A method for evaluating stem cell quality

Abstract

The application provides a stem cell quality evaluation method, comprising: obtaining the expression amount of a characteristic gene related to stem cell quality; calculating a stem cell quality score according to the expression amount of the characteristic gene and the weight coefficient of the characteristic gene; and evaluating the stem cell quality according to the stem cell quality score. The application uses a supervised machine learning model to learn a single-cell gene expression dataset with a quality attribute label to determine a characteristic gene related to stem cell quality and a weight coefficient of the characteristic gene, so as to predict the heterogeneity change of stem cells under the influence of a microenvironment and the quality risk caused thereby. According to the expression amount of the characteristic gene in a stem cell sample to be detected and the weight coefficient of the characteristic gene, the stem cell quality can be accurately, quickly and quantitatively determined, and the safety risk of stem cells caused by stem cell heterogeneity is reduced.

Description

Technical Field

[0001] This invention belongs to the field of stem cell technology and relates to a method for evaluating stem cell quality, particularly a method for evaluating stem cell quality based on the expression level and weight coefficient of characteristic genes. Background Technology

[0002] Stem cell therapy holds promise for fundamentally changing the clinical predicament faced by current medicine in treating intractable diseases such as immune, degenerative, traumatic, and neoplastic diseases by mediating tissue regeneration, replacing and repairing tissue and organ damage caused by aging or disease. It represents the future direction of medical development.

[0003] Obtaining sufficient stem cells through appropriate expansion and culture is a necessary prerequisite for stem cell research and application. However, stem cell proliferation is affected by the microenvironment, leading to altered gene expression and heterogeneity even among stem cells from the same source. This heterogeneity severely hinders the scientific research and clinical application of stem cells and is a significant reason for the potential safety risks associated with stem cell reinfusion. Therefore, identifying and preventing heterogeneity during stem cell proliferation is a crucial prerequisite for the clinical development of stem cell therapy.

[0004] Single-cell RNA sequencing (scRNA-seq) technology offers the possibility of exploring heterogeneity among cells and can preliminarily analyze the heterogeneity of cell subpopulations based on gene expression profiles. However, it cannot clarify the relationship between cell heterogeneity and cell quality, let alone quantitatively determine the quality of stem cells.

[0005] CN113061638A provides a stem cell evaluation system for performing sterility testing, safety testing, cell viability testing, and cell morphology testing on stem cells.

[0006] However, current technologies lack sound and unified norms and standards for evaluating stem cell quality, cannot accurately reveal the impact of the microenvironment on stem cells, and cannot solve the safety issues caused by stem cell heterogeneity in stem cell therapy. The stem cell industry still faces significant challenges such as an incomplete quality control system, incomplete mechanistic research, and non-standardized clinical applications. Summary of the Invention

[0007] To address the shortcomings of existing technologies and practical needs, this invention provides a stem cell quality assessment method. This method is based on single-cell transcriptomics and functional cell subpopulation clustering, identifying key stem cell quality classification criteria at the single-cell level. It constructs a single-cell gene expression dataset of stem cells with quality attribute classification labels, utilizes a supervised machine learning model to build a stem cell quality prediction model, and determines stem cell quality-related characteristic genes and their weight coefficients. This achieves the effect of quantitatively determining the quality risks caused by changes in stem cell heterogeneity.

[0008] The first aspect of this invention provides a method for evaluating stem cell quality, comprising:

[0009] The expression levels of characteristic genes related to stem cell quality are obtained; stem cell quality scores are calculated based on the expression levels and weighting coefficients of the characteristic genes; and stem cell quality is evaluated based on the stem cell quality scores.

[0010] Stem cells used in clinical practice are a class of stem cells with the same cellular biological properties. Their cell fate can change heterogeneously due to the influence of the microenvironment. This invention aims to determine the differentiation direction of stem cells and evaluate their quality by using bioinformatics to identify characteristic genes related to stem cell quality and their weight coefficients. The quality of stem cells is evaluated by detecting the expression levels of characteristic genes in stem cell samples and by considering the expression levels and weight coefficients of these characteristic genes.

[0011] In this invention, the characteristic genes related to stem cell quality and their weight coefficients are determined by learning a single-cell gene expression dataset with quality attribute labels using a supervised machine learning model. The characteristic genes can accurately characterize the heterogeneity of different stem cells in terms of quality. Based on the expression level of the characteristic genes and their weight coefficients in the stem cell sample to be tested, a stem cell quality score can be calculated, thereby achieving a quantitative evaluation of stem cell quality.

[0012] Preferably, the stem cell quality-related characteristic genes are stem cell quality-related characteristic genes determined at the single-cell level.

[0013] Specifically, the method for determining the feature genes and their weight coefficients includes:

[0014] A dataset is formed using single-cell gene expression data and specific quality attributes of stem cells, and is divided into a training set and a test set;

[0015] A supervised machine learning model was trained using the training set. The parameters of the supervised machine learning model were adjusted through cross-validation and testing on the test set. The stem cell quality prediction model, stem cell quality-related feature genes, and the weight coefficients of the feature genes were determined.

[0016] In this invention, single-cell gene expression data of stem cells with known quality attribute labels are used as the dataset, which is randomly divided into training and test sets according to a certain ratio. A supervised machine learning model is trained by using the training set data to determine the number of features of the supervised machine learning model, and using the test set data to adjust the parameters and optimize the supervised machine learning model. A model that performs well in terms of prediction accuracy, precision, recall and F1 score is obtained and used as a stem cell quality prediction model to determine the feature genes and their weights related to stem cell quality.

[0017] The method for obtaining single-cell gene expression data of the stem cells includes:

[0018] Single-cell RNA sequencing was performed on stem cells to obtain single-cell gene expression data.

[0019] The methods for obtaining the specific quality attribute include:

[0020] Specific quality attributes are determined based on the culture microenvironment of stem cells;

[0021] Specific quality attributes are determined based on single-cell epigenetic data of stem cells; or

[0022] Specific quality attributes are determined based on single-cell gene expression data of stem cells;

[0023] The determination of specific quality attributes based on single-cell gene expression data of stem cells includes:

[0024] Pathway enrichment analysis was performed on single-cell gene expression data of stem cells to calculate the enrichment score of each pathway in each stem cell, and a single-cell pathway enrichment score matrix of stem cells was obtained.

[0025] Bioinformatics analysis was performed on the single-cell pathway enrichment score matrix of stem cells to obtain the single-cell subpopulation clustering results of stem cells, which can be used as specific quality attributes of stem cells.

[0026] Preferably, the bioinformatics analysis of the single-cell pathway enrichment fraction matrix of stem cells includes:

[0027] The single-cell pathway enrichment fraction matrix of stem cells was subjected to dimensionality reduction and clustering.

[0028] In this invention, by integrating traditional cell subpopulation clustering, differential gene analysis, and pathway enrichment analysis, a single-cell pathway enrichment score matrix for stem cells is established based on the results of pathway enrichment analysis. Each row of this single-cell pathway enrichment score matrix represents the expression of a pathway in different stem cells, each column represents the expression of all metabolic pathways in different stem cells, and the data in each cell represents the expression of a specific pathway in a specific stem cell. This pathway-based functional single-cell subpopulation clustering method achieves the effect of rapidly discovering functional differences in stem cells.

[0029] Preferably, the method for obtaining the expression level of the characteristic gene includes conventional gene quantification methods in the art, such as single-cell sequencing, high-throughput sequencing, microarray chips, qPCR, etc., with single-cell sequencing being the preferred method for obtaining the expression level of the characteristic gene.

[0030] Preferably, the formula for calculating the stem cell quality score is:

[0031]

[0032] in, Gi For the first i The expression level of each characteristic gene Wi For the first i The weight coefficients of each characteristic gene, n This represents the number of characteristic genes.

[0033] In this invention, the expression levels of characteristic genes in the stem cell sample to be tested are detected and a weighted sum is calculated. The result is then substituted into the above calculation formula to obtain a stem cell quality score. The higher the score, the higher the quality risk of the stem cells.

[0034] Preferably, the method for evaluating stem cell quality based on stem cell quality scoring includes:

[0035] If the stem cell quality score is greater than or equal to the stem cell quality risk threshold, the stem cell is considered a quality risk stem cell.

[0036] If the stem cell quality score is less than the stem cell quality risk threshold, the stem cells are considered non-risk stem cells.

[0037] Preferably, the method for determining the stem cell quality risk threshold includes:

[0038] The stem cell quality score of the dataset was analyzed using the receiver operating characteristic (ROC) curve and the area under the curve. The value of the highest point of the ROC curve was taken as the stem cell quality risk threshold.

[0039] The dataset contains single-cell gene expression data of stem cells with known specific quality attribute labels.

[0040] Preferably, the supervised machine learning model includes any one of the following: perceptron model, K-nearest neighbor algorithm, Naive Bayes model, decision tree model, logistic regression, support vector machine, random forest, boosting method model, EM algorithm, or conditional random field.

[0041] Preferably, the stem cell quality-related characteristic genes contain at least three genes selected from the following genomes: TAGLN, EFEMP1, TPM1, CLU, PTX3, IER3, IGFBP7, MFAP5, IL6, LUM, SERPINE2, CRIM1, and RHOB. Most preferably, the stem cell quality-related characteristic genes include TAGLN, EFEMP1, TPM1, CLU, PTX3, IER3, IGFBP7, MFAP5, IL6, LUM, SERPINE2, CRIM1, and RHOB.

[0042] As a preferred technical solution, the present invention provides a method for evaluating stem cell quality, comprising:

[0043] I. Subpopulation Clustering of Stem Cells

[0044] Preprocessing of single-cell RNA sequencing data of stem cells yields single-cell gene expression data of stem cells;

[0045] Pathway enrichment analysis was performed on single-cell gene expression data of stem cells to calculate the enrichment score of each pathway in each stem cell, and a single-cell pathway enrichment score matrix of stem cells was obtained.

[0046] The single-cell pathway enrichment score matrix of stem cells is standardized, dimensionality reduced, clustered, and visualized to obtain the single-cell subpopulation clustering results of stem cells, which can be used as specific quality attributes of stem cells.

[0047] II. Stem Cell Subpopulation Identification

[0048] The single-cell gene expression data of stem cells with specific quality attribute labels are used to form a dataset, which is divided into a training set and a test set.

[0049] A supervised machine learning model was trained using the training set. The parameters of the supervised machine learning model were adjusted through cross-validation and testing on the test set. The stem cell quality prediction model, stem cell quality-related feature genes, and the weight coefficients of the feature genes were determined.

[0050] III. Stem Cell Quality Scoring

[0051] Obtain the expression levels of characteristic genes in stem cell samples;

[0052] The stem cell quality score is calculated based on the expression level and weight coefficient of the characteristic genes.

[0053] The formula for calculating the stem cell quality score is as follows:

[0054]

[0055] in, Gi For the first i The expression level of each characteristic gene Wi For the first i The weight coefficients of each characteristic gene, n This represents the number of characteristic genes.

[0056] IV. Quality Evaluation of Stem Cells

[0057] Evaluating stem cell quality based on stem cell quality scoring:

[0058] If the stem cell quality score is greater than or equal to the stem cell quality risk threshold, the stem cell is considered a quality risk stem cell.

[0059] If the stem cell quality score is less than the stem cell quality risk threshold, the stem cells are considered non-risk stem cells.

[0060] The method for determining the stem cell quality risk threshold includes:

[0061] The stem cell quality score of the dataset was analyzed using the receiver operating characteristic (ROC) curve and the area under the curve. The value of the highest point of the ROC curve was taken as the stem cell quality risk threshold.

[0062] The dataset contains single-cell gene expression data of stem cells with known specific quality attribute labels.

[0063] A second aspect of this invention provides a method for establishing a stem cell quality prediction model, comprising:

[0064] Single-cell gene expression data and specific quality attributes of stem cells are obtained to form a dataset, which is divided into a training set and a test set;

[0065] A supervised machine learning model was trained using the training set, and its parameters were adjusted through cross-validation and testing on the test set to determine the stem cell quality prediction model.

[0066] Preferably, the method for obtaining single-cell gene expression data and specific quality attributes of stem cells includes:

[0067] Single-cell RNA sequencing was performed on stem cells to obtain single-cell gene expression data.

[0068] Pathway enrichment analysis was performed on single-cell gene expression data of stem cells to calculate the enrichment score of each pathway in each stem cell, and a single-cell pathway enrichment score matrix of stem cells was obtained.

[0069] Bioinformatics analysis was performed on the single-cell pathway enrichment score matrix of stem cells to obtain the single-cell subpopulation clustering results of stem cells, which can be used as specific quality attributes of stem cells.

[0070] Preferably, the bioinformatics analysis of the single-cell pathway enrichment fraction matrix of stem cells includes:

[0071] The single-cell pathway enrichment fraction matrix of stem cells was subjected to dimensionality reduction and clustering.

[0072] Preferably, the establishment method further includes:

[0073] The stem cell quality prediction model was used to determine the characteristic genes related to stem cell quality and their weight coefficients.

[0074] A third aspect of this invention provides a method for classifying the single-cell functions of stem cells, comprising:

[0075] Pathway enrichment analysis was performed on single-cell gene expression data of stem cells to calculate the enrichment score of each pathway in each stem cell, and a single-cell pathway enrichment score matrix of stem cells was obtained.

[0076] Bioinformatics analysis was performed on the single-cell pathway enrichment fraction matrix of stem cells to obtain the single-cell subpopulation clustering results of stem cells.

[0077] Preferably, the bioinformatics analysis of the single-cell pathway enrichment fraction matrix of stem cells includes:

[0078] The single-cell pathway enrichment fraction matrix of stem cells was subjected to dimensionality reduction and clustering.

[0079] Preferably, the method further includes:

[0080] We analyzed differentially expressed genes in single-cell subpopulations of stem cells, selected single-cell subpopulations containing differentially expressed genes related to one or more pathways, and used the differentially expressed genes for dimensionality reduction and clustering to obtain the functional classification results of single-cell stem cells.

[0081] Preferably, the functional pathway can be a thromboembolization-related pathway, including an intrinsic fibrin clot formation pathway, an extrinsic fibrin clot formation pathway, and a fibrin clot-coagulation cascade formation pathway.

[0082] A fourth aspect of the present invention provides a combination of characteristic genes containing at least three genes selected from the following genomes: TAGLN, EFEMP1, TPM1, CLU, PTX3, IER3, IGFBP7, MFAP5, IL6, LUM, SERPINE2, CRIM1, and RHOB, or composed thereof.

[0083] The fifth aspect of the invention provides the use of at least three genes selected from the following genomes for stem cell quality assessment: TAGLN, EFEMP1, TPM1, CLU, PTX3, IER3, IGFBP7, MFAP5, IL6, LUM, SERPINE2, CRIM1 and RHOB.

[0084] A sixth aspect of the present invention provides a server, the server including a processor and a memory storing processor-executable instructions; wherein the processor is configured to execute the stem cell quality evaluation method of the first aspect of the present invention, the stem cell quality prediction model establishment method of the second aspect of the present invention, or the single-cell functional classification method of stem cells of the third aspect of the present invention.

[0085] A seventh aspect of the present invention provides a computer-readable storage medium storing a computer program that executes the stem cell quality evaluation method of the first aspect of the present invention, the method for establishing a stem cell quality prediction model of the second aspect of the present invention, or the single-cell functional classification method of stem cells of the third aspect of the present invention.

[0086] Compared with the prior art, the present invention has the following beneficial effects:

[0087] (1) The stem cell quality evaluation method of the present invention is based on single-cell RNA sequencing technology and functional cell subpopulation clustering method. A stem cell quality standard atlas is established at the single-cell level. Using this stem cell quality standard atlas as a dataset, a stem cell quality prediction model is established based on a supervised machine learning model.

[0088] (2) This invention uses a stem cell quality prediction model to determine the characteristic genes and weight coefficients of stem cell quality. By calculating the weighted sum of the characteristic genes related to stem cell quality, it achieves an accurate and quantitative assessment of stem cell quality. It is a standardized, sound and unified method for evaluating stem cell quality.

[0089] (3) The stem cell quality evaluation method of the present invention achieves the effect of accurately and quantitatively evaluating the heterogeneous changes of stem cells under the influence of the microenvironment;

[0090] (4) The stem cell quality evaluation method of the present invention can be used to screen high-quality stem cells. Attached Figure Description

[0091] Figure 1A The growth kinetics curves for D1M1-P5 are shown. Figure 1B The growth kinetics curves for D1M2-P5 are shown. Figure 1C The cell cycle detection results are for D1M1-P5.Figure 1D The cell cycle detection results are for D1M2-P5. Figure 1E The results of apoptosis detection for D1M1-P5 cells; Figure 1F The results of apoptosis detection for D1M2-P5 cells; Figure 1G The results of adipogenic, osteogenic, and chondrogenic differentiation of D1M1-P5; Figure 1H The differentiation of D1M2-P5 is adipogenic, osteogenic, and chondrogenic.

[0092] Figure 2A The results of lung tissue and HE staining in mice after reinfusion with D1M1-P5, D1M2-P5, and saline were shown. Figure 2B The statistical results of embolism density in mouse lung tissue; Figure 2C The results are from immunofluorescence. Figure 2D These are the results of immunofluorescence statistics;

[0093] Figure 3A The results show the clustering of single-cell sequencing subpopulations of D1M1-P5 and D1M2-P5, where 0, 1, 2, 3, 4, and 5 represent different stem cell subpopulations. Figure 3B Based on the GO-BP database, this study examines the expression of risk genes in different stem cell subpopulations, specifically C0, C1, C2, C3, C4, and C5 (corresponding to...). Figure 3A The stem cell subpopulations 0, 1, 2, 3, 4, and 5 represent different stem cell subpopulations. Figure 3C Based on the KEGG database, this study examines the expression of risk genes in different stem cell subpopulations, specifically C0, C1, C2, C3, C4, and C5 (corresponding to...). Figure 3A The stem cell subpopulations 0, 1, 2, 3, 4, and 5 represent different stem cell subpopulations.

[0094] Figure 4 This is a schematic diagram illustrating the principle of the functional cell subpopulation clustering method.

[0095] Figure 5A Based on the functional cell subpopulation clustering results obtained using the ssGSEA scoring function, A2105C2P5 (i.e., D1M1-P5) are all quality risk stem cells, and A2105C3P5 (i.e., D1M2-P5) are all non-quality risk stem cells. Figure 5B Based on the functional cell subpopulation clustering results obtained using the AUCell scoring function, A2105C2P5 (i.e., D1M1-P5) are all quality risk stem cells, and A2105C3P5 (i.e., D1M2-P5) are all non-quality risk stem cells. Figure 5CBased on the functional cell subpopulation clustering results obtained using the Seurat scoring function, A2105C2P5 (i.e., D1M1-P5) are all quality-risk stem cells, and A2105C3P5 (i.e., D1M2-P5) are all non-quality-risk stem cells.

[0096] Figure 6 This is a schematic diagram of stem cell cross-culturing.

[0097] Figure 7A The results of functional cell subpopulation clustering obtained using the ssGSEA scoring function show that D1M1-P3, D1M1-P5, and D1M2 / M1-P5 are all quality-risk stem cells, while D1M2-P3, D1M2-P5, and D1M1 / M2-P5 are all non-quality-risk stem cells. Figure 7B The results of functional cell subpopulation clustering obtained using the AUCell scoring function show that D1M1-P3, D1M1-P5, and D1M2 / M1-P5 are all quality-risk stem cells, while D1M2-P3, D1M2-P5, and D1M1 / M2-P5 are all non-quality-risk stem cells. Figure 7C The results of functional cell subpopulation clustering obtained using the Seurat scoring function show that D1M1-P3, D1M1-P5, and D1M2 / M1-P5 are all quality-risk stem cells, while D1M2-P3, D1M2-P5, and D1M1 / M2-P5 are all non-quality-risk stem cells.

[0098] Figure 8A The results of lung tissue and HE staining of mice after reinfusion of P3 and P5 generation stem cells and saline obtained under the cross-culture system; Figure 8B The statistical results of embolism density in mouse lung tissue;

[0099] Figure 9A This is a graph showing the results of differentially expressed genes in different single-cell subsets of stem cells obtained from M1 culture medium using a heatmap analysis method. Figure 9B This is a graph showing the results of differentially expressed genes in different single-cell subsets of stem cells obtained from M2 culture medium using heatmap analysis. Figure 9C The results of single-cell functional classification of stem cells obtained from M1 medium culture are shown, where 0 represents quality-risk stem cells and 1 represents non-quality-risk stem cells. Figure 9D The results of single-cell functional classification of stem cells obtained from M2 culture medium, where 0 represents non-quality-risk stem cells and 1 represents quality-risk stem cells;

[0100] Figure 10 A schematic diagram illustrating the principle of developing a stem cell quality prediction model;

[0101] Figure 11The result diagram shows the determination of the number of feature genes using recursive feature reduction technology combined with cross-validation. M1, M2, M3, M4, M5, M6, M7, and M8 represent models with different hyperparameters C and decision functions, respectively.

[0102] Figure 12 To evaluate the performance of quality scores across different test sets and risk score thresholds using receiver operating characteristic (ROC) curves and area under the curve (AUC);

[0103] Figure 13A The distribution of stem cell quality scores for test set 1; Figure 13B The distribution of stem cell quality scores for test set 2; Figure 13C The distribution of stem cell quality scores for test set 3; Figure 13D The distribution of stem cell quality scores for test set 4;

[0104] Figure 14 This study aims to predict the stem cell quality of cross-cultured samples using stem cell quality-related characteristic genes and their weighting coefficients determined by a stem cell quality prediction model. Detailed Implementation

[0105] To further illustrate the technical means and effects of this invention, the following description, in conjunction with embodiments and accompanying drawings, further explains the invention. It is understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Various modifications or variations to the methods and systems of this invention will be readily apparent to those skilled in the art and will not depart from the scope and spirit of the invention. Although the invention has been described in conjunction with specific preferred embodiments, it should be understood that the invention should not be unduly limited to these specific embodiments as claimed, and various modifications and additions can be made to the embodiments within the scope of the invention. Of course, various modifications made to the embodiments by those skilled in molecular biology and related fields for the purpose of implementing this invention fall within the scope of the claims.

[0106] Where specific techniques or conditions are not specified in the examples, they shall be performed in accordance with the techniques or conditions described in the literature in this field, or in accordance with the product instructions. Reagents or instruments whose manufacturers are not specified are all conventional products that can be purchased through legitimate channels.

[0107] definition

[0108] As described in the context, "stem cell" refers to a relatively undifferentiated cell with the potential to differentiate, which actively divides and circulates, and responds appropriately to mature, differentiated, and functional cell lines. The properties defined for said stem cells include: (a) not being terminally differentiated; (b) being able to divide indefinitely throughout an animal's lifetime; (c) having consistent cellular marker characterization results, being a single stem cell and not a mixture of multiple stem cells and / or somatic cells; and (d) upon division, each daughter cell has the option to either remain a stem cell or enter into an irreversible process leading to terminal differentiation.

[0109] As described in the context, "mesenchymal stem cells" are pluripotent stem cells that can differentiate into various cell types. Mesenchymal stem cells have been shown to differentiate into cell types including osteoblasts, chondrocytes, myocytes, and adipocytes, both in vitro and in vivo. Mesenchyme is embryonic connective tissue derived from the mesoderm and differentiates into hematopoietic and connective tissues; however, mesenchymal stem cells do not differentiate into hematopoietic cells.

[0110] As described in the context, "stem cell quality" refers to any of the factors mentioned above related to stem cell safety, including heterogeneous changes in stem cells under the influence of the microenvironment, and the potential risks arising from these changes. Clinical-grade stem cells consist of a single type of stem cell, not multiple stem cells or a mixture of stem cells and somatic cells. They must undergo rigorous third-party quality testing and laboratory testing, including tests for cell viability, biological function, tumorigenicity, embolism, immunogenicity, microorganisms, mycoplasma, and endotoxins. These tests are closely related to the safety, efficacy, and consistency of stem cells. Even qualified stem cells require further release testing before transplantation, including compliance checks for microorganisms, mycoplasma, and endotoxins, to prevent acute or subacute severe adverse reactions such as fever, allergies, and bacteremia during or after transplantation.

[0111] As described in the context, “stem cell quality-related characteristic genes” refer to genes that determine the quality category of stem cells. Once the expression of these genes increases, the quality risk of stem cells will increase or decrease.

[0112] As the context suggests, “expression level” refers to the level at which a gene is expressed.

[0113] As described in the context, "stem cell quality score" refers to the score calculated according to the following formula, based on the expression level of characteristic genes and the weight coefficient of each characteristic gene determined by the stem cell quality prediction model;

[0114]

[0115] in, Gi For the first i The expression level of each characteristic gene Wi For the first iThe weight coefficients of each characteristic gene, n This represents the number of characteristic genes.

[0116] As the context suggests, "gene expression level" refers to the expression level of a specific gene in a cell, measured using methods conventional in molecular biology. Examples include hybridization levels (measurement data) measured in the form of fluorescence intensity between nucleic acids immobilized on the surface of a DNA microarray, numerical estimates of gene expression levels, and so on.

[0117] As described in the context, "specific quality attribute" refers to the sub-clustering result of a single stem cell determined using a sub-clustering method, namely "quality-risk stem cells" or "non-quality-risk stem cells".

[0118] As described in the context, the rows of the “pathway enrichment score matrix” represent the expression of a pathway in different stem cells, the columns represent the expression of all metabolic pathways in different stem cells, and the data in each cell represents the expression of a specific pathway in a specific stem cell; the data analysis methods (including supervised and unsupervised data analysis and bioinformatics methods) are disclosed in Brazma and Viljo J, 2000, FEBS Lett 480(1):17-24.

[0119] As described in the context, "pathway" can be any pathway related to stem cell function, such as developmental signaling pathways like Notch, WNT, Hedgehog, Hippo, and NANOG, and oncogenic signaling pathways like NF-κB, MAPK, PI3K, and EGFR. Those skilled in the art are familiar with the design of cellular pathways related to stem cell function. The pathways of this invention can be any pathway related to stem cell tumorigenicity and immunogenicity. Preferred pathways include intrinsic fibrin clot formation pathways, extrinsic fibrin clot formation pathways, and fibrin clot-coagulation cascade formation pathways.

[0120] The following examples illustrate the risk of stem cell embolism closely related to stem cell quality. Those skilled in the art will understand that, based on the disclosure of this invention, essentially the same methods and means can be used to make the same identification of stem cell tumorigenicity and immunogenicity. For example, characteristic genes related to stem cell quality and tumorigenicity may include: c-myc Characteristic genes associated with immunogenicity may include: dnam-1 , mcp-1 .

[0121] The risk of thromboembolism in stem cells is the most typical risk in stem cell applications and one of the most important factors affecting the quality of stem cell preparations. In the past 20 years, numerous clinical cases of thromboembolic complications have been reported after stem cell therapy (Woodard, JP et al. Pulmonary cytolytic thrombi: a newly recognized complication of stem cell transplantation. Bone Marrow Transpl 25, 293-300 (2000).; Tatsumi, K. et al. Tissue factor triggers procoagulation in transplanted mesenchymal stem cells leading to thromboembolism. BiochemBiophys Res Commun 431, 203-209 (2013).). This demonstrates that those skilled in the art understand that assessing this risk can be used to evaluate the quality of stem cell preparations.

[0122] Example 1: Acquisition and culture of adipose-derived mesenchymal stem cells

[0123] I. Obtaining Adipose-Derived Mesenchymal Stem Cells

[0124] 1. Collection of adipose tissue

[0125] Under sterile conditions, adipose tissue was collected from donor 1 (who tested negative for HIV, hepatitis B virus, hepatitis C virus, human T-cell viral load virus, EB virus, cytomegalovirus and Treponema pallidum).

[0126] Take 50-150 mL of adipose tissue into a sealed container pre-filled with 100 mL of tissue preservation solution (purchased from Tianjin Haoyang Biological Products Technology Co., Ltd.), and store at a constant temperature of 2-8℃ for later use.

[0127] After aspirating 30 mL of tissue preservation solution with a pipette and confirming the absence of bacteria, endotoxins, and mycoplasma contamination, human adipose-derived stem cells (hADSCs) were isolated and cultured.

[0128] 2. Isolation of adipose-derived mesenchymal stem cells

[0129] Add an equal volume of dPBS to the adipose tissue, seal the container containing the tissue, shake vigorously for 20 seconds and let stand for 5 minutes. After the adipose tissue and dPBS have completely separated into layers, aspirate the lower layer of liquid and rinse the adipose tissue repeatedly with dPBS until there is no obvious red color in the lower layer of liquid.

[0130] The washed adipose tissue was aliquoted into 50 mL centrifuge tubes at a ratio of 20 mL to 50 mL. An equal volume of dPBS was added, and the tubes were centrifuged at 400 g for 5 min to separate the upper oil layer, the middle adipose tissue layer, the lower dPBS layer, and the blood cell precipitate. The upper oil layer, the lower dPBS layer, and the blood cell precipitate were then removed.

[0131] Add twice the volume of 1 mg / mL type I collagenase (digestive solution) to the adipose tissue, seal the container containing the tissue, and transfer it to a preheated constant temperature air shaker at 37°C for 120 rpm / min digestion for 1 h.

[0132] 3. Collection of adipose-derived mesenchymal stem cells

[0133] After digestion, the tissue was centrifuged at 500 g for 8 min at room temperature. After centrifugation, it was separated into an upper lipid layer, a middle adipose tissue layer, a lower digestive fluid layer, and a bottom cell pellet. The upper lipid layer, the middle adipose tissue layer, and the lower digestive fluid layer were discarded. The bottom cell pellet was resuspended with dPBS and filtered through a 100 μm sieve into a 50 mL centrifuge tube. The tube was centrifuged at 500 g for 5 min, and the supernatant was removed to obtain a cell pellet containing P0 generation adipose mesenchymal stem cells.

[0134] Add an equal volume of complete culture medium to the adipose tissue into a centrifuge tube, mix well to allow the digested cells to be fully released in the complete culture medium, and obtain a cell suspension containing P0 generation adipose mesenchymal stem cells.

[0135] II. Culture of Adipose-Derived Mesenchymal Stem Cells

[0136] The culture and proliferation of adipose-derived mesenchymal stem cells were carried out using different culture media: M1 (αMEM + 10% FBS, αMEM purchased from Thermo Fisher Scientific, FBS purchased from EcoScience) or M2 (DMEM / F-12 + 5% Helios UltraGRO-Advanced, DMEM / F purchased from Thermo Fisher Scientific, Helios UltraGRO-Advanced purchased from Helios BioScience). The specific steps are as follows:

[0137] 1. Primary culture

[0138] Resuspend the cell pellet in M1 or M2 medium of equal volume to the adipose tissue, and seed 1.5 mL of the cell suspension into a T75 cell culture flask pre-filled with 8.5 mL of M1 / M2 medium.

[0139] After labeling the T75 cell culture flasks, they were transferred to a cell culture incubator and cultured at 37°C and 5% CO2. After 24 hours, the adipose-derived mesenchymal stem cells had basically adhered to the culture vessel. The supernatant was removed and 10 mL of M1 / M2 medium was added. The medium was then changed every three days thereafter.

[0140] Microscopic observation revealed that, in addition to P0 generation adipose-derived mesenchymal stem cells, the obtained primary cells also contained many other cells and stromal components, and the adipose-derived mesenchymal stem cells were typically long spindle-shaped.

[0141] 2. Subculture

[0142] When the confluence of P0 generation cells reaches 50%~70%, remove the culture medium, add 10 mL dPBS to wash once and remove the washing solution, then add 1.5 mL of digestion solution Tryple™-Express (1×) to digest for 1~2 min. When most cells become round and detach, gently tap the culture flask and add 4.5 mL dPBS to stop digestion.

[0143] Collect the liquid after termination into a 50 mL centrifuge tube, add 10 mL dPBS to wash once, centrifuge at 400 g for 5 min. The upper layer is a mixture of digestion solution and dPBS, and the lower white precipitate is the precipitate containing P0 generation adipose mesenchymal stem cells. Remove the supernatant, collect the white precipitate from multiple centrifuge tubes into one centrifuge tube, add M1 / M2 medium to resuspend the cells, bring the volume to 30 mL, mix the cell suspension by pipetting, and take a sample for cell counting.

[0144] After cell counting, centrifuge at 400 g for 5 min, remove the supernatant, resuspend the cells in M1 / M2 medium, mix well by pipetting, and seed into cell culture flasks to achieve a cell density of 5000-6000 cells / cm³. 2 ;

[0145] Cell culture flasks were labeled with information such as cell batch, passage number, and culture time. They were then placed in a cell culture incubator and cultured. When the cell confluence reached approximately 90%, the cells were passaged again to collect P3 and P5 generation stem cells. These cells were cryopreserved to establish a working cell bank and named as follows: D1M1-P3 (representing primary adipose-derived mesenchymal stem cells from donor 1, passaged in M1 medium to P3 generation), D1M2-P3 (representing primary adipose-derived mesenchymal stem cells from donor 1, passaged in M2 medium to P3 generation), D1M1-P5 (representing primary adipose-derived mesenchymal stem cells from donor 1, passaged in M1 medium to P5 generation), and D1M2-P5 (representing primary adipose-derived mesenchymal stem cells from donor 1, passaged in M2 medium to P5 generation).

[0146] Example 2: Routine quality control of adipose-derived mesenchymal stem cells

[0147] In this embodiment, the stem cells from Example 1 were subjected to routine quality control, including microbiological safety testing, cell marker testing, cell viability testing, and biological activity analysis.

[0148] I. Microbiological Safety Testing

[0149] 1. Sterility testing

[0150] For detailed testing procedures, please refer to General Chapter 1101, Sterility Test Method, of the 2020 Edition of the Pharmacopoeia of the People's Republic of China (Part IV), which is briefly described below:

[0151] The filter membrane of a disposable triple sterilizer (purchased from Zhejiang Tailin Biotechnology Co., Ltd.) was pre-wetted with 100 mL of 0.9% sodium chloride injection (purchased from Shijiazhuang No. 4 Pharmaceutical Co., Ltd.). The stem cell sample was then introduced into the sterilizer for filtration. After filtration, the filter membrane was rinsed twice with 300 mL of 0.9% sodium chloride injection.

[0152] Add 100 mL of thioglycolate fluid medium to two of the three culture vessels containing the sample, and add 100 mL of tryptic soy peptone liquid medium to the other culture vessel.

[0153] 0.9% sodium chloride injection was used as a negative control instead of stem cell samples, and Staphylococcus aureus (with a bacterial count of less than 100 CFU) was used as a positive control.

[0154] After inoculation, each experimental group was gently shaken. One culture vessel containing thioglycolate fluid medium was selected for each experimental group and placed at 30-35℃ for incubation. The remaining culture vessels were placed at 20-25℃ for incubation for a total of 14 days. During the incubation period, the presence of bacterial growth was observed and recorded every working day.

[0155] 2. Mycoplasma testing

[0156] For detailed testing procedures, please refer to General Chapter 3301, Mycoplasma Examination Method, of the 2020 Edition of the Pharmacopoeia of the People's Republic of China (Part IV), which is briefly described below:

[0157] Prepare and sterilize mycoplasma broth culture medium, mycoplasma broth culture medium containing arginine, mycoplasma semi-fluid culture medium, and mycoplasma semi-fluid culture medium containing arginine according to the conventional formula. Reconstitute 800,000 units of penicillin sodium for injection (purchased from Jiangxi Dongfeng Pharmaceutical Co., Ltd.) with 1 mL of 0.9% sodium chloride injection for later use. Add 200 mL of fetal bovine serum and 800,000 units of penicillin sodium for injection to every 800 mL of sterilized culture medium, mix well, and store at 2-8℃.

[0158] Take 4 vials of mycoplasma broth culture medium (each containing 10 mL), 4 vials of mycoplasma broth culture medium containing arginine, 2 vials of mycoplasma semi-fluid culture medium, and 2 vials of mycoplasma semi-fluid culture medium containing arginine, inoculate 1.0 mL of stem cell sample, and incubate at 36℃±1℃ for 21 days, observing once every 3 days.

[0159] On the 7th day after inoculation, two vials of mycoplasma broth culture medium inoculated with stem cell samples and two vials of mycoplasma broth culture medium containing arginine inoculated with stem cell samples were used for secondary culture. Each vial of mycoplasma broth culture medium was transferred to two vials of mycoplasma semi-fluid culture medium and two vials of mycoplasma broth culture medium, and each vial of mycoplasma broth culture medium containing arginine was transferred to two vials of mycoplasma semi-fluid culture medium containing arginine and two vials of mycoplasma broth culture medium. The inoculation volume of each culture medium was 1 mL. The culture was placed at 36℃±1℃ for 21 days, and the samples were observed every 3 to 5 days.

[0160] 3. Endotoxin testing

[0161] For detailed testing procedures, please refer to General Chapter 1143, Bacterial Endotoxin Test Method, of the 2020 Edition of the Pharmacopoeia of the People's Republic of China (Part IV), which is briefly described below:

[0162] The endotoxin working standard (purchased from Zhanjiang Andus Biotechnology Co., Ltd.) was reconstituted with 1 mL of water for endotoxin testing (purchased from Zhanjiang Andus Biotechnology Co., Ltd.), mixed with a vortex mixer for 15 min, and then serially diluted. Each dilution was mixed with a vortex mixer for 30 s, and finally diluted to 4λ and 2λ endotoxin standard solutions.

[0163] After rapidly thawing the cell cryopreservation solution in a 37°C water bath, centrifuge at 1200 rpm for 5 min. Take the supernatant and add water for endotoxin testing for serial dilution. Vortex for 30 s at each dilution step. The dilution factor should not exceed the maximum effective concentration dilution factor (MVD). This solution is used as the stem cell sample detection solution. The maximum effective concentration dilution factor (MVD) is calculated according to the formula MVD=C*L / λ, where L is the endotoxin limit of the stem cell sample, C is the concentration of the stem cell sample detection solution, and λ is the labeled sensitivity of the Limulus Amebocyte Lysate (LAL) reagent.

[0164] Separately, take the stem cell sample detection solution and add 4λ endotoxin standard solution at a volume ratio of 1:1, mix with a vortex mixer for 30 s, and use it as the endotoxin positive control solution for the stem cell sample.

[0165] Eight Limulus Amebocyte Lysate (LAL) reagents (purchased from Zhanjiang Andus Biotechnology Co., Ltd.) were reconstituted with 0.1 mL of endotoxin test water. Two LAL reagents were used as parallel positive control tubes (PPC) containing endotoxin from stem cell samples. Two LAL reagents were also used as parallel positive control tubes (PC). Two LAL reagents were used as parallel negative control tubes (NC) containing 0.1 mL of endotoxin test water. Finally, two LAL reagents were used as parallel stem cell sample detection tubes containing 0.1 mL of stem cell sample detection solution diluted no more than MVD.

[0166] Open the reaction well cover of the preheated bacterial endotoxin gel electrophoresis apparatus, insert the reaction tube, and start the countdown for 60 minutes. Remove the reaction tube 1 minute before the end of the 60 minutes and observe and record the results.

[0167] The results are shown in Table 1. After 14 days, the collection container containing the stem cell samples showed sterile growth. The sterility test results of D1M1-P3, D1M2-P3, D1M1-P5, and D1M2-P5 were qualified. The mycoplasma test results of D1M1-P3, D1M2-P3, D1M1-P5, and D1M2-P5 were negative, and the test results were qualified. The endotoxin test results of D1M1-P3, D1M2-P3, D1M1-P5, and D1M2-P5 were negative, and the test results were qualified.

[0168] II. Detection of stem cell markers

[0169] The expression of mesenchymal stem cell-specific surface markers CD73, CD90, CD105, CD11b, CD19, CD34, CD45, and HLA-DR by D1M1-P3, D1M2-P3, D1M1-P5, and D1M2-P5 was detected (reference: M. Dominici et al., Minimal criteria for defining multipotent mesenchymal stromal cells. The International Society for Cellular Therapy position statement. Cytotherapy (2006) Vol. 8, No. 4, 315-317). The steps are as follows:

[0170] Add Tryple™-Express (1×) to stem cells in good growth condition, digest at 37°C for 2-3 min, add PBS (1×) at least 3 times the volume of Tryple™-Express (1×) to stop digestion, slowly rinse to detach and loosen the cells into single cells; transfer the cell suspension to a 50 mL centrifuge tube, centrifuge at 300 g for 5 min; discard the supernatant, add 1× PBS to resuspend the cells, centrifuge at 300 g for 5 min; discard the supernatant, add 1 mL of 1× PBS to resuspend the cells, and set aside.

[0171] Adjust the cell concentration to a viable cell density of (0.5~1)×10⁻⁶. 7 Cells / mL, 100 μL of cell suspension was added to a flow cytometer, followed by 5 μL of FITC-labeled anti-human CD34 antibody, FITC-labeled anti-human CD45 antibody, FITC-labeled anti-human CD11b antibody, FITC-labeled anti-human HLA-DR antibody, FITC-labeled anti-human CD73 antibody, FITC-labeled anti-human CD90 antibody, APC-labeled anti-human CD19 antibody, and PE-labeled anti-CD105 antibody. A control group was set up by adding FITC-labeled mouse IgG1, APC-labeled mouse IgG1, and PE-labeled mouse IgG1. The cells were vortexed to mix the cell suspension with the antibodies and incubated in the dark for 15 min.

[0172] Add 2 mL of sheath fluid to each tube, vortex, centrifuge at 300 g for 5 min, discard the supernatant, resuspend the cells in 300 μL of PBS buffer containing 1% paraformaldehyde, and perform flow cytometry detection.

[0173] The results are shown in Table 2. The expression rates of D1M1-P3, D1M2-P3, D1M1-P5, and D1M2-P5 for the positive markers CD73, CD90, and CD105 on the surface of mesenchymal stem cells were higher than 95%, while the expression rates of the negative markers CD11b, CD19, CD34, CD45, and HLA-DR for mesenchymal stem cells were lower than 2%.

[0174] III. Cell Viability Detection

[0175] 1. Cell viability detection

[0176] Dilute the stem cell suspension with 0.9% sodium chloride injection solution and mix thoroughly with 0.4% trypan blue staining solution at a volume ratio of 9:1. Pipette 10 μL of the mixture into the counting chamber of a disposable counting chamber and observe it under a 10x objective lens. Record the total number of live cells and the total number of dead cells in the four squares. Calculate the cell viability using the following formula.

[0177] Viable cell percentage (%) = (Total number of viable cells / (Total number of viable cells + Total number of dead cells)) × 100%

[0178] The results showed that the viable cell rates of D1M1-P3, D1M2-P3, D1M1-P5, and D1M2-P5 all reached over 80%.

[0179] 2. Growth kinetics detection

[0180] When the confluence of P5 generation stem cells reached 80%–90%, adherent cells were digested using Tryple™-Express (1×) to prepare a stem cell suspension and cell counting was performed. The density of the stem cell suspension was adjusted to 3.2 × 10⁻⁶ cells using culture medium. 5 cells / mL, 1.6 × 10 5 cells / mL, 0.8×10 5 cells / mL, 0.4×10 5 cells / mL, 0.2×10 5 cells / mL, 0.1×10 5 Cells / mL were seeded at 100 μL / well in a 96-well cell culture plate; 100 μL of complete culture medium was added to the blank control wells. Each group was set up with 6 replicates and cultured at 37℃ and 5% CO2 for 4 hours.

[0181] Prepare the test reagent according to the ratio of DMEM / F12 medium (phenol red-free): CCK8 (v / v) = 100:10, and mix thoroughly; discard the culture medium in each well, add the above test reagent at 110 μL / well, and incubate at 37℃ and 5% CO2 for 2 hours.

[0182] The sample was placed in an ELISA reader, and the absorbance OD450 at a wavelength of 450 nm was measured. The average absorbance of the wells with different cell seeding densities was calculated, and the average absorbance of the blank control wells was subtracted to obtain the net absorbance ΔOD450 of the wells with different cell seeding densities. A linear regression curve was fitted with ΔOD450 as the x-axis and the corresponding cell seeding number as the y-axis.

[0183] The density of the cell suspension was adjusted to 0.1 × 10⁻⁶ using culture medium. 5 Cells / mL were seeded at 100 μL / well in 96-well cell culture plates, and blank control wells with only 100 μL of complete culture medium were set up. Each group had 6 replicates, and 8 plates were repeatedly seeded.

[0184] One plate was removed each day, and the culture medium in the sample wells and blank control wells was discarded. The detection reagent (DMEM / F12 medium (phenol red-free): CCK8 (v / v) = 100:10) was added at 110 μL / well and incubated at 37℃ and 5% CO2 for 2 hours.

[0185] The sample was placed in an ELISA reader, and the absorbance OD450 at a wavelength of 450 nm was measured. The net absorbance value ΔOD450 of the stem cell sample was calculated. The sample was continuously measured for 8 days. ΔOD450 was substituted into the standard curve equation to calculate the number of cells per day. The growth curve of human adipose-derived mesenchymal stem cells was plotted with the number of proliferation days as the x-axis and the number of cells per day as the y-axis, and the population doubling time was calculated.

[0186] like Figure 1A and Figure 1B The figures show the growth curves of D1M1-P5 and D1M2-P5, respectively. The stem cells entered the logarithmic growth phase after 3 days of culture, entered the plateau phase after 6 days, and the cell proliferation capacity began to decline after 7 days. The population doubling time of D1M1-P5 was 37.5 hours, and that of D1M2-P5 was 21.9 hours.

[0187] 3. Cell cycle detection

[0188] Add Tryple™-Express (1×) to P5 generation stem cells in good growth condition, digest at 37°C for 2-3 min, place the detached cells in a centrifuge tube, centrifuge at 1000 rpm for 3-5 min, and carefully aspirate the supernatant; add 1 mL of pre-chilled ice-cold PBS to resuspend the cells, centrifuge again to precipitate the cells, and carefully aspirate the supernatant; add 1 mL of pre-chilled ice-cold PBS to resuspend the cells.

[0189] Take 4 mL of pre-chilled ice-cold 95% ethanol and vortex at low speed. At the same time, add 1 mL of cell suspension dropwise (operate on ice). Mix well and fix at 4°C for 2 hours or longer. Centrifuge at 1000 rpm for 3-5 min and carefully discard the supernatant. Add 5 mL of pre-chilled ice-cold PBS to resuspend the cells. Centrifuge again to precipitate the cells. Carefully discard the supernatant. Gently tap the bottom of the centrifuge tube to disperse the cells appropriately and avoid cell clumping.

[0190] Referring to Table 3, using the cell cycle and apoptosis detection kit (purchased from Sizhengbai Biotechnology), iodide pyridine staining solution was prepared according to the number of samples to be tested; then 0.4 mL of iodide pyridine staining solution was added to the stem cell sample, the cell pellet was slowly and fully resuspended, and the sample was incubated at 37°C in the dark for 30 min. Flow cytometry detection was completed within 24 hours.

[0191] like Figure 1C and Figure 1D The results of cell cycle flow cytometry are shown. It can be seen that the proportions of D1M1-P5 cells in G1, S and G2 phases are 85.69%, 12.56% and 1.75%, respectively, while the proportions of D1M2-P5 cells in G1, S and G2 phases are 89.07%, 6.42% and 4.51%, respectively.

[0192] 4. Apoptosis detection

[0193] Add Tryple™-Express (1×) to P5 generation stem cells in good growth condition, digest at 37°C for 2-3 min, place the detached cells in a centrifuge tube, centrifuge at 1000 rpm for 3-5 min, carefully aspirate the supernatant; add 0.8 mL of 1× Binding Buffer (purchased from Sizhengbai Biotechnology) to resuspend the cells for later use;

[0194] Take four flow cytometry tubes and label them as blank tube, Annexin-V-FITC tube, PI tube, and stem cell sample tube, respectively. Add 200 μL of a solution with a density of (2~5)×10⁻⁶ to each flow cytometry tube. 5 Prepare stem cell samples of / mL and add 5 μL Annexin-V-FITC to Annexin-V-FITC tubes and stem cell sample tubes, incubate in the dark for 10 min; after centrifugation, resuspend the stem cells and add 200 μL binding buffer to each tube; before instrumental analysis, add 5 μL PI dye to the PI tube.

[0195] The results are as follows Figure 1E and Figure 1FAs shown, the cell survival rate of D1M1-P5 was 92.0% and the apoptosis rate was 5.75%, while the cell survival rate of D1M2-P5 was 91.4% and the apoptosis rate was 0.52%.

[0196] IV. Biological Activity Analysis

[0197] 1. Detection of adipogenic differentiation

[0198] Before the experiment began, solutions A and B were prepared according to the instructions of the OriCell Adult Adipose-Derived Mesenchymal Stem Cell Adipogenic Differentiation Induction Kit (purchased from Cyagen Biosciences Co., Ltd.), followed by the following steps:

[0199] According to 2×10 4 pcs / cm 2 To achieve the desired cell density, cells were seeded into six-well plates, with 2 mL of complete culture medium added to each well. The plates were then incubated at 37°C and 5% CO2 until 100% cell confluence was achieved.

[0200] Discard the culture medium, add 2 mL of solution A to each well, and induce for 3 days. Then replace with 2 mL of solution B, and replace with solution A again after 24 hours. After alternating between solution A and solution B 3 times, continue to use solution B to maintain the culture for 4-7 days until the lipid droplets become large and round enough. During the maintenance culture period, replace with fresh solution B every 2-3 days.

[0201] Oil Red O staining was used to analyze the red lipid droplets under a microscope.

[0202] 2. Osteogenic differentiation induction detection

[0203] Before the experiment, the induction medium was prepared according to the instructions of the OriCell Adult Adipose Mesenchymal Stem Cell Osteogenic Differentiation Induction Kit (purchased from Cyagen Biosciences Co., Ltd.), followed by the following steps:

[0204] According to 2×10 4 pcs / cm 2 To achieve the desired cell density, cells were seeded into six-well plates, with 2 mL of complete culture medium added to each well. The plates were then incubated at 37°C and 5% CO2 until cell confluence reached 80-90%.

[0205] Discard the culture medium and add 2 mL of induction medium to each well. Change the medium every 3 days. After induction for 2-4 weeks, observe the morphological changes and growth of the cells. Use alizarin red staining to analyze the red calcium nodules under the microscope.

[0206] 3. Chondrogenic differentiation induction detection

[0207] Before the experiment began, a complete chondrogenic differentiation culture medium was prepared according to the instructions of the OriCell Adult Adipose-Derived Mesenchymal Stem Cell Chondrogenic Differentiation Induction Kit (purchased from Cyagen Biosciences Co., Ltd.). The following steps were then performed:

[0208] Add 0.1% gelatin to a 6-well culture plate, gently shake to cover the bottom of the wells, let stand for 30 minutes, discard the gelatin, and allow the culture plate to air dry; then add P5 generation stem cells in good growth condition at a ratio of 1×10⁻⁶. 4 pcs / cm 2 Cells were transferred to 6-well plates, with 2 mL of complete culture medium added to each well. The plates were incubated at 37°C and 5% CO2 until cell confluence reached 80-90%. For the induction wells, the culture supernatant was aspirated, and every 2-3 days, 2 mL of fresh chondrogenic induction complete culture medium and 20 μL of TGF-β3 were added to each well. Induction was continued at 37°C and 5% CO2. Control wells were continuously cultured in complete culture medium. After induction for at least 14 days, the cells were fixed and stained with alexandrite blue. The alexandrite blue staining effect was observed under a microscope.

[0209] like Figure 1G and Figure 1H The diagram shows the differentiation of adipose-derived mesenchymal stem cells in an in vitro culture environment. The adipose-derived mesenchymal stem cells were directed to differentiate into adipocytes, osteoblasts, and chondrocytes. The results of Oil Red O staining, Alizarin Red staining, and Alixin Blue staining show that D1M1-P5 and D1M2-P5 were successfully induced into adipocytes, osteoblasts, and chondrocytes, respectively.

[0210] Based on the above quality inspection results, it can be concluded that the P3 and P5 generation stem cells cultured under different culture media are all adipose-derived mesenchymal stem cells, which meet the basic cell biological characteristics requirements of mesenchymal stem cells.

[0211] Example 3 Heterogeneity detection of adipose-derived mesenchymal stem cells

[0212] I. In vivo reinfusion of adipose-derived mesenchymal stem cells in animals

[0213] Six- to eight-week-old male NCG mice (purchased from Jiangsu Jicui Yaokang Biotechnology Co., Ltd.) were randomly divided into groups and labeled. The mice were restrained using a fixator, and after disinfecting the injection site, the routinely tested and qualified D1M1-P5 or D1M2-P5 suspension prepared in Example 1 was slowly reinfused into the tail vein of each mouse. The reinfused dose was 1 × 10⁻⁶ mg / L. 6 One cell / animal, while a control group was set up with reinfused saline;

[0214] Immediately after stem cell infusion, the mice were placed in clean cages and observed via video for 3 minutes, recording their survival status. The observation revealed that all 6 mice infused with D1M1-P5 died within 3 minutes, while all 6 mice infused with D1M2-P5 and 6 mice infused with saline survived within 3 minutes. After the 3-minute observation period, the mice were euthanized using cervical dislocation, and tissues and organs were harvested.

[0215] II. Immunohistochemical Detection

[0216] 1. Obtain materials

[0217] Cut open the skin and muscle tissue of the mouse to expose the chest cavity. Make a small incision in the right atrial appendage with ophthalmic scissors and insert a syringe into the right ventricle to slowly infuse 5 mL of 0.9% saline solution into the whole body until the outflowing fluid is clear and has no obvious blood color. Then begin the lung sampling of the mouse.

[0218] Open the chest cavity, cut the blood vessels and trachea connected to the lungs, remove the entire lung, clean the surface bloodstains with saline and wipe the blood and body fluids on the surface of the lung with gauze, and observe the gross morphological characteristics of the lung.

[0219] 2. HE staining analysis

[0220] Lung tissue samples were embedded in paraffin, sectioned, stained with hematoxylin and eosin (HE) using conventional methods, and observed and imaged under an optical microscope.

[0221] like Figure 2A The image shows the pathological examination results of lung tissue in mice after infusion of D1M1-P5, D1M2-P5, or saline. Compared with the control group, D1M1-P5 infusion resulted in significant pulmonary congestion and severe pulmonary embolism in mice, while D1M2-P5 infusion did not show significant pulmonary congestion or pulmonary embolism. Figure 2B The pulmonary embolism density also showed that mice that received D1M1-P5 had a large number of venous thrombi in their lungs, with an embolism density much higher than that of the D1M2-P5 group and the control group.

[0222] Further, D1M1-P5 or D1M2-P5 labeled with fluorescent PKH26 was reinfused into mouse lung tissue for immunofluorescence staining to detect thrombus formation.

[0223] The results are as follows Figure 2C and Figure 2D As shown, consistent with the immunohistochemical results, a large amount of PKH26 was observed in the lungs of mice with thrombus formation. + D1M1-P5 indicates that stem cells undergo heterogeneous changes when passaged in different culture media.

[0224] Example 4 Single-cell RNA sequencing

[0225] To explore the heterogeneous changes in stem cells under different culture media, this embodiment uses single-cell RNA sequencing technology to detect the gene expression profile of stem cells at the single-cell level. The steps are as follows:

[0226] I. Preparation of Single-Cell Suspension

[0227] The D1M1-P5 and D1M2-P5 stem cells in the logarithmic growth phase prepared in Example 1 were diluted to a concentration of <1000 cells / μL using sample buffer; 200 μL of cell suspension was taken and 1 μL of Calcein AM dye and 1 μL of LRaq7 dye were added for cell staining.

[0228] After filtering the stained cell suspension through a 40 μm filter membrane, the suspension was added to a cell counter and placed in a cell analyzer (purchased from BD Rhapsody Scanning) to calculate cell concentration and cell viability.

[0229] The cell suspension was diluted according to cell concentration and cell loading.

[0230] II. Single-cell sorting

[0231] The diluted cell suspension was added to a BD Cartridge single-cell sorting plate (purchased from BD Biosciences, Cat: 633733), placed in a cell analyzer, and the cell loading and double-cell rate were calculated to evaluate the single-cell separation effect.

[0232] Wash the unloaded cell suspension with buffer, add the capture beads to the BD Cartridge single-cell sorting plate, place it in the cell analyzer, calculate the co-loading of capture beads and cells and the double cell rate, and assess the number of capture beads bound to the single cell well.

[0233] Wash away any excess capture beads, add cell lysis buffer to a BD Cartridge single-cell sorting plate for cell lysis, and ligate mRNA onto the probes on the surface of the capture beads; then recover the capture beads from the BD Cartridge single-cell sorting plate into centrifuge tubes.

[0234] III. Single-cell cDNA synthesis and library construction

[0235] Single-cell cDNA synthesis and library construction were performed using kits purchased from BD Biosciences, specifically the Single-cell cDNA Synthesis Kit (BD Biosciences, Cat: 633731) and the Library Construction Kit (BD Biosciences, Cat: 633801). The procedures were performed according to the kit instructions, briefly described below:

[0236] The recovered capture magnetic beads were cleaned, and the reverse transcription reagent (Table 4) was added and mixed evenly with the capture magnetic beads. The mixture was then incubated at 37°C for 45 min.

[0237] Add exonuclease, incubate at 37°C for 30 min and 80°C for 20 min to remove probes without mRNA attached to the surface of the capture magnetic beads;

[0238] Add random primer reaction mixture (Table 5), incubate at 95℃ for 5 min, 1200 rpm at 37℃ for 5 min, and 1200 rpm at 25℃ for 15 min; add primer extension reaction mixture (Table 6), incubate at 1200 rpm at 25℃ for 10 min, 1200 rpm at 37℃ for 15 min, 1200 rpm at 45℃ for 10 min, and 1200 rpm at 55℃ for 10 min, then elute the amplified single-stranded DNA with elution buffer;

[0239] Add the PCR amplification mixture containing random primer extension products with universal and specific primers (Table 7), and perform the PCR amplification reaction according to the procedure in Table 8. Enrich the random primer amplification products and purify the products.

[0240]

[0241] Add the whole transcriptome Index PCR amplification mixture (Table 9), and perform PCR amplification reaction according to the reaction program in Table 10 (9 cycles when the molar concentration of random primer amplification product is 1~2 nM, and 8 cycles when the molar concentration of random primer amplification product is >2 nM). Enrich the amplification products and purify the products to obtain a single-cell library.

[0242] IV. Quality Testing of Single-Cell Libraries

[0243] The concentration of single-cell libraries was detected using a Qubit analyzer, and the fragment length of the single-cell libraries was detected using an Agilent 2100 bioanalyzer. The library concentrations were found to be 0.1–100 ng / μL, and the fragment lengths were approximately 460–550 bp.

[0244] V. Single-cell sequencing

[0245] The molar concentration of the single-cell library is calculated to be approximately 1–100 nM based on the concentration and fragment length of the single-cell library. After dilution to a standard molar concentration of 0.2–2 nM, it is mixed with a balanced base library of the same molar concentration at a ratio of 1:(0.05–0.5) and then sequenced.

[0246] VI. Sequencing Data Quality Assessment

[0247] We used BD cwl-runner 3.1 to analyze the total data volume, Q30, clustered data volume, and effective cluster data volume of the sequencing data to conduct a simple assessment of the quality of the sequencing data after sequencing.

[0248] The 5095 D1M1-P5 and 3249 D1M2-P5 cells prepared in Example 1 were sequenced, with an average sequencing depth of 50K / cell.

[0249] Example 5: Subgroup Clustering

[0250] Follow steps one and two according to the BD Protocol. Convert the sequencing data (BCL file) to a FASTQ file, check the sequencing data quality, and perform further analysis.

[0251] I. Data Preprocessing

[0252] Quality control: Filter out sequences with read1 length < 60, read2 length < 42, read1 and read2 base mass < 20, and read1 SNF ≥ 0.55 or read2 SNF ≥ 0.80;

[0253] Alignment and annotation: The quality-controlled valid data are aligned with the reference genome GRCh38, and the alignment results are annotated.

[0254] RSEC algorithm adjustment: By continuously performing the recursive substitution error correction (RSEC) algorithm, the UMI (Unique Molecular Index) information of the alignment results is corrected to obtain the single-cell gene expression matrix.

[0255] II. Cell Filtration

[0256] Using the median absolute deviation (MAD) principle, outlier cells are filtered out at the cellular level: UMI (Unique Molecular Index) is used to count different genes with the same barcode. The number of genes detected in each cell and the UMI are analyzed together. Based on the threshold criteria of UMI > median - MAD, number of genes > median - MAD, and mitochondrial ratio < median + MAD, the effective cells and their number are determined, and low-quality cells, dead cells or empty barcodes are removed.

[0257] Remove genes that are not expressed in cells at the gene level, ensuring that each gene is expressed in at least 10 cells.

[0258] Further, conventional methods were used to eliminate the influence of factors such as cell cycle, sequencing depth, and mitochondrial ratio on the analysis results.

[0259] III. Dimensional Reduction

[0260] To obtain two-dimensional data, Seurat's principal component analysis (PCA) method was used to process the first 2000 hypervariable genes in the zero-mean expression matrix for dimensionality reduction, obtaining low-dimensional spatial information. Subsequently, the uniform manifold approximation and projection (UMAP) method was used to process the first 30 principal components (PCs) to achieve two-dimensional cell visualization. The steps included:

[0261] Data normalization is performed using the NormalizeData function (normalization.method = "LogNormalize");

[0262] The FindVariableFeature function (selection.method = "vst", nfeatures = 2000) selects the top 2000 genes sorted by variance as highly variable genes (HVG).

[0263] The ScaleData function was used to standardize 2000 highly variable genes and remove noise caused by cell cycle and other factors.

[0264] Data dimensionality reduction is performed using the RunPCA function (features = VariableFeatures(object = adsc)).

[0265] A neighborhood graph is constructed using the shared nearest neighbor (SNN) algorithm of the FindNeighbors function;

[0266] The FindClusters function is used to adjust the parameters of the SNN model (resolution = 0.1~1) to determine the number of cell subpopulations.

[0267] Visual dimensionality reduction analysis is performed using the UMAP (uniform manifold approximation and projection) algorithm of the RunUMAP function.

[0268] like Figure 3A The cell subpopulation clustering results for D1M1-P5 and D1M2-P5 included six cell subpopulations (0-5), with significant differences in the proportion of each subpopulation within D1M1-P5 and D1M2-P5. Further analysis using GO and KEGG was conducted to mine stem cell risk genes, such as... Figure 3B and Figure 3C As shown, the expression of risk genes also differs among the subpopulations. This indicates that stem cells undergo heterogeneous changes in different culture media, with completely different gene expression profiles for D1M1-P5 and D1M2-P5.

[0269] IV. Cluster Analysis of Functional Cell Subpopulations

[0270] Based on the single-cell gene expression matrix, all genes in stem cells are scored according to pathways to obtain a single-cell pathway enrichment score matrix. Subsequently, the single-cell pathway enrichment score matrix is ​​subjected to dimensionality reduction and clustering to obtain the functional cell subpopulation clustering results. A schematic diagram of the principle is shown below. Figure 4 As shown, the steps are briefly described below:

[0271] By performing pathway enrichment analysis on gene expression data of single cells, the scoring functions of three analysis software, ssGSEA, AUCell and Seurat, were called to calculate the enrichment score of each pathway enriched in each stem cell, and a single-cell pathway enrichment score matrix was obtained.

[0272] Based on the single-cell pathway enrichment score matrix, single cells are clustered and visualized using dimensionality reduction, common nearest neighbor similarity algorithm (SNN), and unified manifold approximation and projection (UMAP) to obtain the stem cell subpopulation clustering results based on gene expression.

[0273] The results are as follows Figure 5A , Figure 5B and Figure 5CAs shown, three different scoring functions—ssGSEA, AUCell, and Seurat—were used to perform functional clustering of stem cells, yielding consistent results. The cell subpopulation clustering results for D1M1-P5 (i.e., A2105C2P5) and D1M2-P5 (i.e., A2105C3P5) both showed clear boundaries. D1M1-P5 consisted entirely of stem cells at quality risk, while D1M2-P5 consisted entirely of stem cells at non-quality risk. This indicates that significant functional changes occurred in stem cells after culture in different media, consistent with the results of Example 2. This functional cell subpopulation clustering analysis method integrates traditional cell subpopulation clustering, differential gene analysis, and pathway enrichment analysis, achieving the effect of rapidly identifying differences in cell function.

[0274] According to such Figure 6 The stem cell culture method shown uses M1 or M2 culture medium to passage adipose-derived mesenchymal stem cells from donor 1 from generation P0 to generation P3, and then exchanges the culture medium for passage to generation P5. The quality of generation P3 and generation P5 stem cells is determined by functional cell subpopulation clustering analysis.

[0275] The results are as follows Figure 7A , Figure 7B and Figure 7C As shown, the functional cell subpopulation clustering results of stem cells obtained by culture in M1 medium and stem cells obtained by culture in M2 medium showed significant differences. Stem cells obtained by culture in M1 medium were all quality-risk stem cells, while stem cells obtained by culture in M2 medium were all non-quality-risk stem cells. M1 and M2 mediums caused significant heterogeneity changes in stem cells.

[0276] Figure 8A and Figure 8B Animal experiments showed that D1M1-P3 and D1M2 / M1-P5 caused pulmonary embolism in mice, while D1M2-P3 and D1M1 / M2-P5 did not, confirming the correctness of the functional cell subpopulation clustering results.

[0277] V. Single-cell functional classification of stem cells

[0278] Based on the above steps of data preprocessing, cell filtering, dimensionality reduction and functional cell subpopulation clustering analysis, the single-cell RNA sequencing data of D1M1-P5, D1M2-P5, D2M1-P5, D2M2-P5, D3M1-P5 and D3M2-P5 were analyzed to obtain the single-cell subpopulation clustering results.

[0279] Wherein, D1M1-P5 and D1M2-P5 represent stem cells cultured to the P5 generation from donor 1, M1 or M2 culture medium, respectively; D2M1-P5 and D2M2-P5 represent stem cells cultured to the P5 generation from donor 2, M1 or M2 culture medium, respectively; and D3M1-P5 and D3M2-P5 represent stem cells cultured to the P5 generation from donor 3, M1 or M2 culture medium, respectively.

[0280] like Figure 9A and Figure 9B As shown, differentially expressed genes in different single-cell subsets were analyzed using heatmap analysis. It was found that differentially expressed genes related to prothrombotic pathways (intrinsic fibrin clot formation pathway, extrinsic fibrin clot formation pathway, and fibrin clot-coagulation cascade formation pathway) were increased in stem cell subsets 0 and 3.

[0281] Further differentially expressed genes were used to further separate cells into subpopulations 0 and 3, and the results were as follows: Figure 9C and Figure 9D As shown, the stem cells obtained by culturing in M1 medium were mainly high-risk stem cells (subpopulation 0), while the stem cells obtained by culturing in M2 medium were mainly non-high-risk stem cells (subpopulation 0).

[0282] Example 6 Subgroup Identification

[0283] This embodiment uses the dataset in Table 11 to construct a stem cell quality prediction model based on decision trees, random forests, or support vector machines (SVM) to assess stem cell quality risk at the single-cell level. A schematic diagram of the principle is shown below. Figure 10 As shown, D1M1-P5 and D1M2-P5 represent stem cells cultured to the P5 generation from donor 1, M1, or M2 culture medium, respectively; D1M1-P3 and D1M2-P3 represent stem cells cultured to the P3 generation from donor 1, M1, or M2 culture medium, respectively; D2M1-P5 and D2M2-P5 represent stem cells cultured to the P5 generation from donor 2, M1, or M2 culture medium, respectively; and D2M3 / M2-P5 represent stem cells cultured to the P3 generation from donor 2 and M3 (αMEM + 5% Helios UltraGRO-Advanced) culture medium, followed by culture to the P5 generation from M2 culture medium.

[0284] The steps are as follows:

[0285] I. Initial Hyperparameter Settings

[0286] In the random forest model, the estimator (n_estimator) takes values ​​of 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000, and the maximum tree depth (max_depth) is 3, 5, or 7. In the support vector machine model, the model complexity hyperparameter C takes values ​​of 0.2, 0.6, 0.8, 1.0, 1.2, 1.6, 2.0, 2.2, 2.6, and 3.0, and the kernel parameter (kemel) is linear, 'poly', 'rbf', or 'sigmoid'.

[0287] II. Feature Selection

[0288] By using recursive feature reduction technique combined with cross-validation (RFECV) machine learning method, the importance of each gene in the training set in distinguishing between quality-risk stem cells and non-quality-risk stem cells was ranked.

[0289] Starting with the most important gene, add one gene at a time, calculate the cross-validation accuracy, and determine the appropriate feature genes and the number of feature genes.

[0290] like Figure 11 As shown, when the most important first gene is selected as the feature gene, the cross-validation accuracy of the models with different hyperparameters C on the training set is all above 94%. When the most important top 13 genes (TAGLN, EFEMP1, TPM1, CLU, PTX3, IER3, IGFBP7, MFAP5, IL6, LUM, SERPINE2, CRIM1, and RHOB) are selected as feature genes, the cross-validation accuracy of the models with different hyperparameters C on the training set is all 100%.

[0291] III. Training and Testing Models

[0292] Using the 13 most important genes as features, a support vector machine is built, and cross-validation is used to optimize the model coefficient matrix (model weight matrix), which represents the importance score of the feature genes.

[0293] The model was tested using test set 1, test set 2, test set 3, and test set 4 respectively, and the model complexity hyperparameter C was adjusted based on the prediction accuracy.

[0294] Based on the test results shown in Table 12, the model with the highest predictive accuracy on all test sets (C=0.0005) was selected as the stem cell quality prediction model.

[0295] The prediction results of the established stem cell quality prediction model (support vector machine, model complexity parameter C=0.0005) on different test sets for different types of stem cells are shown in Table 13. It has good prediction accuracy, precision, recall and F1 score on the four test sets. The 13 identified feature genes and their corresponding weight coefficients are shown in Table 14.

[0296]

[0297] IV. Stem Cell Quality Scoring at the Single-Cell Level

[0298] Based on the expression levels of characteristic genes and the weighting coefficients of each characteristic gene determined by the stem cell quality prediction model, a stem cell quality score at the single-cell level is calculated to quantify the quality risk of a single stem cell. The calculation formula is as follows:

[0299]

[0300] in, Gi For the first i The expression level of each characteristic gene Wi For the first i The weight coefficients of each characteristic gene, n The number of characteristic genes; Wi A positive value indicates that increased expression of the characteristic gene may increase the risk to stem cell quality. Wi A negative value indicates that increased expression of the characteristic gene may inhibit the quality of stem cells.

[0301] The performance of stem cell quality scores across different test sets and risk score thresholds were assessed using receiver operating characteristic (ROC) curves and area under the curve (AUC).

[0302] like Figure 12 The table shows the ROC curves and corresponding AUCs for test sets 1, 2, 3, and 4. The highest value of the ROC curve for each test set (representing the highest sensitivity and specificity) is used as the threshold for determining whether the stem cells in that test set are quality-risk stem cells or not. It can be seen that the quality-risk threshold for stem cells in test set 1 is 3.961 (AUC=1), the quality-risk threshold for stem cells in test set 2 is 3.961 (AUC=1), the quality-risk threshold for stem cells in test set 3 is 5.312 (AUC=0.986), and the quality-risk threshold for stem cells in test set 4 is 6.680 (AUC=0.993). Specific results are as follows... Figure 13A , Figure 13B , Figure 13C and Figure 13D As shown.

[0303] Example 7: Validation of the stem cell quality prediction model

[0304] Based on the stem cell quality prediction model and the characteristic genes and their weighting coefficients (Table 14), this embodiment detects the expression levels of characteristic genes in D1M1 / M2-P5 and D1M2 / M1-P5 at the single-cell level. Substituting these values ​​into the stem cell quality scoring formula, each stem cell quality score for D1M1 / M2-P5 and D1M2 / M1-P5 is calculated, and stem cell quality is evaluated. The stem cell quality risk threshold is set to 3.961.

[0305] The results are as follows Figure 14 As shown, 99.90% of the stem cells in D1M2 / M1-P5 were of quality risk, and 0.10% were of non-quality risk. However, in D1M1 / M2-P5, 0.24% of the stem cells were of quality risk, and 99.76% were of non-quality risk. Figure 7A , Figure 7B , Figure 7C Functional cell subset clustering results and Figure 8A , Figure 8B The results from the animal experiments shown are consistent. This indicates that the stem cell quality prediction model can accurately predict the quality risk of stem cells.

[0306] The applicant declares that the detailed method of the present invention is illustrated by the above embodiments, but the present invention is not limited to the above detailed method, that is, it does not mean that the present invention must rely on the above detailed method to be implemented. Those skilled in the art should understand that any improvements to the present invention, equivalent substitutions of the raw materials of the product of the present invention, addition of auxiliary components, selection of specific methods, etc., all fall within the protection scope and disclosure scope of the present invention.

Claims

1. A method for evaluating the quality of adipose-derived mesenchymal stem cells, characterized in that, include: Obtain the expression levels of characteristic genes related to stem cell quality; The stem cell quality score is calculated based on the expression level and weight coefficient of the characteristic genes. Stem cell quality is evaluated based on stem cell quality scoring. The stem cell quality-related characteristic genes are stem cell quality-related characteristic genes determined at the single-cell level. The method for determining the stem cell quality-related characteristic genes includes: Single-cell gene expression data and specific quality attributes of stem cells are obtained to form a dataset, which is divided into a training set and a test set; A supervised machine learning model was trained using the training set, and its parameters were adjusted through cross-validation and testing on the test set to determine the stem cell quality prediction model. Identify stem cell quality-related characteristic genes based on stem cell quality prediction models; The weighting coefficients of characteristic genes are determined based on the stem cell quality prediction model; The formula for calculating the stem cell quality score is as follows: ； in, Gi For the first i The expression level of each characteristic gene Wi For the first i The weight coefficients of each characteristic gene, n The number of characteristic genes; The stem cell quality-related characteristic genes are: TAGLN, EFEMP1, TPM1, CLU, PTX3, IER3, IGFBP7, MFAP5, IL6, LUM, SERPINE2, CRIM1, and RHOB.

2. The method for evaluating the quality of adipose-derived mesenchymal stem cells according to claim 1, characterized in that, The method for obtaining single-cell gene expression data of the stem cells includes: Single-cell RNA sequencing was performed on stem cells to obtain single-cell gene expression data.

3. The method for evaluating the quality of adipose-derived mesenchymal stem cells according to claim 1, characterized in that, The methods for obtaining the specific quality attribute include: Specific quality attributes are determined based on the culture microenvironment of stem cells; Specific quality attributes are determined based on single-cell epigenetic data of stem cells; or Specific quality attributes are determined based on single-cell gene expression data of stem cells; The determination of specific quality attributes based on single-cell gene expression data of stem cells includes: Pathway enrichment analysis was performed on single-cell gene expression data of stem cells to calculate the enrichment score of each pathway in each stem cell, and a single-cell pathway enrichment score matrix of stem cells was obtained. Bioinformatics analysis was performed on the single-cell pathway enrichment score matrix of stem cells to obtain the single-cell subpopulation clustering results of stem cells, which can be used as specific quality attributes of stem cells.

4. The method for evaluating the quality of adipose-derived mesenchymal stem cells according to claim 3, characterized in that, The bioinformatics analysis of the single-cell pathway enrichment fraction matrix of stem cells includes: The single-cell pathway enrichment fraction matrix of stem cells was subjected to dimensionality reduction and clustering.

5. The method for evaluating the quality of adipose-derived mesenchymal stem cells according to claim 1, characterized in that, The method for evaluating stem cell quality based on stem cell quality scoring includes: If the stem cell quality score is greater than or equal to the stem cell quality risk threshold, the stem cell is considered a quality risk stem cell. If the stem cell quality score is less than the stem cell quality risk threshold, the stem cells are considered non-risk stem cells.

6. The method for evaluating the quality of adipose-derived mesenchymal stem cells according to claim 5, characterized in that, The method for determining the stem cell quality risk threshold includes: The stem cell quality score of the dataset was analyzed using the receiver operating characteristic (ROC) curve and the area under the curve. The value of the highest point of the ROC curve was taken as the stem cell quality risk threshold. The dataset contains single-cell gene expression data of stem cells with known specific quality attribute labels.

7. The method for evaluating the quality of adipose-derived mesenchymal stem cells according to claim 1, characterized in that, The supervised machine learning model includes any one of support vector machines or random forests.

8. The use of characteristic gene combinations for quality evaluation of adipose-derived mesenchymal stem cells, characterized in that, The characteristic gene combination is: TAGLN, EFEMP1, TPM1, CLU, PTX3, IER3, IGFBP7, MFAP5, IL6, LUM, SERPINE2, CRIM1, and RHOB.

9. A server, characterized in that, The server includes: A processor and a memory storing instructions executable by the processor; The processor executes the method for evaluating the quality of adipose-derived mesenchymal stem cells as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that executes the method for evaluating the quality of adipose-derived mesenchymal stem cells according to any one of claims 1-7.

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