Method for identifying functional cell subpopulations in mesenchymal / stem cell populations

Abstract

The present invention provides a method for identifying functional cell subpopulations in an MSCs cell population, the method comprising performing omics analysis of an MSCs cell population at the single cell level and clustering the MSCs cell population based on the results of the analysis to classify the cell population into cell subpopulations.

Description

Methods for identifying functional cell subpopulations in mesenchymal stromal / stem cell populations

[0001] The present invention relates to methods for identifying functional cell subpopulations within a mesenchymal stromal / stem cell population.

[0002] Mesenchymal stromal / stem cells (MSCs) have already been used clinically as a source of cell therapy for various diseases, and further clinical applications are expected. However, it has been reported that MSCs have unstable clinical efficacy and are a heterogeneous cell population (Non-Patent Documents 1 and 2). Therefore, it has been difficult to establish critical quality attributes and quality standards related to pharmacological effects for MSCs for clinical applications or cell-processed products manufactured by processing MSCs (collectively referred to as "MSC-based cell products"). Previous attempts have been reported to predict the osteogenic differentiation potential and immunosuppressive potential of MSCs by using the percentage of cells exhibiting specific morphologies early in osteogenic differentiation induction and the percentage of cells exhibiting specific morphologies after activation with interferon-γ among isolated or prepared MSCs (Non-Patent Documents 3 and 4).

[0003] Dunn CM, et al. Strategies to address mesenchymal stem / stromal cell heterogeneity in immunomodulatory profiles to improve cell-based therapies. Acta Biomater. 2021 Oct 1;133:114-125. doi: 10.1016 / j.actbio.2021.03.069. Epub 2021 Apr 20. PMID: 33857693.Phinney DG. Functional heterogeneity of mesenchymal stem cells:implications for cell therapy. J Cell Biochem. 2012;113(9):2806-12.Marklein RA, et al. High Content Imaging of Early Morphological Signatures Predicts Long Term Mineralization Capacity of Human Mesenchymal Stem Cells upon Osteogenic Induction. Stem Cells. 2016 Apr;34(4):935-47. doi: 10.1002 / stem.2322. Epub 2016 Feb 29. PMID: 26865267.Marklein RA, et al. Morphological profiling using machine learning reveals emergent subpopulations of interferon-γ-stimulated mesenchymal stromal cells that predict immunosuppression. Cytotherapy. 2019 Jan;21(1):17-31. doi: 10.1016 / j.jcyt.2018.10.008. Epub 2018 Nov 28. PMID: 30503100.

[0004] Considering the rapid development of clinical applications of MSCs in recent years, evaluating the pharmacological effects and safety of MSCs is important not only for ensuring the quality of MSC-based cell products but also for evaluating the pharmacological effects and safety of such products. It is desirable to clarify the heterogeneous composition of MSCs or to identify functional cell subpopulations within MSC cell populations.

[0005] The present inventors have found that by performing omics analysis of MSCs at the single cell level and clustering the cells based on the results of the analysis, it is possible to classify cells in a heterogeneous MSC cell population based on their properties, or to identify functional cell subpopulations contained in an MSC cell population.

[0006] The present invention provides the following as representative embodiments: [1] A method for identifying functional cell subpopulations in a mesenchymal stromal / stem cell population, comprising: performing omics analysis of the mesenchymal stromal / stem cell population at the single-cell level; and clustering and classifying the mesenchymal stromal / stem cell population into cell subpopulations based on the results of the analysis. [2] The method of [1], wherein the omics analysis is gene expression analysis by single-cell RNA sequencing. [3] The method of [1] or [2], wherein the clustering of the cell population is performed by dimensionality reduction of the results of the omics analysis of the cell population. [4] The method of any one of [1] to [3], comprising: identifying genes that are differentially expressed in a given subpopulation among the classified cell subpopulations compared to other subpopulations; identifying the function of the gene; and estimating the function of the gene as a function contributed by the given subpopulation. [5] The method of any one of [1] to [3], comprising: identifying genes that are differentially expressed in a given subpopulation among the classified cell subpopulations compared to other subpopulations; and selecting a functional cell subpopulation from a given mesenchymal stromal / stem cell population using the genes as markers. [6] The method of any one of [1] to [3], comprising: classifying a mesenchymal stromal / stem cell population derived from different mesenchymal stromal / stem cell lines into cell subpopulations by performing the omics analysis and clustering; determining the number of cells derived from each of the different mesenchymal stromal / stem cell lines contained in each of the cell subpopulations; measuring the level of a specific function in each of the cell lines; and inferring a cell subpopulation that has a correlation between the number of cells derived from each of the cell lines contained in the subpopulation and the level of the specific function in each of the cell lines as a cell subpopulation contributing to the specific function. [7] The method of [6], wherein the specific function is pharmacological effect and clinical safety. [8] The method according to [6], wherein the specific function is VEGF secretion ability. [9] The method according to [6], wherein the specific function is angiogenesis ability.

[10] The method according to any one of [6] to [9], comprising: identifying genes that are differentially expressed in a subpopulation predicted to contribute to the specific function from among the classified cell subpopulations, compared to other subpopulations; and predicting the genes as genes involved in the specific function.

[11] A method for regulating a function of a cell subpopulation, comprising: preparing a cell subpopulation predicted to contribute to a specific function according to the method of any one of [6] to [9]; identifying genes that are differentially expressed in the subpopulation predicted to contribute to the specific function, compared to other subpopulations; and regulating the expression level of the genes or their expression products in a given mesenchymal stromal / stem cell, thereby controlling the specific function in the cells.

[12] A method for evaluating the VEGF-secreting ability or angiogenic ability of mesenchymal stromal / stem cells under ischemic conditions, comprising evaluating the VEGF-secreting ability or angiogenic ability of the cells under ischemic conditions based on the expression level of at least one gene selected from the group consisting of the LRRC75A gene, the LGALS1 gene, the CD73 gene, the CD90 gene, and the CD105 gene, and proteins encoded thereby, in the mesenchymal stromal / stem cells.

[13] A method for selecting mesenchymal stromal / stem cells having high VEGF-secreting ability or angiogenic ability under ischemic conditions, comprising selecting mesenchymal stromal / stem cells having high VEGF-secreting ability or angiogenic ability under ischemic conditions based on the expression level of at least one gene selected from the group consisting of the LRRC75A gene, the LGALS1 gene, the CD73 gene, the CD90 gene, and the CD105 gene, and proteins encoded thereby, in the mesenchymal stromal / stem cells.

[14] A method for producing mesenchymal stromal / stem cells having high VEGF secretion ability or angiogenic ability under an ischemic environment, comprising culturing mesenchymal stromal / stem cells having high VEGF secretion ability or angiogenic ability under an ischemic environment selected by the method of

[13] .

[15] A method for regulating the VEGF secretion ability or angiogenic ability of mesenchymal stromal / stem cells under an ischemic environment, comprising regulating the expression level of the LRRC75A gene or its expression product in mesenchymal stromal / stem cells.

[16] A method for evaluating the immunosuppressive ability of mesenchymal stromal / stem cells, comprising evaluating the immunosuppressive ability of the cells based on the expression level of at least one gene selected from the group consisting of the LGALS1 gene, the LRRC75A gene, the CD73 gene, the CD90 gene, and the CD105 gene, and the proteins encoded thereby, in the mesenchymal stromal / stem cells.

[17] A method for selecting mesenchymal stromal / stem cells with high immunosuppressive ability, comprising selecting mesenchymal stromal / stem cells with high immunosuppressive ability based on the expression level of at least one gene selected from the group consisting of the LGALS1 gene, the LRRC75A gene, the CD73 gene, the CD90 gene, and the CD105 gene, and the proteins encoded thereby, in the mesenchymal stromal / stem cells.

[18] A method for producing mesenchymal stromal / stem cells with high immunosuppressive ability, comprising culturing mesenchymal stromal / stem cells with high immunosuppressive ability selected by the method of

[17] .

[0007] According to the present invention, the composition of a heterogeneous MSC population can be clarified by classifying cells in the population based on their properties. Furthermore, according to the present invention, functional cell subpopulations, such as cell subpopulations involved in specific pharmacological effects or safety, can be identified from within the MSC population.

[0008] A: Culture conditions for bone marrow-derived MSCs (BM-MSCs) used in the examples. B: Amount of VEGF secreted into the culture supernatant after culturing 11 strains of BM-MSCs under normal and ischemic culture conditions for 16 hours (mean ± SE, n = 5 for each strain). *: p < 0.05, **: p < 0.01, ns: no significant difference (Student t-test). Two-dimensional UMAP plot based on the mRNA expression profile of each cell of 11 strains of BM-MSCs under normal culture conditions. Eight clusters of cell subpopulations (CL0 to CL7) were detected. Results of gene ontology (GO) analysis of the clusters. Results of GO analysis for clusters 0 to 1. The figure shows a bar graph depicting the hypergeometric distribution test performed on a 2x2 frequency distribution table using the Gene Ontology (GO) annotation information of the significant genes detected for each cluster as categories. P-values ​​after correction for multiple comparisons (adjusted P-values) based on the Benjanni-Hochberg (BH) method were calculated and log10 logarithmically transformed. The bar graph colors represent shades of orange-red according to the number of significant genes corresponding to each GO annotation. (Continued from Figure 3-A.) GO analysis results for Clusters 2-3. (Continued from Figure 3-A.) GO analysis results for Clusters 4-5. (Continued from Figure 3-A.) GO analysis results for Clusters 6-7. (Scatter plot showing the amount of VEGF secreted by each BM-MSC line under ischemic culture conditions against the percentage of cell number in each cluster.) Each figure shows the Spearman rank correlation coefficient (rs) and its p-value between the percentage of cell number for each cell line and the amount of VEGF secretion. The relative expression level of LRRC75A for each cell line is displayed on a two-dimensional UMAP plot. The scale bar represents a color scale of gene expression normalized using the FeaturePlot function in Seurat. Effect of LRRC75A expression suppression in BM-MSCs on VEGF secretion.LRRC75A mRNA expression (A), VEGF secretion (B), and [VEGF secretion under ischemic culture conditions] / [VEGF secretion under normal culture conditions] (C) were measured for a BM-MSC line transfected with control siRNA (siNegative) and a BM-MSC line in which LRRC75A expression was suppressed by siRNA (siLRRC75A#1) under normal and ischemic culture conditions. The bars in each figure represent the mean ± SE (n=3). *: p<0.05, **: p<0.01 (Student's t-test). The relative expression levels of CD73, CD90, and CD105 for each cell type are displayed on a two-dimensional UMAP plot. The scale bar represents the color scale of gene expression normalized using the FeaturePlot function in Seurat.

[0009] Mesenchymal stromal / stem cells (MSCs) are mesodermally derived stem cells present in the body. MSCs can be isolated from various tissues, such as bone marrow, adipose tissue, umbilical cord, and placenta. MSCs can differentiate into mesodermally derived tissues, such as bone, cartilage, blood vessels, and cardiomyocytes. It has also been reported that MSCs can differentiate into ectodermally derived neurons and glial cells, and endodermally derived hepatocytes. The International Society for Cellular Therapy (ISCT) defines MSCs as follows: (1) they proliferate as adherent cells in vitro; (2) they are positive for the cell surface markers CD73, CD90, and CD105, and negative for CD45, CD34, CD14, CD11b, CD79a, CD19, and human leukocyte antigen class II; and (3) they have the potential to differentiate into osteoblasts, chondrocytes, and adipocytes.

[0010] Clinical applications of MSCs typically involve culturing cells collected from living organisms and using the resulting cell lines to treat patients. MSC-based cell products are also commercially available. MSCs possess immunomodulatory capabilities, the ability to promote wound healing, and the ability to differentiate into various cell types, making them promising candidates for applications in wound healing and regenerative medicine. For example, MSCs can exhibit physiological activities such as anti-inflammatory properties, growth factor secretion, and angiogenesis promotion. However, MSCs are known to exhibit diverse biological and functional properties (e.g., proliferation, differentiation, and physiological activity) among cell lines. For example, even if all MSCs meet the ISCT definition, their biological characteristics differ depending on the tissue from which they are derived. This property is known as MSC heterogeneity. MSC heterogeneity can lead to differences in the biological and functional properties among MSC cell lines and can also result in reduced quality, such as unstable pharmacological effects, in MSC-based cell products. Furthermore, the instability of MSC-based cell products not only impairs their pharmacological efficacy and safety, but also makes it difficult to compare products, which may have a serious negative impact on clinical therapeutic efficacy.

[0011] There is a need for objective criteria for evaluating the biological and functional properties of MSCs, and for verifying the quality of MSC-based cell products, for example, for assessing their pharmacological efficacy or safety, and for standardizing the quality of such products based on their pharmacological efficacy or safety.

[0012] The inventors considered that clarifying the composition or properties of heterogeneous MSC cell populations would be important for evaluating the biological and functional properties of MSCs or verifying the quality of MSC-based cell products. The present invention relates to clarifying the composition or properties of heterogeneous MSC cell populations. The present invention also relates to classifying cells in MSC cell populations based on their properties or identifying functional cell subpopulations contained in the populations.

[0013] To understand the heterogeneity of MSCs, the present inventors performed omics analysis of MSC populations at the single-cell level using gene expression analysis technology based on single-cell RNA sequencing (scRNA-Seq), and clustered the MSC populations based on the results of this analysis. As a result, it was discovered that among the cell subpopulations classified by this clustering, there are subpopulations associated with specific functions.

[0014] Thus, in one embodiment, the present invention provides a method for identifying a functional cell subpopulation in a cell population of MSCs. Hereinafter, this method is also referred to as the "method of the present invention." In the method of the present invention, the "functional cell subpopulation" refers to a cell subpopulation characterized by specific biological and functional properties. Preferred examples of the "functional cell subpopulation" include cell subpopulations that exert specific clinically relevant physiological activities, such as cell subpopulations that exert pharmacological effects on living organisms, and cell subpopulations that are related to clinical safety, such as cell subpopulations that exert biological toxicity (e.g., carcinogenicity, teratogenicity, cytotoxicity, induction of allergic or inflammatory responses, etc.).

[0015] The animals from which the MSCs used in the present invention are derived may be humans or non-human mammals such as mice. Tissues from which the MSCs are derived include bone marrow, adipose tissue, umbilical cord, placenta, etc. The MSCs used in the present invention may be those collected from living organisms, commercially available cell lines, or cultured or processed versions of these.

[0016] The method of the present invention comprises performing omics analysis of a cell population of MSCs at the single cell level, and clustering the cell population of MSCs based on the results of the analysis.

[0017] Techniques for single-cell-level omics analysis of the cell population used in the methods of the present invention include genomic analysis, transcriptome analysis, proteome analysis, and metabolome analysis, with gene expression analysis being preferred. For example, gene expression analysis at the single-cell level can be performed by single-cell RNA sequencing (scRNA-Seq). scRNA-Seq can be performed according to procedures known in the art. For example, single-cell droplets and library preparation can be performed using a Chromium Controller provided by 10xGenomics, and then sequencing the resulting droplets using an Illumina next-generation sequencer can be used to obtain gene expression profiles at the single-cell level. This gene expression analysis can provide gene expression profiles for individual cells contained in the MSC cell population.

[0018] Next, MSCs are clustered based on the results of the analysis. For example, the cell population can be clustered by reducing the dimension of the omics analysis data for the cell population (e.g., the gene expression profile obtained by the scRNA-Seq). As a more specific example, the dimensionality of the omics analysis data for the cell population is reduced using Uniform Manifold Approximation and Projection (UMAP), a nonlinear dimensionality reduction method, and then the cell population is clustered according to a clustering algorithm (e.g., the k-means clustering algorithm UMAP-assisted K-means clustering). The UMAP-based analysis can be performed using Seurat (URL: / / satijalab.org / seurat / ). The results of dimensionality reduction and clustering can be visualized in two-dimensional space using UMAP.

[0019] By the clustering, the MSC cell population is classified into subpopulations characterized based on the results of omics analysis, such as gene or protein expression and physiological activity. The cells contained in each subpopulation share common characteristics (e.g., a common gene expression profile pattern) and are therefore considered to share a specific function based on these characteristics. Therefore, the cell subpopulations classified by the method of the present invention can be assumed to be functional cell subpopulations.

[0020] Furthermore, in the present invention, the functions of cell subpopulations classified using the method of the present invention can be identified. In one embodiment, MSC cell populations derived from different MSC lines are subjected to omics analysis and clustering according to the method of the present invention and classified into cell subpopulations. For example, different MSC lines are each subjected to the omics analysis and clustering, and specific cell subpopulations with the same characteristics are extracted from each cell line. Alternatively, cells from different MSC lines are pooled and subjected to the omics analysis and clustering to be classified into cell subpopulations. The number of cells derived from each cell line contained in each of the cell subpopulations is then determined. Meanwhile, the level of a specific function in each cell line (e.g., the activity, production, or secretion amount of a pharmacological substance, etc.) is measured. The correlation between the number of cells derived from each cell line contained in each subpopulation and the level of the specific function in each cell line is examined. Subpopulations with such a correlation can be predicted to be subpopulations that contribute to the specific function. The specific functions include physiological activity, pharmacological effects, and clinical safety desired for MSCs, and are not particularly limited to, for example, the ability to secrete vascular endothelial growth factor (VEGF). VEGF secretion leads to angiogenesis, so a subpopulation that contributes to VEGF secretion can contribute to angiogenesis or wound healing. As used herein, "contribution" to a function encompasses both positive (enhancement) and negative (inhibition) contributions. Generally, a positive contribution is desired with respect to pharmacological effects, and a negative contribution is desired with respect to side effects and biotoxicity, but is not limited to these. Whether a subpopulation's "contribution" to a given function is positive or negative will be apparent to those skilled in the art from the context.

[0021] A specific example of the procedure for identifying the function of a cell subpopulation according to this embodiment is shown in the Examples below. In this Example, the omics analysis and clustering were performed on 11 bone marrow-derived MSC lines, and each cell line was classified into eight cell subpopulations based on the same characteristics. The number of cells derived from each cell line contained in each subpopulation and the amount of VEGF secreted by each cell line under an ischemic environment were examined, and it was found that there was a subpopulation in which the amount of VEGF secreted correlated with the number of cells. This subpopulation was presumed to contribute to VEGF secretion under an ischemic environment. This cell subpopulation is clinically effective because it can promote VEGF secretion under an ischemic environment and contribute to wound healing, such as angiogenesis.

[0022] In a further embodiment, genes that are differentially expressed in a cell subpopulation that is presumed to contribute to the specific function compared to other subpopulations are identified. The differentially expressed genes found in the subpopulation characterize the subpopulation. Thus, the differentially expressed genes can be used as markers to assess the presence or absence or level of the specific function in MSCs.

[0023] In one example, the differentially expressed gene found in the cell subpopulation predicted to contribute to the specific function can be used as a marker to select the cell subpopulation predicted to contribute to the specific function from given MSCs. Also, for example, by collecting cells expressing the differentially expressed gene from given MSCs or further culturing those cells, a cell population of MSCs that more effectively exerts the specific function can be obtained.

[0024] In another example, the differentially expressed genes may be genes involved in a specific function contributed by a subpopulation of MSCs. The function involved by the differentially expressed genes can be identified based on existing knowledge (e.g., public databases) or by known methods (e.g., gene knockdown). For example, differentially expressed genes in a given subpopulation are identified, and the function involved by the genes is identified. The function involved by the differentially expressed genes can be estimated as the function contributed by the given subpopulation.

[0025] In yet another example, if the differentially expressed gene is involved in a specific function, regulating the expression level of the gene or its expression product in MSCs can control the specific function by MSCs. For example, if enhanced expression of the differentially expressed gene results in the enhancement of the specific function (e.g., a pharmacological effect), enhancing expression of the gene can enhance the specific function, while reducing expression of the gene can attenuate the specific function. Alternatively, if reduced expression of the differentially expressed gene results in the enhancement of the specific function, reducing expression of the gene can enhance the specific function, while enhancing expression of the gene can attenuate the specific function.

[0026] As a specific example, as shown in the Examples below, it was found that the subpopulation predicted to contribute to VEGF secretion contained many cells strongly expressing the LRRC75A gene. Inhibiting expression of the LRRC75A gene in this subpopulation reduced VEGF secretion. These results indicate that the LRRC75A gene is involved in VEGF secretion, and that regulating the expression level of the LRRC75A gene or the protein encoded by it in MSCs can control the VEGF secretion ability of the MSCs and, therefore, the wound healing ability (e.g., angiogenesis). Furthermore, these results indicate that the efficacy of VEGF secretion and wound healing, such as angiogenesis, of MSCs can be evaluated, or MSCs effective in VEGF secretion and wound healing, such as angiogenesis, can be selected, based on the expression level of the LRRC75A gene or the protein encoded by it.

[0027] Therefore, in a further embodiment, the present invention provides a method for controlling the VEGF secretion ability of MSCs under an ischemic environment. This method comprises regulating the expression level of the LRRC75A gene or the protein encoded thereby in MSCs. Increasing the expression level of the LRRC75A gene or the protein encoded thereby in MSCs can enhance the VEGF secretion ability, while reducing the expression of the gene or the protein encoded thereby can attenuate the VEGF secretion ability. Furthermore, because high VEGF secretion ability reflects high angiogenic ability, this method can also be used to control angiogenic ability under an ischemic environment.

[0028] The expression level of a gene or protein in a cell population can be measured by known methods, for example, but not limited to, RT-PCR, quantitative RT-PCR, mRNA expression level analysis by RNA sequencing, etc., ELISA, fluorescent staining, mass spectrometry, flow cytometry, etc. Regulation of gene or protein expression can be carried out by known methods, for example, but not limited to, gene knockdown using siRNA or the like, gene modification using the CRISPR-Cas9 system or the like, antibody drugs, etc.

[0029] Furthermore, as shown in the Examples below, it was found that within a population of MSCs, a subpopulation that strongly expresses the LRRC75A gene also strongly expresses mitochondrial function-related genes, the immunosuppressant LGALS1 (galectin 1) gene, and the cell surface antigen CD73 gene, CD90 gene, and CD105 gene. This indicates that this subpopulation is also effective in immunosuppression. It also indicates that MSCs effective in VEGF secretion, wound healing (e.g., angiogenesis), or immunosuppression can be more efficiently selected based on the expression levels of the CD73 gene, CD90 gene, or CD105 gene in addition to the LRRC75A gene or LGALS1 gene.

[0030] Therefore, in a further embodiment, the present invention relates to evaluating or selecting MSCs using LGALS1 (galectin 1), CD73, CD90, and CD105 as markers instead of or in addition to the aforementioned LRRC75A.

[0031] In one embodiment, the present invention provides a method for evaluating the ability of MSCs to secrete VEGF under an ischemic environment using the marker. In another embodiment, the present invention provides a method for selecting MSCs with high ability to secrete VEGF under an ischemic environment using the marker. These methods comprise the step of evaluating the VEGF secretion ability of MSCs or selecting MSCs with high VEGF secretion ability based on the expression level of a marker gene in the MSCs or a protein encoded by the marker gene.

[0032] The marker gene related to the VEGF secretion ability or angiogenesis ability of MSCs includes at least one selected from the group consisting of the LRRC75A gene, the LGALS1 gene, the CD73 gene, the CD90 gene, and the CD105 gene. In a preferred embodiment, the marker gene is the LRRC75A gene. In another preferred embodiment, the marker gene is a combination of the CD73 gene, the CD90 gene, and the CD105 gene. In a more preferred embodiment, the marker gene is a combination of the LRRC75A gene, the CD73 gene, the CD90 gene, and the CD105 gene.

[0033] The higher the expression level of the marker gene or the protein encoded thereby in MSCs or a subpopulation thereof, the higher the MSCs' ability to secrete VEGF under an ischemic environment is evaluated. For example, in given MSCs, cells that express the marker gene (e.g., the LRRC75A gene) at a statistically significantly higher level than the average expression level of the gene in the cell population can be evaluated or selected as cells with high VEGF secretion ability under an ischemic environment. Furthermore, since high VEGF secretion ability reflects high angiogenic ability, these MSCs are cells with high angiogenic ability under an ischemic environment.

[0034] In yet another embodiment, the present invention provides methods for evaluating the immunosuppressive potential of MSCs using the markers. In yet another embodiment, the present invention provides methods for selecting MSCs with high immunosuppressive potential using the markers. These methods comprise the step of evaluating the immunosuppressive potential of MSCs or selecting MSCs with high immunosuppressive potential based on the expression level of a marker gene in the MSCs or a protein encoded by the marker gene.

[0035] The marker gene related to the immunosuppressive ability of MSCs includes at least one gene selected from the group consisting of the LGALS1 gene, the LRRC75A gene, the CD73 gene, the CD90 gene, and the CD105 gene. In a preferred embodiment, the marker gene is the LGALS1 gene. In another preferred embodiment, the marker gene is a combination of the CD73 gene, the CD90 gene, and the CD105 gene. In a more preferred embodiment, the marker gene is a combination of the LGALS1 gene, the CD73 gene, the CD90 gene, and the CD105 gene.

[0036] The higher the expression level of the marker gene or the protein encoded thereby in MSCs, the higher the immunosuppressive ability of the MSCs is evaluated. For example, in given MSCs, cells that express the marker gene at a statistically significantly higher level than the average expression level of the gene in the cell population can be evaluated or selected as cells with high immunosuppressive ability.

[0037] Furthermore, from the above examples, it can be seen that by collecting cells that more strongly express the markers from a given population of MSCs, or by further culturing those cells, it is possible to obtain a cell population of MSCs that can more effectively exert effects such as VEGF secretion, wound healing (e.g., angiogenesis), or immunosuppression. Therefore, in a further embodiment, the present invention provides a method for producing MSCs with high VGF secretion or angiogenic ability under an ischemic environment. For example, this method includes collecting cells evaluated as having high VGF secretion or angiogenic ability based on the expression levels of marker genes related to the VGF secretion or angiogenic ability of the MSCs or proteins encoded thereby, and, if necessary, culturing the collected cells. In another further embodiment, the present invention provides a method for producing MSCs with high immunosuppressive ability. For example, this method includes collecting cells evaluated as having high immunosuppressive ability based on the expression levels of marker genes related to the immunosuppressive ability of the MSCs or proteins encoded thereby, and, if necessary, culturing the collected cells.

[0038] In a more specific example, cells that express a marker gene (e.g., the LRRC75A gene) at a statistically significantly higher level than the average expression level of the gene in the cell population are collected from a given MSC population and cultured, thereby obtaining MSCs with enhanced VEGF secretion ability. Alternatively, a strain that expresses the marker gene more strongly is selected from a group of MSC strains, cultured, and then subcultured. By repeating this process, MSC strains that express the marker gene more strongly and thus have enhanced VEGF secretion ability can be obtained.

[0039] The MSCs thus obtained, which have enhanced VEGF secretion or immunosuppressive ability, have high pharmacological effects and are useful for clinical applications (e.g., wound healing, angiogenesis, immunomodulation, etc.). For example, MSCs with enhanced VEGF secretion ability can be used to produce MSC-based cell products for clinical applications.

[0040] The objectives, features, advantages, and ideas of the present invention will be apparent to those skilled in the art from the description of this specification. The embodiments and examples of the invention described herein show preferred embodiments of the present invention and are shown for the purpose of illustration and explanation, and are not intended to limit the present invention thereto. It will be apparent to those skilled in the art that various changes and modifications of the present invention can be made based on the description of this specification within the spirit and scope of the present invention disclosed herein.

[0041] The present invention will be described in detail below with reference to examples, but the present invention is not limited to these examples. In the following examples, when commercially available reagent kits or measuring devices are used, the protocols attached to those kits or devices are used unless otherwise specified.

[0042] (Cells) Eleven strains of human bone marrow-derived MSCs (BM-MSCs) were obtained from LONZA and ScienCell Research Laboratories (LONZA MSC production lot numbers: 18TL241909, 18TL262066, 18TL282222, 18TL312488, 19TL029340, 19TL058658, 19TL191055, 19TL155677, 19TL281098, 19TL337479, and ScienCell Research Laboratories). Laboratories MSC production lot number: 21580, L2, L3, L4, L5, L6, L7, L10, L11, L13, L14, and SB1, respectively).

[0043] (Cell Culture) The above 11 strains of BM-MSCs were cultured using the MSCGM Bullet Kit. TM The cells were cultured in a medium containing LONZA (5% CO₂, 95% air (approximately 20% O₂), at 37°C. The cells were detached from the culture dish using trypsin / EDTA (for mesenchymal stromal cells) (LONZA) and passaged every 7 days. The medium was changed using the MSCGM Bullet Kit. TM As shown in Figure 1A, all cell lines were cultured using the MSCGM BulletKit up to passage 5. TMAfter culturing in 1% O, the cells were cultured for 16 hours under normal culture conditions (95% air, 5% CO, glucose-containing, 37°C) or under ischemic culture conditions (1% O, 5% CO, no glucose, 37°C).

[0044] (VEGF secretion under ischemic culture conditions) The amount of VEGF in the culture supernatant after 16 hours of culture under ischemic culture conditions was measured by ELISA. VEGF expression was increased under ischemic culture conditions in all cell lines, and VEGF expression differed between the lines ( Figure 1B ).

[0045] (scRNA-Seq) 3'-end scRNA-Seq analysis was performed on 5,000 cells from each of the 11 BM-MSC strains (cultured under standard culture conditions). Library preparation was performed using a 10xGenomics Chromium Controller, distinguishing between individual cells. Single-cell droplet generation and library preparation were performed according to the protocol provided with the 10xGenomics kit. The prepared libraries were sequenced using an Illumina next-generation sequencer.

[0046] (Clustering) The obtained sequence data were processed using the Cellranger analysis pipeline provided by 10xGenomics and then analyzed using Seurat ([URL: / / satijalab.org / seurat / ], version 4.3.0) as follows. Based on the obtained expression profiles, cells were visualized in two-dimensional space using Uniform Manifold Approximation and Projection (UMAP). Using the UMAP-assisted k-means clustering algorithm, each of the 11 BM-MSC lines was classified into eight distinct cell subpopulations (cluster 0 to cluster 7) representing different cell types (Figure 2).

[0047] (Identification of DEGs) Model-based analysis of single cell transcriptomics (Genome Biol, 2015, 16: 278) was used to identify statistically significant differentially expressed genes (DEGs) in each cluster relative to other clusters (FindAllMarkers(only.pos=TRUE, min.pct=0.25, logfc.threshold=0.25)).

[0048] (GO Analysis) To predict the functional characteristics of each cluster, marker genes for each cluster were extracted based on DEGs. Gene ontology analysis (GO analysis) was performed on the marker genes using the plot_all_cluster_go function in the Scillus R package.

[0049] The results of the GO analysis are shown in Figures 3-A to 3-D. The figures show a hypergeometric distribution test performed on a 2x2 frequency distribution table, with the Gene Ontology (GO) annotation information of the significant genes detected for each cluster as categories. Based on the Benjanni-Hochberg (BH) method, the P-values ​​after correction for multiple comparisons (adjusted P-values) were calculated and log10 logarithmically transformed. The bar graphs are colored in shades of orange to red, representing the number of significant genes corresponding to each GO annotation. Many of the cells in clusters 0 and 1 had GO terms such as "Collagen-Containing Extracellular Matrix," while many of the cells in cluster 2 had the GO terms "Focal Adhesion" and "Cell-Substrate Junction." These results suggest that clusters 0, 1, and 2 are cell subpopulations with characteristics related to cytoskeleton formation and intercellular signaling. Cluster 3 contained many cells with GO terms related to "mitochondria," suggesting that it is a cell subpopulation with high cellular function and activated mitochondrial energy metabolism. Mitochondrial energy metabolism function is known to correlate with the ability of MSCs to suppress immune cell function (Stem Cells Transl Med, 2023, 12: 169-182), suggesting that cluster 3 is a cell subpopulation responsible for the ability of MSCs to suppress immune cell function. Clusters 4 and 5 contained many cells with GO terms related to cell division, such as "chromosome" and "nuclear division," suggesting that they were cell subpopulations with high proliferation potential.

[0050] (Identification of Clusters Associated with VEGF Secretion) We attempted to identify clusters associated with VEGF secretion under ischemic culture conditions from the eight clusters by comparing the proportion of BM-MSCs derived from each cell line in each of the eight clusters with the VEGF secretion capacity of each cell line under ischemic culture conditions. Spearman rank correlation coefficients were calculated between the proportion of cells derived from each cell line contained in each cluster and the amount of VEGF secreted by each cell line under ischemic culture conditions. In Cluster 3, a significant positive correlation (Spearman rank correlation, rs = 0.3, p < 0.05) was observed between the proportion of cells from each cell line and the VEGF secretion capacity under ischemic culture conditions (Figure 4). Furthermore, Cluster 3 was found to have high expression levels of VEGF family genes (VEGF-A, VEGF-B, and VEGF-C). These results suggest a relationship between the cell subpopulations in Cluster 3 and VEGF secretion. Although the calculated rs for Cluster 6 was high, the number of cells in the cluster was small and VEGF secretion was detected only from specific cell lines, so it was determined that there was no significant correlation with VEGF secretion.

[0051] (Identification of factors involved in VEGF secretion) To investigate factors involved in VEGF secretion in BM-MSCs under ischemic culture conditions, we focused on genes (DEGs) that were characteristically highly expressed in cluster 3. DEGs expressed in cluster 3 are shown in Table 1. The LRRC75A gene was highly expressed in cluster 3. Figure 5 shows the relative expression level of the LRRC75A gene in MSCs normalized using the FeaturePlot function of Seurat (described above) on a two-dimensional UMAP plot. Table 1 and Figure 5 indicate that the LRRC75A gene is selectively highly expressed in cluster 3 cells.

[0052]

[0053] A loss-of-function assay using siRNA confirmed the involvement of LRRC75A in VEGF secretion under ischemic culture conditions. Changes in VEGF secretion due to LRRC75A gene knockdown using siRNA were observed. BM-MSCs were transfected with LRRC75A gene-specific siRNA or control siRNA and cultured for 16 hours under normal and ischemic culture conditions. After culture, LRRC75A gene expression levels in the cells were measured by qRT-PCR, and VEGF levels in the culture supernatant were measured by ELISA. LRRC75A gene knockdown by siRNA was observed in BM-MSCs under both culture conditions (Figure 6A). Furthermore, LRRC75A gene knockdown by siRNA significantly suppressed VEGF secretion by BM-MSCs under ischemic culture conditions (Figures 6B and C). These results suggest that the LRRC75A-mediated pathway contributes to VEGF secretion by BM-MSCs under ischemic culture conditions.

[0054] (Functional Estimation of Cluster 3 Based on DEGs) As shown in Table 1, LGALS1 was found to be a gene selectively expressed in Cluster 3. LGALS1 is a gene encoding galectin 1, which is thought to play a major role in the immunosuppressive ability of MSCs (Blood, 2010, 116:3770-9). This result, together with the results of the GO analysis described above, suggests that Cluster 3 is a cell subpopulation responsible for the ability of MSCs to suppress immune cell function.

[0055] (Marker Gene Expression in Cluster 3) MSCs are generally characterized as cells positive for the cell surface markers CD73, CD90, and CD105. Based on the scRNA-seq results of this example, Figure 7 shows the relative expression levels of the CD73, CD90, and CD105 genes in MSCs normalized using the FeaturePlot function of Seurat (described above) on a two-dimensional UMAP plot. The gene expression levels of the MSC markers CD73, CD90, and CD105 were significantly higher in Cluster 3 compared to other clusters. This result suggests the possibility that the function of MSCs can be evaluated based on the expression levels of these cell surface markers rather than the positivity rate. Furthermore, this result suggests the possible involvement of these cell surface markers in the primary functions of MSCs, namely, wound healing via VEGF secretion, angiogenesis, or immunosuppression.

[0056] As described above, a cell subpopulation was identified from the MSC cell population that contributes to the main functions of MSCs, i.e., wound healing through VEGF secretion, e.g., angiogenesis, or immunosuppression. This subpopulation exhibited high expression of LRRC75A, which was shown to contribute to the subpopulation's VEGF secretion ability. This subpopulation was also shown to highly express genes involved in immunosuppression, such as LGALS1. This subpopulation was also characterized by high expression of MSC cell surface markers CD73, CD90, and CD105. Therefore, LRRC75A, LGALS1, CD73, CD90, and CD105 are considered to be useful markers for identifying subpopulations of MSCs that contribute to VEGF secretion, wound healing (e.g., angiogenesis), or immunosuppression, or for assessing the VEGF secretion, wound healing (e.g., angiogenesis), or immunosuppressive abilities of MSCs.

Claims

1. A method for identifying functional cell subpopulations in a mesenchymal stromal / stem cell population, comprising: performing omics analysis of the mesenchymal stromal / stem cell population at the single cell level; and clustering and classifying the mesenchymal stromal / stem cell population into cell subpopulations based on the results of the analysis.

2. The method according to claim 1, wherein the omics analysis is gene expression analysis by single-cell RNA sequencing.

3. The method according to claim 1, wherein the clustering of the cell population is performed by reducing the dimension of the results of omics analysis of the cell population.

4. The method of claim 1, comprising: identifying genes that are differentially expressed in a given subpopulation of the sorted cell subpopulations compared to other subpopulations; identifying the function of the gene; and deducing the function of the gene as a function contributed by the given subpopulation.

5. The method of claim 1, comprising: identifying genes that are differentially expressed in a given subpopulation of cells from the sorted cell subpopulations compared to other subpopulations; and using the genes as markers to select functional cell subpopulations from a given mesenchymal stromal / stem cell population.

6. The method of claim 1, comprising: classifying a cell population of mesenchymal stromal / stem cells derived from different mesenchymal stromal / stem cell lines into cell subpopulations by performing the omics analysis and clustering; determining the number of cells derived from each of the different mesenchymal stromal / stem cell lines contained in each of the cell subpopulations; measuring the level of a specific function in each of the cell lines; and inferring that a cell subpopulation that has a correlation between the number of cells derived from each of the cell lines contained in the subpopulation and the level of the specific function in each of the cell lines is the cell subpopulation that contributes to the specific function.

7. The method according to claim 6, wherein the specific functions are pharmacological efficacy and clinical safety.

8. The method according to claim 6, wherein the specific function is the ability to secrete VEGF.

9. The method according to claim 6, wherein the specific function is angiogenic activity.

10. The method of claim 6, comprising: identifying genes that are differentially expressed in a subpopulation of the classified cell subpopulations that is predicted to contribute to the specific function compared to other subpopulations; and predicting that the genes are involved in the specific function.

11. A method for controlling the function of a cell subpopulation, comprising: preparing a cell subpopulation predicted to contribute to a specific function according to the method of claim 6; identifying genes that are differentially expressed in the subpopulation predicted to contribute to the specific function compared to other subpopulations; and regulating the expression level of the gene or its expression product in a given mesenchymal stromal / stem cell, thereby controlling the specific function in the cell.

12. A method for evaluating the VEGF secretion ability or angiogenic ability of mesenchymal stromal / stem cells in an ischemic environment, comprising evaluating the VEGF secretion ability or angiogenic ability of the cells in an ischemic environment based on the expression level of at least one gene selected from the group consisting of the LRRC75A gene, the LGALS1 gene, the CD73 gene, the CD90 gene, and the CD105 gene, and the proteins encoded thereby, in the mesenchymal stromal / stem cells.

13. A method for selecting mesenchymal stromal / stem cells having high VEGF secretion ability or angiogenic ability under an ischemic environment, the method comprising selecting mesenchymal stromal / stem cells having high VEGF secretion ability or angiogenic ability under an ischemic environment based on the expression level of at least one gene selected from the group consisting of the LRRC75A gene, the LGALS1 gene, the CD73 gene, the CD90 gene, and the CD105 gene, and the proteins encoded thereby, in the mesenchymal stromal / stem cells.

14. A method for producing mesenchymal stromal / stem cells having high VEGF secretion or angiogenic ability under an ischemic environment, comprising culturing mesenchymal stromal / stem cells having high VEGF secretion or angiogenic ability under an ischemic environment selected by the method described in claim 13.

15. A method for controlling the VEGF secretion ability or angiogenic ability of mesenchymal stromal / stem cells under an ischemic environment, the method comprising regulating the expression level of the LRRC75A gene or its expression product in mesenchymal stromal / stem cells.

16. A method for evaluating the immunosuppressive ability of mesenchymal stromal / stem cells, comprising evaluating the immunosuppressive ability of the cells based on the expression level in the mesenchymal stromal / stem cells of at least one gene selected from the group consisting of the LGALS1 gene, the LRRC75A gene, the CD73 gene, the CD90 gene, and the CD105 gene, and the proteins encoded thereby.

17. A method for selecting mesenchymal stromal / stem cells with high immunosuppressive ability, the method comprising selecting mesenchymal stromal / stem cells with high immunosuppressive ability based on the expression level of at least one gene selected from the group consisting of the LGALS1 gene, the LRRC75A gene, the CD73 gene, the CD90 gene, and the CD105 gene, and the proteins encoded thereby, in the mesenchymal stromal / stem cells.

18. A method for producing mesenchymal stromal / stem cells with high immunosuppressive potential, comprising culturing mesenchymal stromal / stem cells with high immunosuppressive potential selected by the method described in claim 17.

Images