Methods and devices for detecting cell quality attributes and their uses

Abstract

The present invention provides a method and apparatus for detecting cell quality attributes, and uses thereof. The method for detecting cell quality attributes includes detecting cellular heterogeneity, including diversity in the state of a cell subgroup, where the diversity in the state of a cell subgroup includes any one or a combination of at least two of the following: genome diversity, transcriptome diversity, proteome diversity, metabolome diversity, and epigenomic diversity of the cell subgroup. The present invention uses cellular heterogeneity due to the influence of the microenvironment as a cell quality attribute, which can more accurately evaluate the quality of a cellular product at the single-cell level compared to current common cell quality evaluation indicators. The thus established method for detecting cell quality attributes plays an important role in accurately detecting the quality of cellular products and ensuring the stability of clinical therapeutic effects.

Description

[Technical Field]

[0001] The present invention relates to the field of cell technology, and more particularly to a method, an apparatus and their use for detecting quality attributes of cells. [CROSS-REFERENCE TO RELATED APPLICATIONS]

[0002] This application claims priority from Chinese Patent Application No. 202211677045.3, filed on December 26, 2022, entitled "Method and apparatus for detecting quality attributes of cells and their use," the entire contents of which are incorporated herein by reference. [Background technology]

[0003] In recent years, with the continuous development of fundamental theories, technological methods, and clinical research on stem cell therapy, immune cell therapy, and genome editing, cell therapy products have provided new treatment opportunities for several severe and intractable diseases. Clinical studies have already been conducted in multiple syndrome areas, and the number of clinical studies on cell therapy products is constantly increasing.

[0004] Cell therapy products are characterized by complex origins and compositions, diverse cell types, the ability of cells to survive in the body, self-proliferate and / or differentiate, large variations in production scale and production process, and complex mechanisms of action. The risks of cell therapy products depend heavily on the origin, type, properties, and function of the cells, the production process, non-cellular components, non-target cell populations, the prevention and control of contamination and / or cross-contamination throughout the entire production process, as well as the specific therapeutic route and use. Therefore, it is necessary to determine the risk characteristics and specific control measures according to the specific type and adopt appropriate specific technologies.

[0005] Due to factors such as the complexity of cell origin, cell self-proliferation and / or differentiation, and significant variations in the production process, the cell subgroups that make up a cell product are characterized by inconsistent states, which leads to heterogeneity in cell products, significantly affecting the quality of cell products and ultimately leading to inconsistent clinical therapeutic effects.

[0006] The prior art has not yet fully met the needs for standardizing and guiding the research, development, and registration of cell therapy products and promoting industrial development. Therefore, the field needs indicators that more accurately represent the quality attributes of cells to establish methods for improving and analyzing cell heterogeneity. Summary of the Invention

[0007] In view of the deficiencies in the prior art and actual needs, the present invention provides a method and device for detecting cell quality attributes and their use, which identifies cell heterogeneity due to the influence of the microenvironment as a cell quality attribute, establishes a method and device for detecting the quantification of cell heterogeneity, and uses them in the production of cell therapy products and pharmaceutical research, which helps to establish a sound cell production and standardization of quality control systems.

[0008] In particular, the inventors of the present application have unexpectedly discovered that by analyzing the differences in the states of different cell subgroups in a cell population from angles such as the genome, transcriptome, proteome, metabolome, or epigenome, quantitative detection of the degree of heterogeneity in a cell population can be achieved based on the single-cell expression values ​​of molecular characterization and the average expression values ​​of molecular characterization in a cell population, thereby establishing an improved method for analyzing cellular heterogeneity.

[0009] First, the present invention provides a detection method for detecting cell quality attributes, comprising a step of detecting cellular heterogeneity including diversity of the state of a cell subgroup, wherein the diversity of the state of the cell subgroup includes any one or a combination of at least two of the genomic diversity, transcriptomic diversity, proteomic diversity, metabolomic diversity, and epigenomic diversity of the cell subgroup.

[0010] In some embodiments, the diversity of the state of the cell subgroup is the genomic diversity of the cell subgroup.

[0011] In some embodiments, the diversity of the state of the cell subgroup is the transcriptome diversity of the cell subgroup.

[0012] In some embodiments, the diversity of the state of the cell subgroup is the proteomic diversity of the cell subgroup.

[0013] In some embodiments, the diversity of the state of the cell subgroup is the metabolomic diversity of the cell subgroup.

[0014] In some embodiments, the diversity of the state of the cell subgroup is the epigenomic diversity of the cell subgroup.

[0015] The present invention treats cell heterogeneity as a quality attribute and uses single-cell level omics research methods to analyze the differences in the status of different cell subgroups in a cell population from the perspectives of genome, transcriptome, proteome, metabolome, or epigenome, thereby achieving the effect of accurately detecting the quality of cell products.

[0016] In the present invention, the genomic diversity of cell subgroups in a cell population refers to differences in the type and / or number of genetic information between different cell subgroups, which can be detected by single-cell genome sequencing. The transcriptome diversity of cell subgroups in a cell population refers to differences in the type and / or number of transcripts between different cell subgroups, which can be detected by single-cell transcriptome sequencing. The proteome diversity of cell subgroups in a cell population refers to differences in the type and / or number of proteins between different cell subgroups, which can be detected by single-cell proteome methods based on antibodies or mass spectrometry. The metabolomic diversity of cell subgroups in a cell population refers to differences in the type and / or number of metabolites between different cell subgroups, which can be detected by single-cell metabolome methods based on MALDI imaging mass spectrometry. The epigenomic diversity of cell subgroups in a cell population refers to differences in methylation sites between different cell subgroups, which can be detected by single-cell epigenetic sequencing.

[0017] In some embodiments, determining molecular characterizations and single-cell expression values ​​for said molecular characterizations based on the single-cell molecular expression matrix of the cell population; determining a mean expression value in the cell population of said molecular characterization; and determining a degree of cellular heterogeneity based on the single cell expression values ​​of said molecular characterization and the average expression values ​​of said molecular characterization in a cell population; The above detection method further comprises the step of detecting the degree of cellular heterogeneity comprising:

[0018] In the present invention, the degree of cellular heterogeneity quantitatively indicates the diversity of the states of cell subgroups in a cell population. Based on the expression matrix of single-cell molecules in the cell population, possible molecular characterizations representing the expression status of single-cell molecules are determined, and the degree of cellular heterogeneity is calculated from the single-cell expression values ​​of the molecular characterizations and the average expression values ​​of the molecular characterizations in the cell population. This helps to obtain the main information affecting cellular heterogeneity, reduce noise interference, and achieve the effect of accurately quantifying cellular heterogeneity.

[0019] In some embodiments, the method for obtaining a single cell molecular expression matrix of the cell population comprises: Detecting molecular expression values ​​at a single cell level of the cell population to obtain single cell molecular expression data of the cell population; and pre-processing the single cell molecular expression data of the cell population to obtain a single cell molecular expression matrix of the cell population; Here, the pre-processing includes calibration and / or normalization of the noise.

[0020] In some embodiments, the method for determining molecular characterization comprises: This involves processing single-cell molecular expression matrices through data dimensionality reduction methods to select nonlinearly related, non-biological function-related data for molecular characterization.

[0021] In the present invention, the single-cell molecular expression matrix data is first transformed into linear unrelated, biological function unrelated data in each dimension by using conventional data dimensionality reduction methods (e.g., principal component analysis (PCA), uniform manifold approximation and projection (UMAP)), thereby achieving dimensionality reduction of high-dimensional data.

[0022] In some embodiments, the molecular characterization is any one or a combination of at least two selected from gene characterization, transcriptional characterization, protein characterization, metabolite characterization, and epigenetic site characterization, which can represent a single-cell molecular phenotype of a cell population.

[0023] In some embodiments, the Euclidean distance is calculated from the single cell expression values ​​of the molecular characterization and the mean expression values ​​of the molecular characterization in a cell population to determine the degree of cellular heterogeneity.

[0024] In the present invention, the Euclidean distance between any two cells in a cell population in a multidimensional space is calculated from the single-cell expression values ​​of molecular characterization and the average expression values ​​in a cell population of molecular characterization, and the heterogeneity of the cell population is depicted.

[0025] In some embodiments, the degree of cellular heterogeneity is determined from the single cell expression values ​​of the molecular characterization and the average expression values ​​in the cell population based on a formula for calculating the degree of cellular heterogeneity.

[0026] The formula for calculating the degree of cellular heterogeneity is as follows: JPEG2025530540000002.jpg29170where, X ij represents the single-cell expression value of the jth molecular characterization of the ith cell, n represents the quantity of molecular characterization, m represents the number of cells in the cell population, and μ j represents the average expression value in the cell population for the jth molecular characterization.

[0027] In the present invention, the range of values ​​for the degree of cellular heterogeneity is [0, 100]. When all cells in a cell population are derived from the same clone, the degree of cellular heterogeneity of the cell population is 0, such as in the case of induced pluripotent stem cell (iPSC) products, which are homogeneous and have a uniform composition. When each cell in a cell population is derived from a different clone, the degree of cellular heterogeneity of the cell population is infinite. However, to better apply the degree of cellular heterogeneity to the evaluation of the safety and efficacy of cell products, the maximum value of the degree of cellular heterogeneity is limited to be as close to 100 as possible.

[0028] In some embodiments, the detection method further comprises detecting any one or a combination of at least two of microbiological safety, cellular markers, cellular activity, and biological efficacy.

[0029] In the present invention, detecting microbiological safety includes detecting mycoplasma, bacteria, fungi, endotoxin, mycobacteria, etc. in a cell sample; detecting cell markers includes detecting cell surface markers (e.g., CD73, CD90, CD105, CD11b, CD19, CD34, CD45, and HLA-DR); detecting cell activity includes detecting cell activity rate, cell doubling time, cell cycle, apoptosis, clonogenesis rate, etc.; and detecting biological efficacy includes detecting osteogenic differentiation, adipogenic differentiation, chondrogenic differentiation ability, etc.

[0030] As a preferred technical solution, the present invention provides a method for detecting cell quality attributes, comprising a step of detecting cell heterogeneity, and further comprising a step of detecting any one or a combination of at least two of microbiological safety, cell marker, cell activity, and biological efficacy, comprising: The step of detecting cellular heterogeneity comprises: Detecting the whole genome / exon group at the single cell level of a cell population by single-cell genome sequencing method, and obtaining the expression data of the whole genome / exon group of the single cell of the cell population; preprocessing the single-cell whole genome / exon group expression data of the cell population to obtain a single-cell whole genome / exon group expression matrix of the cell population; determining gene / exon characterizations and single-cell expression values ​​for said gene / exon characterizations based on the single-cell whole genome / exon group expression matrix of the cell population; Determining the mean expression value in the cell population for gene / exon characterization; and Determine cellular heterogeneity based on a formula for calculating the degree of cellular heterogeneity from the single-cell expression values ​​of gene / exon characterization and the average expression values ​​of gene / exon characterization in a cell population. (The formula for calculating the degree of cellular heterogeneity is as follows: JPEG2025530540000003.jpg29170 (where X ij represents the single-cell expression value of the jth gene / exon characterization of the ith cell, n represents the quantity of the gene / exon characterization, m represents the number of cells in the cell population, and μ j represents the average expression value in the cell population of the j-th gene / exon characterization.)

[0031] As a preferred technical solution, the present invention provides a method for detecting cell quality attributes, comprising a step of detecting cell heterogeneity, and further comprising a step of detecting any one or a combination of at least two of microbiological safety, cell marker, cell activity, and biological efficacy, comprising: The step of detecting cellular heterogeneity comprises: Detecting the expression value of transcripts at the single cell level of the cell population by single cell transcriptome sequencing to obtain single cell gene expression data of the cell population; preprocessing the single-cell gene expression data of the cell population to obtain a single-cell gene expression matrix of the cell population; determining transcriptional characterizations and single-cell expression values ​​for said transcriptional characterizations based on the single-cell gene expression matrix of the cell population; determining the mean expression value in the cell population for transcriptional characterization; and determining the degree of cellular heterogeneity from the single cell expression value of the transcriptional characterization and the average expression value of the transcriptional characterization in the cell population based on a formula for calculating the degree of cellular heterogeneity; (The formula for calculating the degree of cellular heterogeneity is as follows: JPEG2025530540000004.jpg29170 (where X ij represents the single-cell expression value of the jth transcriptional characterization of the ith cell, n represents the quantity of the transcriptional characterization, m represents the number of cells in the cell population, and μ j represents the average expression value in the cell population of the j-th transcriptional characterization.

[0032] Secondly, the present invention provides a device for detecting cell quality attributes, including a cell heterogeneity detection module configured to detect diversity in the state of a cell subgroup, including any one or a combination of at least two of the genomic diversity, transcriptome diversity, proteome diversity, metabolomic diversity and epigenomic diversity of the cell subgroup.

[0033] In some embodiments, the diversity of the state of the cell subgroup is the genomic diversity of the cell subgroup.

[0034] In some embodiments, the diversity of the state of the cell subgroup is the transcriptome diversity of the cell subgroup.

[0035] In some embodiments, the diversity of the state of the cell subgroup is the proteomic diversity of the cell subgroup.

[0036] In some embodiments, the diversity of the state of the cell subgroup is the metabolomic diversity of the cell subgroup.

[0037] In some embodiments, the diversity of the state of the cell subgroup is the epigenomic diversity of the cell subgroup.

[0038] In some embodiments, the cellular heterogeneity detection module comprises: a molecular characterization determination unit for determining a molecular characterization based on the single-cell molecular expression matrix of the cell population, and determining a single-cell expression value of the molecular characterization and an average value of the expression value in the cell population; a calculation unit for determining a degree of cellular heterogeneity based on the single cell expression value of the molecular characterization and the average expression value in the cell population of the molecular characterization; The submodule includes a detection submodule for the degree of cellular heterogeneity, including:

[0039] In some embodiments, the cellular heterogeneity detection module further comprises: a single-cell molecular expression data detection unit for detecting molecular expression values ​​at a single-cell level of the cell population to obtain single-cell molecular expression data of the cell population; a single cell molecular expression matrix detection submodule including a data preprocessing unit for preprocessing the single cell molecular expression data of the cell population to obtain a single cell molecular expression matrix of the cell population; Here, the pre-processing includes calibration and / or normalization of the noise.

[0040] In some embodiments, the molecular characterization determination unit is for processing the single-cell molecular expression matrix through a data dimensionality reduction method to select non-linearly related, non-biological function related data as molecular characterizations.

[0041] In some embodiments, the molecular characterization is any one or a combination of at least two selected from gene characterization, transcriptional characterization, protein characterization, metabolite characterization, and epigenetic site characterization that can represent a single-cell molecular phenotype matrix of a cell population.

[0042] In some embodiments, the calculation unit is for calculating a Euclidean distance from a single cell expression value of the molecular characterization and a mean value of the expression value in a cell population of the molecular characterization to determine the degree of cellular heterogeneity.

[0043] In some embodiments, the calculation unit is for determining the degree of cellular heterogeneity from single cell expression values ​​of molecular characterization and average expression values ​​in a cell population based on a calculation formula for the degree of cellular heterogeneity.

[0044] The formula for calculating the degree of cellular heterogeneity is as follows: JPEG2025530540000005.jpg31170where, X ij represents the single-cell expression value of the jth molecular characterization of the ith cell, n represents the quantity of molecular characterization, m represents the number of cells in the cell population, and μ j represents the average expression value in the cell population for the jth molecular characterization.

[0045] In some embodiments, the detection device further includes any one or a combination of at least two of a microbiological safety detection module, a cellular marker detection module, a cellular activity detection module, and a biological efficacy detection module.

[0046] In some embodiments, the detection device includes a cellular heterogeneity detection module, a microbiological safety detection module, a cellular marker detection module, a cellular activity detection module, and a biological efficacy detection module, as described above.

[0047] Thirdly, the present invention provides a use or application of the method for detecting a quality attribute of a cell and / or the device for detecting a quality attribute of a cell in producing a cell product.

[0048] In some embodiments, the cell product comprises genetically modified or unmodified mammalian active cells and any one or a combination of at least two of optional pharmaceutically acceptable auxiliary ingredients, carriers, excipients, and diluents. Here, the mammals include humans.

[0049] In some embodiments, the cell product comprises any one or a combination of at least two of an autologous or allogeneic stem cell product, a stromal cell product, an immune cell product, a tissue cell product, and a cell line product.

[0050] In some embodiments, the stem cells include any one or a combination of at least two of adult stem cells, embryonic stem cells, induced pluripotent stem cells, and stem cells obtained by transformation of adult somatic cells and their derivatives.

[0051] In some embodiments, the stem cells include any one or a combination of at least two of mesenchymal stem cells, mesenchymal stromal cells, multipotent stromal cells, multipotent mesenchymal stromal cells, and drug signal transduction cells.

[0052] In some embodiments, the stem cells comprise any one or a combination of at least two of adipose-derived stem cells, umbilical cord blood-derived stem cells, placenta-derived stem cells, bone marrow-derived stem cells, dental pulp-derived stem cells, menstrual blood-derived stem cells, amniotic epithelial stem cells, and bronchial basal layer cells.

[0053] Fourthly, the present invention provides a use or application of the method for detecting a quality attribute of a cell and / or the device for detecting a quality attribute of a cell in researching and / or developing a cellular product.

[0054] In some embodiments, the cell products include cell stock products and cell preparation products, and the cell stock products include cell seed bank products and work cell bank products, and the research and / or development of the cell products includes researching and / or developing a manufacturing process for the cell seed bank products (including a supplier selection process, a primary isolation process, an expansion culture process, a cryopreservation process, etc.), the storage stability of the cell seed bank products, a preparation process for the work cell bank products (including a supplier selection process, a primary isolation process, an expansion culture process, a cryopreservation process, etc.), the storage stability of the work cell bank products, a preparation process for the cell preparation products (including a process for resuscitating cells from the work cell bank, a washing process, a liquid filling process, etc.), or the stability of the cell preparation products (including storage stability, transport stability, use stability, binding stability, etc.). [Effects of the Invention]

[0055] Compared with the prior art, the present invention has the following beneficial effects: (1) As demonstrated in the examples, the degree of heterogeneity of a cell population is related to the safety and efficacy of the cells, and cell heterogeneity as a cell quality attribute can be used to evaluate cell quality. The present invention uses cell heterogeneity due to the influence of the microenvironment as a cell quality attribute, which can more accurately evaluate the quality of a cell product at the single cell level compared to current conventional cell quality evaluation indicators. The established method for detecting cell quality attributes plays an important role in accurately detecting the quality of cell products and ensuring the stability of clinical therapeutic effects. (2) The present invention has achieved the effect of quantitatively describing the diversity of the states of cell subgroups in a cell population by calculating the degree of cellular heterogeneity from single-cell expression values ​​of molecular characterization and average expression values ​​in a cell population of molecular characterization. (3) The cell quality attribute detection method and detection device of the present invention have important utility in the production, research, and development of cell products, and provide a new evaluation means for determining the production process, storage process, etc. of cell products. [Brief explanation of the drawings]

[0056] [Figure 1] 1 is a diagram showing the scores of the degree of heterogeneity of different cell samples S1, S2, and S3. [Figure 2A] 1 is a schematic diagram showing the main configuration of a cell quality attribute detection device. [Figure 2B] 1 is a schematic diagram showing the main components of a cellular heterogeneity detection module 10. FIG. [Figure 2C] FIG. 1 is a schematic diagram showing the main components of the single cell molecular expression matrix detection submodule 110. [Figure 2D] FIG. 1 is a schematic diagram showing the main components of the cellular heterogeneity detection submodule 120. [Figure 3] This shows the distribution of scores for the degree of cellular heterogeneity in P5, P7, and P10 generation ADSC samples from two lots (A1, A2). [Figure 4] [Figure 4A] QQ normality test of the distribution of the degree of cellular heterogeneity in ADSC samples of different generations. [Figure 4B] QQ normality test of the degree of cellular heterogeneity in ADSC samples of different lots. [Figure 4C] QQ normality test of the degree of cellular heterogeneity in ADSC samples of multiple generations and multiple lots. [Figure 5] [Figure 5A] Differences in the degree of cellular heterogeneity between ADSC samples of different generations but from the same lot. [Figure 5B] Differences in the degree of cellular heterogeneity between ADSC samples of different lots but from the same generation. [Figure 6][Figure 6A] Reference range of the score of the degree of cellular heterogeneity for P5 generation ADSCs. [Figure 6B] Reference range of the score of the degree of cellular heterogeneity for P7 generation ADSCs. [Figure 6C] Reference range of the score of the degree of cellular heterogeneity for P10 generation ADSCs. [Figure 6D] Combined reference range of the score of the degree of cellular heterogeneity for P5, P7, and P10 generation ADSCs. [Figure 7] [Figure 7A] Distribution of scores for the degree of cellular heterogeneity when ADSC injection solution was left to stand at 5±3°C for 6 hours or 24 hours. [Figure 7B] Distribution of scores for the degree of cellular heterogeneity when ADSC injection solution was vibrated at a low speed (100 rpm / min) or a high speed (300 rpm / min) at 5±3°C. [Figure 7C] Distribution of scores for the degree of cellular heterogeneity when ADSC injection solution was left to stand at 5±3°C or room temperature (10-30°C) for 2 hours. [Figure 8] [Figure 8A] QQ normality test of the degree of cellular heterogeneity of ADSC injection solution after different storage times. [Figure 8B] QQ normality test of the degree of cellular heterogeneity of ADSC injection solution after different transportation conditions. [Figure 8C] QQ normality test of the degree of cellular heterogeneity of ADSC injection solution after different use conditions. [Figure 9] QQ normality test of the degree of cellular heterogeneity of ADSC injection solution in the influence of multifactor combination. [Figure 10] The interactive effects of storage time, transportation conditions, and usage conditions on the degree of cellular heterogeneity of ADSC injections. [Figure 11] [Figure 11A] Effect of transportation conditions on the degree of cellular heterogeneity of ADSCs injection solution at a certain storage time. [Figure 11B] Effect of use conditions on the degree of cellular heterogeneity of ADSCs injection solution at a certain storage time. [Figure 11C] Effect of storage time on the degree of cellular heterogeneity of ADSCs injection solution at a certain transportation condition. [Figure 11D] Effect of use conditions on the degree of cellular heterogeneity of ADSCs injection solution at a certain transportation condition. [Figure 11E] Effect of transportation conditions on the degree of cellular heterogeneity of ADSCs injection solution at a certain usage condition. [Figure 11F] Effect of storage time on the degree of cellular heterogeneity of ADSCs injection solution at a certain usage condition. [Figure 12] [Figure 12A] Reference range of the score for the degree of cellular heterogeneity of ADSCs stored for 0-6 hours. [Figure 12B] Reference range of the score for the degree of cellular heterogeneity of ADSCs stored for 6-24 hours. [Figure 12C] Reference range of the score for the degree of cellular heterogeneity of ADSCs stored for 0-24 hours. DETAILED DESCRIPTION OF THE INVENTION

[0057] In order to further explain the technical means employed by the present invention and its effects, the present invention will be further described below with reference to examples and drawings. It will be understood that the specific embodiments described herein are intended to interpret the present invention, but are not intended to limit the present invention. Various modifications or changes to the method and system of the present invention will be obvious to those skilled in the art and will not depart from the scope and essence of the present invention. Although the present invention has been described in combination with specific preferred embodiments, it should be understood that the present invention should not be unduly limited to these specific embodiments, as claimed in the claims, and that various modifications and additions to the above embodiments can be made within the scope of the present invention. It goes without saying that various modifications to molecular biology made to the above embodiments by those skilled in the art in order to implement the present invention are all within the scope of the claims.

[0058] In the examples, unless specific techniques or conditions are described, the techniques or conditions are those described in the literature or in the product instructions. If the manufacturer of the formulation or device used is not specified, they are all ordinary products purchased through official channels. definition

[0059] As used herein, "cell quality attribute" refers to a physical, chemical, biological, or microbiological property of a cell that, in an appropriate limit, range, or distribution, ensures the quality of the desired cell product.

[0060] As stated in the context, a "cell population" refers to a group of cells having one or more identical or different attributes and / or functions of interest. A "cell subgroup" refers to a group of cells within a "cell population" that are further classified based on differences in their state, such as differences in cell morphology, cell function, and / or cell molecular markers.

[0061] As mentioned in the context, "cellular heterogeneity" refers to differences in the state of different cell subgroups within a cell population. Such differences may be genomic, transcriptomic, proteomic, metabolomic, or epigenomic, and may refer to differences among different cell subgroups in terms of cell morphology, cell function, or cell molecular markers. (Reference: Kumar RM, Cahan P, Shalek AK, Satija R, Daley-Keyser A, Li H, Zhang J, Pardee K, Gennert D, Trombetta JJ, Ferrante TC, Regev A, Daley GQ, Collins JJ. Deconstructing transcriptional heterogeneity in pluripotent stem cells. Nature. 2014 Dec 4;516(7529):56-61.)

[0062] As stated in the context, the "degree of cellular heterogeneity" is a quantification value that describes the diversity of the states of cell subgroups within a cell population.

[0063] As stated in the context, the term "single-cell molecular expression matrix" refers to a matrix containing molecular expression information of each cell, where the rows of the single-cell molecular expression matrix represent the expression status of specific molecules in different cells, the columns represent the expression status of specific cells in different molecules, and the data of the matrix represent the expression level of specific molecules in specific cells.

[0064] As stated in the context, "molecular characterization" means gene characterization, transcriptional characterization, protein characterization, metabolite characterization or epigenetic site characterization that can describe the expression status of single-cell molecules in a cell population.

[0065] As stated in the context, "Euclidean distance" means the true distance between two points in a multidimensional space.

[0066] As stated in the context, "cellular product" refers to a live cell product, including genetically modified or unmodified cells, such as autologous or allogeneic immune cells, stem cells, stromal cells, tissue cells, or cell lines, which may also be referred to as a "cell therapy product."

[0067] Example 1 Cell acquisition and culture 1. Cell acquisition

[0068] 1. Adipose Tissue Collection Adipose tissue was collected from donors (negative for detection of AIDS virus, hepatitis B virus, hepatitis C virus, human T-lymphotropic virus, EB virus, cytomegalovirus, and Treponema pallidum) under a sterile environment. 50-150 mL of adipose tissue was placed in a sealed container containing 100 mL of tissue preservation solution (purchased from Tianjin Haoyang Bio-Products Technology Co., Ltd.), and stored at a constant temperature of 2-8°C for future use. 30 mL of tissue preservation solution was aspirated with a pipette, and after detecting and confirming that it was free of bacteria, endotoxin, and mycoplasma contamination, human adipose-derived cells were isolated and cultured.

[0069] 2. Cell Isolation Add the same volume of dPBS to the adipose tissue, seal the container containing the tissue, shake vigorously for 20 seconds, and let it stand for 5 minutes. After the adipose tissue and dPBS have completely separated into layers, aspirate and remove the liquid underneath the tissue. Repeatedly rinse the adipose tissue with dPBS until the liquid underneath no longer has a noticeable red color. The washed adipose tissue was dispensed into 20 mL aliquots in 50 mL centrifuge tubes, and the same volume of dPBS was added. The mixture was centrifuged at 400 g for 5 minutes to separate the tissue into an upper oily layer, a middle adipose tissue layer, and a lower layer of dPBS and blood cell precipitate. The upper oily layer and the lower layer of dPBS and blood cell precipitate were then removed. A double volume of 1 mg / mL type I collagenase (digestion solution) was added to the adipose tissue, the container containing the tissue was sealed, and the tissue was digested for 1 hour at 120 rpm / min in a preheated air-controlled shaker at 37°C.

[0070] 3. Cell Collection After digestion was complete, the tissue was centrifuged at 500 g for 8 minutes at room temperature. After centrifugation, the tissue was separated into an upper oily layer, a middle adipose tissue layer, a lower digestive fluid, and a cellular precipitate in the substratum. The upper oily layer, middle adipose tissue layer, and lower digestive fluid were removed by aspiration, and the cellular precipitate in the substratum was resuspended in dPBS. This was filtered through a 100 μm mesh and added to a 50 ml centrifuge tube. The resulting solution was centrifuged at 500 g for 5 minutes, and the supernatant was removed to obtain a cellular precipitate containing P0 generation adipose-derived mesenchymal stromal cells (ADSCs). A complete medium with the same volume as the adipose tissue was added to the centrifuge tube and mixed evenly so that the digested cells were sufficiently released into the complete medium, thereby obtaining a cell suspension containing P0 generation ADSCs.

[0071] 2. Cell culture ADSCs were cultured and grown in a medium (DMEM / F-12 + 5% Helios UltraGRO-Advanced, DMEM / F-12 purchased from Helios BioScience, and Helios UltraGRO-Advanced purchased from Helios BioScience). The specific procedure was as follows.

[0072] 1. Primary culture The cell pellet was resuspended in the same volume of medium as the adipose tissue, and 1.5 mL of the cell suspension was collected and inoculated into a T75 cell culture flask to which 8.5 mL of medium had been added beforehand. After attaching a label to a T75 cell culture flask, the flask was transferred to a cell culture box and cultured at 37°C and 5% CO2. After 24 hours, when the adipose-derived cells had almost completely adhered to the wall, the supernatant was removed and 10 ml of medium was added. Thereafter, the medium was changed every three days. Microscopic observation revealed that the obtained primary cells contained many miscellaneous cells and matrix components in addition to P0 generation ADSCs, and the ADSCs had a typical long spindle shape.

[0073] 2. Subculture When the P0 generation cell population reached 50% to 70%, the medium was removed, 10 mL of dPBS was added and the cells were washed once. The wash solution was then removed, and 1.5 mL of Tryple®-Express (1x) digestion solution was added and digested for 1 to 2 minutes until most of the cells had fallen off in round shapes. After gently tapping the culture flask, 4.5 mL of dPBS was added to complete the digestion. The resulting liquid was collected in a 50 mL centrifuge tube and washed once with 10 mL of dPBS. The tube was then centrifuged at 400 g for 5 minutes. The upper layer was a mixture of the digestion liquid and dPBS, and the lower white precipitate contained P0 generation ADSCs. The supernatant was removed, and the white precipitates in the multiple tubes were collected in one tube. Culture medium was added to resuspend the cells, and the volume was adjusted to 30 mL. The cell suspension was mixed uniformly by shaking, and a sample was taken for cell counting. After counting the cells, the cells were centrifuged at 400g for 5 minutes, the supernatant was removed, and the medium was added to resuspend the cells. The cells were mixed uniformly by blowing, and the volume was adjusted to the required volume and inoculated into a cell culture flask. The passage cell density was 5000-6000 cells / cm. 2 This is what happened. Information such as cell lot, generation number, and culture time was marked on the cell culture flask, which was then placed in a cell culture box for culture. When cell confluence reached approximately 90%, the cells were passaged again, and P5 generation ADSCs were collected and cryopreserved to establish a working cell bank. In this example, based on the above operation steps, three work cell banks were resuscitated as P5 generation ADSC samples, named S1, S2, and S3, respectively.

[0074] Example 2: Regular quality control In this example, routine quality control was carried out on three samples S1, S2 and S3 based on the following operation steps.

[0075] 1. Microbiological safety detection 1. Sterility detection For details of the detection steps, please refer to General Rule 1101 Sterility Test Method in the Pharmacopoeia of the People's Republic of China 2020 Edition (Part 4), which can be briefly summarized as follows: The filter membrane of a disposable triple-barreled bacterial collector (purchased from Zhejiang Tailin Biotechnology Co., Ltd.) was first filtered and wetted with 100 mL of 0.9% sodium chloride injection (purchased from Shijiazhuang Siyue Co., Ltd.), and then the cell sample was introduced into the bacterial collector and filtered. After the cell sample was filtered, the filter membrane was washed twice with 300 mL of 0.9% sodium chloride injection. Two of the triple-cell harvester vessels containing the cell samples received 100 mL of liquid thioglycollate medium, and the other vessel received 100 mL of liquid soybean casein digest medium. Instead of cell samples, 0.9% sodium chloride injection was used as a negative control, and Staphylococcus aureus (with less than 100 CFU of bacteria added) was used as a positive control. After inoculation, each experimental group was gently shaken, and the incubators containing liquid thioglycolic acid medium were selected from each experimental group and cultured at 30-35°C, while the remaining incubators were cultured at 20-25°C. The incubation period was a total of 14 days, and the presence or absence of bacterial growth was observed and recorded every working day during the incubation period.

[0076] 2. Mycoplasma Detection For details of the detection steps, please refer to the Pharmacopoeia of the People's Republic of China 2020 Edition (Part 4) General Rule 3301 Mycoplasma Testing Method, which can be briefly summarized as follows: Mycoplasma broth medium, arginine-containing mycoplasma broth medium, mycoplasma semi-solid medium, and arginine-containing mycoplasma semi-solid medium were prepared and sterilized according to the standard recipe. 800,000 units of injectable penicillin sodium (purchased from Jiangxi Dongfeng Pharmaceutical Co., Ltd.) were reconstituted with 1 mL of 0.9% sodium chloride injection to prepare a reserve for future use. 200 mL of fetal bovine serum and 800,000 units of injectable penicillin sodium were added to every 800 mL of sterilized medium, mixed uniformly, and stored at 2-8°C. Four tubes of 10 mL of mycoplasma broth medium, four tubes of arginine-containing mycoplasma broth medium, two tubes of mycoplasma semi-solid medium, and two tubes of arginine-containing mycoplasma semi-solid medium were taken, and 1.0 mL of the cell sample was inoculated into each tube. The tubes were cultured at 36°C ± 1°C for 21 days, and observed once every three days. Seven days after inoculation, two tubes of mycoplasma broth medium inoculated with the cell sample and two tubes of arginine-containing mycoplasma broth medium inoculated with the cell sample were taken and subjected to next-generation culture. One tube of mycoplasma broth medium was transferred to two tubes of mycoplasma semi-solid medium and two tubes of mycoplasma broth medium, respectively, and one tube of arginine-containing mycoplasma broth medium was transferred to two tubes of arginine-containing mycoplasma semi-solid medium and two tubes of arginine-containing mycoplasma broth medium, respectively. Each tube had an inoculation volume of 1 mL. The samples were cultured at 36°C ± 1°C for 21 days and observed every 3 to 5 days.

[0077] 3. Endotoxin Detection For details of the detection steps, please refer to the Pharmacopoeia of the People's Republic of China 2020 Edition (Part 4) General Rule 1143 Bacterial Endotoxin Testing Method, which can be briefly summarized as follows: The endotoxin working standard (purchased from Zhanjiang Andos Bio-Co., Ltd.) was reconstituted with 1 mL of endotoxin test water (purchased from Zhanjiang Andos Bio-Co., Ltd.) and mixed uniformly on a vortex shaker for 15 minutes, then gradient diluted, with each dilution step mixed uniformly on a vortex shaker for 30 seconds, and finally diluted to 4λ and 2λ endotoxin standard solutions. The frozen cell solution was rapidly thawed in a 37°C water bath, then centrifuged at 1200 rpm for 5 minutes. The supernatant was then added to endotoxin test water for gradient dilution. Each dilution step was mixed uniformly on a vortex shaker for 30 seconds. The dilution ratio did not exceed the maximum effective concentration dilution ratio (MVD), which was used as the cell sample detection solution. The maximum effective concentration dilution ratio (MVD) was calculated using the formula MVD = C * L / λ, where L is the endotoxin threshold value of the cell sample, C is the concentration of the cell sample detection solution, and λ is the label sensitivity of the Limulus reagent. Separately, the cell sample detection solution was taken, and 4λ endotoxin standard solution was added at a volume ratio of 1:1, and mixed uniformly using a vortex shaker for 30 seconds to prepare the cell sample endotoxin positive control solution. Eight bottles of horseshoe crab reagent (purchased from Zhanjiang Andos Bio Co., Ltd.) were reconstituted with 0.1 mL of endotoxin test water. Two parallel-set positive control tubes (PPC) were prepared: 0.1 mL of cell sample endotoxin positive control solution added to two horseshoe crab reagents. Two parallel-set positive control tubes (PC) were prepared: 0.1 mL of 2λ endotoxin standard solution added to two horseshoe crab reagents. Two parallel-set negative control tubes (NC) were prepared: 0.1 mL of endotoxin test water added to two horseshoe crab reagents. Two parallel-set cell sample detection tubes were prepared: 0.1 mL of cell sample detection solution (dilution ratio not exceeding MVD) added to two horseshoe crab reagents. The reaction hole cover of the preheated bacterial endotoxin gel method measuring instrument was opened, the reaction tube was placed in, and a 60-minute countdown was started. One minute before the end of the 60 minutes, the reaction tube in the reaction was removed and the recorded results were observed. As a result, as shown in Table 1, after 14 days, the cell samples in the cell collection vessels grew aseptically, and the sterility detection results of S1, S2, and S3 passed. The mycoplasma detection results of S1, S2, and S3 were negative, and the detection results were also passed. The endotoxin detection results of S1, S2, and S3 were negative, and the detection results were also passed. [Table 1]

[0078] 2. Detection of cell markers The expression status of mesenchymal stem cell-specific surface markers CD73, CD90, CD105, CD11b, CD19, CD34, CD45, and HLA-DR in cell samples was detected (see 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: A well-growing cell sample was added to Tryple®-Express (1x) and digested at 37°C for 2-3 minutes. The digestion was terminated by adding PBS (1x) at least three times the volume of Tryple®-Express (1x). The cells were then slowly washed away to dissociate them into single cells. The cell suspension was aspirated into a 50 mL centrifuge tube, centrifuged at 300 g for 5 minutes, the supernatant removed, 1x PBS added, the cells resuspended, centrifuged at 300 g for 5 minutes, the supernatant removed, and 1 mL of 1x PBS added to resuspend the cells. The remaining cells were then stored for future use. Viable cell density is (0.5-1) x 10 7 The cell concentration was adjusted to 100 cells / mL, and 100 μL of the cell suspension was aspirated and added to a flow tube. Five μL each 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 was then added. As controls, FITC-labeled mouse-derived IgG1, APC-labeled mouse-derived IgG1, and PE-labeled mouse-derived IgG1 were added, and the cell suspension and antibodies were mixed uniformly by vortexing and incubated for 15 minutes in the dark. Two milliliters of sheath fluid was placed in each tube, vortexed, and centrifuged at 300 g for 5 minutes to remove the supernatant. 300 μL of PBS buffer containing 1% paraformaldehyde was added to resuspend the cells, and flow detection was performed. As a result, the expression rates of mesenchymal stem cell surface positive markers CD73, CD90, and CD105 in S1, S2, and S3 were higher than 95%, while the expression rates of mesenchymal stem cell surface negative markers CD11b, CD19, CD34, CD45, and HLA-DR were less than 2%.

[0079] 3. Detection of cell activity 1. Detection of Cell Activity Rate The cell sample was diluted with 0.9% sodium chloride injection and thoroughly mixed with 0.4% trypan blue staining solution at a volume ratio of 9:1. 10 μL of the mixture was drawn into the counting cell of a disposable counting board and observed under a 10 objective lens. The total number of live cells and the total number of dead cells in each of the four cells were counted, and the cell activity rate was calculated according to the following formula: Cell viability (%) = total number of live cells / (total number of live cells + total number of dead cells) x 100% As a result, the cell activation rates of S1, S2 and S3 were all above 80%.

[0080] 4. Analysis of biological effects 1. Detection of Adipogenic Differentiation Before starting the experiment, solutions A and B were prepared according to the instructions for the Oricell Adult Adipose-Derived Mesenchymal Stem Cell Adipogenic Differentiation Kit (purchased from Seiye Biotechnology Co., Ltd.), and then the following steps were performed. 2×10 4 pieces / cm 2 The cells were seeded into a 6-well plate at a cell density of 100%, 2 mL of complete medium was added to each well, and the cells were cultured at 37°C and 5% CO2 until cell confluence reached 100%. The medium was removed by aspiration, and 2 mL of solution A was added to each well. After 3 days of induction, the medium was replaced with 2 mL of solution B. 24 hours later, the medium was replaced with solution A. Solutions A and B were alternately applied three times, and then the culture was maintained in solution B for 4 to 7 days until the lipid droplets became sufficiently large and round. During the period of maintaining the culture in solution B, the culture was replaced with fresh solution B every 2 to 3 days. Oil Red O staining analysis revealed red lipid droplets under a mirror.

[0081] 2. Detection of Osteogenic Differentiation Before the start of the experiment, induction medium was prepared according to the instructions for the Oricell Adult Adipose-Derived Mesenchymal Stem Cell Osteogenesis Induction Differentiation Kit (purchased from Seiye Biotechnology Co., Ltd.), and then the following steps were performed. 2×10 4 pieces / cm 2 The cells were seeded into a 6-well plate at a cell density of 1000 kJ / well, 2 mL of complete medium was added to each well, and the cells were cultured at 37°C and 5% CO2 until cell confluence reached 90%. The medium was removed by aspiration, and 2 mL of induction medium was added to each well. The medium was changed every 3 days. After 2 to 4 weeks of induction, changes in cell morphology and growth were observed, and red calcium nodules were observed under a mirror using Alizarin Red staining analysis.

[0082] 3. Detection of Chondrogenic Differentiation Before starting the experiment, a complete medium for chondrogenic differentiation was prepared according to the instructions of the Oricell Adult Adipose-Derived Mesenchymal Stem Cell Chondrogenic Differentiation Kit (purchased from Seiye Biotechnology Co., Ltd.), and then the following steps were performed. 0.1% gelatin was added to a 6-well culture plate, gently shaken to cover the bottom of the well, and then allowed to stand. The gelatin was discarded and the culture plate was allowed to dry. 1 × 10 cells in good growth condition were cultured. 4 pieces / cm 2Cells were seeded into 6-well culture plates at a cell density of 1000 kJ / well, 2 mL of complete medium was added to each well, and cultured at 37°C and 5% CO2 until cell confluence reached 80-90%. The culture supernatant from the induction wells was aspirated and removed. Every 2-3 days, each well was replaced with 2 mL of fresh chondrogenic differentiation complete medium and 20 μL of TGF-β3. The cells were then placed at 37°C and 5% CO2 for continued induction culture. Control wells were continuously cultured in complete medium. After 14 days of continuous induction, the cells were fixed and stained with Alcian blue, and the Alcian blue staining effect was observed under a microscope. As a result, S1, S2, and S3 were directionally induced to undergo adipogenic, osteogenic, and chondrogenic differentiation in an in vitro culture environment, and the results of Oil Red O staining, Alizarin Red staining, and Alcian Blue staining showed that S1, S2, and S3 were successfully induced into adipocytes, osteoblasts, and chondroblasts, respectively.

[0083] Example 3 Sequencing of Single-Cell RNA To explore cellular heterogeneity in culture, in this example, we detected the transcriptome spectra of S1, S2, and S3 at the single-cell level using single-cell RNA sequencing technology, and the steps are as follows:

[0084] 1. Preparation of single cell suspension S1, S2, and S3, which were in the logarithmic growth phase, were each diluted with sample buffer to a cell suspension with a concentration of less than 1,000 cells / μL. 200 μL of the cell suspension was taken, and 1 μL of Calcein AM dye and 1 μL of Draq7 dye were added for cell staining. The stained cell suspension was filtered through a 40 μm filter membrane, then placed in a cell counter and placed in a cell analyzer (purchased from BD Rhapsody Scanning) to calculate the cell concentration and cell activity rate. The cell suspension was diluted according to the cell concentration and cell loading.

[0085] 2. Single cell sorting The diluted cell suspension was added to a BD Cartridge single cell sorting plate (purchased from BD Biosciences, Cat: 633733) and placed in a cell analyzer to calculate the cell loading and the rate of double cells, and evaluate the efficiency of single cell separation. The unloaded cell suspension was washed with buffer, and the capture magnetic beads were added to a BD Cartridge single-cell sorting plate and placed in a cell analyzer. The total loading of the capture magnetic beads and cells and the rate of double cells were calculated, and the number of capture magnetic beads bound to the pores of single cells was evaluated. Excess capture magnetic beads were washed away, and the cell lysis solution was added to the BD Cartridge single cell sorting plate to lyse the cells, allowing the mRNA to bind to the probes on the surface of the capture magnetic beads.The capture magnetic beads were then recovered from the BD Cartridge single cell sorting plate into a centrifuge tube.

[0086] 3. Single-cell cDNA synthesis and library construction Single-cell cDNA synthesis and library construction were performed using kits purchased from BD Biosciences, specifically, the Single-Cell cDNA Synthesis Kit (purchased from BD Biosciences, Cat: 633731) and Library Construction Kit (purchased from BD Biosciences, Cat: 633801). The following procedures were performed according to the instructions included with the kit. Briefly, the procedure is as follows. The collected capture magnetic beads were washed, and the reverse transcription reagent (Table 2) was added and mixed with the capture magnetic beads, followed by incubation at 37°C for 45 minutes. [Table 2] Ensonuclease was added and incubated at 37°C for 30 min and then at 80°C for 20 min to remove probes that were not attached to mRNA from the surface of the capture magnetic beads. Random primer reaction mixture (Table 3) was added, and the mixture was incubated at 95°C for 5 minutes, at 1200 rpm and 37°C for 5 minutes, and at 1200 rpm and 25°C for 15 minutes. Primer extension reaction mixture (Table 4) was added, and the mixture was incubated at 1200 rpm and 25°C for 10 minutes, at 1200 rpm and 37°C for 15 minutes, at 1200 rpm and 45°C for 10 minutes, and at 1200 rpm and 55°C for 10 minutes. The amplified single-stranded DNA was eluted with the elution solution. [Table 3] [Table 4] A random primer extension product PCR amplification mixture (Table 5) containing universal primers and specific primers was added, and PCR amplification reaction was carried out according to the program in Table 6 to concentrate the random primer amplification product and purify the product. [Table 5] [Table 6] The whole transcriptome index PCR amplification mixture (Table 7) was added, and the PCR amplification reaction was performed according to the reaction program in Table 8 (9 cycles of amplification were performed when the molar concentration of the random primer amplification product was 1-2 nM, and 8 cycles were performed when the molar concentration of the random primer amplification product was greater than 2 nM). The amplified products were concentrated and purified to obtain a single-cell library. [Table 7] [Table 8]

[0087] 4. Quality detection of single-cell libraries The concentration of the single-cell library was detected by a Qubit meter, and the length of the single-cell library fragment was detected by an Agilent 2100 bioanalyzer, and it was found that the library concentration was 0.1–100 ng / μL and the length of the library fragment was approximately 460–550 bp.

[0088] 5. Single-cell sequencing Based on the concentration of the single-cell library and the length of the fragments, the molar concentration of the single-cell library is calculated to be approximately 1-100 nM. After diluting it to a standard molar concentration of 0.2-2 nM, mix it with a balance base library with the same molar concentration at a ratio of single-cell library:balance base library = 1:(0.05-0.5) and perform online sequencing.

[0089] 6. Sequencing data quality assessment The total data volume, Q30, cluster data volume, and valid cluster data volume of the sequencing data were analyzed using BD cwl-runner3.1 to easily evaluate the data quality of offline sequencing. 3057 S1, 2382 S2, and 2683 S3 prepared in Example 1 were sequenced, with an average sequencing depth of 50K / cell.

[0090] Example 4 Data processing According to the BD Protocol, the following steps 1 and 2 were performed: offline sequencing data BCL files were converted to FASTQ data files, sequencing data quality was confirmed, and it was further analyzed.

[0091] 1. Data preprocessing Quality control: Filtering removed sequences with read1 length <60 and read2 length <42, sequences with read1 and read2 base quality <20, and sequences with read1 SNF ≥ 0.55 or read2 SNF ≥ 0.80. Comparison and annotation: Compare quality-controlled valid data with the reference genome GRCh38 and annotate the comparison results; Adjustment of RSEC algorithm: By repeatedly applying the recursive substitution error correction (RSEC) algorithm, the UMI (Unique Molecular Index) information of the comparison results is corrected to obtain the initial single-cell gene expression matrix file.

[0092] 2. Noise calibration First, we remove rows with non-UMI values ​​in the single-cell gene expression matrix file, and then transpose the matrix to obtain a transposed matrix in which the columns are different cells and the rows are different genes, and each value in the transposed matrix represents the expression level of a specific gene in a specific cell. The R language package "SAVER" (reference Huang et al., SAVER: gene expression recovery for single-cell RNA sequencing. Nature Methods (2018) Vol. 15, 539-542) was used to remove technical noise in the transposed matrix and obtain the true single-cell gene expression matrix.

[0093] 3. Standardization The single-cell gene expression matrix obtained in step 2 was transposed. By using the "Census" function in the R language package "Monocle 2," gene expression levels in the single-cell gene expression matrix were converted into relative transcript expression levels, and in the absence of control sequencing samples, gene expression levels for all cells, including rare and silent cells, were accurately obtained, forming a normalized single-cell gene expression matrix.

[0094] 4. Obtaining core transcriptional characterization of cell populations The standardized single-cell gene expression matrix obtained in step 3 was processed using the uniform manifold approximation and projection (UMAP) method to process the first 2,000 highly variant genes in the standardized single-cell gene expression matrix, and a dimensionality reduction process was performed to obtain the projection of 50 nonlinear correlations, non-biological functional correlations, in the low-dimensional space of highly variant genes, and form a matrix of cell-core transcriptional characterization values ​​as core transcriptional characterizations that accurately distinguish different cell subgroups.

[0095] Example 5 Analysis of the degree of heterogeneity of cell populations Based on the matrix of cell-core transcriptional characterization values, a score of the degree of cellular heterogeneity of the cell population was calculated based on the following formula: JPEG2025530540000014.jpg28170where, X ij represents the single-cell expression value of the jth core transcriptional characterization of the ith cell, n represents the quantity of the core transcriptional characterization, m represents the number of cells in the cell population, and μ j represents the average expression value in the cell population of the jth core transcriptional characterization. Single-cell expression values ​​in more than half of the core transcriptional characterizations were [μ j -3σ j ,μ j +3σ j ] are determined to be abnormal cells and are removed during analysis, where σ j represents the standard deviation of the expression values ​​in the cell population for the jth core transcriptional characterization. The range of values ​​for the degree of cellular heterogeneity is [0, 100]. As a result, as shown in Figure 1, the heterogeneity degrees of S1, S2, and S3 derived from different suppliers were different, with heterogeneity scores of 67.91, 68.83, and 72.85, respectively.

[0096] Example 6: Evaluation of cellular safety and efficacy In this example, the safety and efficacy of cell samples with different degrees of heterogeneity were evaluated, and the possibility of evaluating the quality of cell products by cell heterogeneity as a cell quality attribute was investigated.

[0097] 1. Safety assessment Six to eight-week-old NCG mice (purchased from Jiangsu Jicui Pharmaceutical Co., Ltd.) were randomly divided into four groups, each with three male and three female mice, and numbered. The mice were fixed in a restrainer, and the injection site was disinfected. Each mouse was then injected with 1 × 10 6 The S1, S2 or S3 suspension was slowly administered into the tail vein of mice so that the number of cells was 1 / mouse, and a negative control group was administered with saline. After the cell administration was completed, the mice were immediately placed in a clean cage and observed by video recording, and the survival status of the mice was recorded within 3 minutes. As a result, as shown in Table 9, all 6 mice administered S1 and S2 and 6 mice administered saline survived within 3 minutes, while 1 mouse administered S3 died within 3 minutes and 5 mice survived. The heterogeneity of S3 was higher than that of S1 and S2, and the safety risk of S3 was higher than that of S1 and S2. [Table 9]

[0098] II. Effectiveness evaluation 1. Group division and administration SD rats with a permanent ischemic stroke model were divided into three groups with eight rats in each group. The model cell control group was administered saline + cyclosporine A (concentration 2.5 mg / mL), group 1 was administered cell sample S1 + cyclosporine A, and group 2 was administered cell sample S3 + cyclosporine A. Immediately after evaluating neurological function 24 h after model creation, cells were administered via tail vein injection at a dose of 2 × 107 / kg in a volume of 4 mL / kg. Cyclosporine A was administered intraperitoneally every day before and after model creation. The study endpoint was 28 days after cell treatment.

[0099] 2. Assessment of Neurological Function The 5-level behavioral assessment of Zea Longa and the forelimb placing test were performed before administration and on days 3, 7, 14, 21, and 28 after administration, respectively. Zea Longa's 5-point scoring system: 0 points, no neurological deficits; 1 points, mild focal neurological deficits, inability to fully extend the left front limb; 2 points, moderate focal neurological deficits (turning to the left); 3 points, severe neurological deficits (falling to the left); 4 points, inability to walk spontaneously, reduced level of consciousness. Forelimb placing test: The examiner holds the rat's dorsal skin in his hand, holds its limbs in the air, and touches its whiskers to the edge of a table to test the activity of the forelimbs on that side. Uninjured rats can quickly place their forelimbs on the table, while brain-injured rats have varying degrees of impairment in this movement. Ten measurements were taken on each side of the rat, and the percentage of times the forelimbs touched the edge of the table was the score for that side. The rats in the control group showed clear neurological impairments, such as bending and turning to the hemiplegic side, which gradually improved over time. Compared with the control group, the neurological function scores of Group 1 were significantly reduced from 14 to 28 days after administration (P<0.05-0.01), with an improvement rate of 38.6% (P<0.05) by 28 days after administration. The neurological function scores of Group 2 were significantly reduced from 14 to 28 days after administration (P<0.05), with an improvement rate of 30.7% (P>0.05) by 28 days after administration. Comparing the improvement rates of neurological function scores from 14 to 28 days after administration, it was found that the therapeutic effect of Group 1 was slightly better than that of Group 2 (improvement rate on the 28th day after administration: 38.6% vs. 30.7%, P>0.05). The results are shown in Table 10. * P<0.05, ** P<0.01). [Table 10] The rats in the control group with permanent ischemic stroke model suffered from a contralateral forelimb placement disorder. Compared with the control group, the forelimb placement rate in both Groups 1 and 2 significantly improved from 21 to 28 days after treatment (P<0.01-0.001). The improvement rates at 28 days after treatment were 48.7% (P<0.001) and 44.1% (P<0.001), respectively. Comparing the forelimb placement rate on days 21 to 28 after administration, the treatment effect of Group 1 was found to be slightly better than that of Group 2 (improvement rate on day 28 after administration: 48.7% vs. 44.1%, P>0.05). The results are shown in Table 11. * P<0.05, ** P<0.01, *** P<0.001). [Table 11]

[0100] 3. Measurement of the extent of cerebral infarction On the 28th day after administration, the rats were exsanguinated and their brains were removed. The brain tissue was frozen in a refrigerator at -20°C and then sliced ​​into 2mm-thick sheets. The brain tissue sheets were placed in a 2% tetrazole red (TTC) solution and incubated at 37°C for 5 minutes. Infarcted tissue appeared white, while non-infarcted tissue appeared red. The area of ​​cerebral infarction was measured using Image J software, and the percentage of cerebral infarction area relative to the total brain area was calculated. To exclude deviations due to cerebral edema or cerebral atrophy, the area of ​​cerebral infarction (%) was calculated using the following formula: Cerebral infarct area (%) = (area of ​​healthy lateral hemisphere - area of ​​normal brain on the infarcted side) / (area of ​​healthy lateral hemisphere x 2) x 100% In the permanent ischemic stroke model, rats developed obvious cerebral infarction. Compared with the model control group, the area of ​​cerebral infarction was significantly reduced in both Group 1 and Group 2 on the 28th day after administration (P<0.05), with improvement rates of 19.2% (P<0.05) and 15.7% (P<0.05), respectively. Comparing the extent of cerebral infarction on the 28th day after administration, the therapeutic effect of Group 1 was found to be slightly better than that of Group 2 (improvement rate 19.2% vs 15.7%, P>0.05). The results are shown in Table 12 (*P<0.05). [Table 12] Combining the above evaluation results, the degree of heterogeneity of a cell population correlates with the safety and efficacy of the cells, and cell heterogeneity can be used as a cytoplasmic attribute to evaluate cell quality.

[0101] Example 7: Cell quality attribute detection device As shown in FIG. 2A, this embodiment provides a cell quality attribute detection device including a cell heterogeneity detection module 10, a microbiological safety detection module 20, a cell marker detection module 30, a cell activity detection module 40, and a biological effect detection module 50. The cellular heterogeneity detection module 10 detects the diversity of the state of a cell subgroup (genomic diversity, transcriptomic diversity, proteomic diversity, metabolomic diversity or epigenomic diversity of a cell subgroup). The microbiological safety detection module 20 detects mycoplasma, bacteria, fungi, endotoxins, mycobacteria, etc. in cell samples. The cell marker detection module 30 detects cell surface markers (e.g., CD73, CD90, CD105, CD11b, CD19, CD34, CD45, and HLA-DR). The cell activity detection module 40 detects cell activity rate, cell doubling time, cell cycle, apoptosis, clonogenesis rate, and the like. The biological effect detection module 50 detects the ability of osteogenic differentiation, adipogenic differentiation, chondrogenic differentiation, and the like.

[0102] As shown in FIG. 2B, the cellular heterogeneity detection module 10 includes: a single cell molecular expression matrix detection submodule 110 for detecting a single cell molecular expression matrix of a cell population; and a cellular heterogeneity degree detection sub-module 120 for detecting the degree of cellular heterogeneity of the cell population.

[0103] Here, as shown in FIG. 2C, the single cell molecular expression matrix submodule 110: a single-cell molecular expression data detection unit 1101 for detecting molecular expression values ​​at a single-cell level of a cell population to obtain single-cell molecular expression data of the cell population; and a data pre-processing unit 1102 for pre-processing the single cell molecular expression data of the cell population (wherein the pre-processing includes noise correction and / or normalization) to obtain a single cell molecular expression matrix of the cell population.

[0104] As shown in FIG. 2D, the cellular heterogeneity detection module 120 includes: a molecular characterization determination unit 1201 for processing the single-cell molecular expression matrix through a data dimensionality reduction method, selecting data of nonlinear correlation and non-biological function correlation as molecular characterization, and determining the single-cell expression value of the molecular characterization and the average expression value in the cell population, where the molecular characterization is selected from gene characterization, transcription characterization, protein characterization, metabolite characterization or epigenetic site, which can represent the single-cell molecular expression matrix of the cell population; and a calculation unit 1202 for determining the degree of cellular heterogeneity from the single-cell expression value of the molecular characterization and the average expression value in the cell population based on a calculation formula for the degree of cellular heterogeneity.

[0105] The formula for calculating the degree of cellular heterogeneity is as follows: JPEG2025530540000019.jpg28170where, X ij represents the single-cell expression value of the jth molecular characterization of the ith cell, n represents the quantity of molecular characterization, m represents the number of cells in the cell population, and μ jrepresents the average expression value in the cell population for the jth molecular characterization.

[0106] Example 8 Development of a manufacturing process for ADSCs seed bank In this example, we developed a process for the number of washing times of primary ADSCs in the production process of an ADSC seed bank, and confirmed the parameters of the production process by calculating the degree of cellular heterogeneity of samples in the ADSC seed bank. Twelve 50 ml centrifuge tubes were prepared in advance and marked into four groups, each with three tubes, to be kept in reserve for use. A sample of adipose tissue collected within 24 hours was transferred from the collection bottle to a 50 ml centrifuge tube, and twice its volume of saline was added. The tube was then shaken vigorously for 20 seconds and allowed to stand for 5 minutes. After the adipose tissue and saline were completely separated, the lower layer of the tissue was aspirated and discarded. The adipose tissue was repeatedly rinsed with saline until the lower layer was no longer noticeably red. After washing, the adipose tissue was dispensed into 20 mL portions into 50 mL centrifuge tubes, and the same volume of dPBS was added. The mixture was centrifuged at 400 g for 5 minutes to separate the tissue into an upper oily layer, a middle adipose tissue layer, and a lower layer of dPBS and blood cell precipitate. The upper oily layer, lower dPBS, and blood cell precipitate were then removed. To the adipose tissue, 1 mg / mL type I collagenase (tissue digestion solution) was added in a volume twice that of the adipose tissue, and the tissue was transferred to a preheated air shaker at 37°C and digested at 120 rpm / min for 1 hour. The digested tissue was centrifuged at 500 g for 8 minutes at room temperature, and then separated into an upper oily layer, a middle adipose tissue layer, a lower digestive fluid layer, and a cellular precipitate in the lower layer. The upper oily layer, middle adipose tissue layer, and lower digestive fluid layer were aspirated and discarded, and the cellular precipitate was resuspended in dPBS and filtered through a 100 μm mesh into a 50 ml centrifuge tube. The resulting suspension was centrifuged at 500 g for 5 minutes, the supernatant removed, and a cellular precipitate containing P0 generation ADSCs was obtained. The cell pellet was resuspended in dPBS, mixed uniformly by blowing, and then added to 12 centrifuge tubes equally. Each tube was washed once, twice, three times, or four times with dPBS according to the group, and then centrifuged at 500 g for 5 minutes, and the supernatant was removed. P0 generation ADSCs were cultured in complete medium and then subcultured to P2 generation to detect the degree of cell heterogeneity among P0, P1, and P2 generations of primary ADSCs after different washing times. The above study was completed with adipose tissue from three sources. The results of the detection of the degree of cellular heterogeneity are shown in Table 13. For samples from the same lot, primary ADSCs were washed once before inoculation, leaving many miscellaneous cells. As passages progressed, the degree of cellular heterogeneity of the samples from P0 to P2 generations changed significantly, resulting in inconsistent heterogeneity and making it impossible to obtain a stable-quality ADSC seed bank. For samples from the same lot, primary ADSCs were washed two, three, or four times before inoculation, the degree of cellular heterogeneity of the samples from P0 to P2 generations was consistent, with no significant statistical differences, resulting in a stable-quality ADSC seed bank. The changes in the degree of cellular heterogeneity of the samples from P0 to P2 generations were consistent. Considering the effect of the number of washings on cell viability, during the production process of the ADSC seed bank, the primary ADSCs were washed twice with dPBS, and then inoculated and passaged to produce the ADSC seed bank. [Table 13]

[0107] Example 9: Examination of the stability of ADSCs seed bank In this example, the degree of cellular heterogeneity of ADSC seed bank samples was calculated to explore the law of time-dependent changes in the quality attributes of ADSC seed bank samples stored under liquid nitrogen conditions, providing scientific basis for product production, packaging, storage, transportation conditions, and retesting periods. Nine 50 ml centrifuge tubes were prepared in advance and marked into three groups, each consisting of three tubes. 10 ml of saline containing 1% human serum albumin was added to each tube, and these were kept as spares for future use. Nine tubes were randomly extracted from three lots of ADSC seed banks from the liquid nitrogen tank, three per lot, and placed in a 37°C constant temperature water bath. They were gently shaken to thaw the cells uniformly, then transferred to a biological safety cabinet. All cells from the same lot were aspirated into one 50 ml centrifuge tube, mixed uniformly by blowing, and then added to a centrifuge tube containing 1% human serum albumin in advance for washing. The centrifuge tubes were placed in a centrifuge and centrifuged at 400 g for 5 minutes at room temperature. After centrifugation was complete, the tubes were removed from the centrifuge and transferred to a biological safety cabinet. The supernatant was aspirated and discarded, and 10 mL of saline containing 1% human serum albumin was added to each tube to resuspend the cells, which were then washed by blowing. The centrifuge tubes were placed in a centrifuge and centrifuged at 400 g for 5 minutes at room temperature. After centrifugation was complete, the tubes were removed from the centrifuge and transferred to a biological safety cabinet. The supernatant was aspirated and discarded, and 10 mL of saline containing 1% human serum albumin was added to each tube to resuspend the cells, which were then washed by blowing. The centrifuge tubes were placed in a centrifuge and centrifuged at 400 g for 5 minutes at room temperature. After the centrifugation was complete, the tubes were removed from the centrifuge and transferred to a biological safety cabinet. The supernatant was aspirated and discarded. 10 mL of saline containing 1% human serum albumin was added to each tube, and the mixture was mixed uniformly by blowing. The tubes were then filtered through a 100 μm cell screen into new 50 mL centrifuge tubes. Each tube was sampled and counted, and the same cell concentration was adjusted based on the counting results by adding saline containing 1% human serum albumin. The degree of cellular heterogeneity was detected at 0, 3, 6, 9, and 12 months after preparation of the ADSC seed bank. The results of the detection of the degree of cellular heterogeneity are shown in Table 14. The degree of cellular heterogeneity detected in samples from the same lot at 3 months, 6 months, 9 months, and 12 months did not show any significant statistical difference compared to the degree of cellular heterogeneity detected at 0 month. The detection data for the degree of cellular heterogeneity of samples from the three lots showed good consistency. This indicates that the quality of the ADSC seed bank stored in liquid nitrogen is stable for up to one year. [Table 14]

[0108] Example 10: Development of a manufacturing process for ADSCs work bank In this example, process development was performed on the time that cells come into contact with the cryopreservation solution during the production process of the ADSCs work bank, and the parameters of the production process were confirmed by calculating the degree of sample cell heterogeneity of the ADSCs work bank. Four cryopreservation tubes were prepared in advance and divided into four groups, three per group, and cryopreservation solution was added to each tube to keep them in reserve for future use. One ADSC seed bank tube was randomly extracted from the liquid nitrogen tank, placed in a 37°C water bath, and gently shaken to thaw the cells uniformly. The tube was then transferred to a biological safety cabinet, and all cells were aspirated into a centrifuge tube containing 1% human serum albumin in saline, where they were washed three times. The centrifuge tube was placed in a centrifuge and centrifuged at 400 g for 5 minutes at room temperature. After the centrifugation was completed, the tube was removed from the centrifuge and transferred to a biological safety cabinet. The supernatant was aspirated and discarded, and 10 mL of saline containing 1% human serum albumin was added and mixed uniformly by blowing. The cells were then counted. 5000 cells / cm 2 The cells were inoculated evenly into four T75 cell culture flasks at a density of 10 mL / bottle, supplemented with complete medium up to 10 mL / bottle, transferred to a cell culture box, and cultured at 37°C and 5% CO2. When the cell confluence reached approximately 90%, they were subcultured up to the P5 generation. When the P5 generation cell confluence reached 80%, one T75 cell was taken and digested, and the cell pellet was collected and diluted to 1 x 10 7 The cells were added to three cryopreservation tubes containing cryopreservation solution at an average concentration of 100 cells / mL / tube, and timing was started. The cells in the remaining three T75 tubes were digested and collected after the contact times of the cells with the cryopreservation solution were 3 hours, 2 hours, 1 hour, and 0 hours, respectively, and added to three cryopreservation tubes containing cryopreservation solution on average. The operations of each step were integrated so that each group reached the same time point, and the cells were resuscitated 7 days after cryopreservation with programmed cooling to detect the degree of cellular heterogeneity in each group before adding the cryopreservation solution, before cryopreservation with programmed cooling, and after resuscitation 7 days after cryopreservation. The above test was completed by selecting three lots of ADSCs seed banks. As a result, we found that if samples from the same lot were exposed to the cryopreservation solution for more than two hours before cryopreservation, it had a significant impact on the state of the cell transcriptome, causing significant changes in the degree of cellular heterogeneity between samples before and after cryopreservation, making it impossible to obtain an ADSC workbank of stable quality. There was consistency in the changes in the degree of cellular heterogeneity between samples from the three lots before and after cryopreservation. Therefore, in the process of producing an ADSC work bank, it is necessary to limit the cryopreservation process to a programmed cooling step within 2 hours after the P5 generation cells come into contact with the cryopreservation solution.

[0109] Example 11: Examination of the stability of the ADSCs work bank In this example, the degree of cellular heterogeneity of ADSC work bank samples was calculated to explore the law of time-dependent changes in the quality attributes of ADSC work bank samples under liquid nitrogen storage conditions, providing scientific basis for product production, packaging, storage, transportation conditions, and retesting periods.

[0110] 1. Storage stability Nine 50 ml centrifuge tubes were prepared in advance and marked into three groups, each consisting of three tubes. 10 ml of saline containing 1% human serum albumin was added to each tube, and these tubes were kept as spares for future use. Nine tubes were randomly extracted from three lots of ADSC work banks from the liquid nitrogen tank, three per lot, and placed in a 37°C constant temperature water bath. They were gently shaken to thaw the cells uniformly, then transferred to a biological safety cabinet. All cells from the same lot were aspirated into one 50 ml centrifuge tube, mixed uniformly by blowing, and then added evenly to centrifuge tubes that had previously been supplemented with saline containing 1% human serum albumin, for washing. The centrifuge tubes were placed in a centrifuge and centrifuged at 400 g for 5 minutes at room temperature. After centrifugation was complete, the tubes were removed from the centrifuge and transferred to a biological safety cabinet. The supernatant was aspirated and discarded, and 10 mL of saline containing 1% human serum albumin was added to each tube to resuspend the cells, which were then washed by blowing. The centrifuge tubes were placed in a centrifuge and centrifuged at 400 g for 5 minutes at room temperature. After centrifugation was complete, the tubes were removed from the centrifuge and transferred to a biological safety cabinet. The supernatant was aspirated and discarded, and 10 mL of saline containing 1% human serum albumin was added to each tube to resuspend the cells, which were then washed by blowing. The centrifuge tubes were placed in a centrifuge and centrifuged at 400 g for 5 minutes at room temperature. After the centrifugation was complete, the tubes were removed from the centrifuge and transferred to a biological safety cabinet. The supernatant was aspirated and discarded. 10 mL of saline containing 1% human serum albumin was added to each tube, and the mixture was mixed uniformly by blowing. The tubes were then filtered through a 100 μm cell screen into new 50 mL centrifuge tubes. Each tube was sampled and counted, and the same cell concentration was adjusted based on the counting results by adding saline containing 1% human serum albumin. The degree of cellular heterogeneity was detected at 0, 3, 6, 9, and 12 months after preparation of the ADSCs working bank. As a result, there was no significant statistical difference in the degree of cellular heterogeneity detected in samples from the same lot at 3, 6, 9, and 12 months compared to the degree of cellular heterogeneity detected at 0 month. The data on the degree of cellular heterogeneity detected in samples from the three lots were in good agreement. This indicates that the quality of the ADSCs work bank stored in liquid nitrogen is stable for one year.

[0111] 2. Passage stability The test samples for passage stability were two lots of ADSCs work banks randomly extracted from a liquid nitrogen tank, and cryopreserved cell samples passaged to P7 and P10 generations after their resuscitation using the same production process. Two batches of ADSCs work banks were randomly extracted from the liquid nitrogen tank, totaling six bottles, three per batch. After reviving, they were subcultured up to the P10 generation using the same production process. During the subculture process, P7 and P10 generation cells were extracted, cryopreserved, and stored in liquid nitrogen. After the preparation of the P5, P7, and P10 generation cell cryopreserved samples was completed, they were immediately resuscitated to detect the degree of cellular heterogeneity. Figure 3 shows the distribution of scores for the degree of cellular heterogeneity in P5, P7, and P10 generation ADSC samples from two lots (A1 and A2). As can be seen from Table 15, Figures 4A, 4B, and 4C, the degree of cellular heterogeneity followed a normal distribution among ADSC samples of different generations and different lots. [Table 15] Analysis of variance was performed on the degree of cellular heterogeneity of ADSC samples of P5, P7, and P10 generations from two lots (A1 and A2). As shown in Figures 5A and 5B, there was no significant statistical difference in the degree of cellular heterogeneity between ADSC samples of different generations from the same lot, nor was there any significant statistical difference in the degree of cellular heterogeneity between ADSC samples of different lots from the same generation. This indicates that the quality of the ADSCs workbank stored in liquid nitrogen is stable across generations. Using the t-test method, the 95% and 99% confidence intervals of the cellular heterogeneity scores at different generations were used as the reference range for the cellular heterogeneity of ADSCs with stable quality. The specific results are shown in Figures 6A, 6B, 6C, and 6D.

[0112] Example 12: Development of a production process for ADSCs injection In this example, process development was carried out on the compounding ratio of auxiliary materials in the production process of ADSCs injection, and the parameters of the production process were confirmed by calculating the degree of cellular heterogeneity of the ADSCs injection sample. Forty-five 50 ml centrifuge tubes were prepared in advance, and 30 ml of saline containing 1% human serum albumin was added to each tube as spares for future use. 135 ADSC work banks from the same lot were randomly extracted from the liquid nitrogen tank, placed in a 37°C constant temperature water bath, and gently shaken to thaw the cells uniformly. After that, they were transferred to a biological safety cabinet, and all the cells were aspirated into one 50 ml centrifuge tube. After mixing uniformly by blowing, the cells were added to 45 centrifuge tubes containing saline containing an average of 1% human serum albumin, and washed. The centrifuge tubes were placed in a centrifuge and centrifuged at 400 g for 5 minutes at room temperature. After centrifugation was complete, the tubes were removed from the centrifuge and transferred to a biological safety cabinet. The supernatant was aspirated and discarded, and 30 mL of saline containing 1% human serum albumin was added to each tube to resuspend the cells, which were then washed by blowing. The centrifuge tubes were placed in a centrifuge and centrifuged at 400 g for 5 minutes at room temperature. After centrifugation was complete, the tubes were removed from the centrifuge and transferred to a biological safety cabinet. The supernatant was aspirated and discarded, and 30 mL of saline containing 1% human serum albumin was added to each tube to resuspend the cells, which were then washed by blowing. Place the centrifuge tubes in a centrifuge and centrifuge at room temperature for 5 minutes at 400 g. After centrifugation is complete, remove the centrifuge tubes from the centrifuge and place them in a biological safety cabinet. Aspirate and discard the supernatant. Divide all the centrifuge tubes into 9 groups according to the notation in Table 16, with 5 tubes per group. [Table 16] To each sample, 30 mL of saline containing human serum albumin at the corresponding concentration as shown in Table 16 was added, mixed uniformly by blowing, and filtered into a new 50 mL centrifuge tube using a 100 μm cell screen. Each sample was sampled and counted, and based on the counting results, saline containing human serum albumin was added to adjust the cell concentration to the required concentration for each group. The cells were loaded into 100 mL disposable blood bags, with 5 x 10 cells per bag. 7 Each group contained three bags of human ADSCs, and the injection solution was prepared and stored at 4°C. The degree of cellular heterogeneity was detected at 0 hours, 12 hours, and 24 hours after the preparation was completed, and one bag of preparation was taken out from each group each time. The above studies were completed based on a working bank of three ADSC lots with similar degrees of cellular heterogeneity. The results of the detection of the degree of cell heterogeneity are shown in Table 17. Normal saline containing 1% human serum albumin was used as the auxiliary material, and the cell concentration was 1 × 10 6 / mL, the degree of cellular heterogeneity within 24 hours for samples from the same lot is consistent, and the degree of cellular heterogeneity for samples from different lots is also consistent. Therefore, in the production process of human ADSC injection, 5% human serum albumin (20%):95% saline is selected as the optimal blending ratio of auxiliary materials, and 1 × 10 6 / mL was selected as the optimal cell concentration. [Table 17]

[0113] Example 13: Study on the stability of ADSCs injection solution In this example, the degree of cellular heterogeneity of ADSC injection samples was calculated to explore the law of time-dependent changes in the quality attributes of ADSC injection during storage, transportation, and use, providing scientific basis for formulation, storage and transportation conditions, and retest period.

[0114] 1. Storage stability A total of 18 bags of ADSC injection solution from three lots were randomly selected, six bags per lot, and left to stand at 5±3°C for 24 hours. At 6 and 24 hours, three bags of the preparation per lot were taken to detect the degree of cellular heterogeneity. Figure 7A shows the distribution of scores for the degree of cellular heterogeneity of the ADSC injection solution incubated at 5±3°C for 6 or 24 hours. As can be seen from Table 18 and Figure 8A, the degree of cellular heterogeneity of the ADSC injection solution incubated at 5±3°C for 6 or 24 hours all followed a normal distribution. [Table 18]

[0115] 2. Transportation stability A total of 18 bags of ADSC injection solution from three lots were randomly selected, six bags per lot, and placed at 5±3°C, shaken at a low speed (100 rpm / min) or a high speed (300 rpm / min), and maintained for 12 hours. At 12 hours, three bags of the preparation per lot were taken to detect the degree of cellular heterogeneity. Figure 7B shows the score distribution of the degree of cellular heterogeneity of the ADSC injection solution shaken at a low speed (100 rpm / min) or a high speed (300 rpm / min) at 5±3°C. As can be seen from Table 18 and Figure 8B, when shaken at a low speed (100 rpm / min) or a high speed (300 rpm / min) at 5±3°C, the degree of cellular heterogeneity of the ADSC injection solution all followed a normal distribution.

[0116] 3. Usage stability A total of 18 bags of ADSC injection solution from three lots were randomly selected, six bags per lot, and either left to stand at 5±3°C for 2 hours or left at room temperature (10-30°C) for 2 hours. At 2 hours, three bags of the preparation were taken from each lot to detect the degree of cellular heterogeneity. Figure 7C shows the distribution of scores for the degree of cellular heterogeneity of the ADSC injection solution left to stand for 2 hours at 5±3°C or room temperature (10-30°C). As can be seen from Table 18 and Figure 8C, the degree of cellular heterogeneity of the ADSC injection solution left to stand for 2 hours at 5±3°C or room temperature (10-30°C) all followed a normal distribution.

[0117] 4. Binding stability Based on the above results of detecting the degree of cellular heterogeneity, we analyzed the stability of ADSC injection solutions under the influence of multiple factors. As shown in Figure 9, the degree of cellular heterogeneity in the ADSC injection solution followed a normal distribution under the influence of multiple factors. Analysis of variance for the degree of cellular heterogeneity under the influence of multiple factors revealed no significant statistical difference in the degree of cellular heterogeneity affected by multiple factors, as shown in Table 19. Analysis of the interaction effects between multiple factors revealed that the storage time and usage conditions had an interactive effect on the degree of cellular heterogeneity, as shown in Figure 10. There was no significant statistical difference in the degree of cellular heterogeneity affected by the combination of any two factors, and the results are shown in Figures 11A, 11B, 11C, 11D, 11E, and 11F. This ensures that the quality of the ADSC injection solution is stable under the combined influence of storage time, transportation conditions, and usage conditions. [Table 19] Using the t-test method, the 95% and 99% confidence intervals of the scores for the degree of cellular heterogeneity at different storage times were used as the reference range for the degree of cellular heterogeneity of ADSCs with stable quality. The specific results are shown in Figures 12A, 12B, and 12C.

[0118] The applicant describes and interprets the present invention through the above examples, but the present invention is not limited to the above examples, i.e., it does not mean that the present invention must be implemented depending on the methods, steps, or devices in the above examples. It is obvious that those skilled in the art can make many modifications and variations according to the above teachings. Those skilled in the art should understand that any improvements to the present invention fall within the protection scope and disclosure of the present invention. The scope of the present invention and all equivalents thereof are intended to be limited by the appended claims. [Industrial Applicability]

[0119] The present invention provides a method and apparatus for detecting cell quality attributes, and uses thereof. The method for detecting cell quality attributes includes detecting cellular heterogeneity, including the diversity of cell subgroup states, which includes any one or a combination of at least two of the following: genome diversity, transcriptome diversity, proteome diversity, metabolome diversity, and epigenomic diversity of the cell subgroup. The present invention uses cellular heterogeneity due to the influence of the microenvironment as a cell quality attribute, which can more accurately evaluate the quality of cellular products at the single-cell level compared to current common cell quality evaluation indicators. The thus established method for detecting cell quality attributes plays an important role in accurately detecting the quality of cellular products and ensuring the stability of clinical therapeutic effects, and has good economic value and prospects for use. [Explanation of symbols]

[0120] 10 - cellular heterogeneity detection module, 20 - microbiological safety detection module, 30 - cellular marker detection module, 40 - cellular activity detection module, 50 - biological effect detection module; wherein, 110 - single cell molecular expression matrix detection submodule, 120 - degree of cellular heterogeneity detection submodule; wherein, 1101 - single cell molecular expression data detection unit, 1102 - data preprocessing unit; wherein, 1201 - molecular characterization determination unit, 1202 - calculation unit.

Claims

1. A detection method comprising the step of detecting cellular heterogeneity, including diversity in the state of cell subgroups, The diversity of the state of the cell subgroup includes one or at least two combinations of the following: genomic diversity, transcriptome diversity, proteome diversity, metabolome diversity, and epigenomic diversity of the cell subgroup. The detection method includes the step of detecting the degree of cellular heterogeneity, The step of detecting the degree of cellular heterogeneity is, Based on the single-cell molecular expression matrix of a cell population, determine molecular characterization and the single-cell expression values ​​of said molecular characterization. To determine the average expression value in the cell population of the aforementioned molecular characterization, and This includes determining the degree of cellular heterogeneity based on the single-cell expression value of the molecular characterization and the average expression value in a cell population of the molecular characterization. Here, the degree of cellular heterogeneity is determined based on the formula for calculating the degree of cellular heterogeneity, using the single-cell expression value of the molecular characterization and the average expression value in a cell population of the molecular characterization. A method for detecting the quality attributes of cells, characterized in that the formula for calculating the degree of cellular heterogeneity is as follows. (Here, Xij represents the single-cell expression value of the j-th molecular characteristic in the i-th cell, n represents the quantity of the molecular characteristic, m represents the number of cells in the cell population, and μj represents the average expression value of the j-th molecular characteristic in the cell population.)

2. A method for obtaining the single-cell molecular expression matrix of the aforementioned cell population is: To detect molecular expression levels at the single-cell level in a cell population and obtain single-cell molecular expression data for that cell population, and This includes preprocessing single-cell molecular expression data of the aforementioned cell population to obtain a single-cell molecular expression matrix of the aforementioned cell population. The detection method according to claim 1, characterized in that the preprocessing includes calibration and / or standardization of noise.

3. The method for determining the molecular characterization is: The detection method according to claim 1, characterized by processing single-cell molecular expression matrices using a data dimensionality reduction method and selecting nonlinear and nonbiological function-related data as molecular characterizations.

4. The detection method according to claim 1, characterized in that the molecular characterization is one or at least two selected from gene characterization, transcriptional characterization, protein characterization, metabolite characterization, and epigenetic site characterization, which can represent the single-cell molecular expression of a cell population.

5. The detection method according to claim 1, characterized in that the degree of cellular heterogeneity is determined by calculating the Euclidean distance from the single-cell expression value of the molecular characterization and the mean value of the expression value in a cell population of the molecular characterization.

6. The detection method according to claim 1, further comprising the step of detecting one or at least two combinations of microbiological safety, cell markers, cell activity, and biological efficacy.

7. The system includes a cellular heterogeneity detection module configured to detect the diversity of the state of a cell subgroup, which includes one or a combination of at least two of the following: genomic diversity, transcriptome diversity, proteome diversity, metabolome diversity, and epigenomic diversity. The aforementioned cellular heterogeneity detection module is, A molecular characterization determination unit for determining molecular characterizations based on the single-cell molecular expression matrix of a cell population, and for determining the single-cell expression value of the molecular characterization and the average expression value in that cell population, It includes a computational unit for determining the degree of cellular heterogeneity based on the single-cell expression values ​​of molecular characterizations and the mean expression values ​​of molecular characterizations in a cell population, and a cellular heterogeneity detection submodule, Here, the calculation unit is used to determine the degree of cellular heterogeneity based on a formula for calculating the degree of cellular heterogeneity, using the single-cell expression values ​​of molecular characterization and the average expression values ​​in the cell population. A cell quality attribute detection device characterized in that the formula for calculating the degree of cellular heterogeneity is as follows. (Here, Xij represents the single-cell expression value of the j-th molecular characteristic in the i-th cell, n represents the quantity of the molecular characteristic, m represents the number of cells in the cell population, and μj represents the average expression value of the j-th molecular characteristic in the cell population.)

8. The aforementioned cellular heterogeneity detection module further includes, A single-cell molecular expression data detection unit for detecting molecular expression levels at the single-cell level in a cell population and obtaining single-cell molecular expression data for the cell population, Includes a data preprocessing unit for preprocessing single-cell molecular expression data of a cell population to obtain a single-cell molecular expression matrix of the cell population, and a single-cell molecular expression matrix detection submodule, The detection apparatus according to claim 7, wherein the preprocessing includes calibration and / or standardization of noise.

9. The detection device according to claim 7, wherein the molecular characterization determination unit processes single-cell molecular expression matrices using a data dimensionality reduction method, selects nonlinear relationship and nonbiological function relationship data as molecular characterizations, and determines the single-cell expression value of the molecular characterization and the mean value of its expression value in a cell population.

10. The detection device according to claim 7, characterized in that the molecular characterization is one or at least two selected from gene characterization, transcriptional characterization, protein characterization, metabolite characterization, and epigenetic site characterization, which can represent the single-cell molecular representation matrix of a cell population.

11. The detection device according to claim 7, characterized in that the calculation unit calculates the Euclidean distance from the single-cell expression value of the molecular characterization and the average expression value in a cell population of the molecular characterization, and determines the degree of cellular heterogeneity.

12. The detection device according to claim 7, further comprising one or at least two of the following: a microbiological safety detection module, a cell marker detection module, a cell activity detection module, and a biological efficacy detection module.

13. Use of the cell quality attribute detection method and / or cell quality attribute detection apparatus according to any one of claims 1 to 6 in the manufacture of a cell product.

14. The use according to claim 13, characterized in that the cell product comprises active cells of a genetically modified or unmodified mammal (wherein the mammal is human) and one or at least two combinations of pharmaceutically acceptable selectable auxiliary materials, carriers, excipients, and diluents.

15. The use according to claim 13, characterized in that the cell product includes one or at least two of the following: an autologous or allogeneic stem cell product, an immune cell product, a tissue cell product, and a cell line product.

16. The use according to claim 15, characterized in that the stem cells include one or at least two of the following: adult stem cells, embryonic stem cells, inducible pluripotent stem cells, and stem cells and derived cells obtained by transformation of mature cells.

17. The use according to claim 15, characterized in that the stem cells include one or at least two of the following: mesenchymal stem cells, mesenchymal stromal cells, pluripotent stromal cells, pluripotent mesenchymal stromal cells, and drug signaling cells.

18. The use according to claim 15, characterized in that the stem cells include one or at least two of the following: adipose-derived stem cells, umbilical cord blood-derived stem cells, placenta-derived stem cells, bone marrow-derived stem cells, dental pulp-derived stem cells, menstrual blood-derived stem cells, amniotic epithelial stem cells, and bronchial basal cells.

19. Use of the cell quality attribute detection method and / or cell quality attribute detection apparatus according to any one of claims 1 to 6 in the research and / or development of cell products.

20. The use according to claim 19, wherein the cell product comprises a cell stock product and / or a cell preparation product, and the cell stock product comprises a cell seed bank product and / or a cell work cell bank product.

21. The use according to claim 19, characterized in that the research and / or development of the cell product includes researching and / or developing one or at least two of the following: a manufacturing process for cell cell seed bank products, storage stability of cell cell seed bank products, a preparation process for cell work cell bank products, storage stability of cell work cell bank products, a preparation process for cell formulation products, or the stability of cell formulation products.

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