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

Abstract

To clarify the heterogeneous composition of mesenchymal stromal / stem cells (MSCs) and identify functional subpopulations of MSCs.SOLUTION: Provided is a method for identifying functional cell subpopulations in a cell population of MSCs, comprising: performing omics analysis of the cell population of MSCs at the single cell level; and clustering the cell population of MSCs based on the results of the analysis to classify it into cell subpopulations.SELECTED DRAWING: None

Description

[Technical Field]

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

[0002] Mesenchymal stromal / stem cells (MSCs) are already being 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 as indicators (Non-Patent Documents 3 and 4). [Prior art documents] [Non-patent literature]

[0003] [Non-Patent Document 1] 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. [Non-patent document 2] Phinney DG. Functional heterogeneity of mesenchymal stem cells: implications for cell therapy. J Cell Biochem. 2012;113(9):2806-12. [Non-patent document 3] 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. [Non-patent document 4] 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. Summary of the Invention [Problem to be solved by the invention]

[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 these products. It is desirable to clarify the heterogeneous composition of MSCs or to identify functional cell subpopulations within MSC cell populations. [Means for solving the problem]

[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 population based on their properties, or to identify functional cell subpopulations contained in an MSC population.

[0006] The present invention provides the following as representative embodiments. [1] A method for identifying a functional cell subpopulation in a mesenchymal stromal / stem cell population, comprising: Omics analysis of mesenchymal stromal / stem cell populations at the single cell level, and clustering the mesenchymal stromal / stem cell population based on the results of the analysis to classify it into cell subpopulations; A method comprising: [2] The method described in [1], wherein the omics analysis is gene expression analysis by single-cell RNA sequencing. [3] The method described in [1] or [2], wherein the clustering of the cell population is performed by reducing the dimension of the results of omics analysis of the cell population. [4] Identifying genes that are differentially expressed in a given subpopulation of the sorted cell subpopulations compared to other subpopulations; identifying the function of said gene; estimating the function of said gene as a function contributed by said given subpopulation; The method according to any one of [1] to [3], comprising: [5] Identifying genes that are differentially expressed in a given subpopulation of the sorted cell subpopulations compared to other subpopulations; Selecting a functional cell subpopulation from a given mesenchymal stromal / stem cell population using the gene as a marker; The method according to any one of [1] to [3], comprising: [6] classifying mesenchymal stromal / stem cell populations derived from different mesenchymal stromal / stem cell lines into cell subpopulations by 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 particular function in each of the cell lines; Presuming 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 that contributes to the specific function; The method according to any one of [1] to [3], comprising: [7] The method described in [6], wherein the specific functions are pharmacological effect and clinical safety. [8] The method described in [6], wherein the specific function is the ability to secrete VEGF. [9] The method described in [6], wherein the specific function is angiogenic ability.

[10] Identifying genes that are differentially expressed in a subpopulation of the sorted cell subpopulations that is predicted to contribute to the specific function compared to other subpopulations; Predicting the gene as a gene involved in the specific function; The method according to any one of [6] to [9], comprising:

[11] A method for regulating the function of a cell subpopulation, comprising: Preparing a cell subpopulation that is predicted to contribute to a specific function according to any one of the methods described in [6] to [9]; Identifying genes that are differentially expressed in the subpopulations that are predicted to contribute to the specific function compared to other subpopulations; Regulating the expression level of said gene or its expression product in a given mesenchymal stromal / stem cell to control said specific function in said cell; A method comprising:

[12] A method for evaluating the VEGF secretion ability or angiogenic ability of mesenchymal stromal / stem cells under an ischemic environment, comprising: Evaluating the VEGF secretion ability or angiogenic ability of mesenchymal stromal / stem cells in an ischemic environment based on the expression level of at least one gene selected from the group consisting of LRRC75A gene, LGALS1 gene, CD73 gene, CD90 gene, and CD105 gene, and proteins encoded by these genes; A method comprising:

[13] A method for selecting mesenchymal stromal / stem cells having high VEGF secretion ability or angiogenic ability under an ischemic environment, 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 LRRC75A gene, LGALS1 gene, CD73 gene, CD90 gene, and CD105 gene, and proteins encoded by these genes, in the mesenchymal stromal / stem cells; A method comprising:

[14] A method for producing mesenchymal stromal / stem cells that have 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 described in

[13] . A method comprising:

[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; A method comprising:

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

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

[18] A method for producing mesenchymal stromal / stem cells with high immunosuppressive ability, comprising:

[17] Culturing mesenchymal stromal / stem cells with high immunosuppressive ability selected by the method described in

[17] . A method comprising: [Effects of the Invention]

[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. [Brief explanation of the drawings]

[0008] [Figure 1] A: Culture conditions for bone marrow-derived MSCs (BM-MSCs) used in the examples. B: Amount of VEGF secreted into the culture supernatant after 16 hours of culture of 11 BM-MSC lines under normal and ischemic conditions (mean ± SE, n = 5 per line). *: p < 0.05, **: p < 0.01, ns: not significant (Student t-test). [Figure 2]Two-dimensional UMAP plot based on the mRNA expression profiles of individual cells from 11 BM-MSC lines under normal culture conditions. Eight cell subpopulation clusters (CL0–CL7) were detected. [Figure 3-A] Gene Ontology (GO) analysis results for clusters 0-3. The figure shows bar graphs of adjusted P-values ​​calculated after correction for multiple comparisons using the Benjanmini-Hochberg (BH) method, based on a 2x2 frequency distribution table in which the Gene Ontology (GO) annotation information of significant genes detected for each cluster was used as categories. The values ​​are then transformed into log10 values. The bar graph colors are shades of orange-red, representing the number of significant genes corresponding to each GO annotation information. [Figure 3-B] Continued from Figure 3-A. GO analysis results for clusters 4 to 7. [Figure 4] Scatter plots of the VEGF secretion levels of each BM-MSC line under ischemic culture conditions against the percentage of cell numbers in each cluster. Each figure shows the Spearman rank correlation coefficient (rs) and its p-value between the percentage of cell numbers and VEGF secretion levels of each cell line. [Figure 5] Relative expression levels of LRRC75A in each cell type are displayed on a 2D UMAP plot. The scale bar represents the color scale of gene expression normalized using the FeaturePlot function in Seurat. [Figure 6] Effect of LRRC75A suppression on VEGF secretion in BM-MSCs. LRRC75A mRNA expression (A), VEGF secretion (B), and [VEGF secretion under ischemic conditions] / [VEGF secretion under normal 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. Bars in each figure represent mean ± SE (n = 3). *: p < 0.05, **: p < 0.01 (Student's t-test). [Figure 7] Relative expression levels of CD73, CD90, and CD105 for each cell type displayed on a 2D UMAP plot. The scale bar represents the color scale of gene expression normalized using the FeaturePlot function in Seurat. DETAILED DESCRIPTION OF THE INVENTION

[0009] Mesenchymal stromal / stem cells (MSCs) are mesodermally derived stem cells present in the body. MSCs can be isolated from various tissues, including 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, but 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 for patient treatment. MSC-based cell products are also commercially available. MSCs possess immunomodulatory capabilities, wound healing promotion, and differentiation into various cell types, making them promising candidates for wound healing and regenerative medicine. For example, MSCs 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 activities) among cell lines. For example, even if all MSCs meet the ISCT definition, their biological characteristics vary depending on the tissue from which they are derived. This characteristic is known as MSC heterogeneity. MSC heterogeneity can lead to differences in the biological and functional properties among MSC cell lines and can also lead to reduced quality, such as inconsistent pharmacological effects, in MSC-based cell products. Furthermore, the instability of MSC-based cell products not only compromises 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, as well as 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 present inventors considered that clarifying the composition or properties of heterogeneous MSC 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 populations. The present invention also relates to classifying cells in MSC 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, the presence of subpopulations associated with specific functions was found among the cell subpopulations classified by the clustering.

[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 associated with 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 dropletization 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 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 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 (e.g., the gene expression profile obtained by scRNA-Seq) for the cell population. As a more specific example, the omics analysis data for the cell population is reduced in dimension 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., UMAP-assisted K-means clustering, a k-means clustering algorithm). 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 specific functions 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 by 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 include, but are not limited to, 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 can be understood from the context herein by those skilled in the art.

[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 cell number. This subpopulation was presumed to contribute to VEGF secretion under an ischemic environment. This cell subpopulation is clinically useful because it promotes VEGF secretion under an ischemic environment and can contribute to wound healing, such as angiogenesis.

[0022] In a further embodiment, genes that are differentially expressed in a cell subpopulation that is predicted to contribute to the specific function compared to other subpopulations are identified. The differentially expressed genes found in the subpopulation are genes that characterize the subpopulation. Therefore, 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 these 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, when the differentially expressed gene is involved in a specific function, the specific function of MSCs can be controlled by adjusting the expression level of the gene or its expression product in MSCs. For example, when enhanced expression of the differentially expressed gene results in the enhancement of the specific function (e.g., a pharmacological effect), enhancing the expression of the gene can enhance the specific function, while reducing the expression of the gene can attenuate the specific function. Alternatively, when reduced expression of the differentially expressed gene results in the enhancement of the specific function, reducing the expression of the gene can enhance the specific function, while enhancing the expression of the gene can attenuate the specific function.

[0026] Specifically, as shown in the Examples below, the subpopulation predicted to contribute to VEGF secretion was found to contain many cells strongly expressing the LRRC75A gene. Inhibiting LRRC75A gene expression in this subpopulation reduced VEGF secretion. These results demonstrate that the LRRC75A gene is involved in VEGF secretion and that regulating the expression level of the LRRC75A gene or its encoded protein in MSCs can control the VEGF secretion ability of the MSCs and, in turn, the wound healing ability (e.g., angiogenesis). These results also demonstrate that the expression level of the LRRC75A gene or its encoded protein can be used to evaluate the efficacy of MSCs in VEGF secretion and wound healing, e.g., angiogenesis, or to select MSCs effective in VEGF secretion and wound healing, e.g., angiogenesis.

[0027] Therefore, in a further embodiment, the present invention provides a method for controlling the VEGF secretion ability of MSCs under ischemic conditions. 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 ischemic conditions.

[0028] The expression level of a gene or protein in a cell population can be measured by known methods, including, but not limited to, mRNA expression analysis by RT-PCR, quantitative RT-PCR, RNA sequencing, etc., ELISA, fluorescent staining, mass spectrometry, flow cytometry, etc. Regulation of gene or protein expression can be carried out by known methods, including, 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 expresses mitochondrial function-related genes, the immunosuppressant LGALS1 (galectin 1) gene, and the cell surface antigens CD73, CD90, and CD105. This indicates that this subpopulation is also effective in immunosuppression. This 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, CD90, or CD105 genes in addition to the LRRC75A 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 VEGF secretion ability 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 gene 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 VEGF secretion ability of the MSCs under ischemic conditions. 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 ischemic conditions. Furthermore, since high VEGF secretion ability reflects high angiogenic ability, the MSCs are cells with high angiogenic ability under ischemic conditions.

[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 above markers from given MSCs, or by further culturing these 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 having high VEGF secretion ability or angiogenic ability under an ischemic environment. For example, this method includes collecting cells that are evaluated as having high VEGF secretion ability or angiogenic ability based on the expression levels of marker genes related to the VEGF secretion ability or angiogenic ability of the MSCs or proteins encoded thereby, and further, if necessary, culturing the collected cells. In yet another embodiment, the present invention provides a method for producing MSCs with high immunosuppressive potential. For example, the method includes collecting cells that are evaluated to have high immunosuppressive potential based on the expression levels of marker genes related to the immunosuppressive potential 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, with 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. [Example]

[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] (cell) Eleven lines 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 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 BulletKit. TM The cells were cultured in LONZA medium under conditions of 5% CO₂ / 95% air (approximately 20% O₂) at 37°C. Subculture was performed every 7 days after detaching the cells from the culture dish using trypsin / EDTA (for mesenchymal stromal cells) (LONZA). Medium changes were performed using the MSCGM BulletKit. TM As shown in Figure 1A, all cell lines were cultured using the MSCGM BulletKit up to passage 5. TM After culturing in 100°C, the cells were cultured for 16 hours under normal culture conditions (95% air, 5% CO2, glucose-containing, 37°C) or under ischemic culture conditions (1% O2, 5% CO2, no glucose, 37°C).

[0044] (VEGF secretion under ischemic culture conditions) After 16 hours of culture under ischemic conditions, VEGF levels in the culture supernatants were measured by ELISA. VEGF expression was increased in all cell lines under ischemic conditions, and the expression levels varied among the cell lines (Fig. 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] (DEG identification) Using model-based analysis of single cell transcriptomics (Genome Biol, 2015, 16:278), we identified 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 were extracted for each cluster based on DEGs, and 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 3A and 3B. The figures show bar graphs of adjusted P-values ​​calculated by the Benjanmini-Hochberg (BH) method after correction for multiple comparisons, using a hypergeometric distribution test based on a 2x2 frequency table with the Gene Ontology (GO) annotation information of the significant genes detected for each cluster as categories. The values ​​are then transformed by log10. The bar colors are shades of orange to red, representing the number of significant genes corresponding to each GO annotation information. 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 GO terms such as "Focal Adhesion" and "Cell-Substrate Junction." These results suggest that clusters 0, 1, and 2 are cell subpopulations with properties 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 a cluster involved in VEGF secretion) We attempted to identify the clusters associated with VEGF secretion under ischemic conditions by comparing the proportion of BM-MSCs derived from each cell line in each of the eight clusters and their VEGF secretion capacity under ischemic conditions. Spearman rank correlation coefficients were calculated between the proportion of cells derived from each cell line in each cluster and the amount of VEGF secreted by each cell line under ischemic conditions. A significant positive correlation (Spearman rank correlation, rs = 0.3, p < 0.05) was observed between the proportion of cells derived from each cell line and their VEGF secretion capacity under ischemic conditions in Cluster 3 (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 suggested a relationship between the cell subpopulations in Cluster 3 and VEGF secretion. Although Cluster 6 had a high calculated rs, it was determined that there was no significant correlation with VEGF secretion due to the small cell numbers in the cluster and the fact that VEGF secretion was detected only in certain cell lines.

[0051] (Identification of factors involved in VEGF secretion) To investigate factors involved in VEGF secretion by BM-MSCs under ischemic culture conditions, we focused on genes (DEGs) that were specifically 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 levels 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] [Table 1]

[0053] We confirmed the involvement of LRRC75A in VEGF secretion under ischemic culture conditions by a loss-of-function assay using siRNA. We also examined the effects of LRRC75A gene knockdown using siRNA on VEGF secretion. BM-MSCs were transfected with LRRC75A-specific siRNA or control siRNA and cultured for 16 hours under normal and ischemic culture conditions. After culture, LRRC75A gene expression in the cells was 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 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 encodes 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 in this example, Figure 7 shows the relative expression levels of 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 than in other clusters. This result suggests that the function of MSCs can be evaluated based on the expression levels of these cell surface markers rather than the positivity rate. This result also suggests the possible involvement of these cell surface markers in the primary functions of MSCs, namely wound healing via VEGF secretion, angiogenesis, and immunosuppression.

[0056] As described above, a subpopulation of MSCs was identified that contributes to the primary functions of MSCs, i.e., wound healing via VEGF secretion, e.g., angiogenesis, or immunosuppression. This subpopulation exhibited high expression of LRRC75A, suggesting that LRRC75A contributes to the subpopulation's VEGF secretion. Furthermore, this subpopulation was shown to 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 may be used as markers to identify MSC subpopulations that contribute to VEGF secretion, wound healing (e.g., angiogenesis), or immunosuppression, or to assess the VEGF secretion, wound healing (e.g., angiogenesis), or immunosuppression capabilities of MSCs.

Claims

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

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. identifying genes that are differentially expressed in a given subpopulation of cells compared to other subpopulations among the sorted cell subpopulations; identifying the function of said gene; estimating the function of said gene as a function contributed by said given subpopulation; 2. The method of claim 1, comprising:

5. identifying genes that are differentially expressed in a given subpopulation of cells compared to other subpopulations among the sorted cell subpopulations; selecting a functional cell subpopulation from a given mesenchymal stromal / stem cell population using the gene as a marker; 2. The method of claim 1, comprising:

6. classifying mesenchymal stromal / stem cell populations derived from different mesenchymal stromal / stem cell lines into cell subpopulations by 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 particular function in each of the cell lines; Presuming 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 that contributes to the specific function; 2. The method of claim 1, comprising:

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. Identifying genes that are differentially expressed in the subpopulations of the sorted cells that are predicted to contribute to the specific function compared to other subpopulations; Predicting the gene as a gene involved in the specific function; 7. The method of claim 6, comprising:

11. 1. A method for regulating a function of a cell subpopulation, comprising: Providing 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 subpopulations that are predicted to contribute to the specific function compared to other subpopulations; Regulating the expression level of said gene or its expression product in a given mesenchymal stromal / stem cell to control said specific function in said cell; A method comprising:

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

13. A method for selecting mesenchymal stromal / stem cells having high VEGF secretion ability or angiogenic ability under an ischemic environment, 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 LRRC75A gene, LGALS1 gene, CD73 gene, CD90 gene, and CD105 gene, and proteins encoded by these genes, in the mesenchymal stromal / stem cells; A method comprising:

14. A method for producing mesenchymal stromal / stem cells having high VEGF secretion ability or angiogenic ability under ischemic conditions, comprising: Culturing mesenchymal stromal / stem cells having high VEGF secretion ability or angiogenic ability under an ischemic environment, selected by the method according to claim 13; A method comprising:

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

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

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

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 according to claim 17; A method comprising:

Images