; DTNBP1; DYNC1H1; DYNLT1; DYRK1A; E2F1; ECE1; ECEL1; EDN1; EEA1; EEF2; EFHD1; EGF; EGFR; EGLN3; EGR1; EIF2AK2; EIF3E; EIF4A3; ELAVL2; ELK1; EPB41L1; EPHA8; EPHB2; EPM2A; EPOR; ERBB4; ERMARD; ESR1; ESR2; EWSR1; EXTL3; EZH2; F2R; FAAH; FAM107A; FAS; FASLG; FAT4; FBXO31; FCGR2B; FCRL1; FECH; FES; FEZ1; FGD1; FGF14; FGF2; FGFR1; FGFR2; FIG4; FKBP1A; FKBP4; FLI1; FMR1; FNBP1L; FOS; FOXD3; FOXO1; FOXO3; FOXP2; FOXP3; FRMD7; FSCN1; FUS; FXYD1; FYN; GABRA2; GABRA6; GABRB3; GAN; GAP43; GAPDH; GAS7; GBA; GCG; GDNF; GFRA1; GIT1; GJA1; GJD2; GJD3; GLI3; GLP1R; GLRX; GLS2; GMPPB; GNB2L1; GNRH1; GPM6A; GPM6B; GPR35; GPR50; GPSM2; GRB2; GRIK4; GRIN3A; GRM2; GRM3; GRM4; GRN; GSK3B; GULP1; GZMB; HCRT; HDAC1; HDAC3; HES1; HEXIM1; HFE; HGF; HIF1A; HIP1; HIP1R; HIPK2; HK1; HLA-DRB1; HMOX1; HNRNPK; HOXA1; HOXC9; HRAS; HSD11B1; HSP90AA1; HSPA1A; HSPA4; HSPA6; HSPA8;HSPA9; HSPB1; HSPB2; HSPD1; HTR1A; HTR1B; HTR1D; HTR6; HTRA2; HTT; ICA1; ID1; ID2; ID3; ID4; IGF2BP1; IGFBP3; IGFBP5; IGLON5; IKBKAP; IL13; IL1B; IL2; IL3; IL6; IL7; ILK; IMPG1; INA; INS; IQGAP1; IRF1; IRS2; ITGA2; ITGA3; ITGA5; ITGA9; ITGB1; ITGB3; ITM2B; ITPR1; ITSN2; JAK2; JUN; KALRN; KAT2B; KCNA1; KCND2; KCNE3; KCNH2; KCNIP3; KCNJ10; KCNJ11; KCNJ9; KCNK2; KCNMB4; KCNN3; KCNQ2; KCNQ3; KCNQ4; KDM1A; KDM5C; KDR; KEAP1; KEL; KHDRBS1; KIAA0319; KIAA2022; KIF13B; KIF1A; KIF1BP; KIF2B; KIF5A; KLF11; KRT19; KRT7; L1RE1; LANCL1; LEP; LEPR; LGALS1; LGI1; LGMN; LHX8; LMTK2; LMX1A; LOC643387; LRRK2; LRRN1; MAFD2; MAGEA12; MAOA; MAP1B; MAP1LC3A; MAP2; MAP2K1; MAP2K4; MAP3K10; MAP3K11; MAP3K5; MAPK1; MAPK10; MAPK13; MAPK14; MAPK3; MAPK7; MAPK8IP1; MAPT; MARCKSP1; MARK1; MARK4; MBP; MCFD2; MCL1; MCRS1; MDGA1; MED12; MED19; MED26; MEF2C; MET; MFGE8; MFN2; MFSD8; MGAT5B; MIR106B; MIR124-1; MIR126; MIR132; MIR137; MIR138-1; MIR142; MIR200C; MIR21; MIR212; MIR26B; MIR302B; MIR338; MIR34A; MIR432;MIR497; MIR9-1; MIRLET7B; MLLT11; MME; MMP1; MMP2; MMP3; MMP9; MPO; MPPED2; MRAS; MST1; MT3; MTHFR; MTNR1A; MTOR; MYC; MYCN; NAAA; NAE1; NAIP; NAMPT; NBAT1; NBN; NBR1; NCAM1; NCOA7; NCS1; NDEL1; NEDD4; NEFL; NEU4; NEWENTRY; NF2; NFASC; NFATC4; NFE2L2; NFKB1; NFKBIA; NFX1; NFYB; NGB; NGF; NGFR; NKX2-2; NLGN3; NLRP1; NLRP3; NMNAT1; NMNAT2; NNAT; NNT; NODAL; NOS1AP; NOS2; NOS3; NOX1; NPDC1; NPTN; NPTX1; NPTX2; NQO1; NR4A1; NRCAM; NRD1; NRF1; NRP1; NRTN; NSF; NTF3; NTRK2; NUMB; OGDH; OLFML2A; OLIG2; OPA1; OSTM1; OTX1; OTX2; OXTR; P2RX4; P2RX7; P2RY4; PA2G4; PACRG; PAFAH1B1; PANK2; PARD3; PARK2; PARK7; PARP1; PAWR; PBX1; PCSK5; PDCD10; PDGFC; PDIA2; PDPK1; PDYN; PEBP1; PEG3; PFN1; PHF21A; PHLPP1; PHOX2A; PHOX2B; PICK1; PIK3C3; PIK3CG; PIN1; PINK1; PIP4K2A; PIP5K1C; PLA2G1B; PLA2G3; PLA2G6; PLAT; PLAUR; PLCG1; PLCH2; PLP1; PLXNB3; PMAIP1; PNPLA6; POLB; POLG; POMGNT1; POMT1; POU4F1; PPARD; PPARG; PPARGC1A; PPIA; PPID; PPP1CA; PPP2R1A; PPP2R2B; PPP2R4; PPP4R2; PPT1; PRDX5; PREX1; PRIMA1;PRKAA1; PRKACA; PRKCE; PRKCSH; PRKCZ; PRKDC; PRNP; PROM1; PSEN1; PSEN2; PSMC5; PSMD1; PTEN; PTGDS; PTGER3; PTGS2; PTPN1; PTPN5; RAB1A; RAB39B; RAB3A; RAB5A; RAB6B; RAC1; RAC3; RAE1; RANBP2; RAP1A; RARA; RARG; RARRES3; RASA1; RB1; RBFOX1; RCAN1; REEP1; REEP2; REG1A; RELA; REM2; REST; RET; RGS6; RHOA; RIC3; RIT1; RNF19A; ROBO4; ROCK2; ROGDI; RPS6KA5; RPS6KB1; RRAS; RTN3; RTN4; RTN4RL2; RUNX1; RUSC1; RXRA; S100A10; SALL2; SCAMP5; SCD5; SCG2; SCG3; SCGN; SCN1B; SCN3A; SCNN1D; SDC2; SEMA3A; SEPP1; SEPW1; SERPINE1; SESN2; SET; SETDB1; SETX; SF1; SGK1; SH2B3; SIL1; SIRT1; SIRT2; SKP2; SLC10A4; SLC11A2; SLC12A2; SLC12A6; SLC16A2; SLC16A7; SLC17A5; SLC1A3; SLC22A4; SLC25A14; SLC25A27; SLC25A38; SLC29A1; SLC2A13; SLC2A3; SLC2A4; SLC2A4RG; SLC44A1; SLC44A4; SLC5A1; SLC6A4; SLC8A3; SLC9A1; SLC9A5; SLIT2; SMARCA1; SMARCA4; SMN1; SMPD1; SNCA; SNCAIP; SOCS3; SOD1; SOD2; SORL1; SOX1; SOX2; SOX6; SP1; SPAST; SPG20; SPRY2; SPTLC1; SQSTM1; SRC; SRF; SRR; SRRM2; SSH1; ST6GAL2; STAT3; STAT5A; STAU2; STK11;STK3; STRADA; STUB1; SUMO1; SYNGAP1; SYP; TAAR1; TAGLN3; TARDBP; TAX1BP1; TBP; TCAP; TCF3; TGFBR2; TGFBR3; TGM1; TIMM50; TIMP2; TIMP3; TJP1; TLR2; TLR3; TLR7; TLR8; TLX2; TMED10; TMEM100; TNF; TNFSF10; TOP2A; TOP2B; TOR1A; TP53; TP73; TPH1; TPH2; TPI1; TPM3; TPP1; TRAP1; TRIO; TRPA1; TRPC1; TRPM4; TRPM7; TRPM8; TRPV1; TSC1; TSC2; TSPAN12; TTC3; TUBA1A; TUBA1B; TUBB2B; UBB; UBE2A; UBE2K; UBE3A; UBE3B; UBQLN1; UBR4; UCHL1; UCP2; UCP3; UGCG; UPF3B; USP14; USP47; USP9X; VAMP2; VAMP7; VAPB; VCAN; VEGFA; VRK1; VTI1A; WASL; WDR5; WEE1; WIPF1; WNK3; WNT1; WNT11; WNT4; WT1; WWC1; WWOX; YARS; YWHAE; YY1; ZBTB18; ZC3H14; ZEB2; ZFYVE27; ZNF148; ZNF175; ZNF395; ZNRF1; ZNRF2; ZSCAN10; TIAM1; LRP4; SIPA1L1; RPS6KA1; RAF1; IKBKB; MAPK9; LIMK1; IGF1R; HSF1; CAMK2B; GRIA1; SIK2; NFKB2; SORBS3; DOCK6; RPS3; ACSL6; CNOT8; GLI2; MIB1; NDUFS4; PBX2; SCYL1; SNAP91; TENM4; CASZ1; CHRDL1; EPHA2; GDF11; GPC2; HSPA1B; HSPA5; MBD1; MYEF2; NDP; NKX6-1; PIGT; RBPJ;

[0437] In some embodiments, the neuron-related factors may be 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 or more factors selected from a set of all factors searched in a database [e.g., Harmonizome, Comparative Toxicogenomics Database, etc.], including neuron, neurodevelopment, synapse, etc., or all factors.

[0438] Information on hepatocyte-related factors

[0439] In some embodiments, the hepatocyte-related factors (e.g., liver-related genes) may be selected from factors belonging to the following factor set 2. For example, the hepatocyte-related factors may be 100, 150, 200, 250, 300, 350, 400, 450, or 500 or more factors selected from factors belonging to the following factor set 2. In some embodiments, the hepatocyte-related factors may include 100, 150, 200, 250, 300, 350, 400, 450, or 500 or more factors selected from factors belonging to the following factor set 2. A collection of hepatocyte-related factors may be referred to as a set of hepatocyte-related factors. The set of hepatocyte-related factors may serve as a criterion or reference for selecting hepatocyte-related genes from among the genes belonging to each cluster. In some embodiments, the hepatocyte-related factors may be all factors belonging to factor set 2 below.

[0440] [Factor Set 2] - A total of 521 types of factors (genes)

[0441] ALB; SERPINA1; HHEX; HNF4A; CYP2E1; NR1I3; AFP; APOA1; SERPINA6; APOA2; TTR; APOM; GCK; LRP5; SLC10A1; NOS2; ATP7B; GJB2; FGFR4; PROX1; CRP; GRP; SLC2A2; TFR2; KIF13B; LIPC; VDR; ASGR1; ARG1; G6PC; OTC; UGT1A1; ZHX2; FOXA3; FOXA2; FOXA1; CYP7A1; CYP3A7; CYP3A4; CYP2D6; CYP2C9; CYP2C8; CYP2C19; CYP2B6; CYP1A2; CDH1; GPC3; AHR; CYP1A1; CPS1; GLS2; PCK1; FCNA; TAT; WT1; PRRG4; SULT1A1; APOH; CTNNB1; ABCC3; FGB; AQP3; PLSCR1; FGA; APOB; ANG; ANXA13; SAT2; SFRP5; A1CF; BNIP3; FGL1; AZGP1; FGG; APOC3; DEFB1; TM4SF4; GC; AMBP; ORM1; SERPINA1C; GULO; CYP2F2; HAL; ASS1; CYP2A7; SCD; HMGCS1; ACSS2; TM7SF2; SEC16B; SLBP; RND3; BCHE; GHR; ALDH6A1; MASP2; AKR1C1; HPR; HAMP; GLUL; ACLY; ASL; MUP3; TMEM97; CP; SLPI; ACAT2; TM4SF5; MSMO1; LEPR; RCAN1; AR; PLIN1; RPP25L; HSD11B1; TKFC; G0S2; PON3; C1ORF53; TTC36; GOLT1A; FST; MCC; AQP9; GSTA2; NNT; SAA4; MRPS18C; OCIAD1; APOA5; ENTPD5; C4B; EID2; TP53INP2; ATIC; SERPINH1; SAMD5; GRB14; ABCD3; RHOB; EPB41L4B; GPAT4; SPTBN1; SDC2; PHLDA1; WTAP; ACADM;FOSL1; EPPK1; UCP2; CYP2A6; CEBPA; PAH; ABCA1; ABCC2; ABCC4; ACE; ADIPOQ; AGT; AKT1; AOX1; APEX1; AQP8; ARHGAP12; ARHGEF7; ASGR2; ATM; ATP8B1; BBC3; BCL2L1; BECN1; BMX; BSG; CASP12; CASP3; CASP7; CAT; CBLC; CCND2; CD2; CD40; CD40LG; CD74; CD81; CD82; CDH2; CDKN1A; CDKN1B; CDKN2A; CDX2; CFTR; CIT; CLDN1; CLDN2; COMMD1; CREBBP; CSF3; CXCL16; DDIT3; DNM2; DPP4; EGF; EGFR; EGR1; EPHA2; ERBB2; ETFA; ETS1; ETV6; EZR; FAM110C; FAS; FASLG; FES; FGF19; FGF2; FGF7; FGFR2; FIGF; FOXM1; FOXO1; GAB1; GADD45B; GATA3; GATA6; GFER; GIT1; GOLM1; GPSM2; GRB2; HAVCR2; HAX1; HDAC1; HDAC2; HDAC3; HFE2; HGF; HGFAC; HGS; HIF1A; HIST3H3; HLX; HMGB1; HMOX1; HNF1A; HNF1B; HPN; ICAM3; ID1; IDH1; IDH2; IDO1; IER3; IFNB1; IFNG; IFNL3; IGF1; IGF1R; IGFBP3; IL11; IL22; IL6; INS; INSR; ITGB1; ITGB4; JAG1; JAK2; JUN; KDM1A; KIAA0101; KLK3; KLRK1; KRAS; KRT7; LDLR; LIF; LIMK1; LITAF; MAP4K4; MAPK1; MAPK8; MARK2; MBL2; MCL1; MET; MIR122; MIR124-1; MIR133B; MIR181D; MIR192; MIR27B; MIR30C1; MIR421; MIR499A; MKI67; MMP9;MST1; MTOR; MTTP; MYC; NAMPT; NEWENTRY; NF2; NFE2L2; NFYC; NR0B2; NR1H4; NR1I2; NR2F1; NR2F2; NRP1; NRP2; NUMA1; OCLN; ONECUT1; PAK1; PAK2; PAK4; PAK6; PCBD1; PCSK9; PDCD1; PDGFRA; PDIA3; PEG10; PGF; PGR; PIK3CA; PIK3CG; PKHD1; PKM; PLAT; PLK1; POU5F1; PPARA; PPARG; PPARGC1A; PRKCA; PRKCZ; PTGS2; PTK2; PTPN1; RASSF1; RET; RETN; RGS17; RPS6KB1; RSF1; RXRA; S1PR2; S1PR3; SCARB1; SDC1; SELE; SERPINA7; SERPINC1; SGK2; SI; SIRT1; SKP2; SLC2A8; SLC39A14; SLCO1B1; SLCO1B3; SMAD2; SMAD3; SMAD4; SOAT2; SOCS3; SP1; SP3; SPINT1; SPINT2; SPTBN2; SRC; SRD5A1; SREBF2; ST14; STAM; STAM2; STAT3; STEAP4; STK11; SULT2B1; TBP; TGFA; TGFB1; THBS1; TJP1; TLR4; TNC; TNF; TNFRSF1A; TNFRSF1B; TNFRSF8; TNFSF10; TP53; UGT1A9; VCAM1; VEGFA; VHL; WASF2; WNT3A; WNT8B; XIAP; AQP1; CD34; CLEC4M; CXCL12; CXCR4; CDC25A; CISH; MIR98; NRAS; RBP1; C1QA; CLEC4F; CD163; TIMD4; VSIG4; CLEC1B; GPIHBP1; MNDA; SLC15A3; MARCO; EAR2; HFE; CD38; ADGRE4; CCR5; CLEC4E; FOLR2; PLTP; FTL; IRF7; SPIC; CSF1R; C1QC; C1QB; G6PD; LYZ;MPO; PROK2; STARD5; MSR1; TLR9; MMP13; CD14; CHIT1; OSM; PPARD; ADGRE1; IL1B; SLC40A1; SIGLECF; CLEC4G; SLC11A1; DNASE1L3; TREM1; TIMP1; IGFBP6; COL1A2; COL3A1; SPARC; TPM2; MYL9; DCN; SYP; CYGB; VCL; COL1A1; TAGLN; MEG3; BGN; IGFBP7; CYR61; OLFML3; CCL2; COLEC11; PNPLA3; ADAMTS13; RGS5; FOXF1; CTGF; SEMA7A; AGTR1; FGF10; GFAP; NGFR; ACTA2; MYB; FAP; SLC8A1; RELN; DES; KRT8; KRT18; SMAD5; MAP2K4; GGT1; DLK1; KRT14; ALB; CDH1; KRT8; KRT18; MET; AFP; HHEX; HNF1B; SMAD5; MAP2K4; PROX1; HNF4A; GGT1; FOXM1; DLK1; KRT14; CEBP;

[0442] In some embodiments, the hepatocyte-related factors may be at least 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 factors selected from a set of all factors searched in a database [e.g., Harmonizome, Comparative Toxicogenomics Database, etc.], including hepatocytes, hepatic stellate cells, Kupffer cells, liver sinusoidal endothelial cells, etc., or all factors.

[0443] Information on pancreatic cell-related factors

[0444] In some embodiments, the pancreatic cell-related factors (e.g., pancreatic cell-related genes) may be selected from factors belonging to Factor Set 3 below. For example, the pancreatic cell-related factors may be at least 100, 150, 200, 250, 300, or 350 factors selected from factors belonging to Factor Set 3 below. In some embodiments, the pancreatic cell-related factors may include at least 100, 150, 200, 250, 300, or 350 factors selected from factors belonging to Factor Set 3 below. A collection of pancreatic cell-related factors may be referred to as a set of pancreatic cell-related factors. The set of pancreatic cell-related factors may serve as a criterion or reference for selecting genes associated with pancreatic cells from among genes belonging to each cluster. In some embodiments, the pancreatic cell-related factors may be all factors belonging to Factor Set 3 below.

[0445] [Factor Set 3] - A total of 385 types of factors (genes)

[0446] PRSS1; AMY2A2; KLK1; PNLIP; CTRC; AKR1C3; DUOXA2; ALDOB; REG3A; SERPINA3; PRSS3; REG1B; CFB; CTRB1; GDF15; MUC1; C15ORF48; ANPEP; ANGPTL4; OLFM4; GSTA1; LGALS2; PDZK1IP1; RARRES2; CXCL17; UBD; GSTA2; LYZ; AMY2A5; RBPJL; PTF1A; TRY4; CELA3A; SPINK1; ZG16; CEL; CELA2A; AMY2A3; CPB1; CELA1; PNLIPRP1; RNASE1; AMY2B; CPA2; CPA1; CELA3B; CTRB2; PLA2G1B; PRSS2; CLPS; REG1A; SYCN; GCG; TTR; PCSK2; FXYD5; LDB2; MAFB; CHGA; SCGB2A1; GLS; FAP; DPP4; GPR119; PAX6; NEUROD1; LOXL4; PLCE1; GC; KLHL41; FEV; PTGER3; RFX6; SMARCA1; PGR; IRX1; UCP2; RGS4; KCNK16; GLP1R; ARX; POU3F4; NKX2-2; RESP18; PYY; SLC38A5; TM4SF4; CRYBA2; SH3GL2; PCSK1; PRRG2; IRX2; ALDH1A1; PEMT; SMIM24; F10; SCGN; SLC30A8; NKX6-1; DEPP1; INS; IAPP; INS2; GJD2; MAFA; NPY; INS1; SLC2A2; PDX1; ADCYAP1; TGFBR3; HOPX; CASR; EDARADD; PFKFB2; ISL1; RGS16; SMAD9; SIX3; BMP5; PIR; STXBP5; DLK1; MEG3; GCGR; LMX1A; JPH3; CD40; HAMP; EZH1; NTRK1; NKX6-2; FXYD2; RIMS1; EFNA5; NPTX2; PAX4; G6PC2; ERO1LB; IGF2; SYT13; FFAR2; SIX2; SST; GABRB3;FRZB; MS4A8; BAIAP3; BCHE; UNC5B; EDN3; PRG4; GHSR; GABRG2; POU3F1; BHLHE41; EHF; LCORL; ETV1; LEPR; GPC5-AS1; HHEX; RBP4; PCSK9; FFAR4; CFTR; TFF1; KRT19; S100A10; LGALS4; PERP; PDLIM3; WFDC2; SLC3A1; AQP1; ALDH1A3; VTCN1; CTSH; PIGR; TFF2; KRT7; CLDN4; LAMB3; TACSTD2; CCL2; DCDC2; CXCL2; HNF1B; KRT20; ONECUT1; AMBP; ANXA4; SPP1; CLDN1; MMP7; DEFB1; SERPING1; TSPAN8; CLDN10; SLPI; SERPINA5; GHRL; TM4SF5; HRH2; CALCR; SLC6A16; PCSK6; ADAMTS6; COL22A1; FAM124A; COL12A1; CD109; THSD4; CORIN; ACSL1; MS4A8A; VTN; APOH; VSTM2L; SPTSSB; S100A6; KRT8; GRAMD2; ANXA13; PHGR1; BMP4; HMGCS2; OLFML3; ASGR1; COX6A2; NPY1R; FAXDC2; SLC7A9; MYO1A; C2ORF54; GHRLOS; BHMT; OPRK1; PTGER4; ITGA4; EYA4; XYLB; ELOVL2; AFF3; PPY; ABCC9; FGB; ZNF503; MEIS1; LMO3; EGR3; CHN2; PTGFR; ENTPD2; AQP3; THSD7A; CARTPT; APOBEC2; SEMA3E; SLITRK6; SERTM1; PXK; PPY2P; CMTM8; CXCR4; EPAS1; NEUROG3; SOX9; RPL23A; CTNNB1; YAP1; MFNG; COL6A1; RGS5; COL1A1; MMP11; THY1; COL6A3; SFRP2; COL1A2; TNFAIP6; TIMP3; SPARC; COL3A1;MGP; COL6A2; COL4A1; FN1; SPON2; TIMP1; TGFB1; INHBA; PDGFRA; NDUFA4L2; MMP14; CTGF; CYGB; KRT10; PDGFRB; DYNLT1; GEM; MPZ; EGFL8; GFRA1; OLFML2A; GULP1; VGLL3; GFRA3; INSC; SLITRK2; FIGN; LRRTM1; SEMA3B; NGFR; GFRA2; PNPLA2; PON1; POSTN; PRF1; PROK1; PRSS22; PRSS27; PTAFR; PTCH1; PTGDR2; PTGS2; PTPRN; QSOX1; RASSF1; RB1; RBPJ; REG3G; REG4; RELA; RIOK3; RREB1; RRM2; RXRA; S100P; SAA1; SBDS; SCTR; SDC1; SEPSECS; SERPINB10; SERPINB5; SERPINB8; SHH; SLC18A2; SLC2A4; SMAD4; SMO; SOX4; STAR; STARD13; STK11; SYNE1; TAS1R3; TBX4; TCF7L2; TFF3; TGFA; THBD; TMEM192; TMEM97; TMSB4X; TNC; TNF; TNFAIP8; TNFSF10; TNS4; TP53; TRIM63; TRPV1; TRPV6; TYMP; UBR1; UBR5; UCHL1; UGT1A7; UTRN; VEGFA; VHL; WNT16; YWHAQ;

[0447] In some embodiments, the pancreatic cell-related factors may be at least 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 factors, or all factors, selected from a set of all factors searched in a database [e.g., Harmonizome, Comparative Toxicogenomics Database, etc.], including alpha cells, beta cells, delta cells, epsilon cells, PP cells, etc.

[0448] Grouping (classification by conditions) overview

[0449] In some embodiments, grouping may be performed to select the first candidate genes. In some embodiments, grouping may select factors that are relatively related to or closely related to 3D differentiation. In other words, grouping is a process for selecting factors that are relatively related to or closely related to 3D differentiation, and is a process of selecting genes that satisfy one or more predetermined conditions. Through grouping, genes may be divided into groups that satisfy one or more predetermined conditions and groups that do not, and genes in the group of genes that satisfy one or more predetermined conditions are selected as candidate genes (e.g., first candidate genes). In other words, grouping in this table of contents may be understood as a process of selecting genes based on one or more conditions. In some embodiments, grouping may be performed by, but is not limited to, a person, a server, or a control unit of a device.

[0450] For example, a screening method according to some embodiments of the present disclosure may include selecting a plurality of genes (or a plurality of gene vectors) based on predetermined criteria. The predetermined criteria may be one or more conditions for selecting genes that are relatively related to or closely related to three-dimensional differentiation, as described below.

[0451] In some embodiments, the selection of genes by grouping or by one or more conditions may be performed in place of the clustering and selection of candidate clusters described above.

[0452] Selection of factors relatively related to or closely related to 3D differentiation

[0453] Factors that are relatively related to 3D differentiation or closely related to 3D differentiation may be factors that are judged to be more related to 3D differentiation than to 2D differentiation based on the gene profiles obtained during the 2D and 3D differentiation processes.

[0454] In some embodiments, factors relatively related to or closely related to 3D differentiation may be genes whose expression levels continuously increase as 3D differentiation progresses. For example, if the expression level of a gene measured at the m-th time point of 3D differentiation is greater than the expression level measured at the m-1-th time point of 3D differentiation, the gene may be a factor relatively related to or closely related to 3D differentiation. Here, m may be an integer from 2 to M. For example, assuming that the gene expression levels of gene A are measured at the first, second, third, and fourth time points of the 3D differentiation process, gene A may be classified as a factor relatively related to or closely related to 3D differentiation if it satisfies the following condition: "expression level at the 4th time point of 3D differentiation>expression level at the 3rd time point>expression level at the 2nd time point>expression level at the 1st time point."

[0455] The "expression level at the fourth time point of three-dimensional differentiation > expression level at the third time point > expression level at the second time point > expression level at the first time point" introduced for gene sorting is a classification standard that can sequentially confirm the process of differentiation from before differentiation.

[0456] These conditions allow for the selection of factors that increase during differentiation into specific target cells in 3D differentiation methods. 3D differentiation can practically replicate the tissue differentiation process, making it a viable model for normal tissues.

[0457] On the other hand, two-dimensional differentiation cannot fully replicate the entire tissue differentiation process, and thus can be used as a model for imbalances such as diseases or disorders. For this reason, one or more additional conditions may be considered to exclude factors strongly associated with two-dimensional differentiation during the two-dimensional differentiation process, or to select factors more closely related to three-dimensional differentiation than two-dimensional differentiation.

[0458] In some embodiments, factors relatively related to or closely related to 3D differentiation may be genes additionally selected by one or more conditions, in addition to the condition of "gene expression levels continuously increasing as 3D differentiation progresses in 3D differentiation," to exclude factors strongly associated with the induction of 2D differentiation during the 2D differentiation process, or to select factors more related to 3D differentiation than 2D differentiation. For example, the genes may be additionally selected by one or more conditions selected from the following conditions:

[0459] In two-dimensional differentiation, gene expression levels do not increase continuously as two-dimensional differentiation progresses;

[0460] In two-dimensional differentiation, gene expression levels continuously decrease as two-dimensional differentiation progresses;

[0461] The degree of change in gene expression levels in two-dimensional differentiation is small;

[0462] The 3D gene expression level measured at the last measurement point of 3D differentiation is higher than the 2D gene expression level measured at the last measurement point of 2D differentiation; and

[0463] The degree of change in expression level during the three-dimensional differentiation process is greater than the degree of change in expression level during the two-dimensional differentiation process.

[0464] Conditions for selecting factors that are relatively related to or closely related to the three-dimensional differentiation described above can be divided into essential conditions and additional conditions as follows, and exemplary conditional expressions related to each of the essential conditions and additional conditions are described below:

[0465] [Required Condition] [Example Condition 1]

[0466] Gene expression level measured at the mth time point of 3D differentiation > Gene expression level measured at the m-1th time point of 3D differentiation (wherein, the gene expression level in the process of 3D differentiation is measured at each of the 1st to Mth time points, and m can be an integer from 2 to M)

[0467] [Additional conditional expression]

[0468] [Example Condition 2]

[0469] The condition of the gene expression level measured at the nth time point of the two-dimensional differentiation > the gene expression level measured at the n-1th time point of the two-dimensional differentiation is not satisfied (here, the gene expression level is measured at each of the 1st to Nth time points in the process of the two-dimensional differentiation, and n can be an integer from 2 to N).

[0470] [Example Condition 3]

[0471] Gene expression level measured at the nth time point of two-dimensional differentiation <gene expression level measured at the n-1th time point of two-dimensional differentiation (wherein, the gene expression level is measured at each of the 1st to Nth time points in the process of two-dimensional differentiation, and n can be an integer from 2 to N.)

[0472] [Example Condition 4]

[0473] Standard deviation of gene expression levels measured at the first to Nth time points of two-dimensional differentiation <p,

[0474] Here, p can be an integer from 1 to 50, but is not limited thereto. For example, p can be 20.

[0475] [Example Condition 5]

[0476] Expression level at the Mth time point of 3D differentiation (e.g., the last measurement time point in the 3D differentiation process) > Expression level at the Nth time point of 2D differentiation (e.g., the last measurement time point in the 3D differentiation process)

[0477] [Example Condition 6]

[0478] The average of the degree of change in gene expression level (fold-changes level) during the 2D differentiation process < The average of the degree of change in gene expression level (fold-changes level) during the 3D differentiation process

[0479] Example condition 1 can be expressed, for example, as follows:

[0480] ,

[0481] Here, 3Dexp m is the gene expression level of a certain gene measured at the mth point of 3D differentiation, where m can be an integer from 2 to M.

[0482] For example, if M is 4, the gene expression level for a certain gene A is measured at each of the first, second, third, and fourth time points of the three-dimensional differentiation process,

[0483] At this time, if the three-dimensional gene expression levels of gene A satisfy the conditions of 3Dexp1<3Dexp2<3Dexp3<3Dexp4, gene A may be a gene corresponding to the exemplary condition 1 above.

[0484] Example condition 2 can be expressed, for example, as follows:

[0485] Not ,

[0486] Here, 2Dexp nis the gene expression level of a certain gene measured at the nth point of two-dimensional differentiation, where n can be an integer from 2 to N.

[0487] For example, when N is 4, the gene expression level for a certain gene A is measured at each of the first, second, third, and fourth time points of the two-dimensional differentiation process.

[0488] At this time, if the two-dimensional gene expression levels of gene A do not satisfy the conditions of 2Dexp1<2Dexp2<2Dexp3<2Dexp4, gene A may be a gene corresponding to the exemplary condition 2 above.

[0489] Example condition 3 can be expressed, for example, as follows:

[0490] ,

[0491] Here, 2Dexp n is the gene expression level of a certain gene measured at the nth point of two-dimensional differentiation, where n can be an integer from 2 to N.

[0492] For example, when N is 4, the gene expression level for a certain gene A is measured at each of the first, second, third, and fourth time points of the two-dimensional differentiation process.

[0493] At this time, if the two-dimensional gene expression levels of gene A satisfy the conditions of 2Dexp1>2Dexp2>2Dexp3>2Dexp4, gene A may be a gene corresponding to the exemplary condition 3 above.

[0494] Example condition 4 can be expressed, for example, as follows:

[0495] ,

[0496] Here, 2Dexp n is the gene expression level of a certain gene measured at the nth point of two-dimensional differentiation, where n can be an integer from 2 to N.

[0497] For example, when N is 4, the gene expression level for a certain gene A is measured at each of the first, second, third, and fourth time points of the two-dimensional differentiation process.

[0498] At this time, if the standard deviation of the gene expression levels of gene A measured in the two-dimensional differentiation process is less than 20, gene A may be a gene corresponding to the exemplary condition 4 above.

[0499] Example condition 5 can be expressed, for example, as follows:

[0500] ,

[0501] Here, 2Dexp N is the gene expression level of a gene measured at the Nth time point of 2D differentiation, and 3Dexp M It can be the expression level of any gene measured at the M point of 3D differentiation.

[0502] For example, if the expression level of gene A measured at the last measurement point of three-dimensional differentiation is higher than the expression level of gene A measured at the last measurement point of two-dimensional differentiation, gene A may be a gene corresponding to the exemplary condition 5 above.

[0503] Example condition 6 can be expressed, for example, as follows:

[0504] ,

[0505] Here, 3Dexp m is the gene expression level of a gene measured at the mth point of 3D differentiation, where m can be an integer from 2 to M. Here, 2Dexp n is the gene expression level of a certain gene measured at the nth point of two-dimensional differentiation, where n can be an integer from 2 to N.

[0506] Among the conditions for selecting factors that are relatively related to or closely related to 3D differentiation, the above essential condition is significant in that it selects factors that more strongly or actively induce 3D differentiation. Furthermore, among the conditions for selecting factors that are relatively related to or closely related to 3D differentiation, at least one of the conditional expressions of the above additional conditions is significant in that it excludes factors that are strongly related to the induction of 2D differentiation during the 2D differentiation process, or selects factors that are more related to 3D differentiation than to 2D differentiation. Accordingly, in order to select factors that are relatively related to or closely related to 3D differentiation, it is preferable to use the above essential condition and the additional condition together.

[0507] In some embodiments, factors that are relatively related to or closely related to three-dimensional differentiation may be factors that satisfy one or more of the essential conditions (e.g., exemplary condition 1) and additional conditions (e.g., exemplary condition 2 to 6).

[0508] In some embodiments, factors relatively related to or closely related to three-dimensional differentiation may be factors satisfying any one of the following combinations of conditions, but are not limited thereto:

[0509] [Example Condition 1] and [Example Condition 2]

[0510] [Example condition 1] and [Example condition 3] or [Example condition 4] or [Example condition 5]); and

[0511] [Example Condition 1] and [Example Condition 5] and [Example Condition 6].

[0512] The selection of the first candidate genes is described in detail. For example, the first candidate genes can be selected through clustering based on multiple gene vectors, or grouping or selection based on multiple gene vectors.

[0513] The first candidate genes selected in this way may be related to the lineage to be selected, or may be clustered or grouped to be related to lineage differentiation or lineage fate determination.

[0514] Selection of resetting genes

[0515] Overview of the selection of resetting genes

[0516] A genetic screening method according to some embodiments of the present disclosure comprises selecting reset genes (or candidates for reset genes). For example, a genetic screening method according to some embodiments of the present disclosure may comprise selecting reset genes from a first set of candidate genes. The reset genes may be genes associated with one or more of metabolic, catabolic, and wound healing functions among the first set of candidate genes. In some embodiments, the process of selecting genes associated with one or more of metabolic, catabolic, and wound healing functions among the first set of candidate genes may be performed by, but is not limited to, a human, a server, or a control unit of a device.

[0517] Finally, in some embodiments of the present disclosure, the resetting genes may be genes that belong to the group of first candidate genes and satisfy the condition of being associated with at least one of metabolic, catabolic, and wound-healing functions. Accordingly, the genes associated with at least one of metabolic, catabolic, and wound-healing functions may be identified before or after the selection of the first candidate genes, without limitation.

[0518] Some embodiments of the present disclosure provide methods for screening for resetting genes (or candidates for resetting genes). In some embodiments, the screening method may further comprise determining the cell resetting effect of one or more selected candidates for resetting genes.

[0519] In some embodiments, a genetic screening method (e.g., a method for screening resetting genes) may comprise selecting genes associated with one or more of metabolic, catabolic, and wound-healing genes from among the first candidate genes. For example, the genetic screening method may comprise selecting metabolic genes from among the first candidate genes. For example, the genetic screening method may comprise selecting catabolic genes from among the first candidate genes. For example, the genetic screening method may comprise selecting wound-healing genes from among the first candidate genes.

[0520] In certain embodiments, a genetic screening method (e.g., a method of screening for resetting genes) may comprise selecting, from among the first candidate genes, genes that are associated with at least two of metabolic, catabolic, and wound-healing. For example, the genetic screening method may comprise selecting, from among the first candidate genes, genes that are associated with both metabolic and catabolic functions. For example, the genetic screening method may comprise selecting, from among the first candidate genes, genes that are associated with both metabolic and wound-healing functions. For example, the genetic screening method may comprise selecting, from among the first candidate genes, genes that are associated with both catabolic and wound-healing functions.

[0521] In certain embodiments, a genetic screening method (e.g., a method for screening resetting genes) may include selecting genes associated with both metabolism, catabolism, and wound healing from among the first candidate genes. In this case, the selected resetting genes are schematically represented in FIG. 06. In FIG. 06, genes associated with metabolism are represented as metabolic genes, genes associated with catabolism are represented as catabolic genes, and genes associated with wound healing are represented as wound healing genes.

[0522] Information about genes associated with metabolism, catabolic, and / or wound-healing can be obtained from known literature, accessible databases, knowledge bases, and / or programs. Known databases include, but are not limited to, The Database for Annotation, Visualization and Integrated Discovery (DAVID), National Center for Biotechnology Information (NCBI), ArrayXPath, BioLattice, GenBank, European Molecular Biology Laboratory (EMBL), DNA Data Bank of Japan (DDBJ), Protein Data Bank (PDB), Protein Information Resource (PIR), PROSITE, Pfam, Kyoto Encyclopedia of Genes and Genomes (KEGG), UniProt, Harmonizome, The Comparative Toxicogenomics Database (CTD), or Online Mendelian Inheritance in Man (OMIM).

[0523] Below, genes associated with metabolic, catabolic, and / or wound-healing are described.

[0524] Genes associated with metabolism (genes associated with metabolism)

[0525] For example, if a function of a gene, a process involved, and / or a gene ontology (GO) is associated with metabolism, the gene may be referred to as a gene associated with (or related to) metabolism (or a metabolic process), a metabolic gene, or a metabolic factor. For example, a gene associated with (or related to) metabolism may be a gene in which one or more of the gene's GO terms are associated with metabolism (or a metabolic process), and / or one or more of the GO annotations are associated with metabolism. In the present disclosure, the terms "gene associated with metabolism" and "gene related to metabolism" may be used interchangeably.

[0526] In some embodiments, the functions of the genes associated with metabolism may be related to metabolism. In some embodiments, the processes involved in the genes associated with metabolism may be related to metabolism. In some embodiments, the gene ontology of the genes associated with metabolism may be related to metabolism.

[0527] In some embodiments, genes associated with metabolism can be obtained by searching for keywords including "metabolic" in one or more of the aforementioned databases. For example, genes associated with metabolism can be factors or portions thereof searched for using keywords related to "metabolic," "metabolism," or "metabolic" in one or more of the aforementioned databases (e.g., DAVID, Harmonizome, Comparative Toxicogenomics Database).

[0528] For example, if a gene plays a role in the metabolic process of an organism (e.g., a cell or a living organism), the GO (specifically, a GO term or GO annotation) of said gene may be associated with or correspond to metabolism. These metabolism-associated genes may be known to play a specific role in the metabolic process of the organism.

[0529] Among the genes searched through the keyword of metabolism or metabolism, one or more additional criteria may be introduced to extract genes associated with metabolism, but are not limited thereto.

[0530] Genes associated with catabolism (genes associated with catabolism)

[0531] For example, if a function of a gene, a process involved, and / or a gene ontology (GO) is associated with catabolic, the gene may be referred to as a gene associated with (or related to) catabolic (or catabolic process), a catabolic gene, or a catabolic factor. For example, a gene associated with (or related to) catabolic may be a gene in which one or more of the gene's GO terms are associated with catabolic and / or one or more of the GO annotations are associated with catabolic. In the present disclosure, the terms gene associated with catabolic and gene associated with catabolic may be used interchangeably.

[0532] In some embodiments, the functions of the genes associated with catabolism may be associated with catabolism. In some embodiments, the processes involved in the genes associated with catabolism may be associated with catabolism. In some embodiments, the gene ontology of the genes associated with catabolism may be associated with catabolism.

[0533] In some embodiments, genes associated with catabolism can be obtained by searching for keywords containing "catabolic" in one or more of the aforementioned databases. For example, genes associated with catabolism can be factors or portions thereof searched for using keywords related to "catabolic," "catabolism," or "catabolic" in one or more of the aforementioned databases (e.g., DAVID, Harmonizome, Comparative Toxicogenomics Database).

[0534] For example, if a gene plays a role in the metabolic process of an organism (e.g., a cell or a living organism), the GO (specifically, a GO term or GO annotation) of said gene may be associated with or correspond to catabolism. These catabolism-associated genes may be known to play a specific role in the catabolic process of the organism.

[0535] Among the genes searched through the keyword catabolic or catabolism, one or more additional criteria may be introduced to extract genes associated with catabolic, but are not limited thereto.

[0536] Genes associated with wound healing

[0537] When the function, process involved, and / or gene ontology (GO) of a gene is associated with wound healing, the gene may be referred to as a gene associated with (or related to) wound healing (or the wound healing process), a wound healing gene, or a wound healing factor. For example, a gene associated with wound healing may be a gene in which one or more of the gene's GO terms are associated with wound healing, and / or one or more of the gene's GO annotations are associated with wound healing. In the present disclosure, the terms "gene associated with wound healing" and "gene associated with wound healing" may be used interchangeably.

[0538] In some embodiments, the functions of the genes associated with wound healing may be associated with wound healing. In some embodiments, the processes involved in the genes associated with wound healing may be associated with wound healing. In some embodiments, the gene ontology of the genes associated with wound healing may be associated with wound healing.

[0539] In some embodiments, genes associated with wound healing can be obtained by searching for keywords including "wound healing" in one or more of the aforementioned databases. For example, genes associated with wound healing can be factors or portions thereof searched for using keywords related to wound healing or wound healing in one or more of the aforementioned databases (e.g., DAVID, Harmonizome, Comparative Toxicogenomics Database).

[0540] For example, if a gene plays a role in the wound-healing process of an organism (e.g., a cell or a living organism), the GO (specifically, a GO term or GO annotation) of said gene may be associated with or correspond to wound-healing. These genes associated with wound-healing may be known to play a specific role in the wound-healing process of an organism.

[0541] Among the genes searched through the keyword of wound healing, one or more additional criteria may be introduced to extract genes associated with wound healing, but are not limited thereto.

[0542] For example, metabolic, catabolic, and wound-healing genes can be the set of all genes searched in one or more databases (e.g., Harmonizome, Comparative Toxicogenomics Database) that include the names metabolic, catabolic, and wound-healing.

[0543] Screening therapeutic genes

[0544] Overview of therapeutic gene screening

[0545] A genetic screening method according to some embodiments of the present disclosure comprises identifying or selecting therapeutic genes (e.g., candidate therapeutic genes). For example, a genetic screening method according to some embodiments of the present disclosure may comprise identifying or selecting therapeutic genes from first candidate genes. As another example, a genetic screening method according to some embodiments of the present disclosure may comprise identifying or selecting therapeutic genes from reset genes. The therapeutic genes may be genes among the first candidate genes or reset genes that exhibit abnormal expression levels in a specific disease or patient or model of the disease. In some embodiments, the process of identifying or selecting genes among these first candidate genes or reset genes that exhibit abnormal expression levels in the disease or condition may be performed by, but is not limited to, a human, a server, or a control unit of a device.

[0546] In some embodiments, a gene with an abnormal expression level in a disease or condition may be referred to as, but is not limited to, a gene associated with the disease or condition. In some embodiments, a gene with an abnormal expression level in a disease or condition may be referred to as, but is not limited to, a disease-specific abnormally expressed gene.

[0547] In some embodiments, the screening method may further comprise determining the disease or disorder therapeutic effect of one or more selected candidate therapeutic genes.

[0548] In some embodiments, the process of identifying or selecting therapeutic genes may include selecting genes with abnormal expression levels in a specific disease or condition. The process of selecting therapeutic genes may include selecting genes among the first candidate genes with abnormal expression levels in a specific target disease or condition (disease-specific abnormally expressed genes), or identifying genes among the resetting genes with abnormal expression levels in a specific disease or condition. Consequently, in some embodiments of the present disclosure, the therapeutic genes may be genes that belong to the group of first candidate genes and also belong to the group of genes with abnormal expression levels in a specific target disease or condition; or genes that belong to the group of resetting genes and also belong to the group of genes with abnormal expression levels in a specific target disease or condition. Accordingly, the identification of which genes are disease-specific abnormally expressed genes may be performed before or after the selection of the first candidate genes and before or after the selection of the resetting genes, and is not limited thereto.

[0549] For example, through a process of confirming the expression levels of selected first candidate genes or selected resetting genes in a patient or model of a specific disease or disorder, genes among the selected first candidate genes or selected resetting genes that have abnormal expression levels in the disease or disorder can be identified. For example, the expression levels of the selected first candidate genes or selected resetting genes in the disease or disorder, the patient or model of the disease, can be confirmed through known literature or databases (e.g., disease databases).

[0550] As another example, genes having abnormal expression levels in a patient or model of a disease or condition can be identified, and by intersecting the genes having the identified abnormal expression levels with the selected first candidate genes or the selected resetting genes, genes having abnormal expression levels in the disease or condition can be identified among the first candidate genes or the resetting genes.

[0551] Here, the term "therapeutic gene" refers to a gene that has the potential to be used to treat a disease or condition, as described above. In some embodiments, the therapeutic gene may be a gene for the recovery of an abnormality. In some embodiments, the disease or condition that is the target or target of treatment for the therapeutic gene may be associated with a specific target cell (e.g., a neuron) that is a differentiating cell (e.g., a neural stem cell) and / or a differentiated cell used or identified in the process of selecting the first candidate genes, or may be associated with a specific target tissue (e.g., the brain) associated with the specific target cell. In some embodiments, the disease or condition that is the target of treatment for the therapeutic gene may be a disease or condition caused by damage or dysfunction of a specific target cell or a specific target tissue. As described above, a disease or condition associated with a specific target cell and / or a specific target tissue may be referred to as a specific target disease or condition. That is, a gene screening method according to some embodiments of the present disclosure may include a process of screening for a therapeutic gene for a specific disease or condition.

[0552] specific disease or illness

[0553] A specific disease or condition may be caused by damage or dysfunction of a specific target cell or tissue. Damage may, but is not limited to, a structural abnormality of a specific target cell or tissue, and damage encompasses the death or loss of a specific target cell. Dysfunction may encompass abnormal function of a specific target cell or tissue. For example, if the specific target cell is a neuron, dysfunction of the neuron may refer to a case where the neuron fails to perform its function normally. More specifically, if the specific target cell is a dopaminergic neuron, dysfunction of the dopaminergic neuron may indicate a decrease in dopamine synthesis or secretion by the dopaminergic neuron. Here, the decrease encompasses a case where the dopaminergic neuron fails to synthesize or secrete dopamine. As another example, if the specific target cell is a pancreatic cell (particularly a beta cell associated with insulin secretion), dysfunction of the pancreatic cell may indicate a decrease in insulin secretion. Such dysfunction may be, but is not limited to, dysfunction caused by structural damage, dysfunction caused by causes other than structural damage, or a combination thereof.

[0554] In some embodiments, the specific disease or condition may be, for example, any one selected from a nervous system disease, a central nervous system disease, a peripheral nervous system disease, a neurodegenerative disease, a brain disease, a liver disease, and a pancreatic disease.

[0555] In some embodiments, the particular disease or condition may be a neurological disease. Neurological diseases include, for example, Acute Spinal Cord Injury, Alzheimer's Disease, Amyotrophic Lateral Sclerosis (ALS), Ataxia, Bell's Palsy, Brain Tumors, Cerebral Aneurysm, Epilepsy, Seizures, Guillain-Barre Syndrome, Headache, Head Injury, Hydrocephalus, Lumbar Disk Disease (Herniated Disk), Meningitis, Multiple Sclerosis, Muscular Dystrophy, Neurocutaneous Syndromes, Parkinson's Disease, Stroke (Brain Attack), Cluster Headaches, Tension Headaches, and Migraine. Headaches), Huntington's disease, tauopathies, amyotrophic lateral sclerosis, autism spectrum disorder, spinal muscular atrophy, prion disease, and encephalitis, but is not limited thereto.

[0556] In some embodiments, the specific disease or condition may be a neurodegenerative disease. The neurodegenerative disease may be, for example, but is not limited to, one or more of Alzheimer's disease, Parkinson's disease, Huntington's disease, multiple sclerosis, amyotrophic lateral sclerosis, Batten disease, and Creutzfeldt-Jakob disease.

[0557] For example, if the specific target cell is a central nervous system cell (e.g., a neuron, astrocyte, microglia, or ependymal cell), the specific disease or condition may be a disease or condition caused by damage or dysfunction of the central nervous system cell or the central nervous system. For example, the specific disease or condition may be Parkinson's disease, Alzheimer's disease, Huntington's disease, tauopathies, amyotrophic lateral sclerosis, autism spectrum disorder, spinal muscular atrophy, or prion diseases.

[0558] In some embodiments, the specific disease or condition may be liver disease. In some embodiments, the specific disease or condition may be, but is not limited to, one or more selected from liver failure, hepatic fibrosis, Wilson's disease, and cirrhosis. For example, if the specific target cells are hepatocytes (e.g., hepatocytes, hepatic stellate cells (HSCs), Kupffer cells (KCs), or liver sinusoidal endothelial cells (LSECs)), the specific disease or condition may be a disease or condition caused by damage or dysfunction of hepatocytes or liver tissue.

[0559] In some embodiments, the specific disease or condition may be diabetes (type 1 or type 2), pancreatitis, or diabetic ketoacidosis. For example, if the specific target cell is a pancreatic cell (e.g., an alpha cell, a beta cell, a PP cell, a delta cell, or an epsilon cell), the specific disease or condition may be a disease or condition caused by damage or dysfunction of the pancreatic cell or pancreatic tissue.

[0560] Disease-specific abnormally expressed genes

[0561] As described above, a method for screening therapeutic genes may include selecting genes with abnormal expression levels in a specific disease or condition from among first candidate genes, or selecting genes with abnormal expression levels in a specific disease or condition from among reset genes. Here, genes with abnormal expression levels in a specific disease or condition may be referred to as disease-specific abnormally expressed genes, but are not limited thereto.

[0562] Disease-specific abnormally expressed genes refer to genes that exhibit abnormal expression amounts or levels in the disease. For example, disease-specific abnormally expressed genes may exhibit decreased expression levels compared to normal levels in the disease, and / or increased expression levels compared to normal levels. A decreased expression level may be, but is not limited to, an expression level that is 0.95, 0.9, 0.85, 0.8, 0.75, 0.7, 0.65, 0.6, 0.55, 0.5, 0.45, 0.4, 0.35, 0.3, 0.25, 0.2, 0.15, 0.1, or 0.05 times lower than the expression level in the normal state. The increased expression level may be 1.05, 1.1, 1.15, 1.2, 1.25, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2, 2.2, 2.4, 2.6, 2.8, 3.0, 3.2, 3.4, 3.6, 3.8, 4, 4.2, 4.4, 4.6, 4.8, 5, 5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 times or more than the expression level in the normal state, but There are no limitations. Meanwhile, criteria for reduced and / or increased expression levels can be appropriately established by those skilled in the art, and these criteria can be defined by other expression methods than the aforementioned fold change. Furthermore, preprocessed or normalized data can be used to determine whether an abnormal expression level exists, but this is not limited to these.

[0563] In certain embodiments, disease-specific abnormally expressed genes may be genes that have a reduced expression level compared to a normal state in the disease. For example, disease-specific abnormally expressed genes may be genes that have a lower expression level in the disease model or subject compared to a healthy model or subject. For example, disease-specific abnormally expressed genes may be genes that have a lower expression level (e.g., a relatively low expression level) compared to a normal state (e.g., a healthy model or subject).

[0564] Obtaining information on disease-specific abnormally expressed genes

[0565] In some embodiments, information about disease-specific abnormally expressed genes (e.g., which genes are disease-specific abnormally expressed genes) can be obtained from known literature or databases. For example, a person skilled in the art can refer to a known literature (e.g., [Irmady et al., Nature communications, 2023]) that discloses information about the expression levels of genes in a specific disease or condition, and can identify which genes correspond to disease-specific abnormally expressed genes from this reference. As another example, a person skilled in the art can identify which genes correspond to disease-specific abnormally expressed genes from a database (e.g., SCAD-Brain) that discloses information about the expression levels of genes in a specific disease or condition.

[0566] In some embodiments, information on disease-specific abnormally expressed genes can be obtained from a disease or disease model, or from clinical data. For example, information on disease-specific abnormally expressed genes can be obtained from, but is not limited to, in vitro disease models, animal disease models, or data (e.g., clinical data) on humans (e.g., patients). For example, those skilled in the art can create disease models, such as in vitro models or animal models, for a specific disease or disease, and measure the expression levels of genes in these disease models to identify information on disease-specific abnormally expressed genes.

[0567] As described above, information on disease-specific abnormally expressed genes can be obtained through various routes and is not otherwise limited.

[0568] Exemplary embodiments

[0569] Below, exemplary embodiments of the invention provided according to some embodiments of the present disclosure are provided. The invention provided by the present disclosure is not limited to the examples below.

[0570] Exemplary Embodiment 1 - Screening of Resetting Genes

[0571] A01. A method for screening one or more cell resetting genes for recovery of damage or dysfunction of a specific target cell or a specific target tissue related to the specific target cell, including:

[0572] (i) Obtaining gene vectors for multiple genes;

[0573] At this time, the gene vectors for the above multiple genes

[0574] Obtained from a two-dimensional gene expression profile data set for two-dimensional differentiation of differentiable cells capable of differentiating into the above-mentioned specific cells and a three-dimensional gene expression profile data set for three-dimensional differentiation,

[0575] At this time, the two-dimensional gene expression profile data set for the two-dimensional differentiation is composed of two-dimensional gene expression profiles for multiple genes,

[0576] At this time, the two-dimensional gene expression profile for each of the above multiple genes is

[0577] It consists of the first to Nth gene expression levels of the two-dimensional differentiation for each of the plurality of genes measured at each of the first to Nth time points of the two-dimensional differentiation process,

[0578] At this time, the 3D gene expression profile data set for the 3D differentiation is composed of 3D gene expression profiles for multiple genes,

[0579] At this time, the 3D gene expression profile for each of the above multiple genes is

[0580] Consists of the first to Mth gene expression levels of the three-dimensional differentiation for each of the plurality of genes measured at each of the first to Mth time points of the three-dimensional differentiation process;

[0581] (ii) Classifying the gene vectors for the above plurality of genes into two or more clusters using a clustering algorithm;

[0582] (iii) based on predetermined cluster selection criteria, selecting one of the two or more clusters and determining the first candidate genes as genes belonging to the selected cluster; and

[0583] (iv) Among the first candidate genes, genes related to at least one of metabolic, catabolic, and wound healing are determined as the cell resetting genes (or candidate cell resetting genes).

[0584] A02. In A01,

[0585] A method for screening one or more cell resetting genes, wherein the plurality of genes is 2000 or more.

[0586] A03. In any one of A01 to A02,

[0587] A method for screening one or more cell resetting genes, wherein N is an integer from 2 to 6.

[0588] A04. In any one of A01 to A03,

[0589] A method for screening one or more cell resetting genes, wherein M is an integer from 2 to 6.

[0590] A05. In any one of A01 to A04,

[0591] A method for screening one or more cell resetting genes, wherein information on the genes related to the above metabolism is obtained from a knowledge base on gene ontology.

[0592] A06. In any one of A01 to A05,

[0593] A method for screening one or more cell resetting genes, wherein at least one of the GO terms of the above metabolism-related genes is related to metabolism.

[0594] A07. In any one of A01 to A06,

[0595] A method for screening one or more cell resetting genes, wherein information on the genes related to the above catabolism is obtained from a knowledge base on gene ontology.

[0596] A08. In any one of A01 to A07,

[0597] A method for screening one or more cell resetting genes, wherein at least one of the GO terms of the above catabolic-related genes is related to catabolism.

[0598] A09. In any one of A01 to A08,

[0599] A method for screening one or more cell resetting genes, wherein information on genes associated with the above wound healing is obtained from a knowledge base on gene ontology.

[0600] A10. In any one of A01 to A09,

[0601] A method for screening one or more cell resetting genes, wherein at least one of the GO terms of the genes associated with wound healing is associated with wound healing.

[0602] A11. In any one of A01 to A10,

[0603] The above (iv) is a method for screening one or more cell resetting genes, wherein among the first candidate genes, genes related to two or more selected from metabolic, catabolic and wound healing are determined as the cell resetting genes.

[0604] A12. In any one of A01 to A11,

[0605] The above (iv) is a method for screening one or more cell resetting genes, wherein among the first candidate genes, genes related to metabolic, catabolic and wound healing are determined as the cell resetting genes.

[0606] A13. In any one of A01 to A12,

[0607] A method for screening one or more cell resetting genes, wherein the above-determined cluster selection criteria are determined based on the number of genes associated with the specific target cell belonging to each of the two or more clusters.

[0608] A14. In any one of A01 to A13,

[0609] A method for screening one or more cell resetting genes, wherein the above-described cluster selection criterion is to select a cluster having the highest cluster score calculated by one of the following formulas among the two or more clusters:

[0610] Cluster score = ;

[0611] Cluster score = x / S g ; and

[0612] Cluster score = x / N a ,

[0613] Here, x is the number of genes associated with a specific target cell belonging to each of two or more clusters,

[0614] S g is the sum of the number of genes associated with the specific target cell in all clusters,

[0615] N a is the number of genes belonging to each of two or more clusters.

[0616] A15. In any one of A13 to A14,

[0617] The specific target cell is a neuron,

[0618] A method for screening one or more cell resetting genes, wherein the genes associated with the specific target cell are neuron-related factors (e.g., neuronal factors).

[0619] A16. In A15,

[0620] A method for screening one or more cell resetting genes, wherein the neuron-related factors are factors belonging to factor set 1.

[0621] A17. In any one of A13 to A14,

[0622] The specific target cells are hepatocytes,

[0623] A method for screening one or more cell resetting genes, wherein the genes associated with the specific target cell are hepatocyte-related factors (e.g., liver factors).

[0624] A18. In A17,

[0625] A method for screening one or more cell resetting genes, wherein the above hepatocyte-related factors are factors belonging to factor set 2.

[0626] A19. In any one of A13 to A14,

[0627] The specific target cells are pancreatic cells,

[0628] A method for screening one or more cell resetting genes, wherein the genes associated with the specific target cell are pancreatic cell-related factors.

[0629] A20. In A19,

[0630] A method for screening one or more cell resetting genes, wherein the above pancreatic cell-related factors are factors belonging to factor set 3.

[0631] A21. In any one of A01 to A20,

[0632] (ii) Classifying the gene vectors for the above multiple genes into two or more clusters using a clustering algorithm.

[0633] Determine the optimal number of clusters, and

[0634] Including classifying the gene vectors for the above plurality of genes into the optimal number of clusters,

[0635] A method for screening one or more cell resetting genes.

[0636] A22. In A21,

[0637] The above optimal number of clusters can be determined through a calculation formula for the score for the number of clusters to determine the optimal number of clusters,

[0638] A method for screening one or more cell resetting genes, characterized in that the calculation formula for the score for the number of clusters at this time is as follows:

[0639] Score for number of clusters = ,

[0640] Here, x is the number of genes associated with a specific target cell belonging to each of the two or more clusters,

[0641] μ is the mean of the x values ​​of each cluster,

[0642] Nc is the number of clusters,

[0643] S g is the sum of the number of genes associated with the specific target cell in all clusters,

[0644] N g is the number of genes related to the specific target cell belonging to the above plurality of genes.

[0645] A23. In any one of A21 to A22,

[0646] A method for screening one or more cell resetting genes, wherein the optimal number of clusters is not 2.

[0647] A24. In any one of A01 to A22,

[0648] A method for screening one or more cell resetting genes, wherein the two or more clusters are first to R-th clusters (i.e., R clusters).

[0649] A25. In A24,

[0650] A method for screening one or more cell resetting genes, wherein R is an integer from 2 to 8.

[0651] Exemplary Embodiment 2 - Screening of Therapeutic Genes

[0652] B01. A method for screening one or more therapeutic genes for treating a specific disease or disorder, including:

[0653] Among one or more cell resetting genes for repairing damage or dysfunction of a specific target cell selected from any one of A01 to A25 or a specific target tissue associated with said specific target cell, genes having an abnormal expression level in said specific disease or disorder are identified.

[0654] B02. In B01,

[0655] A method for screening one or more therapeutic genes, wherein the specific disease or disorder is caused by damage or dysfunction of the specific target cell or specific target tissue.

[0656] B03. In any one of B01 to B02,

[0657] A method for screening one or more therapeutic genes, wherein genes having abnormal expression levels in the specific disease or condition are genes having low expression levels in the specific disease or condition (e.g., a model or individual of the specific disease or condition) compared to a normal state (e.g., a healthy model or individual).

[0658] B04. In any one of B01 to B03,

[0659] A method for screening one or more therapeutic genes, comprising: identifying genes having an abnormal expression level in a specific disease or disorder among the one or more cell resetting genes.

[0660] Identifying the expression level of one or more of the above cell resetting genes in a specific disease or illness database.

[0661] B05. In any one of B01 to B03,

[0662] A method for screening one or more therapeutic genes, wherein genes having abnormal expression levels in the above-mentioned specific disease or disorder are identified by a method comprising:

[0663] Preparing a disease model for the above specific disease or condition;

[0664] Obtaining a disease model gene expression profile from the above disease model; and

[0665] Identify genes with abnormal expression levels in a specific disease or condition from the above disease model gene expression profile.

[0666] B06. In any one of B01 to B05,

[0667] A method of screening one or more therapeutic genes, further comprising: determining one or more genes among the cell resetting genes having an abnormal expression level in a specific disease or disorder as therapeutic genes.

[0668] Exemplary embodiment 3 - Method for screening cell resetting genes for recovery of damage or dysfunction of nerve cells

[0669] C01. A method for screening one or more cell resetting genes for recovery of damage or dysfunction of nerve cells, including:

[0670] (i) Obtaining gene vectors for multiple genes;

[0671] At this time, the gene vectors for the above multiple genes

[0672] Obtained from a two-dimensional gene expression profile data set for two-dimensional differentiation of neural stem cells capable of differentiating into the above neural cells and a three-dimensional gene expression profile data set for three-dimensional differentiation,

[0673] At this time, the two-dimensional gene expression profile data set for the two-dimensional differentiation is composed of two-dimensional gene expression profiles for multiple genes,

[0674] At this time, the two-dimensional gene expression profile for each of the above multiple genes is

[0675] It consists of the first to Nth gene expression levels of the two-dimensional differentiation for each of the plurality of genes measured at each of the first to Nth time points of the two-dimensional differentiation process,

[0676] At this time, the 3D gene expression profile data set for the 3D differentiation is composed of 3D gene expression profiles for multiple genes,

[0677] At this time, the 3D gene expression profile for each of the above multiple genes is

[0678] Consists of the first to Mth gene expression levels of the three-dimensional differentiation for each of the plurality of genes measured at each of the first to Mth time points of the three-dimensional differentiation process;

[0679] (ii) Classifying the gene vectors for the above plurality of genes into two or more clusters using a clustering algorithm;

[0680] (iii) based on predetermined cluster selection criteria, selecting one of the two or more clusters and determining the first candidate genes as genes belonging to the selected cluster; and

[0681] (iv) Among the first candidate genes, genes related to at least one of metabolic, catabolic, and wound healing are determined as the cell resetting genes (or candidate cell resetting genes).

[0682] In C02. C01,

[0683] A method for screening one or more cell resetting genes, wherein the plurality of genes is 2000 or more.

[0684] C03. In any one of C01 to C02,

[0685] A method for screening one or more cell resetting genes, wherein N is an integer from 2 to 6.

[0686] C04. In any one of C01 to C03,

[0687] A method for screening one or more cell resetting genes, wherein M is an integer from 2 to 6.

[0688] C05. In any one of C01 to C04,

[0689] A method for screening one or more cell resetting genes, wherein information on the genes related to the above metabolism is obtained from a knowledge base on gene ontology.

[0690] C06. In any one of C01 to C05,

[0691] A method for screening one or more cell resetting genes, wherein at least one of the GO terms of the above metabolism-related genes is related to metabolism.

[0692] C07. In any one of C01 to C06,

[0693] A method for screening one or more cell resetting genes, wherein information on the genes related to the above catabolism is obtained from a knowledge base on gene ontology.

[0694] C08. In any one of C01 to C07,

[0695] A method for screening one or more cell resetting genes, wherein at least one of the GO terms of the above catabolic-related genes is related to catabolism.

[0696] C09. In any one of C01 to C08,

[0697] A method for screening one or more cell resetting genes, wherein information on genes associated with the above wound healing is obtained from a knowledge base on gene ontology.

[0698] In any one of C10. C01 to C09,

[0699] A method for screening one or more cell resetting genes, wherein at least one of the GO terms of the genes associated with wound healing is associated with wound healing.

[0700] In any one of C11. C01 to C10,

[0701] The above (iv) is a method for screening one or more cell resetting genes, wherein among the first candidate genes, genes related to two or more selected from metabolic, catabolic and wound healing are determined as the cell resetting genes.

[0702] C12. In any one of C01 to C11,

[0703] The above (iv) is a method for screening one or more cell resetting genes, wherein among the first candidate genes, genes related to metabolic, catabolic and wound healing are determined as the cell resetting genes.

[0704] C13. In any one of C01 to C12,

[0705] A method for screening one or more cell resetting genes, wherein the above predetermined cluster selection criteria are determined based on the number of genes associated with the neural cells belonging to each of the two or more clusters.

[0706] C14. In any one of C01 to C13,

[0707] A method for screening one or more cell resetting genes, wherein the above-described cluster selection criterion is to select a cluster having the highest cluster score calculated by one of the following formulas among the two or more clusters:

[0708] Cluster score = ;

[0709] Cluster score = x / S g ; and

[0710] Cluster score = x / N a ,

[0711] Here, x is the number of genes associated with neurons belonging to each of two or more clusters,

[0712] S g is the sum of the number of genes associated with the above neurons in all clusters,

[0713] N a is the number of genes belonging to each of two or more clusters.

[0714] C15. In any one of C13 to C14,

[0715] A method for screening one or more cell resetting genes, wherein the genes related to the above-mentioned nerve cells are genes belonging to factor set 1.

[0716] C16. In any one of C01 to C15,

[0717] (ii) Classifying the gene vectors for the above multiple genes into two or more clusters using a clustering algorithm.

[0718] Determine the optimal number of clusters, and

[0719] Including classifying the gene vectors for the above plurality of genes into the optimal number of clusters,

[0720] A method for screening one or more cell resetting genes.

[0721] In C17. C16,

[0722] The above optimal number of clusters can be determined through a calculation formula for the score for the number of clusters to determine the optimal number of clusters,

[0723] A method for screening one or more cell resetting genes, characterized in that the calculation formula for the score for the number of clusters at this time is as follows:

[0724] Score for number of clusters = ,

[0725] Here, x is the number of genes related to neurons belonging to each of the two or more clusters above,

[0726] μ is the mean of the x values ​​of each cluster,

[0727] N c is the number of clusters,

[0728] S g is the sum of the number of genes associated with the above neurons in all clusters,

[0729] N g is the number of genes related to the nerve cells belonging to the above plurality of genes.

[0730] C18. In any one of C01 to C17,

[0731] A method for screening one or more cell resetting genes, wherein the two or more clusters are first to R-th clusters.

[0732] In C19. C18,

[0733] A method for screening one or more cell resetting genes, wherein R is an integer from 2 to 8.

[0734] Exemplary Embodiment 4 - Screening of therapeutic genes for neurological diseases

[0735] D01. A method for screening one or more therapeutic genes for treating a neurological disease, including:

[0736] Among one or more cell resetting genes for recovery of damage or dysfunction of nerve cells selected from any one of C01 to C19, genes having abnormal expression levels in the neurological disease are identified.

[0737] D02. In D01,

[0738] A method for screening one or more therapeutic genes, wherein genes having abnormal expression levels in the above neurological disease are genes having low expression levels in a model or subject of the above neurological disease, compared to a healthy model or subject.

[0739] D03. In any one of D01 to D02,

[0740] A method for screening one or more therapeutic genes, comprising: identifying genes having abnormal expression levels in the neurological disease among the one or more cell resetting genes;

[0741] Check the expression level of one or more of the above cell resetting genes in the neurological disease database.

[0742] D04. In any one of D01 to D02,

[0743] A method for screening one or more therapeutic genes, wherein genes having abnormal expression levels in the above neurological disease are identified from a method comprising:

[0744] Preparing a disease model for the above neurological disease;

[0745] Obtaining a disease model gene expression profile from the above disease model; and

[0746] From the above disease model gene expression profiles, genes with abnormal expression levels in neurological diseases are identified.

[0747] D05. In any one of D01 to D04,

[0748] A method of screening one or more therapeutic genes, further comprising:

[0749] Among the above cell resetting genes, one or more genes having abnormal expression levels in neurological diseases are determined as therapeutic genes.

[0750] D06. In any one of D01 to D06,

[0751] A method for screening one or more therapeutic genes, wherein the above neurological disease is caused by damage or dysfunction of nerve cells.

[0752] D07. In any one of D01 to D06,

[0753] A method for screening one or more therapeutic genes for a neurodegenerative disease, wherein the above neurological disease is a neurodegenerative disease.

[0754] D08. In any one of D01 to D06,

[0755] A method for screening one or more therapeutic genes for a neurological disease, wherein the neurological disease is Parkinson's disease, Alzheimer's disease, Huntington's disease, tauopathies, amyotrophic lateral sclerosis, autism spectrum disorder, spinal muscular atrophy, or prion diseases.

[0756] Exemplary embodiment 5 - Screening of resetting genes (including differentiation process)

[0757] E01. A method for screening one or more cell resetting genes for recovery of damage or dysfunction of a specific target cell or a specific target tissue related to the specific target cell, including:

[0758] Preparing differentiable cells capable of differentiating into the above specific target cells;

[0759] Differentiating the first population of said differentiable cells into said specific target cells in a two-dimensional manner for a predetermined two-dimensional differentiation period;

[0760] While differentiating the differentiable cells of the first group in a two-dimensional manner, at each of the first to Nth time points selected within the predetermined two-dimensional differentiation period, first to Nth gene expression levels of the two-dimensional differentiation for a plurality of genes are obtained;

[0761] Differentiating the second population of said differentiable cells into said specific target cells in a three-dimensional manner for a predetermined three-dimensional differentiation period;

[0762] While differentiating the differentiating cells of the second group in a three-dimensional manner, at each of the first to M-th time points selected within the predetermined three-dimensional differentiation period, first to M-th gene expression levels of the three-dimensional differentiation for the plurality of genes are obtained;

[0763] Generating gene expression profiles for the plurality of genes based on the first to Nth gene expression levels of the two-dimensional differentiation for the plurality of genes obtained above and the first to Mth gene expression levels of the three-dimensional differentiation for the plurality of genes obtained above.

[0764] At this time, the gene expression profile for each of the plurality of genes includes a two-dimensional gene expression profile composed of the first to Nth gene expression levels of the two-dimensional differentiation and a three-dimensional gene expression profile composed of the first to Mth gene expression levels of the three-dimensional differentiation;

[0765] Based on the gene expression profiles for the plurality of genes, the plurality of genes are classified into two or more clusters using a clustering algorithm;

[0766] Based on a predetermined cluster selection criterion, selecting one of the two or more clusters and determining genes belonging to the selected cluster as first candidate genes; and

[0767] Among the first candidate genes, genes related to at least one of metabolic, catabolic, and wound healing are determined as the cell resetting genes (or candidate cell resetting genes).

[0768] Exemplary Embodiment 6 - Screening of Therapeutic Genes (Including Differentiation Process)

[0769] F01. A method for screening one or more therapeutic genes for treating a specific disease or disorder, including:

[0770] Preparing differentiated cells capable of differentiating into specific target cells;

[0771] Differentiating the first population of said differentiable cells into said specific target cells in a two-dimensional manner for a predetermined two-dimensional differentiation period;

[0772] While differentiating the differentiable cells of the first group in a two-dimensional manner, at each of the first to Nth time points selected within the predetermined two-dimensional differentiation period, first to Nth gene expression levels of the two-dimensional differentiation for a plurality of genes are obtained;

[0773] Differentiating the second population of said differentiable cells into said specific target cells in a three-dimensional manner for a predetermined three-dimensional differentiation period;

[0774] While differentiating the differentiating cells of the second group in a three-dimensional manner, at each of the first to M-th time points selected within the predetermined three-dimensional differentiation period, first to M-th gene expression levels of the three-dimensional differentiation for the plurality of genes are obtained;

[0775] Generating gene expression profiles for the plurality of genes based on the first to Nth gene expression levels of the two-dimensional differentiation for the plurality of genes obtained above and the first to Mth gene expression levels of the three-dimensional differentiation for the plurality of genes obtained above.

[0776] At this time, the gene expression profile for each of the plurality of genes includes a two-dimensional gene expression profile composed of the first to Nth gene expression levels of the two-dimensional differentiation and a three-dimensional gene expression profile composed of the first to Mth gene expression levels of the three-dimensional differentiation;

[0777] Based on the gene expression profiles for the plurality of genes, the plurality of genes are classified into two or more clusters using a clustering algorithm;

[0778] Based on predetermined cluster selection criteria, one of the two or more clusters is selected and genes belonging to the selected cluster are determined as first candidate genes;

[0779] Among the first candidate genes, genes associated with at least one of metabolic, catabolic, and wound healing are determined as the cell resetting genes (or candidate cell resetting genes); and

[0780] Among the above cell resetting genes, genes having abnormal expression levels in the specific disease or disorder are identified, and genes having abnormal expression levels among the above cell resetting genes are determined as the therapeutic genes (or candidate therapeutic genes).

[0781] In F02. F01,

[0782] A method for screening one or more therapeutic genes, wherein the specific disease or disorder is caused by damage or dysfunction of the specific target cell or specific target tissue.

[0783] Example

[0784] Hereinafter, the invention provided by the present disclosure will be described in more detail through experimental examples and examples. These examples are intended to illustrate the content provided by the present disclosure, and the scope of the content provided by the present disclosure is not limited by these examples.

[0785] Conventional candidate gene screening methods, such as therapeutic gene screening, screen for candidate genes by identifying genes with abnormal expression levels in disease models. However, as mentioned above, these methods suffer from the following problems: (1) because they select genes based on gene expression levels in previously induced disease models, they fail to reflect information related to the disease pathogenesis, or damage or functional decline of cells or tissues during the disease process; and (2) because the number of genes with abnormal expression levels in disease models is simply too large. Due to these issues, conventional candidate gene screening methods incur significant resource losses.

[0786] To address the problems of these conventional candidate gene screening methods, the inventors of the present application developed a novel gene screening method. Two-dimensional differentiation is a method in which cells are cultured on flat dishes, forming a monolayer. While this method facilitates the study of physiological and biochemical characteristics, it is difficult to mimic cell-to-cell interactions and the three-dimensional structure of tissues. Three-dimensional differentiation involves culturing cells in a three-dimensional structure, allowing them to grow in multiple layers. Three-dimensional differentiation can accurately recreate the microenvironment of actual human tissues and includes complex interactions between cells and between cells and the matrix [Lancaster et al. 2014].

[0787] Thus, the inventor of the present application gave the meaning of a normal differentiation model to three-dimensional differentiation that can substantially simulate the differentiation process into a specific target cell or tissue, and gave the meaning of an imbalance model such as a disease or illness to two-dimensional differentiation that cannot substantially simulate the differentiation process into a specific target cell or tissue.

[0788] The inventors of the present invention have devised a method for primarily selecting resetting genes and genes associated with specific target cells or tissues by identifying and / or comparing the expression profiles of genes in three-dimensional differentiation, which represents a normal differentiation model, with the expression profiles of genes in two-dimensional differentiation, which represents an abnormal differentiation model. The genes thus primarily selected are referred to as first candidate genes.

[0789] When cellular homeostasis and self-healing mechanisms occur, changes occur in genetic factors that maintain cellular metabolism and homeostasis within the body. The inventors of the present application believed that by additionally selecting genes related to cellular metabolism and / or recovery from the first candidate genes, genes more closely associated with cell or tissue damage or dysfunction could be screened. Based on this, the inventors of the present application devised a method for screening resetting genes, which comprises selecting genes related to metabolism and / or recovery from the first candidate genes.

[0790] Furthermore, the inventors of the present application believed that by selecting genes associated with a disease or condition (e.g., genes exhibiting abnormal expression patterns in a disease or condition model) from the selected resetting genes, genes more closely related to the disease or condition could be selected. Accordingly, the inventors of the present application devised a method for screening therapeutic genes for a disease or condition, comprising selecting genes associated with the disease or condition from the resetting genes.

[0791] Furthermore, in order to verify the designed screening methods, the inventor of the present application set specific target cells related to the reset and / or disease or disorder as brain cells, and set the disease or disorder as Parkinson's disease, a nervous system disease, and screened genes, and confirmed that the selected genes had a cell reset effect and a therapeutic effect on Parkinson's disease.

[0792] Example 1. Acquisition of two-dimensional and three-dimensional differentiation and gene expression profiles.

[0793] 1-1. Preparation of cells capable of differentiating into specific target cells and determination of specific target cells

[0794] The inventors of the present invention developed and verified a screening method for resetting genes and therapeutic genes. The screening objectives were determined to be the screening of resetting genes for the restoration of dysfunction or damage in nerve cells or the nervous system (e.g., central nervous system tissue), and the screening of therapeutic genes for nervous system diseases (particularly, Parkinson's disease). Furthermore, the specific target cells used in the screening method were set to be neurons, which are important cells in the nervous system or central nervous system tissue. Neural stem cells (neural stem cells) were used as cells capable of differentiating into neurons, and the neural stem cells were purchased from Sigma Aldrich (United States).

[0795] 1-2. Differentiation of neural stem cells (NSCs) into neurons - 2D differentiation

[0796] Neural stem cells were differentiated into neurons in two dimensions using the following method.

[0797] T75 flasks were coated with 1% Matrigel (in DMEM / F12) for 1 hour. The T75 flasks to be subcultured were removed, the media was aspirated, and washed with PBS. Accutase (Sigma) was added to the T75 flasks and incubated at 37°C. DMEM / F12 (Gibco) (with 1x B-27 (Gibco), FGF2 (20 ng / ml, Prospec), EGF (20 ng / ml, Prospec), 0.2% Heparin (STEMCELL technologies), and 0.5% Penicillin / streptomycin (Gibco)) was then added to the T75 flasks, and the neural stem cells were transferred to a 15 ml conical tube. The 15 ml conical tube was then centrifuged at 250 × g for 5 minutes. The supernatant was aspirated, and the cells were seeded in appropriate dishes. The medium was changed every other day. To differentiate NSCs, the medium was replaced every two days with DMEM / F12 (Gibco) (with 1x B-27 (Gibco), 0.2% Heparin (STEMCELL technologies), 0.5% Penicillin / streptomycin (Gibco)).

[0798] The results of differentiation of neural stem cells into neural cells were confirmed 18 days from the start of differentiation (day 0).

[0799] Bright field and immunofluorescence photographs were taken using a versatile confocal microscope (Olympus).

[0800] 1-3. Differentiation of neural stem cells into specific cells - 3D differentiation

[0801] Neural stem cells were 3D differentiated into neurons using the following method.

[0802] The method for passage of neural stem cells was similar to that described in "1-2. Differentiation of neural stem cells (NSCs) into neurons - 2D differentiation." One 3D organoid was 0.5 x 10 6 The cells were calculated to be 100 cells, placed in 50% Matrigel, and seeded in a ball shape on a dish, and solidified at 37℃ for 30 minutes. The formed organoids were collected in a petri dish, added DMEM / F12 (Gibco) (with 1x B-27 (Gibco), FGF2 (20 ng / ml, Prospec), EGF (20 ng / ml, Prospec), 0.2% Heparin (STEMCELL technologies), 0.5% Penicillin / streptomycin (Gibco)), and cultured on an orbital shaker. The medium was changed every other day. To differentiate NSCs, the medium was changed once every other day with DMEM / F12 (Gibco) (with 1x B-27 (Gibco), 0.2% Heparin (STEMCELL technologies), 0.5% Penicillin / streptomycin (Gibco)).

[0803] The differentiation of neural stem cells into neurons was confirmed 18 days after the start of differentiation (day 0). Bright field and immunofluorescence staining photographs were taken using a versatile confocal microscope (Olympus).

[0804] Figure 7 shows brightfield and immunofluorescence images of neural stem cells differentiated into neurons in two and three dimensions. As shown in Figure 7, neural cell markers (TUJ1, MAP2) were confirmed in brightfield and immunofluorescence images taken on day 18 of differentiation. The expression of the neural cell markers TUJ1 and MAP2 indicates the generation of neurons.

[0805] 1-4. Acquisition of gene expression levels and gene expression profiles

[0806] To obtain gene expression profiles, the expression levels of multiple genes were identified at different differentiation time points (days 0, 3, 8, and 18 from the start of differentiation) in two- and three-dimensional differentiation. To confirm the expression levels of multiple genes at different differentiation time points, samples were acquired at each differentiation time point of each differentiation process, and proteomic analysis was performed on each sample. The expression levels of each of the multiple genes at each time point were confirmed from the proteomic analysis results at each time point, and gene profiles for the multiple genes were obtained from these results.

[0807] For proteomic analysis, sampling was performed at multiple time points during the 2D and 3D differentiation processes, and these samples were analyzed via LC-MS / MS. Specifically, samples were sampled on days 0, 3, 8, and 18 of the 2D and 3D differentiation processes, respectively. That is, sampling was performed on days 0, 3, 8, and 18 of the 2D differentiation process, and on days 0, 3, 8, and 18 of the 3D differentiation process. Each sample was centrifuged at 500 × g for 3 minutes. After carefully removing the supernatant, a buffer (urea in ammonium bicarbonate) was added, and the cells in each sample were lysed by sonication. The lysed samples were fractionated using a micro-scale fractionation system (Advion). Fractionated samples were automatically fractionated and prepared for LC-MS / MS analysis. MS analysis was performed using an Orbitrap Eclipse (Thermo Fisher Scientific). The Proteome Discoverer platform, version 2.1 (Thermo Fisher Scientific) was used for protein identification and quantitation using the UniProt human reference proteome (UP000005640). LC-MS / MS analysis results identified a total of 7,334 genes (i.e., proteins expressed from 7,334 genes).

[0808] The expression levels of genes were obtained through LC-MS / MS analysis at each time point in 2D and 3D differentiation. The expression levels of a total of 7,334 genes were identified. The expression levels of the identified genes were sorted by the expression levels on Day 0, Day 3, Day 8, and Day 18 of 2D differentiation, and by the expression levels on Day 0, Day 3, Day 8, and Day 18 of 3D differentiation.

[0809] For example, expression levels for genes were sorted as follows:

[0810] [Table 11] Gene expression level alignment, gene expression profile (or gene vector)

[0811]

[0812] In this way, gene expression levels for any gene aligned through the type of differentiation (2D or 3D) and differentiation time point (0, 3, 8, or 18 days) can be referred to as gene expression profiles (2D gene expression profiles and 3D gene expression profiles) or gene vectors.

[0813] For example, the expression levels identified in the 2D and 3D differentiation for randomly selected gene 1 (FABP5P3) and gene 2 (HSPA6) from a total of 7334 genes are summarized as follows.

[0814] [Table 12] Gene expression profile (or gene vector) of gene 1 (FABP5P3)

[0815]

[0816] [Table 13] Gene expression profile (or gene vector) of gene 2 (HSPA6)

[0817]

[0818] In addition to HSPA6 and FABP5P3, the gene expression levels at each differentiation time point of the two-dimensional differentiation and each differentiation time point of the three-dimensional differentiation were confirmed for the remaining 7332 types of genes, as described above, and gene expression profiles (or gene vectors) for each of the multiple genes were obtained from the expression levels. In other words, gene expression profiles (or gene vectors) were obtained for 7334 types of multiple genes.

[0819] Example 2. Primary Screening: Screening of First Candidate Genes - Clustering and Cluster Selection

[0820] 2-1. K means clustering

[0821] Clustering results (k=4)

[0822] Through gene profiles (or gene vectors) for multiple genes (7334), the 7334 genes were clustered into four clusters using R. K-means clustering was used as the clustering technique.

[0823] As a result of K-means clustering (k=4), 1,780 types of genes were clustered into Cluster 1 (Cluster 1; C1), 2,432 types of genes were clustered into Cluster 2 (Cluster 2; C2), 2,275 types of genes were clustered into Cluster 3 (Cluster 3; C3), and 1,663 types of genes were clustered into Cluster 4 (Cluster 4; C4). The clustering results are shown in Table 14. Meanwhile, there were some overlapping genes in the clusters, resulting in a difference between the total number of genes belonging to the clusters (8,150) and the number of genes identified through LC-MS / MS (7,334).

[0824] [Table 14] K means clustering (k=4) results

[0825]

[0826] The genes belonging to each cluster are described below.

[0827] [Cluster 1] 1780 types of genes

[0828] ANKRD30A; PPL; SPP1; APOE; BDNF; RASGRP1; KHK; CRLF1; FGB; FGA; AGRN; PRKCA; COL10A1; CLU; RACGAP1; INS; ARG1; WNT5A; THBS2; WSCD1; MFGE8; SEMA3A; LAMC1; NDST3; C6orf120; FGG; HS3ST2; ZMYND12; LAMA1; CLEC18B; CDHR1; KAT5; BMP1; METRN; ACSL6; CRISPLD1; BHMT; ALDH1A1; KIF23; SRPX; NID1; CHRDL1; LAMB1; SPTB; HS3ST3B1; LFNG; CCDC80; OTC; ADH7; CBFA2T3; AOX1; BCKDK; BGN; TAF4B; SSC5D; ALDH8A1; HTRA1; MYH7; METTL7A; PCOLCE2; ALDOB; CHST6; HBB; EMILIN1; ITIH3; PDGFC; LMAN2L; COL4A2; ITGA6; MLIP; PPP2R2B; TTYH2; CHST11; COL4A6; LPL; PRSS23; LIPG; ZBTB20; C3; TINAGL1; MBTPS1; RFT1; PCDHB9; ITGA7; LAMB2; DAG1; GALNT16; TIMP3; TMCC3; COL4A1; MAOB; LAMA2; TNFRSF11B; MUTYH; PON1; TOE1; BTBD3; LTF; ARRDC1; METTL7B; COL18A1; BLZF1; SNED1; IDH3B; LRRC17; SCN9A; PTGFRN; GAL3ST4; CHST14; PC; ENDOD1; ABCA1; ST3GAL5; RBKS; SDCBP; POSTN; PRCP; PLA2G12A; CBFA2T2; RRAGA; ABHD6; NME3; TSPAN5; IGSF3; SQOR; CNEP1R1; MAOA; ITIH4; AK4; TTYH1; TGM2; ITGB1; TMEM201; SYBU; PRC1; NKAP; RAB31; ATG9B; ADAMTS4; CES2;VWA1; DIP2C; TNS1; NTN1; ADCY2; DLL3; C3orf58; HSPG2; SUMF1; TMEM120A; HBA1; FAM186B; APOC3; TMLHE; PXYLP1; SPATA21; RAB29; TMEM17; LTBP1; BCS1L; ALG6; ANGPTL4; IGFBP3; FAM69C; RAB11FIP5; CPNE4; FBLL1; IL10RB; CHST7; FN1; F3; DHRS3; ABTB2; VANGL1; CYC1; FGL2; RRAGD; NIPSNAP2; FBXO6; MFSD3; RRAGB; PLGRKT; BCAM; SLC4A4; BLVRB; RAB27A; PIP5K1C; GHDC; AEBP1; TM2D1; DIRAS2; MCUB; FDXR; TSPAN14; SULF2; SLC35B1; HPR; PLSCR3; TIAM1; MT2A; KCNJ10; BMPER; TRIM47; OGDHL; ITGA3; RAP2B; TMEM19; TRIM37; PDCD6IP; N6AMT1; SLC25A46; ALG11; TMED10; B4GALT4; PCDHGC3; KBTBD11; CPQ; THBS1; CHID1; NRP1; ALG12; FBXL8; DPY19L3; MCU; ADAMTS15; APMAP; RAB39B; GNPTAB; C17orf62; TOR1B; ARSA; ANPEP; AGPAT4; SLIT1; AGPAT5; COMT; FAM102A; ANKH; SENP3; TLCD1; TEFM; UBE2A; ACOT8; TMEM245; GPATCH2L; PRAF2; C17orf53; NKTR; SSBP1; GIPC2; RAB3IP; TMEM14C; COL8A1; DOCK6; PLOD2; TMEM209; GGH; MRAS; EEF1A2; GPRC5B; FBXO2; CTSF; SAMM50; MTX1; ATPAF2; RAB18; HSPA12A; IDH3G; FAM234A; GPSM2; TAP2; NDUFAF6; TBC1D10A; GPR89B;MBD1; GRIK2; SPHK2; STAT2; PROM1; NCEH1; SERPINC1; TLR3; ETFA; PLAT; MGP; PCYOX1; CMAS; TSKU; HKDC1; SLC38A9; ABCB6; CPT1A; RHOG; PRKAG1; EXTL2; EXT2; PRKAG2; GPAA1; VAV3; GJA1; TBC1D9; C4orf36; ARF6; ILVBL; ABCB7; ERLIN1; LMAN1; GPD2; GPX1; HPX; TMED4; LTBP3; SERPING1; SH3BP4; SLC2A1; CD81; MCUR1; TTYH3; LHFPL3; TENM4; SAT2; RRAGC; ABHD5; FARP1; AGPAT1; COMMD9; PIGT; ERGIC1; SLC25A11; ALB; CYB5B; SEC61A2; FAT1; PIP4P1; POMT2; DNAJB5; XPO6; ATP2A2; FUCA2; DPP7; GANAB; PTGDS; VDAC2; SFI1; IKBIP; IDH3A; ARFRP1; PLTP; B4GAT1; ARL10; FCGRT; ERAP2; TMEM63B; EXOC2; RAB21; DIP2B; SESN2; IARS2; DNAH8; MOGS; KLHL26; PTPRO; DNAJC10; GGT7; SUGP2; LAMA5; ATP5A1; RECQL5; STOML3; NARS2; PCCB; ZBTB12; KDSR; BDH1; CISD1; NTPCR; PTAR1; FKTN; DLAT; GATM; RAB32; CYFIP2; RNF114; MLYCD; PCCA; VPS53; PLEKHB1; STN1; NMRK1; CTSC; RAP1A; PARS2; DNAJC25; WDR24; ERCC3; TCTN3; C3orf33; MLEC; ADAM10; TRIM3; ETFB; SAC3D1; RRAS2; ATP2B2; PLEKHH3; DCXR; OPA3; GALNS; CPNE5; EIF2AK3; DHRSX; GNPTG; EPHB3; RENBP; CXXC1;PPP2R2D; ABCA13; SLC12A7; MADD; L2HGDH; IVNS1ABP; FBL; CANX; XXYLT1; GALNT17; GEM; B3GAT3; NEU1; B3GLCT; FAM129C; MBOAT2; SLC27A1; CPE; ITGA5; MYEF2; SIPA1; FAM162A; TTC7B; RETREG1; PHLDA3; SLC6A8; SNX33; FAM49A; TAPBP; CEPT1; C19orf54; CFI; LPIN1; SLC15A4; TTPAL; FAM69B; KAT8; RBM20; TMED1; CHPF; RAB1A; RAB13; ITPR1; RAP2C; DBT; STK32C; MTHFD2L; MTX2; HS2ST1; KRAS; SLC27A3; NOP56; WRNIP1; TSPO; TMED7; PGS1; MTMR11; CPNE2; UCKL1; MINDY2; POMGNT1; RALB; RXRB; CDK11A; RPN1; SAR1B; RDH14; PHKA1; VDAC1; TRMT2A; RAB28; MRPL22; ATP6AP1; IVD; ANXA4; PIGO; ATP5C1; SLC25A22; RALA; TMEM11; CPVL; RER1; HIBADH; UNC45A; STOM; FAF2; LRP4; COL6A3; SFXN3; ST8SIA5; TXNRD2; PIP4P2; SARDH; MAN2B2; LRRC8A; UAP1L1; LAMP1; CSNK1E; KIF16B; THNSL1; CHST10; RAB33B; RDH13; CYB5R3; MBLAC2; C16orf58; TMED9; DOCK4; SMURF2; MBOAT7; ERLIN2; DPY19L1; ATP6V0D1; TAP1; ENPP5; ATP6V0A1; HLA-A; CDIPT; DRG2; LARS2; DDOST; EMC2; DRAM2; OSTC; SEC61A1; PLOD1; MTX3; COG1; DNAJC3; POFUT2; HSPD1; CCPG1; WDR81; ARNT2; PNPO;TMEM205; SACM1L; KIF26B; FLOT2; PPARD; ATP2C1; PFKL; LRRN1; FLOT1; SFXN4; OS9; P2RX4; GTF2H4; MTOR; PDHX; AK7; STIM2; DYNLT3; TMEM192; BNIP2; MPP1; MIB1; ETHE1; MVB12B; MAN2A2; RPN2; CARM1; NKIRAS2; NEIL2; FLAD1; RAB5C; GDAP2; NAGA; MORC3; RIN1; TRAPPC13; TOM1L1; CERS4; TERF1; VDAC3; TEX264; NAT8L; APAF1; ADAM15; WDFY2; ACVR1B; RIC8B; ACSF3; SESN3; RAB7A; OSBPL5; ASPH; KDM6B; TIMM22; ERGIC2; SLC1A3; FAS; ODR4; PDS5B; CYP2U1; MARK4; TMTC3; SERPINE2; BPHL; EEFSEC; POFUT1; JMY; GPC6; APOBEC3F; TNS3; PANK2; NCLN; CERS5; ATP13A1; LDAH; LONP1; LMCD1; MPC2; SUCLG2; KRT78; KNG1; POR; CORO2B; BNIP1; ECI2; ABCA5; ICAM1; FAM3C; SNTB2; SUN1; CD9; TMCO1; SLC25A12; MAPRE2; MFN2; CPNE8; SGPL1; GLUD2; PCDH15; RABL3; CDC16; FHOD3; GPAM; DSEL; CTC1; PCK2; ARNT; VPS37B; NEK7; RAB14; GLDC; TUFM; TSG101; SIAH1; EFR3A; IGF1R; ITPR2; CAMK2D; SYT1; NID2; RBM12B; ATP6V1C1; RDH10; SERPINH1; ATP6AP2; EPHA2; CRNKL1; NUBP2; ULK3; SPIN2B; LAMA4; SELENOK; EXT1; PRKDC; TMEM43; COX15; SNX17; GALNT4; SLC39A11;SLC25A13; SLC26A6; RTKN; TXNDC15; MPP6; KRTCAP2; SLC25A52; STK38; STT3A; RAB10; UPRT; PLP1; ARL5A; ADCY7; RAB35; PHKG2; DGKA; PFKM; STAT1; NFAT5; PIP5K1A; EMC1; ERCC4; TRIM22; SFRP1; ANXA7; CNP; TUBA4A; H2AFY2; RIPOR1; CNNM3; SSR1; ZHX3; GNB5; HARS2; CPNE1; TRAF7; CTBP1; NPTN; GYS1; NFS1; KLHL9; TOB2; STRADA; 45352; HDHD3; NAPB; MCCC2; CLN3; COLGALT2; CRLS1; USMG5; ZADH2; HSP90B1; TUBB2A; RPS3; DYRK1A; PIGK; PMVK; EMC3; KIAA0232; NBAS; SERINC3; SLC25A3; DARS; SRSF12; UNC119; MREG; TMEM63A; ATAD1; RAB3GAP1; CAPN5; ATP1A3; OAT; GXYLT1; PHB2; VPS16; OAS3; ACVR1; RPL10; DOCK9; SGSH; RRP8; GOLGA7; ARHGEF1; HMOX2; RBPJ; ZNF800; NSF; VIPAS39; RELB; NDUFC2; ZNF627; CD151; SPG7; CERS6; PXK; ATP5F1; ZNF296; TXLNB; PDE4DIP; PLA2G16; RCN2; RBL2; LGALS3BP; FBXL19; STEAP3; CS; PAFAH2; CNTNAP1; NDUFS7; CLN8; CARS2; CDC23; SLIT2; COG4; LETM1; DPAGT1; TBC1D7; MYOF; MPND; GRAMD1A; HNRNPLL; ANKRD44; MTCH2; MASP1; NIPSNAP1; CTSB; CNNM4; CCDC51; IPO13; SLC39A10; XPO7; HSD17B11; CWC22; PLIN2; QRSL1;PRPF38B; TRADD; HNRNPH3; CNPY3; EMC8; NEDD4; LIMK1; ALKBH4; CLCN7; TRNT1; DFFB; MAP2K3; LPCAT4; TNKS; PCBP4; MTFR1L; PEX11B; GRIA1; CPNE3; NDUFB5; UNC13B; SLC35G2; TOP2B; LRRC8D; NT5C3A; TOMM70; TMEM167A; CAV2; RAB9A; CKM; ASMTL; BBS10; NFXL1; BTBD10; ATG4A; PIGU; FARP2; HOXB3; KAT2B; BPIFA3; NUDT6; PSMD3; PHB; IKBKB; UQCRC2; ILDR2; NRM; CLDND1; AHCYL1; METTL16; PRPF39; HEXB; SUPV3L1; PLEKHF1; CCDC120; MAGT1; IRAK4; CHD2; TRIM21; SELENOT; VPS28; MT-ATP8; APOA4; TAF2; CA2; TRAPPC5; SMYD3; AKAP17A; ZDHHC6; MAN1C1; RAB6A; CLPTM1L; FAM210B; LGALS8; ATP1A1; FRMD8; TAMM41; ELMO2; C16orf70; ATP1B1; UVRAG; PSMD12; EPB41L3; POM121C; NOL9; ERCC2; MAU2; COQ3; PAN3; SIL1; UQCRQ; GORASP1; LYRM4; VARS; PLAA; KIAA0355; RGL1; HSPA5; NCSTN; KANSL2; COMMD3; PI4KA; RGS12; MINOS1; CASZ1; ARAF; VARS2; ATP5B; MACROD1; PPIG; USP35; THEM6; HTD2; KIAA1958; POLG2; MYO1B; DNM1; MYD88; AP4B1; ASCC3; RAF1; ITPR3; PDIA4; KALRN; MAP2K1; GLG1; ERGIC3; RPS16; DCUN1D3; DGKZ; TBC1D22B; TAF5L; COX18; FAM109A; NKX6-1;SLC29A3; XPO1; ACADM; SNF8; MCC; SLC16A1; TNFAIP2; PTPMT1; TOR1A; TMEM41A; P4HB; SHMT2; PANK1; RPL35A; MYO1E; GRK2; UGGT1; SLC29A1; EMP3; STT3B; NDUFB7; BSG; ACSS3; RPS9; MFSD10; MT-ATP6; SLC9B2; ARL6; ALG2; ACSBG1; TUBB1; OGT; PHRF1; TM7SF3; RNPEPL1; AP2A1; ATAD3B; MARS2; OXCT1; ATL3; ZNRF1; FBXO45; ORC4; COMMD5; PGAP1; PPM1E; RNF13; RNF220; PI4K2A; AUH; TWNK; BMPR1A; PSMC6; TM9SF4; FAM57A; ME2; LRRN2; EOGT; LMBRD2; GFPT1; RUVBL2; RASA3; WDR45; ARMC8; DDX31; IL1RAP; OSBPL9; FKBP11; PRKAA1; RARS; DOCK10; PPP1R37; ERAP1; C9orf72; ARL1; ARL14EP; SNX5; DOPEY2; EPB41L4B; LNPEP; NFKB2; WDR25; SEC63; MGST2; COG8; ATAD3A; PAM16; ALG1; MYL3; DES; DNM3; LCMT1; RGS6; DNAJA3; EPS8L2; SMCR8; DHX33; SPPL2A; ASTE1; CCZ1B; STX4; GSTK1; CSNK1A1; ARHGEF3; TTC3; SALL2; PPP6R1; IGF2BP2; NDUFB3; FOXK1; HYOU1; PLXDC2; TIMM21; BOK; CSNK2A1; PIK3C3; INPP5A; MYO9B; SVIL; RNF213; PSMC2; PLA2G15; BICRA; TFE3; GTPBP4; PCLO; LAMP2; RPS15A; PSMC1; IFI35; TST; EFL1; WDFY1; EI24; RAB24; 45540; PTDSS1; GRIK3; VPS25;RPS24; TBC1D25; CCNL1; PSMD2; CGRRF1; CSNK2B; TUBGCP4; STARD3NL; PPP2R2A; MYO1C; COMMD4; C7orf25; ARPC2; SESTD1; TMED5; NUP160; TOMM40; SPTBN2; COL22A1; CFAP20; GALNT2; RTN4IP1; CYR61; NUP85; PTPRN2; RAB3GAP2; TOR1AIP1; FAM96A; MTRF1L; NEK6; FLII; CCNYL1; MED20; FMC1; ABHD11; NPAS3; NDUFV1; CD59; NGEF; MYCBP2; ZNF326; CELF1; LCAT; PRKACB; CARD8; ADAR; KIF1C; PITPNC1; TIMM44; CIAO1; TBC1D12; DCAF11; FNDC3B; IMPAD1; TRIM56; ALDH1B1; DEPDC5; EVI5L; TIMP4; GET4; PIK3R4; DSTYK; DESI1; SHE1L; PFKFB3; CALU; SDF2; RMC1; MRPL46; SART3; RCAN1; MICU1; MMACHC; SEC24C; RCN1; GPC2; TANC2; PDE12; FTL; PRKAA2; ISOC2; PSMC5; TOMM20; PTPN14; HIF1AN; SPRYD4; ILK; ATP5G3; VEZF1; SAMHD1; SIK2; SPCS2; CDK19; GTPBP6; MTIF2; PDE4B; GMPPA; MAPK9; DNAJB11; RAB8A; RPL15; PPP1R9B; SCFD2; HM13; NSMCE1; CHPF2; ATP11A; ITM2B; MVB12A; TRIM9; SLC12A9; HNRNPUL2; HSD17B7; TRIM32; METTL13; ARL2; GMDS; FBXO30; SLC5A3; CMTR1; ITPKC; FAM120A; RILPL1; CNOT6L; RIOX1; HSPA1B; CLN5; GPX4; ADO; PLCH1; NDUFS1; CUL7; GPAT4; UQCC1;MIOS; GNS; MAPK8; RPS4X; MOB4; UBE4B; TGFB1I1; DNAJC19; GSPT1; GALNT1; CNOT8; SMAD1; EHD1; RIMKLB; MANBA; KIRREL1; ATP5E; DNAL4; DAD1; BMI1; SEC61B; HAGHL; SEC23A; CDAN1; PLBD2; BRIX1; TRAPPC2L; SMARCB1; CYTH2; PLEKHM2; MRPL35; RAB11FIP2; DNAAF5; AP1AR; PPP2CB; TBK1; PSMB9; SLC46A1; UPB1; MP68; PPCS; PIK3CA; YIPF3; RAD9A; PIGBOS1; RNF123; IRAK1; UBE2F; RBMS1; TSPAN6; APRT; TRIM41; RAB1B; CAMK1D; HAX1; CLYBL; GOT2; BTBD1; SEC24A; CSNK1D; NOMO3; GIT1; LIPA; CDK4; RUVBL1; ABCA2; OTUD1; CSK; ATG4C; GLUL; CDK5RAP3; CLPB; RHPN2; RECQL; SRD5A3; LIMS4; GLRX5; CUL2; E2F5; COL6A2; FAM84B; TBC1D2B; RANBP10; PFKP; MED23; PSMC3; XPR1; NPC1; P3H1; ATP5I; PYCR3; SIPA1L1; TRIM65; OSTM1; STXBP1; EHD2; TMX1; AP3D1; RAB2B; ID4; SORBS3; VPS33A; THOC5; CAMK1; SLC7A5; RPL18A; TOMM22; GALK2; TYW5; SCD5; VTA1; SPR; CACNB2; RIT1; SPIRE1; INTS14; HDHD5; RPL18; ATP6V1H; TIGAR; PDHB; RFWD2; CCDC47; PNKP; HSF1; MED28; TMEM199; GCAT; PRPF6; CTSA; HDAC10; THY1; HIP1; ABR; MAP2K4; AKT1; ASPM; POLR2B; COQ8A; CHP1;OSTF1; SSR4; MRPL39; TOMM6; LEMD2; ZC3HAV1L; MAPRE3; COPS5; PRKD3; ELP3; SCYL1 ; PARP9; PDK3; DYM; GATB; GPN3; FBXO38; XYLB; ACTG1; UQCC2; PYROXD1; CP; PTN; MRPL3; GUF1; RGP1; PISD; CIB1; STK4; UBP1; ATP6V1A; UPP1; ASH2L; MDH2; CLPP; FTH1; PRKAR2B; GRN; SCAMP1; KLHL20; CHMP2A; PSMD14; PSMD6; DCAF12; EPDR1; KDM4A; IDS; CAMKK2; STRN3; MINDY3; KPNA1; RPS27L; RTCB; EFHD2; COPS6; FSD1; SLC8A2; ACTR1B; APOB; MRPL45; ACTR2; MRPS24; ANK3; CAAP1; ELOVL2; GMPR; PLCG2; RB1CC1; EGLN1; CTGF; NBR1; BICD1; CKMT1A; PARP4; ATP9B; PDK2; RPL14; PATL1; GPR158; MYO5A; MTSS1; VPS36; HDAC8; HS6ST2; 45544; GNG2; SLC25A14; MAPKBP1; SMPD2; CAV1; PTTG1IP; BRAP; SSU72; SDK2; SP100; GC; RBM47; DLG1; PEAK1; CHMP1A; OTUD7B; PIEZO1; OLFM2; PABPN1; CAVIN1; RLBP1; TTC38; HMOX1; SLC9A3R2; HTRA2; TTN; NSUN4; GALNT7; RPL36AL; SRM; KCTD16; HSPE1; SOGA1; EBF2; AHNAK2; SEC14L2; ITFG2; MRPL13; SPEG; RPL10A; COL6A1; ZNF532; ABCA3; YDJC; PAM; PPP4R2; EIF3F; CRYM; SSFA2; SEC23B; APOBEC3C; ATP6V1F; TNNT2; OXA1L; PLS3; DCTD;PSMB10; CHMP7; CD63; SAP18; ATP6V1E1; GLIPR1; ENPP4; USP33; HPS5; RPS23; B2M; CELF2; TRMT1L; CFH; COL4A3BP; HSPA2; PBX2; SENP5; SEC24D; CHKA; FBLN1; AP1S1; PPP3CA; MCTS1; TBC1D1; RPAP1; THOC7; PLEKHG2; OXR1; RRM2B; TRAFD1; RPS27A; WLS; TSPAN31; CTSD; SNAPC3; AGAP1; PCBD1; CEP41; HNRNPH2; TBC1D22A; RAC3; SRP19; ADPRHL2; TXNL1; SIDT2; TMEM260; FGF2; DEGS1; TF; APBA3; SFSWAP; S100A11; SRP14; YTHDF2; PDCD10; ATP5J; CHMP5; FBLIM1; COTL1; FLNC; HEXIM1; PMF1; DSN1; NDUFS4; PAAF1; SAP130; TAF15; TMOD1; SNCA; FABP5P3; HSPA6; ITIH2; MBP; ACTA2; SPTY2D1; DYRK1B; ADGRA3; CDKN1A; VGF; DNAJC6; BRCA1; ZNF521; SLC2A13; GTF3A; KLHL24; ATP7A; UBASH3A; JMJD4; MTERF1; PRR14; FUNDC2; ATRAID; PEG10; TMEM169; GPR155; VKORC1; ZNF121; SMAD3; MOB2; ODC1; AMFR; NHS; HBD; NACC2; FGD1; DCAF10; LIMK2; PPTC7; RRAS; TSPAN3; SMIM37; SEC14L3; WDR59; ST3GAL2; GLI2; CCDC90B; TXNDC11; SNX32; SYN1; ZBTB45; TAPT1; PRNP; ROMO1; FAM57B; HRG; PLEKHM1; SAMD4A; TPRG1L; CMIP; MUM1; CAMK2B; DAB2; CHADL; SLC16A3; ZFP62; PTP4A3;VKORC1L1; TRIM23; BHLHE40; EDA2R; SCARF2; SLC1A2; TXNDC16; CRY2; LGR4; ZNF667; MAST1; QSOX1; ZBTB47; INO80B; NUDT22; ASB8; CYP24A1; ATP8B2; RHBDF2; SHQ1; MT-CYB; PLG; CCM2; ST3GAL4; C2orf42; OTUD3; ASB13; LITAF; TAF5; REV3L; SEZ6; ARSJ; NXPE3; RPS6KA1; SNAP47; VSNL1; ZNF740; BTBD7; TMEM168; TTLL1; ZNF865; RGS19; LRP10; HS3ST3A1; SCAPER; XYLT2; FKRP; UST; UXS1; MMP11; MOB3C; ZBTB9; DET1; ZNF576; PRKCI; TMEM117; ANGPTL2; NDUFB8; DGCR2; NHLRC3; ICAM5; SGCB; SLC48A1; HLA-DRB1; TMEM104; ACTN2; FBXO21; ASB7; ARSE; PPP1R16A; RIC1; CHRM3; ATP13A2; PDGFRL; RUNDC1; INTS6L; GDF11; GLCE; AES; B3GNT5; EVA1B; PPFIA3; JCHAIN; NDP; ADA2; SUPT3H; HBP1; LACTB; LOXL4; CALHM2; PLCL2; CAMK2A; LRRN3; DMTN; TMEM161A; SNAP91; SLC6A1; SYNGR1; FEM1C; THBD; GBA3; ABHD14A; INSR; STX1B; CLCN4; HGFAC; DDO; TRAF3; ST8SIA1; ALG14; MYH1; VN1R5; C1QTNF5; IGLL5; GPX3; AGAP2; KLHDC8B; SMAD9; CTSZ; CADM2; ADAMTS16; DISP2; SLC16A4; PYGM; PTK2B; A2M; PCDHGC5; AHSG;

[0829] [클러스터 2] 2432 종류의 유전자들

[0830] MSL2; CEP290; TP53; CIT; KIF14; P3H4; SLC5A6; ARL4C; ZNF460; ITGA4; UQCRH; TRIP10; RSL24D1; PXDN; GDF15; MYL2; LATS1; LAS1L; DENND6A; PAK1; POU2F1; NT5DC2; CEP55; CISD2; FREM2; HEG1; AAAS; POMK; KDM3A; ELMSAN1; SLC3A2; MAN1A2; PTPRK; FBXW11; SMAD5; MLX; SCAF8; UBASH3B; IGHMBP2; SP1; SRPRB; HGH1; GOLIM4; SLC38A2; RFK; DROSHA; SLC25A6; CDK7; MCOLN3; UBN1; IMPA2; FEM1B; CHST3; C1GALT1C1; TEX2; GTF3C1; GTF3C6; LZTR1; NSMCE3; AIFM2; SLC25A5; GTF3C3; DHX35; SPG21; SMPD4; LEMD3; TMX3; RBM39; DOHH; ANLN; PSD3; SLC39A14; SRCAP; RBM19; SLC25A19; MAP7D3; SELENON; ZW10; TIMMDC1; ADPGK; CD276; MDN1; DHX34; KIAA0319L; LYN; AQR; URB1; ZNF827; NOP9; MIER3; RFFL; TBCD; MARS; TMEM120B; HSPA4L; CLK4; TPBG; YIPF4; MRPL27; TBC1D15; KLHDC4; LGR5; YEATS2; ORC5; SEC11A; GAPVD1; DCK; TGFBR1; TOR3A; KMT2B; LRCH1; YARS2; XAB2; RTFDC1; GIPC1; RBM28; SLC25A15; GFPT2; TTC12; IQGAP3; SQSTM1; FXR2; FBXL12; CHD8; HACD2; MYO15B; KBTBD6; OSBPL3; THUMPD3; NOL10; NOMO2; LTN1; RAD1; SMCHD1; SPATA5L1; RASSF2; ZC3H7B; TYW1;ACSL3; NAB2; PNPT1; CD320; CEBPZOS; NDOR1; ABT1; MGAT2; ATP1B3; TGFBI; SMC5; RBM23; STAT5B; DHODH; FOXK2; ARIH2; ATL2; TRA2B; KIF20A; FOXO1; PAXBP1; SLC1A5; HSPBP1; LENG8; MYO9A; NUP188; EIF2D; TVP23C; IMPDH2; KPNA6; NKRF; PES1; SPRY2; CACUL1; MGAT1; GATC; FJX1; SIX5; SRP68; USP19; TANGO6; DNA2; FBXO3; KDM5C; SRBD1; BRAT1; NDC1; PTGES3; URGCP; TCEANC2; PHF23; MAN1B1; TRIO; GNE; SETD1A; RPL9; ZNF451; FAM83D; NOC2L; TMEM214; JMJD1C; CD46; USP15; NLRP2; MEN1; ORC3; DDX47; HSPA9; INTS13; KDM1B; RIOK1; TTC17; NFRKB; RCCD1; RBM7; QPCTL; DUS2; RBM45; CDK6; ZMYND11; MRPL48; AKAP8; UBR2; MCAM; CFAP36; SLC19A1; PNO1; WDR6; NR2F6; IPO11; RAB34; RPL28; KIAA2013; IMP4; TBPL1; L3MBTL2; PDE8A; EIF2AK1; NME7; DNAJA2; UBE3C; INTS11; SLC12A2; ASB6; PTBP3; XPO5; SRRM1; TGS1; POP5; PHF8; KIAA1024; DCP2; HERC2; CASP8; SLC7A1; NMD3; SDF2L1; UTP6; METTL21A; TTC13; TBL2; EN1; NXF1; EXTL3; PVR; TUSC3; SPATS2; UBE2G2; DNAJB12; PELO; MCRS1; M6PR; ABCE1; CD97; STARD13; C7orf26; BAZ1B; IGF2R; TDG; WDR26; PTPN12;ZNF574; P3H2; PANX1; CRTAP; SLC4A7; REC8; KIF4A; ITCH; KTI12; TRAF4; SIRPA; LYRM7; ZC3HC1; GTF3C5; SHCBP1; ECSIT; SLMAP; FRMPD1; EIF2B2; SNRNP40; KLHL13; MYO5B; DNAJA1; BAG5; DNM1L; CDC73; SMG1; LRWD1; TSR3; SPCS1; C1orf109; PDIA6; ERICH1; REXO4; WAPL; COLGALT1; SIPA1L3; RPL35; RNF40; PDS5A; AIMP2; ANAPC7; TRMT10C; BRMS1; IMP3; RPS6KA4; TXNIP; AKT2; RPS6KB1; NUP93; ZNF687; DDX49; RRP12; UTP3; HUS1; RPL38; MAD2L2; INTS10; CCDC106; UGGT2; INCENP; IWS1; PPP2R5E; UTP4; RNPS1; SRP72; CUL4A; TMEM109; GTF3C4; ILKAP; GPD1L; CDK10; ZCCHC10; SH3RF2; HEATR1; ANAPC5; TBL1X; MRPS17; GTF2H2; DDX6; HFE; DDX52; THOC2; PTPN2; MRPL36; MTHFD1; COPB2; KIAA1549; GOLPH3L; THUMPD2; SLC4A2; TRABD; FTSJ1; PPP1CB; COPA; ZBTB11; THADA; TARBP1; CBWD2; TXLNA; SIRT6; INTS1; TARS2; KIF1BP; RPL11; PTPN9; DNAJB1; AUP1; DIS3; P3H3; AATF; FBLN2; PMS2; SDHA; P4HA2; WDR36; DDX20; PRKCD; WNK3; ACACA; PDK1; BANP; INTS2; U2AF1; NOS1AP; BCAP31; NOSIP; MICAL3; TFIP11; PRR14L; PWP2; QSOX2; EIF2AK4; COPG1; MSI2; URB2; TTC4;SLC35A4; CLK3; PRPF4B; MRPL21; DHX40; COPB1; TTC5; MAP3K4; PTPRC; SMARCA5; HAUS3; TIMM17B; TECR; MTMR6; CDC123; MICU2; NAT10; CAD; EPRS; ENC1; NCBP1; CTU1; REEP4; TBL3; SMN1; FARSB; SAP30; NVL; GABPA; NDUFAF1; ALDH18A1; METTL6; TUT1; HIRA; ARFGEF2; NDUFA3; NOCT; KIAA0586; ULBP3; TRIM27; SPATA5; PPID; DDX41; MED4; TMEM181; MEF2D; DDX55; PAPD5; UBR5; DNAJC30; TM9SF2; SQLE; ICE1; WDR41; GPX8; ABCF1; NDUFAF4; PLEKHA4; LRIF1; GNL3L; BTAF1; TSFM; SRRM2; IPO8; GCN1; PRDX4; BCL2L12; JTB; DAXX; KCTD1; SUPT20H; CINP; PDXDC1; WDR53; PKN1; POLR3B; PAPOLG; POLG; RPS6; RTTN; CPSF3; EEF1A1; C12orf43; MRM3; IFI16; KCTD3; ARID2; MED27; KRT2; NIP7; YTHDC2; AFF4; DNAJC1; PLOD3; SH3BP1; PML; PREX1; FAM129B; EMG1; GEMIN4; PARP2; NSMCE4A; UMPS; MRPL58; RPF1; LPCAT1; KIF7; MTPAP; PRPS2; SV2A; CDK11B; DDX19A; SACS; SINHCAF; ZMYM2; EPHB4; KBTBD2; CANT1; CHD1L; RPS2; DCUN1D5; MRPL19; SIK3; ELP2; DHX30; UHRF2; EIF4A3; THOC3; ISCA1; DCAF13; UTP23; SLC35B2; HSPH1; CLP1; INTS4; CCT6A; NFKB1; ANAPC1; TANC1; POGK;CDYL; NSUN2; METTL2B; SKA2; U2SURP; ARHGEF10; PARN; CLIC6; DDX10; NOC3L; DNTTIP1; ITPRIP; SRPRA; APP; TMEM2; YY1; CDK5RAP2; USP34; GRAMD1C; LARP4B; MRPL1; BRF1; MED18; MLH1; NAA15; PUM1; SHROOM2; SPATS2L; C1QBP; UGDH; RFC5; EIF2S3; MTO1; EIF3I; GMEB1; KDM1A; DHX38; ZNF280D; CDK13; ARID4B; EIF2AK2; GEMIN5; MRPL47; UTP20; SEC23IP; ZNF24; TRAF2; FOXRED2; PMPCA; PTPN1; FXR1; ASNSD1; STX6; NOC4L; EXOC6; ATIC; AURKB; FAM208B; OSBPL11; BIRC6; INTS5; SMARCAD1; FERMT1; SCAMP3; RNGTT; RNF20; TRAP1; MICA; MTA1; WDR46; FMNL3; INPP5F; MYDGF; ATP11C; ACSL4; ZNF639; GCLM; C7orf50; EIF2B5; RMND5A; SERPINF1; NAA50; TEX30; ABCF2; HTATIP2; TMEM126B; MAP2K7; AP4E1; SMG7; ZFAND2B; NONO; ODF2; DUS1L; RPL23; ECD; BLM; RP9; ARMC6; NQO1; TTC27; MRM1; TYSND1; APEX2; DDX3X; PUS1; SETD2; OBSL1; ARPC5L; FARSA; GINM1; RCC1L; L3MBTL3; ABCF3; FYCO1; LIMS1; POLR1C; CUL1; GPAT3; WDR75; TAF6; ERCC1; FBXW8; INTS3; DDX3Y; NCDN; JAM3; ESS2; HSPA14; TMEM223; POLDIP2; INF2; RFC3; ACBD6; MMP14; CPNE7; RAVER1; TTF2; PFKFB4;ELP1; FRG1; ETV6; EXOSC3; SPOUT1; EHD4; MED25; FTSJ3; DCAF7; MED10; IARS; PDCD11; WDCP; NEPRO; ATG101; UPF1; HDAC2; FDX1; RPL22; ANKLE2; RANBP9; PRPF19; VAMP5; BEND3; PPP4R3B; CPSF2; YARS; SMARCA1; POLRMT; RBM25; DUSP23; ATM; PLK1; TSPYL1; SREK1IP1; TDP2; CSNK1G2; ZNF738; FBXO22; PLCE1; WRN; RPIA; RPRD1A; FOCAD; ATP6V0A2; HCCS; EIF2B3; CCDC115; IFRD2; MED17; CTNNBL1; RPUSD4; ABHD17B; CCT7; WDR3; MSH2; DHX32; TINF2; RACK1; PMEL; RFC2; RFC4; NABP2; FAM25G; ACTR5; FAM98A; DNAJC7; PUS7L; PGAM5; MTERF4; DBR1; CDC27; MXRA7; NGDN; GTF3C2; FHOD1; WDR5; RIOK2; SLC35F1; ALKBH8; CHD4; VAMP7; TRPT1; PRPF18; CLPX; PYCR1; NAA40; SMG5; QTRT1; TOP1; PCGF6; MRPL9; DIMT1; ANK1; USP36; TSEN34; RPS4Y1; SLAIN1; NDUFAF3; NUS1; MTG2; RPL12; PPIL2; NBN; KCTD10; HDAC3; FCF1; CDK2; PAK1IP1; FRA10AC1; EHMT2; CALCOCO2; MYBBP1A; NOL11; BRD1; TRIM28; KIAA1671; PPP4R1; PNISR; SLC30A7; BRD9; THAP11; CDR2; MBIP; SEC14L1; ZMYM1; CDKAL1; SS18L2; ALKBH1; ACTR8; SRPK1; CWC27; NXT1; STXBP4; CD44; RCOR1; MAP4K5; GART;MORC2; MRPS16; ZNF318; RPL37A; DDX51; DUS3L; LRPPRC; MAEA; MICALL1; QSER1; RBM34; TSR1; CCT4; UTP18; CHERP; MKLN1; PIBF1; TMED3; PM20D2; B4GALT1; RPS20; GFM1; MRPL53; ARHGAP31; LMO4; DAGLB; MIA3; IRF3; UTP11; ZNHIT6; SDC2; FBRS; POLD1; LUC7L2; NAAA; SMC1A; IGF2BP3; RBM26; MSANTD4; ZBED4; QARS; CRLF3; AARSD1; EDEM3; UTP15; CCT2; PPFIBP1; SENP1; EIF2S2; WDYHV1; ECM29; TTLL12; GLE1; AHCY; MMRN2; CHAIN1; SPTLC2; TCP1; KATNB1; DDX39A; CBX2; TMEM185A; MRNIP; GAC; G6PD; LARP4; FASTKD5; TELO2; SEPTEMBER; METAP1; LZTS2; INTS6; ARMC9; GRPEL1; RPS11; FKBP10; ATG4B; PPAN; NOL6; KANK2; RPL13A; PARVB; MRPL30; DNMBP; PTPN13; CTR9; ZDHHC13; ZNF830; PYCR2; MANEA; KIAA0754; HAUS2; CIP2A; PODXL; HNRNPA3; EIF3D; DDX46; TUBGCP5; FALSE1; ITGB1BP1; FPGS; TRUB1; DHX8; PARP14; DUSP6; PDZRN3; GAP43; GTPBP10; LNG3; YRDC; EIF5B; DHX15; HSP90AB1; DDX50; COBLL1; DDX24; CCT5; CPD; DNAAF2; RPL7L1; MTFP1; ZC3H7A; HDLPP; MRPL11; DPH1; HAUS4; RPS5; ZMYND8; IBTK; TWISTNB; TRMT112; CCT3; PKM; NT5DC3; ZMAT5; MRPS34; XPNPEP3;PRKAR1B; HOUSE8; SUPT5H; BALL2; RCL1; SUZ12; NOL8; MAN2A1; GAPDH; DIEXF; TBRG4; YME1L1; ARAP3; TRIM24; RPA1; NSUN5; NAA10; DHX16; DHX36; MRPS30; NUDCD3; FADD; RRN3; TCF12; GGNBP2; PUM3; RNF138; KBTBD4; MRPL44; PRMT5; TCP11L1; POLR1A; SIRT1; GTF2H3; MRPL38; SNX1; NSL1; RASSF8; BAG6; PCNT; FAM189B; KRR1; SMARCD2; CPSF1; GPHN; MCMBP; ANAPC2; TXLNG; CSDE1; IPO7; LPGAT1; SETX; GOLGA3; HSP90AB4P; DDX54; SLK; KIF3C; TIMM50; ADNP; CTNNAL1; RPS27; FADS3; CHORDC1; RBM6; METTL1; SYS1; KIAA1217; MLLT1; IRF2BP1; C2CD2; DHX37; SH3D19; HSPA8; SNX9; EEF2KMT; SRSF8; E2F4; ANP32E; THOC6; PRPF4; UBE2D3; HELZ; KMT2D; UBE2O; AKAP13; RAE1; ECE1; MSH6; POLR1E; DCAF1; GARS; SLC38A1; UXT; RPS14; GNB1L; FAM49B; SUPT16H; STK10; METTL15; PIAS4; SDAD1; POLR3E; C12orf73; FAM20B; TOMM34; BUD31; NCBP2; ZNHIT2; MKNK1; DPF1; SMARCC1; ZGPAT; PKN2; ZNF148; PI4K2B; MATR3; CDK12; SPEN; CRCP; METTL18; CTDSPL2; RPL3; XRCC1; MAGOH; RPS17; BRD8; RB1; RBFA; AKAP1; SELENOO; CACYBP; RPLP0; MRPS10; PUS3; SF3B1; ARCN1; ROCK2; COPS8;CSE1L; POLR3A; NUDCD1; CMSS1; EFNB2; MINPP1; MRPL51; TRIP13; RSBN1L; NUDT15; NTMT1; POLR3D; AACS; EFTUD2; CABIN1; DDX56; USP13; MED7; ARHGEF2; SON; HSCB; ALG5; POLR2A; TAF9B; NOB1; DDX18; MRPS5; MED21; B3GALT6; DAP3; PPWD1; RFC1; RPSA; RBM4; CLUH; LRRC41; LONRF2; RPL17; MRPS12; TCF25; SSB; MTMR2; SPTLC1; ZC3H11A; COIL; EHMT1; INTS7; MRPL28; NCOA4; ITGB3; DGCR8; PCID2; GOPC; HECTD1; RAI14; HNRNPF; RANGRF; NDUFAF5; GTF2H1; ADA; PSMG4; RSU1; POLR1B; EIF1; CYP51A1; LIG4; DDX27; MTRR; TAF12; ZFYVE16; NSUN6; SCO2; HPDL; BMS1; EED; SPRY1; COL11A1; HMCES; AAGAB; GOLPH3; KANK1; NDEL1; NCR3LG1; USP10; SRSF9; WDR43; MAP3K1; EIF4E2; EEF2; CHD1; MRPS2; MGA; FKBP4; OXNAD1; MRPL15; DUSP12; MRPL4; CGREF1; STK24; DTX3L; KLF16; IPO4; EIF2A; EIF3CL; TCERG1; UBE3A; BOP1; PCF11; CDC40; NUP35; TTI1; ZDHHC5; RPL21; LRCH3; MRPS23; NUBP1; MSTO1; P4HTM; RSL1D1; GRB10; DDX28; RPL32; INTS9; EIF5A; RUNX1; LRP8; CTU2; UBE2L6; CLSTN1; SLF1; GGA2; SLC35A2; MORF4L1; CEBPZ; EXOSC1; SEMA7A; AHSA1; ERAL1; SLC4A1AP;ZCRB1; SND1; MACF1; ATR; AHCTF1; UBE2W; AKAP11; PAF1; STUB1; ABL1; MTHFD1L; NCOA7; CTDP1; YPEL5; DDX21; FAM111B; KDELC2; TSEN2; MTHFS; RNMT; ASNS; WDR55; BCAS2; IMPDH1; RPL30; ZMAT3; RPS3A; EIF3J; MRPS7; TXNDC5; ZNF706; CHEK2; WDR12; NFIC; RAB36; MAK16; PTCD3; ZC3HAV1; LSG1; DNAJB14; WDR77; KIF2C; HMGCR; YEATS4; ANKRD17; CSTF1; PRKACA; FKBP14; POLR3C; TRMT1; SUGT1; TARBP2; HEATR6; FAM96B; RSAD1; NME4; UBE2S; TIMM23; EXOSC10; RPL19; FAM206A; CLMN; LGALSL; TFB2M; PABPC4; C1D; UBL5; UCK2; R3HDM1; USP7; SH3RF1; EIF2S1; MRPS26; PRMT3; DIAPH1; MRE11; SRRT; CCDC85B; EIF3A; MSANTD2; TXNDC8; EDC3; SDHB; PGK2; SAP30BP; AAMP; BICD2; NFE2L2; MRPS27; SUPT6H; ZNF330; CNOT6; PAPOLA; EIF3E; WDR73; POLR3F; FAM76A; DPP9; CMTM7; GADD45A; SMU1; SNU13; POLA2; UNG; RPS18; ADGRB1; TFB1M; ATXN10; TACC2; EEF1AKMT4; SCD; HOXB4; TBC1D4; EIF4A1; SYNCRIP; RABEPK; EEF1G; NIFK; WIZ; AAR2; ORC1; EIF6; ALKBH2; RPGRIP1L; SDCCAG3; MRPS25; CDR2L; FSCN1; ACLY; METAP2; GNPNAT1; SMC2; MGAT5; AFAP1L2; LUC7L3; STRAP; ERCC6L;PCBP1; UBE2Q1; HMMR; RRP7A; PHF10; SERPINB10; CA12; DNMT1; KARS; RPS13; CDCA8; RIF1; TNFRSF12A; HEATR3; ZNF598; ORC2; BUB3; GON4L; RPL22L1; FASTKD2; BYSL; STK17A; GTF2B; OR4K3; PPAT; VRK2; HK2; KLHL7; C20orf27; PSME4; STX8; TTF1; BANF1; CETN3; GNL2; GPATCH8; RPL13; TRDMT1; TRUB2; CCNT1; ADSS; RPL27A; PCBP3; KANSL3; DUSP16; EIF3B; TACO1; BRPF1; APLP2; FAM184B; PSTPIP2; CPSF7; CCT8; LIMA1; DHCR24; CBFB; CDK8; NUFIP1; KIN; MIER1; EIF3H; ZBTB10; TMEM237; CENPN; PPIL4; TYMS; QTRT2; PRMT7; UBE2M; TRMT6; SYNJ2; HSPA4; RRP1; POLR2E; CWF19L1; UBE2E1; MRPS6; DCP1B; DPYD; RPL24; ABRAXAS1; COPS7A; PHC2; SDHAF3; NOP14; NSA2; HAUS1; PPP1CA; ARMC2; ESF1; MRPS22; TMA16; NAMPT; SMC6; TRAF3IP2; TFRC; SPDL1; ALAS1; PLD3; BUD23; DPH2; TRIP6; PBDC1; BZW2; ISY1; CWC25; LCHN; HAUS6; SRF; SETD7; EDEM1; TXNDC9; MRPS31; KPNA2; SMYD2; WDR70; PAPSS2; PRKAR1A; LSM12; GID8; PDCD2; OTUD4; RAPGEF6; METTL5; MRPS28; EIF3L; RSF1; PRIM1; SSRP1; CBX5; NPRL3; LSM11; KMT2A; CUTC; ZNF281; CDK9; SCG2; SMC4; PPME1; BAZ1A;ZNF644; ZCCHC6; OGFR; ZRANB2; ATG16L1; DEFA4; CTPS1; MIPEP; GPN1; MTREX; MRPL17; PPT2; MRPL43; PA2G4; RBM5; BID; FADS2; AK9; FADS1; WEE1; PARG; KCTD12; MTERF3; TRMT10A; PDLIM4; MPHOSPH6; IK; IGBP1; RPS19BP1; POMP; DNAJC17; MRPL57; SLIRP; SDC1; SYNGR2; MRPS18B; RPS7; MMP2; MRPL24; ANKHD1; NANP; NSUN3; RBFOX2; BIN3; WDR34; ARPC1B; ANKRD52; UBL7; TFPT; PNN; CDKN2A; CDK5RAP1; SDE2; CPOX; NT5DC1; MALT1; SFT2D3; RCC2; NCAPD2; DNTTIP2; IGFBP2; LDLR; ETF1; RPL26L1; MYL6; TIMM17A; NFU1; MPHOSPH10; INTS12; POLR2H; MAZ; NRDC; HAUS7; NAF1; ZC3H15; ZRANB3; KEAP1; TXNL4B; NCBP3; SDF4; MCM4; NSD2; EARS2; MBD3; IL6ST; EZH2; BRI3BP; GTF2E2; AASDHPPT; KRI1; TPT1; GRWD1; VRK1; SBNO1; MGME1; DNAJC2; SNRPB; ZNF48; ALDH3A1; MRFAP1; SRA1; EIF4G1; LMX1A; CDK1; RPS12; BIRC5; CIAPIN1; BAG2; NUP37; MCM3; UBA2; PPP2R1B; MAT2A; MTFR1; DDI2; MTA2; SRSF5; UBR7; DNLZ; GCFC2; WWTR1; DDX5; STAU1; COA7; GPR108; PACSIN2; PRMT1; C14orf119; TIAL1; SNAPC5; TOGARAM1; POLD2; GEMIN2; RPP30; DSCC1; EIF4E; RPL5; FASN; CACTIN;EEF2K; KIF15; DIAPH3; EIF1AD; ZNF346; RNF126; ZPR1; WDR89; GPX7; PPHLN1; KNTC1; WDR74; RIOX2; GRSF1; PACSIN3; NCAPG; EFHC1; PWP1; NPM1; DDA1; Cxorf56; ZNF106; FHL2; UBE2B; REPIN1; SGF29; ACOT7; TIA1; ASS1; GIGYF2; ATRIP; TDRD6; OGFOD1; KCTD20; ZNF280C; KIF11; EIF5; BRD4; RPP40; TARS; PANK3; CAMK4; DNAJB6; POP4; GMPS; PDRG1; HELLS; G3BP2; HOMER1; LEO1; UCHL5; MTAP; SSNA1; RGS14; MYH9; C12orf57; PUS7; PCM1; RRP9; TXNL4A; POLDIP3; METTL17; RPS10; KLHL12; LARP1; PRRC1; CCNDBP1; IRF2BPL; RBM8A; PITPNB; GLS; NUFIP2; NUPL2; SART1; ELL; NUDT1; NBEAL2; SERPINB8; FAM50A; PABPC1; NAA25; PHLDB1; DCP1A; KIAA2012; EIF1AY; DOT1L; WDR62; ING5; SLC26A2; RBM17; AGAIN; STX10; PITRM1; ANKRD40; WARS; MEMO1; HOMER2; IPO5; RRM1; RC3H2; MTHFD2; INO80E; CEP170B; PBC; MAD2L1; TDP1; SMAP2; RAD23A; MRTO4; SRFBP1; C21orf59; RNASEH2C; PIH1D1; ISG20L2; MRPL32; TONSL; C18orf21; CPSF4; GLRX3; ZMAT2; ERI1; BCCIP; SECISBP2; TGFB1; URI1; RTF1; MIS12; CCDC50; FZD3; ACTN1; NCAPG2; NLE1; MED15; CEP131; PPIF; NOA1; UBAP2;NOLC1; RPAP3; NLN; CHTF18; XRN2; ELAC2; SNRPD2; SF3B3; ATF7IP; ZWILCH; ELOA; CHAF1B; RRP1B; RANGAP1; REXO2; MED26; ETNK1; ZNF700; IDE; RBM27; C8orf33; UIMC1; EIF4H; SYAP1; AMD1; WDHD1; AVEN; NOP2; GSKIP; DNAJC24; SHC1; SF3A3; SF3B6; PIN4; TFAP4; GTF2E1; CLNS1A; NUDC; HYPK; TNIP1; HAT1; WTAP; HMGCS1; RBBP7; PFDN6; GEMIN6; TIMELESS; PSMG1; EXOSC8; SPAG5; POP1; ATXN2L; CDCA7L; ALMS1; NOP53; RNF219; ZCCHC3; GPATCH11; MED29; CIZ1; PLA2G4A; UQCC3; CHML; MNAT1; PDCL3; POLD3; PNMA2; PXN; MCM7; ZNF217; CHAC2; C9orf78; ATAD2; MRPL41; CGN; UTP14A; FDFT1; EIF3G; PFDN2; NDUFAB1; MCM2; PDCD5; NDE1; LIG1; GATAD2A; DAZAP1; EDF1; TWSG1; CGGBP1; MCM6; UAP1; PPM1G; NSMAF; CENPF; FBXO9; HIRIP3; GADD45GIP1; POLR2L; RPL37; UBE2G1; FAR1; SPC24; MDC1; RPL27; PMS1; TMF1; SAAL1; G3BP1; GPRIN1; KHDRBS3; UBAP2L; BCL7B; PTX3; CCNA2; NDC80; LYAR; CDC5L; C19orf53; POLE4; SURF6; LMNA; FANCD2; HLTF; NCOA3; NUP153; NCL; BOD1; MEA1; NCOR2; PTS; TAF10; TOP2A; EXOSC6; POLA1; EXOSC2; PLS1; ASDURF; MEPCE; NELFE; BIN1;NSRP1; NEFL; AK6; FAM192A; DNAJC8; MZT2A; PSMG2; CNN3; POLK; DNAJC21; POP7; ZFAND1; RRP15; EBNA1BP2; POLR2G; FEN1; KNOP1; C9orf16; EXOSC9; ZNF622; NCAPH; PRRC2A; CAPRIN1; LRRFIP1; CCNB1; MRPL49; CCNL2; IFITM2; BRD2; SYF2; SOX9; FBN2; BCAT1; YTHDF3; PTMA; SERF2; SS18; ECT2; RNASEH2B; GPATCH4; RWDD4; NRBF2; NEFM; GPATCH1; SNRNP70; BOLA1; CDV3; CDC20; CENPO; ADAT2; LUZP1; SF3A2; CENPH; RPL29; RNF181; DCBLD2; CLINT1; GINS3; POLR2F; NUP50; CKS1B; SCAF11; EXOSC5; SUGP1; CHCHD5; MCM5; RNF113A; NAP1L1; ZFAND3; FANCI; CCND1; TRIAP1; HNRNPAB; MYOM1; YAE1D1; NUF2; RPS21; FAM207A; WDR4; OFD1; CHEK1; AKAP2; JUND; RRS1; IGFBP5; PJA2; SNRPA; CBSL; NCS1; NFATC2IP; CCDC86; EXOSC4; PHAX; TIMM13; POLE3; TNRC6A; MEAF6; DUT; GPKOW; CSRP2; POLR2D; CCDC137; CD3EAP; BUD13; ZYX; PRRC2B; NPM3; PSMG3; RNASEH2A; EXOSC7; PRRC2C; HSPA1L; PQBP1; GLB1L2; RBBP4; NASP; ZCCHC8; MLLT11; NOL12; RPP25L; C9orf40; UHRF1; JPT1; CRYBG1; CFDP1; TMSB10; C1orf174; AKIRIN2; S100A4; CBX3; C1orf131; COA4; LTV1; DCTPP1; WAC; CRIPT;FOSL1; PINX1; C12orf45; YBX1; IRF2BP2; KNSTRN; NACA; PCNA; TPM2; NOP16; RPS15; BAG4; FNBP4; CENPX; TACC3; DHFR; PSMC3IP; DHPS; CWC15; CMC2; POLR3G; MND1; CHAF1A; DIXDC1; SPAG7; ENDOG; SOAT1; PRIM2; PSME3; TIMM8B; PPP1R14B; KIFC1; MKI67; UBE2T; MYCBP; BTF3; JPT2; DNAJC9; HPRT1; RGS10; SENP6; RRP36; TRIR; PAIP2; C8orf59; CENPU; CNBP; ASF1B; C11orf98; KRT8; KIAA1143; DLGAP5; CKAP2; GINS1; RPUSD2; FAM32A; LZTS1; YBX3; ZFAND5; CDC42EP3; RRM2; TPX2; ID1; GMNN; ZFAND6; ZNF593; KRT18; NUSAP1; RPL26; SKP2; TTK; TCF3; EIF1AX; CFAP97; ID3; RNASEH1; TRMT13; SNRNP27; CENPE; KIF22; BAP1; BUB1B; CDC25C; CEP85; GINS2; RPAP2; GMEB2; DDHD1; CHST2; ZCCHC9; ZNHIT3; TLK1; CASKIN2; CEP192; ZWINT; CARNMT1; USP28; CCDC94; CTSV; TRIT1; MTG1; UBIAD1; FAM3A; CASP2; PTRH1; NAB1; PTCD2; LIN37; USP3; KCTD15; SNAPC4; NRIP1; RBMX2; PKD1L1; TK1; SKA3; PASK; C5orf22; SRXN1; RFX7; ATXN7L3; SH2D4A; HDAC7; HJURP; PIAS2; SCLT1; KIF20B; KDM2A; ZNF516; CDPF1; TSPAN13; SGO1; CHD3; ARHGAP29; SEMA6A; ATP13A3; ORAOV1; POLE2;TRMT12; MCM9; ANKRD27; DIS3L; IRF2; INO80; NGRN; VAX2; LYRM2; KCTD9; MYO19; SMC1B; ZNF584; RXRA; C19orf47; SUPT4H1; SGO2; CCP110; SSBP4; KDM2B; FASTKD1; USP22; TOPBP1; BRCA2; UBE3D; UBE2E2; CEP57L1; RFWD3; NOM1; NKAPD1; SLC7A2; ATXN1L; DDX59; KANSL1; KLHL11; ARHGAP39; RPUSD3; NCAPD3; PTPN3; NPAT; TMEM67; POC5; PGPEP1L; ATAD5; CDCA5; NFATC2; LYSMD2; EURL; PROSER2; CENPI; ZNF773; ZNRD1; E2F6; CEP83; CHRAC1; ITPRIPL1; FANCL; ZNF75CP; SNUPN; EAPP; GABPB1; TFDP1; TAF9; DDX11L8; LPCAT2; ENTPD4; DUSP11; DGKH; TOP3A; PLAU; JAK3; LRRC58; MID1IP1; RAD54B; CEP63; ZFP1; ZNF483; APOBEC3B; JUN; CHD9; ADCY9; AEBP2; ATP11B; SNX11; HASPIN; ZNF718; MTF1; ELOVL1; JUNB; PKP4; ZNF3; PPDPF; SSH2; FAM222B; RNF169; EAF1; TRIM14; KRT75; C11orf49; CDCA2; KIAA0930; PRELID3B; GIGYF1; LIPE; SFMBT2; EMC9; NAA20; PHLDA2; GTPBP2; PHF20; ZNF398; SPRYD7; CRYBG3; CDC7; CCNB2; HERPUD1; TOB1; ZNF614; NSMCE2; GBX2; SOX12; CLSPN; ZZZ3; WRAP53; POLH; PDCD7; KRT7; RILPL2; TMEM5; FANCM; LIN52; MMTAG2; MBD4; HIVEP1; ZNF721;CEP164; FGFBP3; INTU; AMBRA1; RTEL1; STK35; BAZ2A; RNF166; ANKRD26; CCDC59; POLR2K; EGR1; GRPEL2; LCORL; FAM173B; KCTD17; PHF19; LIN9; USP1; MAP7D2; TMEM131L; SHPRH; IREB2; ZNF691; C1orf52; LRR1; MIER2; FASTKD3; ANAPC16; GPR180; TUFT1; COA1; UBXN2A; LIN54; NSD1; PER3; KRBA1; POLR1D; PDP2; CAPN15; BICC1; MRM2; ADNP2; TTI2; ERCC8; BTBD2; CEP162; E2F3; ADAT1; DHDDS; PALM2; TULP3; UVSSA; MMP16; RNF10; NPAS2; STYX; GTPBP8; ZCCHC4; CHCHD10; ZNF845; TTLL4; ZNF260; CRY1; TAF8; TMEM115; MED31; FBXL14; PRDM2; MDK; FBXO28; TDRP; RAI1; C16orf72; PRIMPOL; FAM92A; MEF2C; TSTD2; RNF187; AGGF1; OGG1; SSX2IP; OSMR; MTERF2;

[0831] [Cluster 3] 2275 types of genes

[0832] ANOS1; SLC25A18; IGSF8; SLC39A7; SLC30A9; DPYS; ASAH1; NMNAT3; CAT; TMEM68; MEGF9; PGAM2; CCDC127; NPRL2; ATP9A; NCAN; ALDH5A1; CYB5R1; AGPAT3; SLC25A4; SLC25A20; EPHX1; HSDL2; CHDH; ARL8A; FGF1; PCDHGA2; MTTP; JAK2; ASL; ALDH2; PBXIP1; POMGNT2; ALDH6A1; RAB30; BAIAP3; PLXNA2; PTGR2; BCAP29; ARL8B; NDRG4; PPM1L; SFXN5; NNT; SNTB1; LMAN2; CTBS; AP2S1; SLC25A25; NLRX1; DHRS7; MOSPD2; LEFTY2; EGFR; MESD; NOTCH3; TBC1D32; STBD1; ITFG1; 45538; SCAP; SRR; IDUA; NR2F1; FBXL20; RAB6C; CAMK2G; RAB22A; ALDH3A2; ALDH1L1; ALDH4A1; MAP3K5; VPS50; SAMD8; ATL1; SIRT2; ABHD12; FAM213A; RALGAPB; HADHB; VAC14; PPOX; GPM6A; FAHD1; ABHD16A; HSD17B12; EPHB1; AQP4; GSTM2; NFKBIL1; MAPT; ACVR2B; IGFBP7; RHOT1; RHBDF1; TMEM65; SNX8; SCARB2; RHOQ; TMTC4; GDAP1; RFX4; TSC2; GNAO1; FIG4; GLUD1; PPP2R5B; CCDC168; RDH11; SCCPDH; PON2; NUBPL; BCAN; LRP1; ATP1B2; FBXO44; NEDD4L; MCF2L; MAPKAP1; ADCY8; DNAJC11; SLC25A1; KIAA0100; PHPT1; ACSF2; SEL1L; ABAT; SUCLA2; CNTFR; CPS1; RAB8B; HEPACAM; ANTXR2; LHPP; VLDLR;SLC25A24; H6PD; AHCYL2; MYO6; BCKDHB; MOXD1; CDS2; PNPLA8; NRBP2; MT1F; CCDC136; TSC1; CARMIL1; PIGS; APOOL; HECTD4; GDPD2; VCAN; GSTM3; DNAJB4; CLIP3; PHKB; RNF170; EFR3B; SCAI; PPP1CC; C16orf62; PPP2R5A; MID1; GSDME; AKR1C3; KCTD21; PLSCR4; CA14; RTN3; GCDH; C18orf32; PHLDA1; ENTPD5; AMIGO2; AKR7A2; RAB2A; AP3B2; PNPLA6; FZD7; TPPP; COMMD2; PEX16; CKAP4; ELMOD1; ARL6IP1; HSDL1; NADK2; COASY; INPP5K; GGT1; HADHA; PEX14; TAOK3; APBB1; CDK20; ZDHHC4; FUCA1; NGLY1; CLCC1; ITGB5; ANKIB1; PLXNA4; ANO10; MAP2K6; SMOC1; DARS2; FGD3; TGFBRAP1; CERS1; PCDH7; ATP5H; RTCA; RAB5B; RAB4B; PICK1; YES1; ATP2B4; DDX58; RHOC; MGST1; SUCLG1; HACD3; RAB12; GALE; CDC42; LNPK; MGST3; ABLIM1; SARM1; WDR37; MOB3B; RAP1B; SORL1; SARAF; MRVI1; ZNF512; ABHD4; SH3RF3; PEX7; HSD17B10; PHYHIPL; ADAM17; SFXN1; RAB6B; DDR1; MUT; MPDU1; STK38L; IFIH1; FAM122A; TPP1; BCR; DNAJC5; PDGFRB; SDR39U1; DLST; PCYOX1L; EXOC1; GALK1; DHTKD1; PEX12; BBS5; PIP4K2C; KIFAP3; GAB2; ACOT13; ABCD4; TRIM4; FAM160B1; UBA6; CRYZ;PLXNC1; ATP5O; PRKCE; AGK; RPTOR; ZFYVE26; CERCAM; ADGRG1; ARL6IP5; HSPA13; PDPR; GDPD1; DGLUCY; NECTIN2; COG3; RAB3A; TDRD7; VPS11; CPT2; RINT1; AP2M1; RNASET2; CALR; GALC; TBC1D24; DCAF5; RAB5A; RGL2; LPIN2; RUFY3; PLXNA1; ZFYVE27; DTWD2; TBCE; OSBPL7; AASS; PGRMC2; AFG1L; SNTA1; ANKRD16; TOM1; RNF141; MEGF8; RMND1; SNX18; MCCC1; TMCO3; EXOC3; TMEM9B; KRIT1; MLC1; GNA13; EXOC4; ITGAV; RFTN1; SLC1A4; EXOC5; APOL2; TRAPPC8; EML2; PRPSAP1; MAP2K5; MAPK3; CRYL1; D2HGDH; GDE1; GNAI2; FRMD5; AP2A2; ERLEC1; LMF2; ATP1A2; VPS8; STK33; ALDH9A1; USP46; OXLD1; SLC35E2B; CD47; PIK3C2A; GNPAT; DYNC1H1; SPATA20; CROT; EMC7; CNRIP1; INPPL1; VPS18; TOR2A; WIPI2; EXOC6B; TRAF3IP1; ARHGEF37; SESN1; DHRS7B; SCGN; USP9X; WASHC5; ANO6; BTN3A3; PDPK1; C2orf76; JAK1; NRP2; GSTT1; FMN2; LRRC1; TMTC2; CST3; C12orf4; PEX2; EIPR1; ERP44; ADCYAP1R1; DHRS4; ATAT1; UBTD1; DYNC1LI2; ERP29; IBA57; COQ5; BMPR1B; JAKMIP2; HMGCL; CENPV; SZT2; NFIX; TMED2; CBR4; FPGT; FYN; VANGL2; DECR1; ADGRB2; SLC15A2; PANK4; TRIM16;RNF185; OSBPL6; EFNB1; ZFYVE21; ITGB4; ARSD; DSCR3; RAB3D; OSBPL2; CCDC88C; GNAQ; SNX13; BCKDHA; CACFD1; ARMCX3; TRIQK; ITGB8; PURA; SRGAP3; GNA11; WWOX; GBF1; FBXL17; CRBN; ARHGAP1; MMAA; VPS51; SLC33A1; PHF20L1; ANO8; SYT17; FAM169A; POGLUT1; PIP4K2B; KIF5C; CBR1; PTRH2; EMC4; ATP2B1; TARSL2; HERC1; RHEB; VPS9D1; HS6ST1; FAM177A1; FAM45A; EXOC8; BRMS1L; HSD17B4; ZFYVE1; ITPKB; MICALL2; MFF; PDIA5; KIDINS220; FMNL2; PDHA1; PHKA2; SRC; PEX10; WASHC4; EXOC7; ANKRD50; SMAD2; DCAKD; TNPO1; ATG7; SRGAP1; APBA2; SBF2; STAT3; SIPA1L2; ALS2; LAMTOR3; DNAJC13; ALCAM; NIT1; TM9SF3; VWA8; TNR; AKR1D1; TM7SF2; EPS8; DIP2A; ACVR2A; TAOK2; ULK1; FGFR2; EPHB2; RIC8A; STXBP3; VPS52; IDH2; BBS1; KLHL22; ALG9; VPS39; LYPD1; ATRN; HSD17B8; BCHE; BBS9; RUFY2; NDUFA9; RASA1; CCDC93; GYS2; BCAS3; HACL1; LRP2; MTCH1; RICTOR; SLC39A6; CSPG5; RHOB; UQCRFS1; GRAMD4; NIPSNAP3A; CRELD1; CSNK2A2; FNDC3A; RETSAT; TLN2; LPCAT3; METTL9; CHCHD6; ADGRL3; TBC1D9B; AKR1C1; TRAPPC10; KCND3; SH2B1; UQCRB; TTC37; SIAE;ACAD9; RTN4; BBS4; SMPD1; LYRM9; NTRK2; NUDT16L1; PDXK; ENTPD1; ARHGEF10L; HS1BP3; ANK2; C21orf33; SPNS1; AXL; TECPR1; PDIA3; TTC21B; KIAA1109; BBS2; HRAS; ANKFY1; TUBGCP6; CDO1; PKD2; SNX27; FNBP1; PPP1R7; PCDH9; SERAC1; WDR7; USP30; GSK3B; TAOK1; ATG2A; CLCN3; COX20; IMPACT; SIRT5; BTD; KLC4; APPL2; NOTCH2; DENND5B; MANSC1; AP5S1; LRPAP1; HIBCH; GNL1; DEPTOR; SORCS2; PTPRE; GAA; NAA35; MAPK14; INPP4A; EHHADH; TMEM222; OSGEPL1; NPR2; ALDH7A1; HNMT; HSPB1; USE1; ACAA2; NAGK; SAR1A; MOB3A; ACAD11; CAPN1; DHRS1; GYG2; PYROXD2; PLCD1; RSPRY1; ASAP1; TRAPPC12; TRAPPC9; ABCD3; FITM2; PIKFYVE; IQGAP2; SUFU; TDRKH; RAP2A; RASA4; RMDN3; DCLK2; NKIRAS1; TOLLIP; MON2; SUCO; AIDA; OSBPL1A; GRID1; SCO1; ADD3; DVL3; IFFO1; CDK16; MARK3; TRAPPC11; VCPIP1; WDR91; EXD2; ZSWIM8; SEC62; AP5B1; SDSL; STRIP1; UQCRC1; RNASEL; MAPK7; TENM3; SURF4; WDTC1; TANK; CCDC22; KLHL5; CDK14; CEP68; DPM1; NRAS; CMBL; MEST; ARSG; FARS2; MPZL1; TBC1D17; GLB1; LONP2; ARHGEF26; MARK2; UBR4; RAC1; KIF3B; LRRC14; EFCAB14;ARHGAP21; TTC28; DDAH1; PPP6C; STX18; NF1; CCDC91; MAGED4; PSMD11; ACBD5; STIM1; FBXL2; TOMM5; USP32; NUDT19; SERGEF; PTPRA; MAPK1; ARHGAP5; TBCK; OPHN1; SEC11C; AGPS; ECHS1; BTN2A1; RAPGEF1; RBCK1; PLCD3; FAM120C; DENND5A; SKIV2L; KIF3A; PPA2; 45356; MAGED2; ACOT9; HEXIM2; LMO1; GSTCD; SYTL2; ARF4; TBC1D23; RNF7; NCAM1; PEA15; KIF1A; MTMR10; UTRN; IQSEC1; DCD; EPB41L2; WFS1; PRUNE1; ERBB4; ACAP2; COL26A1; LLGL1; NLGN3; BECN1; PARP16; ELMOD2; CRYAB; OPA1; FAM118B; FGFR1; FH; TMEM132A; MSMO1; PRKRA; THTPA; HP1BP3; CROCC; GM2A; SREBF1; C5orf51; ACAA1; CDC42BPB; NCKAP1; VPS41; FAM126A; PEX26; UFSP2; SOS1; NUMB; IRF9; GPM6B; MAP3K2; CACNA2D1; HGS; APC; VPS33B; BBS7; SPG11; HLA-C; VAV2; ACYP2; CYFIP1; DZIP3; HERC4; OPTN; C4B; TK2; VPS13D; RAPGEF2; SPARC; DYNC1LI1 ; SNX21; TRIM25; PIGN; ARHGAP35; MTHFR; GSN; GABARAPL2; KIF21A; AAK1; PACS1; PCNX3; HECTD3; FHIT; SARS2; USP4; TXNRD3; PRKAB1; COG7; C6orf89; AMDHD2; ARMC10; ARSB; IRF4; RNF5; HK1; GLCCI1; ECH1; NSMF; RND2; SNX14; FEZ1; CPEB4;NCOA1; SLC38A7; ARPC1A; WDFY3; GNAS; PEX1; HBS1L; AMOTL2; TPST1; CLASP2; SMG8; ARSK; IFT80; KCNN3; KIF5B; VMA21; TCTN1; PDCL; C1orf50; SLC22A18; ABHD10; GPR37L1; CRMP1; ZNF423; C3orf38; CASK; QPRT; ELP6; PGGT1B; PECR; PEX3; LANCL2; EIF4A2; FAM69A; UFL1; TUBA1A; SPART; LAMTOR1; KIAA1468; RPS6KC1; 45543; POLR3GL; CMTR2; KIF2A; COG5; ATP5J2; OGFRL1; SNX15; FN3K; ARL3; PLA2G6; MUL1; DUSP19; DYNLT1; NCALD; DAPK1; TBCEL; PLEKHA2; FBXL4; FKBP2; ZMPSTE24; COMMD7; PGM1; PIP4K2A; UBXN6; ACADVL; EBF3; TTC8; TECPR2; HACE1; PSMD13; CCNY; CALCOCO1; MAP2K2; BDH2; CCDC28B; ASNA1; S1PR1; DCAF8; MAP4K3; PIK3CB; SRI; 45542; TGFB2; BBOX1; ARHGAP18; CDK17; GYG1; NT5C2; ST5; PGPEP1; EZR; CDH4; C8orf82; ARL15; MAST3; GRHPR; C11orf96; PTGES2; FAM53C; CRAT; GNB1; DTHD1; IMMT; NRBP1; MTMR3; ATP5S; BET1; NDUFB6; FGD4; PFKFB2; WDR19; AP2B1; OSBP; UBE4A; PPIC; TUBB2B; EPB41L5; TMEM230; MAN2C1; CLTC; AP4S1; RNF31; GNAZ; ANKRD45; DCX; CEP97; RETREG3; SH3GLB1; PIK3CG; ATP5L; PLXNB2; DOCK1; FRMD4A; APOA1; ENGASE;PCDH8; CTH; JAM2; TMEM161B; TTC30B; GAMT; DDAH2; RFTN2; PRPS1; ZFPL1; Cxorf38; ATG5; RELA; SEPSECS; PPP1R21; RWDD3; ATPAF1; EPM2AIP1; DCLK1; AGT; AP1G1; CASC4; H2AFY; ASAP3; DGUOK; GABBR2; PTEN; GIT2; ADGRL2; TIPRL; FAM120B; DAAM1; STK3; MT-ND5; GPSM1; SYT11; PACS2; ELAVL3; SEC22B; ARHGEF12; TRAF6; EPHA7; MSI1; PXMP2; GNB4; STARD3; SAP30L; PIGA; VAT1; OR52N4; POMT1; RABGAP1; GUK1; LBH; PLCB1; NAXD; MACROD2; SENP8; DNM2; FEZ2; SBF1; IFT88; PIGX; ABCB8; C4A; SHARPIN; GNB2; AMZ2; NSDHL; MANBAL; IFT122; MSRB2; CAMLG; ADAM9; DALRD3; ABCC10; MIEF1; KIF13B; ACOX1; ARHGEF6; TMOD2; NEK9; LYPLAL1; GLIPR2; PLPP3; PYGL; ANXA6; NEBL; SMYD4; DECR2; LRIG2; MPI; GGCX; IFT172; AGO3; NIN; ELMO1; MPP5; CC2D1A; BRK1; TRIM2; MECR; LAMTOR2; SH3BP5L; CD99L2; FN3KRP; RIOK3; SH3PXD2B; WASH2P; MGMT; SNX24; ACP2; TIMM9; CHN1; SOS2; IFIT1; ABCC4; LRRC40; ACOX3; PYGB; ANKRD13A; NUDT16; STK39; CADM4; SBDS; ISCA2; PTPN23; NADSYN1; OCIAD1; TEP1; GFOD2; EEF1AKMT1; ARHGEF40; SPARCL1; SRGAP2C; TBC1D10B; ABHD14B; MIGA1;PGM5; SARS; USP40; NF2; C6orf203; CTPS2; AP5Z1; SNX29; TSTA3; DAZAP2; CAPN2; SCAMP2; TRAK1; FLRT3; CHMP6; COQ6; C11orf68; SNX6; RGS20; ARHGAP42; IQCB1; COPS7B; SEMA4B; GUCY1B1; DMXL1; PRDX3; VPS4A; WDR35; PREX2; CELSR1; ZHX2; SLC23A2; AMPD2; ZZEF1; RIDA; HLA-B; LRRC57; DTNA; SUGCT; LANCL1; GDPGP1; DERL2; TRO; MAN2B1; AP3M1; STX5; ARAP1; PFN2; 45537; ITM2C; NAPRT; WDR11; FXYD6; RALGAPA1; CELSR2; CTNND2; BLOC1S1; SC5D; SLC38A3; PBX1; PRKD1; CEP250; PIPOX; TMEM231; DYNC2LI1; TNPO2; EPN2; ALG13; PPP2R5D; AP5M1; ERBB2; POTEKP; ZNFX1; EDNRB; FAM117A; RABEP1; UBA1; TIMP2; NPC2; C21orf2; EPB41L1; KIF5A; HSD11B2; SLC7A11; WASHC3; STON2; COG6; HEATR5B; TANGO2; CXADR; ATP6V1G2; MGEA5; VPS26B; RBSN; CGNL1; MON1B; PGM3; NCK2; MAP1A; SUMF2; DYNC1I2; ANKMY2; TMEM184B; MAPKAPK3; GHITM; UBE2D1; SYNM; DICER1; AP1B1; COMMD10; USP5; CSAD; SNX2; ZNF618; NXN; PLPBP; PHYH; MAP1S; SLC6A11; KLC1; RMDN2; ATP5D; GMPR2; LAP3; CUL5; QDPR; ORMDL3; RALBP1; CDC37L1; FGGY; HUWE1; LCLAT1; ACADSB; ADH5; FAM81A;ARHGEF11; IFIT2; PHGDH; CRB2; MYO5C; ENO2; SCRN1; LRRC4B; USP47; MAPK11; PRDX5; DMXL2; FHL1; GBA; TRIOBP; C1orf43; NOVA2; HSPB8; DAB2IP; B4GALNT4; MGRN1; RYDEN; TBC1D5; CUL9; ZFP36L1; PRKD2; PLXNB1; HINT2; DENND4C; PRPSAP2; C2orf72; CEP112; FUK; ERMP1; SLC25A17; DTX3; GFM2; PNMA8B; DPY19L4; HTT; ACTR10; PELI2; TUBA1C; HEATR5A; TUBB4A; ARF3; CTNND1; CLIP2; CNPY2; TMCC1; RND3; REEP6; LRP1B; MAP7; HERPUD2; RP2; BBIP1; ARHGAP12; PSEN1; ACTR1A; TBCB; RHOA; PICALM; SNX16; GAREM2; TMEM167B; RNH1; BMP2K; SCFD1; PCDH10; 45539; DNASE2; MED22; RAB4A; ATG9A; MAPK12; TNFAIP1; NPEPPS; UBA7; RABGEF1; DCTN1; INPP4B; HINT1; WASL; CACHD1; NHLRC2; GALM; AGO1; ARPC4; ARVCF; LSS; MTHFSD; TMEM263; C14orf37; ABI1; TSPYL4; HPCAL1; EHD3; PTPRZ1; POTHEUS; LASP1; UGP2; GLB1L; TXN2; SPAST; PCYT1A; POLB; STOX2; MPDZ; IFT27; ARHGAP11B; FRYL; PEF1; TROVE2; ACADS; SYNE1; PCYT2; SCRN3; EEPD1; SMYD5; PSMC4; WDR1; PSMD10; NAGLU; AHDC1; SERINC1; ARHGEF18; SAMD4B; CYHR1; BTBD9; COPG2; STX17; ENOPH1; EPS15; PTPRF; TLDC1;GNG5; CTIF; CORO1B; TBC1D16; ROGDI; AP1M1; DCTN6; TKFC; FAM160B2; TRMT11; PDCD6; NAPG; CUL3; ZER1; IFT140; HEXA; TMEM132E; MTM1; DBNDD1; TP53I3; INAFM2; CRELD2; NMNAT1; FAIM; DLD; UBR1; MOCS3; SOGA3; EPG5; GPN2; BROX; ANXA5; SIRT3; KCTD6; FAM91A1; VPS13A; AKTIP; MFHAS1; PAQR7; ADD1; SAYSD1; PARD6B; FHL3; IFT46; SLC12A4; DCTN3; FAHD2A; MSN; SRP54; FER; ERBIN; SCRIB; PTPRS; LRBA; GNPDA1; LRIG1; ERG28; IDH1; PDE3A; WDR47; RDX; STX7; TBC1D13; GGPS1; REEP5; AGL; DPH5; CC2D1B; IFT52; CDKN2C; ARHGAP10; STAM; HIP1R; SEPHS2; PPFIA1; STAM2; RCBTB1; PAK3; OTUD6B; NR3C1; CCDC97; RHBDD2; HLCS; SNX12; PUDP; MCFD2; SLC38A10; EFCAB7; PTPN11; NAA38; MYO18A; LZIC; FSTL1; MECP2; CA8; YKT6; UBXN4; VPS37C; SCAMP4; UBL3; NAV1; DNAJB2; WDR45B; LRRC47; WDR13; EEF1AKMT2; COQ9; MTUS1; MTMR9; B9D1; SLC44A1; ACAT1; NEMF; KRT5; SCARB1; TFAM; TRAPPC3; METTL26; MAP1LC3B; TFCP2; RABL2A; PPP1R9A; KCTD18; MVP; C8orf37; POLI; THYN1; VEPH1; UBAC1; RAP1GAP; WASF2; HECA; PSMD1; CMPK1; VPS37A; PPM1B; KRT1; CYB5A; MYH10;AMOT; STAU2; HADH; GMIP; RNF34; XDH; ADK; C16orf45; KCTD5; WDR44; CNNM2; PDZD8; LSAMP; USP11; UBR3; GCLC; HDHD2; SYNJ1; RFX2; TUBB3; PRKG1; GDI1; GNG4; AKR1A1; KSR1; PLXNA3; ATG3; REPS1; GLTP; NEK11; FNIP1; VPS45; RTL8C; CAP2; OCLN; VWA5A; GCA; ASCC1; FBXL18; GRIPAP1; ISOC1; EHBP1; DLG5; ATE1; C10orf76; BPGM; TCP11L2; VAPA; LIX1L; PEX6; PTK7; ABI2; UBE2H; HOMER3; SMUG1; KIF13A; TPMT; GSPT2; VPS13C; SPAG9; MAP9; RCOR3; KIAA1211; RBBP9; FZD2; SH3BGRL2; GFAP; DYNLL2; BSDC1; POM121; AHR; USP8; TAGLN3; IFT81; XPNPEP1; ISPD; SPAG1; PCBD2; TUBB; RAP1GDS1; FAM98C; SDCCAG8; PMPCB; ALDH1L2; GBE1; WWC1; TSC22D4; CRABP1; GNG12; TMEM134; GSTT2B; MAP3K8; IMPA1; S100B; HYI; CBLB; CHKB; SLC6A9; SNCAIP; SEC24B; XPOT; BRSK2; GLRX; CHMP3; PARD3B; FERMT2; ADD2; DNPEP; DNALI1; AGAP3; STX2; MAPK8IP1; FAM213B; NEO1; MAP2; IGHV4-34; GOLGA2; ELOC; BORCS7; CACNB3; FAM114A2; PIK3C2B; ERI3; CLPTM1; EFNB3; CRADD; ALDOC; SPTBN1; PLEKHO2; AGFG1; MAPK8IP3; PAFAH1B1; TOM1L2; LTA4H; DPYSL5; DLGAP4; H1F0; ESD; CHMP1B;NEK1; MAP1LC3A; SURF1; MTIF3; DCTN4; WDR20; MYH14; MAGED1; STX12; PRMT9; PEX19; SH3BGRL3; DHCR7; HDAC6; CCDC92; 45353; NAP1L5; SRPK2; ELAVL4; FKBP15; STAMBP; PTGR1; GPR107; CSPG4; BORCS5; NLGN2; LIN7C; PSAP; DPYSL4; MX1; RNPEP; FABP3; USP54; IKBKE; RPE; AKAP7; PCIF1; KYAT1; FCHO1; PSMB8; LRSAM1; GNG10; PPP2R1A; VAPB; PAG1; PDE6D; INPP1; PALM; SYNC; CASP6; RAB27B; SETD3; CDH2; CAPZB; CAB39L; DMWD; PDLIM3; PPP2R5C; CARD19; GPT2; PPP3R1; CKB; NMI; TAX1BP1; ACTR3; NACAD; GPC4; TUBB4B; ST13; SCP2; PCMT1; JAG1; UBTD2; NECAP1; TBC1D8; EMD; SYNPO; NR2C2AP; CUX1; KPNA4; UNKL; FBXW9; IAH1; TCEAL1; ELP5; SNX3; MRC2; AARS; CHMP4B; RIPK1; PGM2L1; PPFIA2; CTNNB1; RNF146; ACOT1; NAE1; KLHL42; BAG3; MAPKAPK2; ABRAXAS2; SH3GLB2; PAFAH1B2; ZFYVE19; CCDC71; ASIC1; PPP3CB; DDT; TWF1; PEX5; CAMKV; WASHC2C; SEPT11; CBL; GSTA4; TUBB6; QKI; CAPNS1; NUDT12; IFT20; MMP15; PITPNM1; MVD; UBA3; SPECC1; GATD1; PPP1R12B; CDC42BPA; NBEA; HELZ2; STX16; HAGH; AK1; HDAC5; CHMP2B; C5orf30; ISG15; F2R; GMPPB; IGDCC4;CAMSAP2; MAP3K7; NAT9; UROD; PCSK1N; GPR39; MMAB; GSR; FKBP9; BORCS8; VMAC; TMEM106B; R3HCC1; MOCS2; NDRG3; UROS; GSTP1; MEGF10; DST; PDZD11; GNG11; PLEC; COQ10B; PRR36; DCTN5; IFT57; CTNNA1; KRT10; BABAM1; MAP7D1; ACTC1; SASH1; NARS; IPO9; LRRC20; ACSS2; SCYL2; SLC30A5; ARPIN; SPTAN1; CRYZL1; CLTB; PGLS; SELENOM; FNBP1L; PTRHD1; CFL1; CCS; NOL3; PLPP1; YWHAZ; KATNAL1; ITSN1; SELENBP1; ATP6V1B2; CYLD; TP53BP1; PPM1A; FAM107B; AKAP12; GPC1; GRB2; LRRCC1; STRN4; SLITRK5; SOD2; WIPI1; CAND1; DPYSL2; FAM167A; NDRG2; CRKL; CLGN; MAP6; MT-ND4; TNC; RABGGTA; PEPD; PEBP1; DUSP3; MELTF; ADAL; GAN; PLD2; PRUNE2; DBNL; AAMDC; MTR; ASPSCR1; TSC22D2; CNDP2; FNTA; KLHL25; BABAM2; MAP1B; PRODH; ZFP36L2; KPTN; PODXL2; ALAD; CASP3; EPN1; AK3; OR1M1; AKAP6; CTSL; STON1; YWHAQ; CLUAP1; ACY1; GCSH; KIAA1191; ME1; MINDY4; IFIT3; SLC44A2; LYPLA2; TATDN3; PSPH; MPRIP; NRCAM; UBXN1; CRTC3; SH3BGRL; SCRN2; NAXE; ZSCAN18; ECI1; BAIAP2; UBA5; WBP2; RASAL2; HSBP1; LIMCH1; CNOT7; FIS1; PALMD; CALB1; HEBP2; WIPF2;PARL; KLC2; CTTNBP2NL; PBLD; NUTF2; CPPED1; NMT2; EFHD1; CRTC1; NAPA; PITPNA; SEMA6D; CADM1; SLC9A3R1; C12orf29; EEA1; RBP1; FABP5; C1orf21; ARHGAP32; DCTN2; NFIB; C1orf198; CSTB; UFM1; PGAM1; PRDX1; ZNF703; NECAP2; C11orf54; OPLAH; GOLGA1; PRDX2; UBE2Z; PSAT1; DYNLRB1; UCHL1; GSTZ1; KYAT3; SH3GL1; YWHAE; EXOG; PRDX6; CHL1; SMS; DBN1; SOD1; GNPDA2; GMFB; DNPH1; GSS; SS18L1; NPEPL1; TLN1; NES; PARK7; RABGAP1L; S100A16; SYVN1; DGKQ; AMPH; DPP8; RHOU; PIR; PLEKHA5; STMN3; FLNA; ENO3; GDI2; CCT6B; MAVS; GBP1; ARHGEF17; SORBS1; NINJ1; NSFL1C; CYGB; MARCKS; VCAM1; C1orf123; CHCHD2P9; TFG; GLOD4; MSRA; YWHAB; ZNF385D; HDGFL3; DPYSL3; MT-ND2; SLC9A6; ENAH; PAQR6; PALD1; SERPINB6; ZC2HC1A; RASSF9; CD99; DTD2; CDC42EP4; KAZN; MAGI2; SELENOW; MAP4; CALB2; EPS15L1; ASRGL1; MTPN; RGMA; NNMT; ARMC3; IFRD1; NKX2-2; ACAD8; PLIN3; CDC42EP1; GSTM4; ATPIF1; UBE2N; MDM1; SOX6; WDR17; RNF11; CNTN2; GAB1; CDKN1B; DAP; HMGN3; BAX; DBI; UBQLN2; VCL; PPIL1; BASP1; VASN; LRRFIP2; ACBD7; ZNRF2; NQO2; ECHDC3;LCA5; DENND1B; INA; TAGLN2; S100A1; PJA1; CASP1; TSNAX; CPLX1; LGALS3; PHYKPL; SH3BP2; FYTTD1; HINT3; FXN; PPP1R1B; TTC25; FBXO18; SCOC; HDGFL1; TAGLN; RPE65; MT1X; CHCHD2; ATG12; INPP5B; RABL2B; AP3M2; ASAP2; MINK1; BCL2L2; FDX2; ZFYVE9; ZCCHC24; MYLK3; MAGI3; HEXDC; SLC25A26; CCSER2; C17orf80; AMN1; PIK3R1; GALNT10; CBX8; ADPRM; GULP1; FBXO4; RASSF4; CNPY4; PARD6G; METAP1D; TMEM126A; PBX3; PRAG1; TTC30A; PXDC1; MAP4K2; CARHSP1; MAPK10; ITGAE; TAX1BP3; SMIM20; OSBP2; RNF139; CMC4; FRS2; ANKS6; MAPK6; PLEKHO1; GPD1; NALCN; TLE3; VPS13B; SLC25A44; STK36; CSF1; BCLAF3; GNA12; HPS4; GFER; DVL1P1; ADAM22; MLLT3; MTURN; CASKIN1; NAPEPLD; EVL; GGACT; PTPRD; SCYL3; MAN1A1; PRPF40B; ERCC6; ZNF502; ZSWIM9; UBE3B; EPPK1; MAPKAPK5; DSP; OR5K2; C20orf194; SCARA3; NBEAL1; CPTP; WNK2; TCEAL3; STS; LARP6; FSD1L; CCDC138; SOWAHC; MANEAL; SLC12A6; ZSCAN31; HCFC2; ELN; RBMXL1; GNAI1; HAND1; AMACR; HPS1; C7orf43; CADPS; DMD; SLC25A51; TMEM259; FSBP; FCHSD1; TTC39A; ANKRD13D; ANKRD30B; C11orf95; PCDHB14;TUBB8; ANKRD46; APBB2; JCAD; DUS4L; NFIA; MROH1; BLOC1S3; CDKL5; GOSR2; PPP1R15B; PDE2A; SPRED1; CCDC177; PIGB; CELF3; ZDHHC3; LETMD1; ZHX1; C1orf228; PLPP6; ZNF579; LYSMD1; SVIP; FAM199X; N4BP2; RNF17; KBTBD3; GSTM1; SEMA4A; CBARP; C8orf76; NECAB3; UNC5B; KIAA0513; RSG1; RCBTB2; GRB14; PROX1; PCDHGA4; TCAIM; SPPL2B; CADM3; NYNRIN; PLEKHA6; SHF; TMEM94; MOCS1; DDX60; PLEKHM3; PRKN; GK; DENND2A; USP2; IL17D; BMPR2; SGSM2; PPP1R3G; KLF9; CTNNA2; HYKK; TMEM143; ROBO2; LDB3; CDH11; FRMPD3; SLC25A53; SLC4A8; ADGRA1; GTF2IRD2B; RSPH9; SYNGAP1; WWC2; GAS2L1; GUCY1A1; ACOX2; CD38; RIMS3; PHKG1; TRIL; RSPH1;

[0833] [클러스터 4] 1663 종류의 유전자들

[0834] NCOA6; SMARCD3; ZNF490; UQCR10; ETFDH; GUSB; VMP1; POTEJ; SUN2; PRPF38A; REL; FAT3; WARS2; TUBGCP2; TYRO3; HDAC1; NUP205; SSR3; ABCB10; VPS72; PREB; NUP210; KDM6A; POU3F2; TREX1; OSBPL8; NT5E; ANKZF1; RCOR2; CHUK; RAD17; TYK2; MAIP1; KDM4B; NUP155; XPC; CHD7; CNOT10; ZBTB40; SLC16A2; RGS3; SETD1B; WDR48; ATAD2B; GATAD1; MSH3; UHRF1BP1; MIC13; ZEB2; CISD3; PNMA1; CEP78; MAST2; PELP1; ARL6IP4; ZNF714; CDK5; NEMP1; ZNF592; TEX10; STOML2; P4HA1; DDIT4; ITGA2; RPS26; MAP4K4; ELF2; DNAJC16; PUM2; VEZT; ACSS1; LPP; CAND2; FKBP7; ERO1A; SYNE3; EMSY; TRRAP; RHOBTB3; APOO; TRA2A; TIMM29; OGDH; SLC30A1; BRWD1; PDF; HTATSF1; KDM5A; AFG3L2; TMEM131; PUS10; ZNF143; RAD50; POU3F3; SMARCAL1; TMX4; ZNF813; ZCCHC11; AP3B1; KDM5B; CCDC134; SNX4; RBM14; HNRNPU; MLST8; ARF5; PRKCSH; ARMC1; PNMA8A; NOP58; TLE1; ALKBH5; AGO2; APTX; PBRM1; ZNF512B; AARS2; GLYR1; JAGN1; DTWD1; STK11; YIF1A; MED14; AKAP10; RBM4B; NDUFS2; USP43; H3F3A; ESYT2; TMEM9; TAF6L; TRIP12; COG2; WDR18; CSNK1G1; PSMD7; RFX3; ASCC2; MMS19;TMEM33; VPS35; STK11IP; FUT8; FLCN; NDUFS3; CCDC43; INTS8; TRMU; FAM185A; NUDCD2; THG1L; SDHC; SNX7; RANBP6; TARDBP; NDUFA10; TPRKB; LAMTOR5; GTF2I; FAM208A; LAGE3; MFN1; TTC26; PDP1; NDUFA11; YBEY; GNAI3; EPHA3; UACA; CHD6; RALGAPA2; N4BP1; GSK3A; SYMPK; TCTN2; TRIM33; LIN7A; RPS6KA3; UBN2; TP53RK; EIF2B4; WDSUB1; GPS1; FAM172A; SIN3B; TOR1AIP2; PLP2; MED12; ENTPD2; DNMT3A; CNOT1; TNPO3; TUBGCP3; ARFGEF1; POGZ; TTL; PRPF31; MAD2L1BP; CTBP2; RUFY1; ZNF605; NUP107; SEC13; CLCN6; RAB11B; ZNF608; SLC25A10; PPIP5K2; DTYMK; CTAGE5; AMOTL1; UBXN2B; FAM98B; NUMBL; DVL2; ATG2B; ANXA11; FIBP; LIFR; CENPB; TMEM248; SMG6; PROSER1; USP48; FUT11; LIG3; AKAP9; CLIC1; NUDT2; CNOT9; PKNOX1; ANAPC4; SURF2; BAZ2B; MAP3K20; NDUFB10; NFATC1; SMIM13; AKT3; LYRM1; MYO10; CECR2; NUB1; PRPF40A; SNX30; SYNJ2BP; EYA4; QRICH1; NELFCD; ELP4; ZNF638; ZNF292; NDUFA6; GOLM1; NUDT4; CCDC167; COX4I1; ZMYM3; NAA16; TRAPPC4; LRP6; PHIP; EDC4; PRDM10; UBTF; USP24; FAH; CCNT2; NUCB1; LDB1; NT5C3B; C2CD5; PIK3R2; BCL2L13;SRSF7; PRPF8; EIF4G2; COMMD8; HPS3; RPP38; FCHSD2; SPCS3; NUP133; GGA3; PHACTR4; NUP88; KIF1B; ZNF182; TTC9C; C1orf167; NDUFB11; RNF14; NRDE2; GSDMD; NUP98; ZBED1; FKBP8; ATXN1; WDR33; CHM; USP39; SRSF4; SCAF4; EP400; TEAD1; KHNYN; USP6NL; DOCK7; LRRC59; COMMD6; BOC; NDUFS8; DDRGK1; MED24; HPF1; HPS6; EEF1E1; SEC31A; SENP2; UPF2; EPB41; HNRNPUL1; FBXO7; NTAN1; RBM15B; PSMD8; EIF4G3; MSL1; TMX2; HNRNPH1; TNIK; KXD1; DMAP1; RPS8; SLC35F6; NISCH; NCOA2; MARF1; COMMD1; FECH; RSBN1; CTCF; ARHGAP17; PAN2; MED16; C17orf75; NELFA; COBL; MCAT; VTI1A; RBM15; PPP1R2; HIKESHI; VPS29; PGRMC1; ATP6V1D; GLMN; TRMT5; HNRNPC; BLOC1S4; MPHOSPH8; CUL4B; COL14A1; DDX1; TM9SF1; HSP90AA1; SRSF1; MTFMT; ANKRD28; BORCS6; ABCC1; BLVRA; KAT7; IFIT5; PHF21A; TRIM5; HNRNPM; RING1; PTBP2; ATRX; STK25; CFL2; PPIB; RALY; ANGEL2; ATXN3; NCKIPSD; PAFAH1B3; BPTF; NSD3; PPP2CA; ADRM1; TRIM59; PAK4; USF1; OXSM; RWDD2B; PPP1R12C; NDUFB4; PHF6; ZNF629; TBP; DDHD2; CMTM6; TMEM97; NIPBL; RAN; ZKSCAN8; SELENOF; SFXN2; NOTCH1;SRGAP2; VPS4B; RPF2; RHOT2; HAUS5; DOCK5; FMR1; ESYT1; DENND4A; MCEE; CORO7; DOK1; ARRB2; C2orf69; NUP62; PHF2; OCRL; NEK4; DHX9; DKC1; CNTROB; BRD3; NIF3L1; RNF167; LRCH4; SECISBP2L; TCAF1; NUP58; MOB1B; CEP135; MED1; OSCP1; BZW1; RNF216; TRNAU1AP; NUCB2; GOLGA4; AGTPBP1; SRSF10; NACC1; HNRNPA2B1; WWP2; DIAPH2; KDELC1; STAG1; COPRS; SHOC2; MRRF; BCL10; PPP4R3A; CHCHD3; RABAC1; STRN; UEVLD; PAICS; ARID4A; SMC3; RPS6KB2; CKAP5; ARID1B; TUBG1; DHX29; SNRNP200; COX11; KIAA0391; MOV10; YTHDF1; ARGLU1; C2CD3; MPG; IST1; SETDB1; AIP; NDUFB9; PLXND1; ACO2; LMNB2; ZSCAN21; PARD3; MAD1L1; ZKSCAN1; AP1S2; TSC22D1; ISCU; GPR98; CBR3; MAGIC1; KDM3B; BRCC3; LUCK1; RARS2; ANKS1A; DHRS11; CEP44; BOLA3; SIN3A; LRRC49; ZNF146; PAK2; USP16; FCHO2; TGOLN2; HSPB11; COPS2; PTP4A1; PHF14; ROCK1; XPO4; GLT8D1; DPF3; PAXX; NDUFA12; CORO1A; AVL9; LARS; FGFR1OP2; MRPL10; RBX1; NDUFA13; CBX6; GTPBP1; KAT6A; PI4KB; ECHDC1; PATH1; HMBOX1; ARPC3; ANTXR1; APPL1; FBXO42; THOC1; RPL7; BLOC1S2; NDUFAF7; MICAL1; FAM168A;MCM3AP; POLR2C; EIF2B1; GLIS3; RPRD2; ZMYM4; F8A1; DXO; BBX; HEBP1; NCK1; USO1; SLTM; LARP7; SORT1; AKAP8L; ZNF277; RAD21; PARP1; YIPF1; ALDH3B1; OXSR1; WDR61; DEK; HNRNPR; SYNE2; RPL7A; ITSN2; BCAT2; DDX19B; MIF4GD; DYNLL1; COA3; GAS7; CCDC88A; NUDT11; CLEC16A; CLASP1; COPS4; LGALS1; DKK3; EPC2; RNPC3; TJP1; MFAP1; DHX57; ZYG11B; LSM1; RBM22; KPNA3; MRPL16; DIS3L2; NDUFA5; BCL2L1; PSRC1; HNRNPL; BCLAF1; SWAP70; TRAPPC2B; RABEP2; WDR92; MRPL20; ZBTB21; APEX1; CPSF6; ZBTB33; CCAR1; SMG9; KPNB1; RTRAF; USF2; PARVA; UFC1; OARD1; CAB39; HIST1H3A; SIGMAR1; PTER; TRIP4; RFX1; COX6B1; NELFB; OLA1; TERF2IP; ERF; RSRC1; MKRN2; NPLOC4; LOXHD1; RPL36A; THAP12; PSPC1; SHTN1; DDX39B; CAMSAP1; PPP6R3; VPS26A; UBFD1; AS3MT; NDUFA8; PLCG1; GEMIN8; MRPS18A; ZBTB43; ELOB; HNRNPA1; SLC27A4; OVCA2; SMARCC2; AFTPH; CUTA; RBBP5; MRPL2; SLC37A4; ZNF22; PrEP; WASHC2A; ELAVL1; PCDH18; NUDT9; MRPS15; LENG9; SCAF1; IP6K1; METTL3; SLC7A6OS; PURB; RPA2; CCDC82; TJP2; FUBP3; MYORG; THRAP3; MRPL23; COX7C; GABARAP;AP3S2; TRIM13; TXNDC12; DRG1; PPP6R2; LSM14B; NEDD1; MAT2B; MEX3A; TJAP1; SGTB; TIMM8A; COX7A2L; SMAD4; TRIP11; MBNL1; RANBP2; CCAR2; YTHDC1; FAM114A1; PRCC; NDUFV2; GOLGB1; EIF3K; GAR1; IKZF5; DCAF6; MDH1; 45541; DPH6; COA6; BAG1; ZNF462; ARMT1; TRAPPC6A; RPL6; RPRD1B; NUP214; PDCD4; GORASP2; CCDC25; ARFIP2; CORO1C; NMT1; RIPK2; SERPINB1; NUP43; VAMP3; REEP3; TMEM184C; ACAD10; IQGAP1; KCMF1; TP53BP2; PSMD5; FOXRED1; NAT14; FKBP5; XRN1; BRD7; MIEN1; SMARCA4; MTDH; DACH1; EIF1B; ZBTB14; DSTN; PGD; ATF6B; CNIH4; TMPO; MRPL37; ARFIP1; CASTOR2; HIST2H3A; SHPK; SMARCD1; SMAP1; MED8; VRK3; COX7B; ARIH1; PAX6; CDC34; ADPRH; RNF25; YAP1; FAM76B; CREBBP; AIFM1; ZNF316; LSM4; PPM1F; PCBP2; SNAP23; CLASRP; LYPLA1; ACBD3; CCDC85C; CARS; CRK; MRPS9; COX6C; ARID1A; LACTB2; RFXANK; HMG20A; GCC2; SRSF3; NR2C2; SOX11; AFAP1; CSTF2; IFT74; GGA1; TLE4; GOLGA5; SKP1; ARFGAP2; VIRMA; SGTA; L3HYPDH; DDX23; COQ8B; AFDN; BPNT1; DPCD; TMEM87A; EVI5; EIF4ENIF1; COPS3; CAPZA2; NUP54; BLOC1S5; NFX1; ACO1; MTCL1;SNRPD1; PGM2; DERA; FNTB; UNC119B; OTULIN; MPST; PATZ1; WNK1; SNAPIN; ARHGEF7; NDUFA4; WDR82; ZC3H12C; PREPL; APPBP2; SYNRG; SRSF11; AIMP1; TAB1; C12orf10; SP3; LMNB1; NFKBIB; CYP20A1; NUDT3; RPL34; ARFGAP3; NXT2; POLR2M; TERF2; HCFC1; PPIL3; SYPL1; OTUB1; GPS2; TLK2; HNRNPA0; STAG2; COX7A2; MANF; ITPA; FKBP3; USP25; TADA3; STIP1; PPP5C; XRCC5; RCHY1; CDKN2AIP; USP14; ARFGAP1; PTBP1; CAP1; JUP; SRSF6; SELENOH; NPHP4; SCPEP1; LUC7L; NHSL1; SFPQ; PAIP1; KDM8; SCLY; SALL1; ACTL6A; CAPN7; ARPP19; ABL2; MEIS1; ADI1; PHF12; HMGXB4; FZR1; PTK2; PSMA7; OSGEP; HIST1H4A; ZBTB8OS; TRPS1; NAALADL2; MTMR14; TXNDC17; GSTO1; EIF3M; MBD2; SGSM3; PPP1R18; ACP1; CD2BP2; SMARCE1; DTNBP1; BACH1; PMM1; RANBP1; SPA17; ZFR; CEP128; SPIN1; PSMF1; DYNC2H1; PSMA2; KTN1; FKBPL; RREB1; FUOM; NAP1L4; POLR2I; COX5B; MRPL40; TPP2; PSME2; DIDO1; ATG13; VCP; RCC1; PHLPP1; PAPSS1; GCC1; PDXP; FGFR1OP; PSMD4; CTDSP1; PTP4A2; ROBO1; TWF2; NIT2; TCEA1; NDUFV3; BAP18; PLXNB3; RBM12; DCAF16; TMOD3; UNK; PPT1; MRPS21; PIN1;MRPL50; RABGGTB; DNAL1; NADK; CNOT11; CNOT3; LIAT1; TACC1; RBM10; ERC1; DPP3; MRPL42; HNRNPK; DDX17; SUB1; LSM14A; RAPH1; WASF1; C2orf49; CLIP1; PSMB6; XRCC4; ZNF8; SNAP29; SSBP3; EML3; HIST2H2AB; CIC; SPICE1; KRT9; RLIM; ANKRD11; HMGN4; XRCC6; AP3S1; ANXA1; PRKAR2A; SPATA33; YWHAG; TIMM10; NUDT10; PHF3; MZT2B; RBM33; HOOK3; DDB1; PPP3CC; PSMA1; GLA; ALDH16A1; TCF4; WDR60; SUV39H2; IRS2; BCL9; SORD; VIM; CHAMP1; TNKS1BP1; HMBS; TBCC; RPL4; COX17; CXXC5; RBMS2; PPA1; NHP2; GOSR1; CASP7; HABP4; DAPK3; UBQLN4; CHMP4A; AKR1B1; EWSR1; ATOX1; APEH; PSME1; SCRG1; CLIC4; NENF; HDGFL2; PNP; SNRPA1; SPECC1L; DCUN1D1; RPGRIP1; DPF2; 45545; HDDC2; CCDC83; UHRF1BP1L; MTSS1L; SELENOS; XIAP; CNOT4; EML1; VTI1B; TMEM165; EML4; MAPRE1; CENPC; NCOR1; CSRNP1; TPI1; LBR; GATAD2B; ERCC5; MRPS11; HIST2H2BF; PTPA; CSTF2T; PSMA6; CEP170; AKT1S1; TSEN15; PFN1; TTC39C; TDRD3; PSMA5; CCDC6; LSM2; PSMD9; HECW2; ARPC5; FOXO3; YLPM1; CMC1; RPL8; TSR2; CNOT2; FAF1; NANS; SNRPG; UBE2L3; PDLIM5; ILF2; CHURC1; GTF2F2;METTL14; CSRP1; MARCKSL1; CERS2; PSIP1; DENR; ATXN2; BTF3L4; DFFA; MYL6B; CLN6; CWF19L2; PGK1; TCEAL4; PHF5A; TPD52L2; TAF7; PIGG; H1FX; SUDS3; MRI1; UCHL3; KHDC4; CCDC58; UBQLN1; NOVA1; SLC11A2; ARHGDIA; PSMB3; PAWR; C19orf25; CAPG; CLTA; PLEKHA7; MTMR1; MT-CO2; IKBKG; ACP6; GSE1; UBAP1; PRPF3; TXN; NDUFS5; PPP1R12A; SEC16A; CETN2; NUMA1; LSM3; COPE; ATF7; TTC1; RBBP6; SHROOM3; UFD1; PLCB3; NDUFA2; HDAC4; TBL1XR1; ZNF609; SNRPE; PALLD; GGCT; ADSL; WDR54; GPI; KHDRBS1; NUDT5; S100A10; POLR2J; UBE2R2; IRGQ; RBM42; RRBP1; GLO1; S100A13; ACIN1; TRIM26; WIPF1; CALM3; HIST1H1C; PPIA; CAPZA1; CBX1; ATXN7L3B; NFYC; MKL1; IFT43; LZTFL1; ZSCAN26; SRSF2; PFDN4; RNF214; ZSWIM5; TAF4; RPL31; DTD1; SHMT1; VASP; CASC3; VAMP2; CRIP2; SAE1; UBE2V2; PET100; LDHB; ACYP1; PSMB1; ZC3H18; SNRPD3; CBX4; UPF3A; ANXA2; COX5A; RPA3; COPZ1; RPS25; NEDD8; PSMA4; ENOSF1; PYGO2; TKT; HNRNPDL; TSN; THUMPD1; TES; SZRD1; PPIE; RERE; ZNF358; TPM1; RFXAP; BLMH; FKBP1A; CD2AP; PRTFDC1; NMRAL1; IFT22; YWHAH; RSRC2;ISYNA1; TALDO1; TXNRD1; CREB1; PPP2R3A; ZNF219; PAXIP1; PSMA3; TATDN1; ZC3H13; FTO; UBE2NL; MED13; CAST; ALDOA; SLU7; UBL4A; DENND1A; TAF3; FAM117B; NDUFA7; CTTN; FLNB; ACTN4; VBP1; TNRC6B; U2AF2; DCPS; UBE2K; RPLP1; ELAC1; HMGB1; GOT1; FKBP1B; TCEA2; MRPS35; SSSCA1; ZEB1; PSMB5; EEF1D; NME1; MRPL55; MRPS33; DR1; STRBP; RPS29; C1orf122; THOP1; UBE2V1; PDLIM2; MDP1; PMM2; MTMR12; LSM8; PUF60; CYCS; ZC3H14; HNRNPD; KHSRP; SOX13; MVK; RPLP2; PPIH; PDLIM1; DPH7; MCRIP1; SOX5; DRAP1; GON7; AHNAK; CSTF3; UBLCP1; ZNF768; NDUFAF2; GTF2H5; ANP32A; MRPL33; PDAP1; H2AFV; ZBTB18; PPCDC; FABP7; SF3B2; TTC19; NDUFS6; MIF; RABL6; NUCKS1; GTF2A1; NCOA5; PPP1R8; AGTRAP; S100A6; AK2; LSM7; PFAS; UBE2I; NFYB; RAD23B; HIST2H2BE; HDGF; TCTEX1D2; SNRNP35; SORBS2; NAA30; CDC37; NME2; PSMB4; PFDN5; TCOF1; RFX5; HMGN2; CCDC124; MAPK1IP1L; CALD1; H2AFX; KRT14; PSMB2; TMSB4X; SF3B5; RBMX; HARS; NT5C; PDLIM7; IDI1; API5; MRPL52; HGSNAT; SDHAF4; PTPRG; MRPL12; PTMS; NHEJ1; SNRPC; SOX21; LDHA; LSM6; DIABLO;N4BP2L2; AURKAIP1; CCDC9; C1orf226; WBP11; UPF3B; FDPS; PFDN1; TPR; DDX42; MYL12B; SEPHS1; LSM5; SUMO3; PSMB7; ENO1; LCMT2; ASF1A; FAM136A; EIF4B; TPPP3; TOX4; MRPL14; GPALPP1; TBCA; PKIG; SRP9; TPD52; PYM1; TPM3; GTF2F1; KMT2C; RPS19; LGMN; UBXN7; EEF1B2; ACAT2; AIF1L; ZFP91; SNW1; ENY2; SF3B4; FUBP1; AUTS2; GPBP1L1; CCDC102A; NOP10; HMGN1; BCL7A; PPP1R10; RANBP3; ALYREF; SARNP; MRPS14; PCNP; SF1; ELOF1; PRKRIP1; MT-CO1; SH3KBP1; PLRG1; ILF3; DDB2; SLAIN2; RMDN1; ZC3H4; COA5; SERBP1; CD109; CAPS; RNF2; HIST2H2AA3; SUMO1; STMN1; HIST1H1E; CDK2AP1; ERH; ENSA; GINS4; NUDT21; ZNF207; PITHD1; SUOX; BOD1L1; FUS; SAFB2; ZCCHC7; RPL36; SAFB; SOX2; CTNNBIP1; CCDC12; CCNK; RPP21; RWDD1; NOL7; RPL23A; GTF2A2; FIP1L1; SMNDC1; PGP; FAU; ATP6V1G1; FAM103A1; PRR12; CHTF8; HMGB2; APIP; SERPINB9; TPM4; RPL39; RPS28; KCNH1; CIRBP; ANP32B; SMAP; CNN2; DOPEY1; WBP4; PGGHG; SF3A1; MRPS36; SET; HIST1H1B; ZNF787; HMGA1; CHCHD1; SNRPB2; LIMD2; HMGB3; MAML2; C5orf24; MAX; FILIP1L; NFYA; CHTOP; HIST1H1D;EIF4EBP1; HIST1H2BB; SUMO2; COX8A; HIST1H2BM; HIST1H2AJ; MRPL34; MKL2; PIAS3; SEMG2; CCDC174; ING4; BCAR1; LRCH2; SOX4; ARL2BP; VGLL4; TAB2; MPP2; TOP3B; CA13; NDUFB1; MORF4L2; FAM193A; SP2; HOMEZ; MAPK4; COX19; FBXL3; SREBF2; MED6; BIRC2; ATN1; FAM161A; ZNF770; FAM181B; ZMIZ1; MZT1; SP8; TNRC6C; STAR; GPATCH2; TSSC4; CCDC186; FLYWCH2; TRMT61B; EYA1; SLC25A40; MEIS2; HTR1B; BNIP3; GPBP1; HMG20B; ZFP37; SEMA4D; SPATA6; PRPH; TADA1; TBC1D20; NDUFAF8; ZNF428; ATP23; BRWD3; MDFIC; EIF4EBP3; LRRC45; IL17RD; FAM53B; ZNF783; EPC1; ZBTB24; SNRK; KMT5B; HDDC3; TCF20; CC2D2A; CCDC9B; NEK3; NR2C1; UBAC2; PPP4C; URM1; TNRC18; BCL9L; HSP90AA4P; PPIL6; ABRACL; CEP120; TMC1; TEX26; RYR1; DOCK11; CEND1; SP9; THEM4; NPHP3; HIST3H2BB; ICE2; DPM3; FAM160A2; ZDHHC8; C12orf60; ZSWIM7; TMA7; TNK2; CCDC121; ZNF131; SLC35A1; CRTC2; TAF1; GAREM1; CCDC28A; TMEM132C; PARP12; THAP1; ALG8; RTL8A; USP38; TCF7L1; ACOT11; TMEM246; CEP89; ZFX; FAM193B; KIAA0556; ESCO1; TOX2; TRPC4AP; SIKE1; CCDC148; CNTRL; TSPY10;PIAS1; STK19; MTRF1; MLXIP; CPEB2; MAMLD1; PCYT1B; ZNF445; ZNF35; C11orf74; C11orf70; ZNF777; ZNF276; CLK1; PLEKHA8; PLEKHG4B; UTS2; IGSF11; PIK3IP1;

[0835] Based on the clustering results, a heatmap analysis was performed on gene expression levels at each differentiation time point. The heatmap analysis used the Log(Fold Change) values ​​obtained from the expression levels measured for each gene at each differentiation time point (Fold Change (FC) = expression level in the experimental group / expression level in the control group). The heatmap analysis results are shown in Figures 8 and 9. The values ​​of the results in Figure 8 are shown in Figure 9. Specifically, Figure 9 numerically represents the average Log(Fold Change) values ​​of the genes belonging to each cluster.

[0836] Determining the optimal k value

[0837] Furthermore, the inventor of the present application verified the scores for the number of clusters for various k values, including k=4. Among the various k values, the score for the number of clusters was confirmed to be the highest for k=4.

[0838] The score for the number of clusters was calculated based on the number of neuron-related factors, which are factors associated with a specific target cell or tissue. Neuron-related factors (neuron-related genes) were identified through database searches. Specifically, the keywords "neuron," "synapse," "dopaminergic neuron," "cholinergic neuron," "serotonergic neuron," and "glutamatergic neuron" were searched in the databases Harmonizome and The Comparative Toxicogenomics Database (CTD) to obtain a list of neuron-related factors. After deduplication, a total of 1,553 factors were identified, which are disclosed in "Factor Set 1" herein. Neuron-related factors may be referred to as "neuron-related genes."

[0839] As a result of the verification, it was confirmed that all genes (multiple genes) belonging to the clusters contained 564 types of Neuronal-related genes.

[0840] [Factor set 04] 564 types of Neuronal-related genes

[0841] APOE; BDNF; PRKCA; INS; MFGE8; SEMA3A; ACSL6; CHRDL1; ANOS1; PDGFC; PPP2R2B; C3; TIMP3; TP53; COL18A1; SCN9A; ABCA1; MAOA; ITGB1; JAK2; IGFBP3; PIP5K1C; NNT; TIAM1; KCNJ10; ITGA3; TMED10; EGFR; NRP1; RAB39B; SRR; COMT; UBE2A; MAP3K5; SIRT2; DOCK6; MRAS; GPM6A; CTSF; AQP4; GPSM2; MAPT; MBD1; TSC2; PROM1; FIG4; TLR3; PLAT; GJA1; ARF6; ATP1B2; TENM4; PIGT; CYB5B; PTGDS; SESN2; PTPRO; TSC1; VCAN; RAP1A; HDAC1; ADAM10; RTN3; ATP2B2; PNPLA6; APBB1; ITGA5; MYEF2; SP1; RAB1A; PICK1; ITPR1; POMGNT1; SORL1; RAB6B; LRP4; TPP1; DNAJC5; DLST; PRKCE; RAB3A; HSPD1; ARNT2; PPARD; LRRN1; RAB5A; P2RX4; MTOR; ZFYVE27; STIM2; MIB1; MAPK3; APAF1; SLC16A2; SLC1A3; FAS; DYNC1H1; MARK4; ATAD2B; PANK2; SCGN; USP9X; PDPK1; CST3; ADCYAP1R1; MFN2; ZEB2; FYN; IGF1R; CAMK2D; SYT1; EPHA2; PRKDC; WWOX; CDK5; PLP1; NPTN; ITGA2; SRC; STRADA; CLN3; APBA2; RPS3; DYRK1A; STAT3; ALS2; FGFR2; EPHB2; RBPJ; NSF; RASA1; CLN8; SLIT2; CTSB; OGDH; RTN4; SMPD1; NTRK2; NEDD4; LIMK1; AXL; HRAS; GRIA1; TOP2B; GSK3B; CAV2; BTBD10;SQSTM1; KAT2B; MAPK14; IKBKB; ALDH7A1; HSPB1; CAPN1; PRKCSH; ATP1A1; MAPK7; ATP1B1; SIL1; UBR4; RAC1; FOXO1; STK11; HSPA5; NF1; CASZ1; ARAF; SPRY2; MAPK1; DNM1; RAF1; KALRN; KDM5C; MAP2K1; NCAM1; KIF1A; NKX6-1; ERBB4; NLGN3; TRIO; BECN1; TOR1A; OPA1; FGFR1; SLC29A1; NUMB; GPM6B; HSPA9; APC; MTHFR; ZNRF1; TARDBP; HK1; FEZ1; PRKAA1; KCNN3; SLC12A2; C9orf72; NFKB2; TUBA1A; RGS6; EXTL3; PLA2G6; TTC3; DYNLT1; ROOM2; MCRS1; DAPK1; PIP4K2A; PIK3C3; MED12; DNMT3A; TUBB2B; DCX; PIK3CG; DNM1L; ATG5; RELA; AGT; GABBR2; PTEN; AKT2; RPS6KB1; AKAP9; STK3; POMT1; AKT3; HFE; KIF13B; RCAN1; GPC2; PSMC5; KIF1BP; CC2D1A; ILK; SIK2; PRKCD; WNK3; NOS1AP; MAPK9; NF2; ITM2B; TRIM9; SMN1; PPID; LANCL1; HSPA1B; CLN5; SRRM2; PBX1; MAPK8; POLG; CNOT8; TIMP2; EPB41L1; KIF5A; DOCK7; PREX1; EIF4A3; CLP1; NFKB1; ADH5; ENO2; USP47; PRDX5; APP; YY1; GBA; FECH; KDM1A; EIF2AK2; GIT1; VTI1A; PTPN1; CDK4; CSK; HTT; TRAP1; HSP90AA1; PSEN1; PHF21A; RHOA; BLM; GIRL1L1; NQO1; OSTM1; DCTN1; ATXN3; WASL; ID4; SORBS3; SPAST;CAMK1; TBP; POLB; SCD5; RIT1; NOTCH1; ROGDI; FMR1; YARS; SMARCA1; HSF1; ATM; NMNAT1; DLD; HIP1; MAP2K4; AKT1; ASPM; WDR5; VAMP7; HIP1R; MCFD2; MECP2; CA8; NBN; HDAC3; DNAJB2; SCYL1; SETDB1; SLC44A1; PARD3; ACTG1; PSMD1; SDC2; STAU2; NAAA; CRLF3; CIB1; TUBB3; GRN; GFAP; CAMKK2; GAP43; TAGLN3; WWC1; S100B; PARP1; SNCAIP; ANK3; GAPDH; GLRX; ITSN2; GAS7; MAPK8IP1; RB1CC1; MAP2; SIRT1; LGALS1; SETX; NBR1; BICD1; TIMM50; TJP1; HSPA8; PAFAH1B1; DPYSL5; RAE1; BCL2L1; MAP1LC3A; ECE1; MYO5A; APEX1; HDAC6; ZNF148; SLC25A14; RB1; CAV1; CACYBP; ROCK2; PPP2R1A; VAPB; PLCG1; CASP6; CDH2; TAX1BP1; DLG1; SPTLC1; COIL; ITGB3; NAE1; BAG3; NDEL1; ASIC1; EEF2; FKBP4; UBE3A; CBL; RANBP2; HMOX1; HTRA2; RUNX1; AK1; BAG1; CHMP2B; F2R; GMPPB; STUB1; ABL1; NCOA7; IQGAP1; DST; SMARCA4; PRKACA; PAX6; CREBBP; AIFM1; PPP4R2; DIAPH1; FNBP1L; CFL1; NFE2L2; EIF3E; AKAP12; GRB2; SOD2; FSCN1; NFX1; DPYSL2; DNMT1; PEBP1; GAN; MAP1B; CCNT1; CASP3; CTSL; DHCR24; PBX2; USP14; HSPA4; NRCAM; PPP1CA; NAMPT; SRF; EFHD1; EEA1;DTNBP1; SCG2; PA2G4; WEE1; PHLPP1; CTDSP1; ROBO1; PLXNB3; PPT1; MMP2; PIN1; UCHL1; YWHAE; HNRNPK; SOD1; KEAP1; EZH2; NES; PARK7; VRK1; IRS2; LMX1A; CDK1; BIRC5; EWSR1; RAC3; TPI1; CYGB; PFN1; FOXO3; DFFA; DPYSL3; CLN6; CAMK4; UBQLN1; SLC11A2; PAWR; GLS; FGF2; KHDRBS1; S100A10; CALB2; WIPF1; PPIA; ACTN1; VAMP2; NKX2-2; MED26; FKBP1A; PDCD10; SOX6; CREB1; CNTN2; CDKN1B; CAST; BAX; UBE2K; HEXIM1; ZC3H14; ANP32A; ZBTB18; NDUFS4; ZNRF2; NFYB; TOP2A; NEFL; INA; CAPRIN1; BCAT1; UPF3B; TPM3; LGMN; CCND1; IGFBP5; NCS1; SF1; SUMO1; PQBP1; MLLT11; FUS; SOX2; DIXDC1; SET; SNCA; HSPA6; ID1; SKP2; TCF3; ID3; MBP; AP3M2; CDKN1A; VGF; SLC2A13; RXRA; GULP1; ATN1; CDCA5; MAPK10; HTR1B; FGD1; JUN; RRAS; CSF1; GLI2; SYN1; PRNP; KRT7; CAMK2B; CEND1; UBE3B; EGR1; SLC12A6; RPS6KA1; CEP89; CDKL5; HLA-DRB1; MEF2C; ATP13A2; GDF11; NDP; CAMK2A; SNAP91; ADGRA1; SYNGAP1; AGAP2; PTK2B;

[0842] The formula used to calculate the score for the number of clusters is as follows:

[0843] [Calculation Formula 1]

[0844] Score for number of clusters = .

[0845] Here, x is the number of factors related to a specific target cell or a specific target tissue belonging to each cluster. Here, x is the number of Neuronal-related genes belonging to each cluster.

[0846] Here, μ is the average of the x values ​​of each cluster.

[0847] At this time, N c is the number of clusters. For e...

Claims

1. A method for screening one or more cell resetting genes for recovery of damage or dysfunction of a specific target cell or a specific target tissue related to the specific target cell, including: (i) Obtain vectors for multiple genes; At this time, the vectors for the above multiple genes Obtained from a two-dimensional gene expression profile data set for two-dimensional differentiation of differentiable cells capable of differentiating into the above-mentioned specific cells and a three-dimensional gene expression profile data set for three-dimensional differentiation, At this time, the two-dimensional gene expression profile data set for the two-dimensional differentiation is composed of two-dimensional gene expression profiles for multiple genes, At this time, the two-dimensional gene expression profile for each of the above multiple genes is It consists of the first to Nth gene expression levels of the two-dimensional differentiation for each of the plurality of genes measured at each of the first to Nth time points of the two-dimensional differentiation process, At this time, the 3D gene expression profile data set for the 3D differentiation is composed of 3D gene expression profiles for multiple genes, At this time, the 3D gene expression profile for each of the above multiple genes is Consists of the first to Mth gene expression levels of the three-dimensional differentiation for each of the plurality of genes measured at each of the first to Mth time points of the three-dimensional differentiation process; (ii) Classifying the gene vectors for the above plurality of genes into two or more clusters using a clustering algorithm; (iii) based on predetermined cluster selection criteria, selecting one of the two or more clusters and determining the first candidate genes as genes belonging to the selected cluster; and (iv) Among the first candidate genes, genes related to at least one of metabolic, catabolic, and wound healing are determined as the cell resetting genes.

2. A method for screening one or more cell resetting genes, wherein the plurality of genes in the first paragraph is 2000 or more.

3. A method for screening one or more cell resetting genes, wherein N is an integer from 2 to 6 in any one of claims 1 to 2.

4. A method for screening one or more cell resetting genes, wherein M is an integer from 2 to 6 in any one of claims 1 to 3.

5. A method for screening one or more cell resetting genes, wherein information on the genes related to metabolism in any one of claims 1 to 4 is obtained from a knowledge base on Gene Ontology (GO).

6. A method for screening one or more cell resetting genes, wherein information on the genes related to catabolism is obtained from a knowledge base on gene ontology in any one of claims 1 to 5.

7. A method for screening one or more cell resetting genes, wherein the information on the genes related to wound healing in any one of claims 1 to 6 is obtained from a knowledge base on gene ontology.

8. A method for screening one or more cell resetting genes, wherein in any one of paragraphs 1 to 7, (iv) determines, among the first candidate genes, genes associated with two or more selected from metabolic, catabolic, and wound healing as the cell resetting genes.

9. A method for screening one or more cell resetting genes, wherein in any one of paragraphs 1 to 8, (iv) determines genes related to metabolic, catabolic, and wound healing among the first candidate genes as the cell resetting genes.

10. A method for screening one or more cell resetting genes, wherein the predetermined cluster selection criteria are determined based on the number of genes associated with the specific target cell belonging to each of the two or more clusters, in any one of claims 1 to 9.

11. A method for screening one or more cell resetting genes, wherein the predetermined cluster selection criterion is to select a cluster having the highest cluster score calculated by one of the following formulas among the two or more clusters: Cluster score = ; Cluster score = x / S g ; and Cluster score = x / N a , Here, x is the number of genes associated with a specific target cell belonging to each of two or more clusters, S g is the sum of the number of genes associated with the specific target cell in all clusters, N a is the number of genes belonging to each of two or more clusters.

12. In any one of paragraphs 10 to 11, the specific target cell is a neuron, A method for screening one or more cell resetting genes, wherein the genes associated with the specific target cell are neuron-related factors (e.g., neuronal factors).

13. A method for screening one or more cell resetting genes, wherein the neuron-related factors in paragraph 12 are factors belonging to factor set 1.

14. In any one of paragraphs 10 to 11, the specific target cell is a hepatocyte, A method for screening one or more cell resetting genes, wherein the genes associated with the specific target cell are hepatocyte-related factors (e.g., liver factors).

15. A method for screening one or more cell resetting genes, wherein the hepatocyte-related factors in paragraph 14 are factors belonging to factor set 2.

16. In any one of paragraphs 10 to 11, the specific target cell is a pancreatic cell, A method for screening one or more cell resetting genes, wherein the genes associated with the specific target cell are pancreatic cell-related factors.

17. A method for screening one or more cell resetting genes, wherein the pancreatic cell-related factors in paragraph 16 are factors belonging to factor set 3.

18. In any one of paragraphs 1 to 17, (ii) the gene vectors for the plurality of genes are classified into two or more clusters using a clustering algorithm. Determine the optimal number of clusters, and Including classifying the gene vectors for the above plurality of genes into the optimal number of clusters, A method for screening one or more cell resetting genes.

19. In paragraph 18, the optimal number of clusters can be determined through a calculation formula for a score for the number of clusters to determine the optimal number of clusters, A method for screening one or more cell resetting genes, characterized in that the calculation formula for the score for the number of clusters at this time is as follows: Score for number of clusters = , Here, x is the number of genes associated with a specific target cell belonging to each of the two or more clusters, μ is the mean of the x values ​​of each cluster, Nc is the number of clusters, S g is the sum of the number of genes associated with the specific target cell in all clusters, N g is the number of genes related to the specific target cell belonging to the above plurality of genes.

20. A method for screening one or more cell resetting genes, wherein the optimal number of clusters is not 2, in any one of claims 18 to 19.

21. A method for screening one or more cell resetting genes, wherein the two or more clusters are first to R-th clusters (i.e., R clusters) in any one of claims 1 to 18.

22. A method for screening one or more cell resetting genes, wherein R is an integer from 2 to 8 in the 21st paragraph.

23. A method for screening one or more therapeutic genes for treating a specific disease or disorder, including: A method for screening one or more cell resetting genes according to any one of claims 1 to 22, wherein among one or more cell resetting genes for recovery of damage or dysfunction of a specific target cell or a specific target tissue related to the specific target cell, genes having an abnormal expression level in the specific disease or disorder are identified.

24. A method for screening one or more therapeutic genes in claim 23, wherein the specific disease or disorder is caused by damage or dysfunction of the specific target cell or specific target tissue.

25. A method for screening one or more therapeutic genes, wherein the genes having an abnormal expression level in the specific disease or disorder are genes having a low expression level in a model or individual of the specific disease or disorder compared to a healthy model or individual, in any one of claims 23 to 24.

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