Mesenchymal stem cell wound repair dominant functional clusters and their identification and use

Abstract

The present invention discloses mesenchymal stem cell wound repair dominant functional cluster (Hr-MSC) and its identification and use. The mesenchymal stem cell wound repair dominant functional cluster has a combination of biomarkers including CD29, CD142 and CYR61 or S100A9. The wound repair dominant functional cluster of the present invention can efficiently promote the healing of wounds (including various types of acute wounds, chronic refractory wounds, etc.), can be used to promote clinical healing of various types of acute and chronic wounds, and can intervene at each stage of wound healing to achieve the goal of promoting scarless wound healing. It also provides sufficient data support and technical support for the research and development of innovative stem cell drugs, facilitates the customized development of "innovative stem cell drugs" that promote the clinical use of innovative stem cell drugs to shorten the course of diseases and improve the quality of wound healing, and finally builds a scientific and rational stem cell application standardization system.

Description

Detailed Description of the Invention

[0001] This application claims priority to Chinese patent application CN202210249611.4, filed on March 14, 2022. This application cites the above Chinese patent application in full.

[0002] [Technical field] The present invention is in the field of biology, and specifically relates to mesenchymal stem cell wound repair dominant functional clusters and their identification and use.

[0003] [Background technology] Human umbilical cord mesenchymal stem cells (HUCMSCs) are one of the ideal seed cells and have shown good potential for use in the field of tissue damage repair. However, after reviewing many studies, it was found that the therapeutic effects of stem cells vary, and both the transplantation efficiency and integration rate are low, which are fundamentally due to the functional mutations and cellular heterogeneity of mesenchymal stem cells (MSCs).

[0004] Single-cell transcriptome sequencing technology can realize gene duplication within a single cell, which is of great significance for discovering cellular heterogeneity, clarifying cellular functions, explaining cross-omes relationships, and constructing a complete map.

[0005] Single-cell multi-omics ATAC+GEX (Assay for transposase-accessible chromatin and gene expression) technology can simultaneously detect RNA and chromatin accessibility in single cells, characterize cell types of different lineages in greater depth, capture cellular heterogeneity, infer a complete picture of molecular regulatory networks combining epigenetic and gene expression profiles, and obtain more realistic and systematic molecular change information, which is a brand new direction for the panoramic analysis of spatiotemporal heterogeneity of umbilical cord mesenchymal stem cells.

[0006] Flow cytometry (FCM) is a new advanced cell analysis technology that integrates laser technology, photoelectric measurement technology, computer technology, fluid dynamics and cell immunofluorescence chemical technology, and monoclonal antibody technology, and is a rapidly developing biomedical analysis technology. A flow cytometer is an instrument built using flow cytometry. It allows multi-parameter analysis of particles, including particle shape and size, cell cycle, intracellular cytokines, bacterial surface antigens, cell DNA content, etc., by making cells or particles flow in a liquid flow, passing incident light one by one, and recording scattered light and various fluorescent signals with a highly sensitive detector. The particles detected by FCM are generally live cells, and various biological information inside the cells can be obtained from the intensity and color of the fluorescent substance labeled on the cells, and the intensity of the scattered light by exciting it as a laser light source. In addition, FCM can also perform cell sorting, which can sort according to the specific characteristics of the detected cells, and if the sorting process is performed under sterile conditions, these cells can also be used for culture.

[0007] [Summary of the Invention] The technical problem to be solved by the present invention is to overcome the shortcomings of existing techniques such as inconsistent repair effect, low reproducibility, and low transplantation rate of mesenchymal stem cells for wound repair, and to provide mesenchymal stem cell wound repair dominant functional cell clusters and their identification and use. The wound repair dominant functional cell clusters of the present invention have the same functional properties, excellent reproducibility, and can be used to promote wound healing.

[0008] Through experiments, the inventors have discussed and demonstrated the heterogeneity of mesenchymal stem cells such as umbilical cord mesenchymal stem cells, and used single-cell multi-omics ATAC+GEX and spatial transcriptome sequencing to comprehensively map the heterogeneity of umbilical cord mesenchymal stem cells in multiple dimensions by applying single-cell multimodal omics. Furthermore, by combining the inventors' clinical practice and research results in wound repair, flow cytometry sorting technology was used to finally select the main functional clusters related to wound healing, which were amplified, identified and preliminarily verified in animal models.

[0009] The present invention solves the above technical problems through the following technical solutions.

[0010] A first aspect of the present invention provides a combination of biomarkers for wound repair dominant functional cells, including CD29, CD142 and CYR61.

[0011] The CD29 is also called ITGB1, and the CD142 is also called F3.

[0012] A second aspect of the present invention provides a combination of biomarkers of wound repair dominant functional cells including CD29, CD142 and S100A9.

[0013] In the present invention, the wound refers to damage to normal skin caused by an external wound factor, resulting in impaired skin function, including loss of part of normal tissue, necrosis of part of tissue cells, and stress reaction of part of tissue.

[0014] In the present invention, the combination of biomarkers may further comprise one or more biomarkers selected from DCN, TGFB1, COL3A1, COLIA2 and COLIA1.

[0015] In the present invention, the combination of biomarkers may further comprise the markers DCN, TGFB1, COL3A1, COLIA2 and COLIA1.

[0016] In the present invention, the combination of biomarkers may further comprise one or more biomarkers selected from DCN, TGFB1, COL3A1, COLIA2, COLIA1, NEAT1, TMP1, GFBP4, SAT1, TGM2, LUM, SERPNF1, MXD4, FOS, ACTA2, HTRA3, HTRA1, PNRC1, APOE and CDKN1C.

[0017] In the present invention, the combination of biomarkers further comprises the markers DCN, TGFB1, COL3A1, COLIA2, COLIA1, NEAT1, TMP1, GFBP4, SAT1, TGM2, LUM, SERPNF1, MXD4, FOS, ACTA2, HTRA3, HTRA1, PNRC1, APOE and CDKN1C.

[0018] A third aspect of the present invention provides a wound repair dominant functional cell preparation comprising CD29, CD142 and CYR61 positive stem cells.

[0019] A fourth aspect of the present invention provides a wound repair dominant functional cell preparation comprising CD29, CD142 and S100A9 positive stem cells.

[0020] In the present invention, the stem cells are preferably mesenchymal stem cells.

[0021] In the present invention, said stem cells preferably express one or more biomarkers selected from DCN, TGFB1, COL3A1, COLIA2 and COLIA1.

[0022] In the present invention, the stem cells more preferably express one or more biomarkers selected from NEAT1, TMP1, GFBP4, SAT1, TGM2, LUM, SERPNF1, MXD4, FOS, ACTA2, HTRA3, HTRA1, PNRC1, APOE and CDKN1C.

[0023] A fifth aspect of the present invention provides a cell cluster comprising CD29, CD142 and CYR61 positive stem cells, wherein the proportion of said stem cells in said cell cluster is at least 90%.

[0024] A sixth aspect of the present invention provides a cell cluster comprising CD29, CD142 and S100A9 positive stem cells, wherein the proportion of said stem cells in said cell cluster is at least 90%.

[0025] In the present invention, the positive expression ratio of said biomarkers in said cell clusters is preferably at least 92%, at least 95%, at least 97%, at least 98%, at least 99% or at least 100%.

[0026] In the present invention, said stem cells are preferably as described in the third or fourth aspect.

[0027] A seventh aspect of the present invention provides a pharmaceutical composition comprising stem cells and a pharma- ceutically acceptable carrier and / or excipient, said stem cells being as described in the third or fourth aspect.

[0028] An eighth aspect of the present invention provides a pharmaceutical combination comprising a first therapeutic agent and a second therapeutic agent, said first therapeutic agent comprising a pharmaceutical composition as described in the seventh aspect.

[0029] In some embodiments of the invention, the second therapeutic agent is selected from one or more of an immunosuppressant, an analgesic, and an anti-infective agent.

[0030] A ninth aspect of the present invention provides the use of a biomarker combination as defined in aspect 1 or aspect 2, a wound repair dominant functional cell preparation as defined in aspect 3 or aspect 4, a cell cluster as defined in aspect 5 or aspect 6, a pharmaceutical composition as defined in aspect 7 or a pharmaceutical combination as defined in aspect 8 in the manufacture of a preparation for promoting wound repair.

[0031] A tenth aspect of the present invention provides a method of promoting wound repair comprising administering to a subject in need thereof a wound repair dominant functional cell preparation as described in aspect three or four, a cell cluster as described in aspect five or six, a pharmaceutical composition as described in aspect seven, or a pharmaceutical combination as described in aspect eight.

[0032] An eleventh aspect of the present invention provides a method for identifying wound repair dominant functional cell clusters for non-diagnostic purposes, the method comprising: (1) performing molecular typing on test cells based on single-cell multimodal omics technology to obtain target candidate cell clusters; (2) performing signature gene display and classification on the target candidate cell cluster obtained in (1) to obtain a cell cluster containing signature genes, the cell cluster being a wound repair dominant functional cell cluster; Including, The signature genes express a combination of biomarkers as described in the first or second aspect.

[0033] In the present invention, the non-diagnostic purpose refers to the fact that the wound repair dominant functional cell cluster obtained by the identification method cannot be used as a single clinical indicator of wound repair, which still requires the experience and professional analysis and judgment of clinical medical personnel, and the wound repair dominant functional cell cluster in the present invention is an important reference indicator.

[0034] In some embodiments of the invention, the positive expression rate of the combination of biomarkers described in the first or second aspects in the cell cluster is at least 90%, such as at least 92%, at least 95%, at least 97%, at least 98%, at least 99% or at least 100%.

[0035] In some embodiments of the present invention, the single-cell multimodal omics technology may be a conventional technology in the art, including a single-cell multiomics technology such as ATAC+GEX technology.

[0036] In some embodiments of the invention, the cells are mesenchymal stem cells, for example umbilical cord mesenchymal stem cells.

[0037] In the present invention, the mesenchymal stem cells are wound repair dominant functional clusters of human umbilical cord mesenchymal stem cells (Hr-MSCs), a concept proposed for the first time by the inventors.

[0038] In some embodiments of the present invention, (1) includes (1-1): obtaining localization information of the target candidate cell clusters through spatial transcriptomics, and the localization information includes enrichment status and localization status.

[0039] A twelfth aspect of the present invention provides a system for identifying wound repair dominant functional cluster cells, comprising a typing module and a gene display module.

[0040] wherein the typing module performs molecular typing on the test cells by single-cell multimodal omics technology to obtain target candidate cell clusters; the gene display module performs characteristic gene display and classification on the target candidate cell clusters obtained by the typing module to obtain functional cells expressing characteristic genes; and the functional cells are wound repair dominant functional cluster cells; Said signature genes comprise / express a combination of biomarkers as described in the first or second aspect.

[0041] In some embodiments of the invention, the positive expression rate of the combination of markers described in the first or second aspects in the functional cell cluster is at least 90%, such as at least 92%, at least 95%, at least 97%, at least 98%, at least 99% or at least 100%.

[0042] A thirteenth aspect of the invention provides a kit for identifying a combination of biomarkers as defined in the first or second aspect, or a cell cluster as defined in the fifth or sixth aspect.

[0043] In some embodiments of the invention, the kit comprises reagents for detecting the expression level of a combination of biomarkers as described in aspects 1 or 2, such as reagents for detecting the protein expression level of the markers or reagents for detecting the gene or transcription level of the markers.

[0044] In some embodiments of the invention, the kit comprises a combination of markers as described in the first or second aspect.

[0045] The cell preparation of the present invention can be used to produce a material for regenerative medicine.

[0046] In the present invention, the stem cells are cells that have the ability to differentiate into various cells and the ability to self-renew, including embryonic stem cells, mesenchymal stem cells, umbilical cord stem cells, umbilical cord blood stem cells, adipose stem cells, pluripotent stem cells, induced pluripotent stem cells, etc.

[0047] In the present invention, said positive means that the cells can be classified into positive cells by FACS or the expression of the corresponding marker can be detected by RT-PCR.

[0048] Based on the results of single-cell multimodal omics clustering and characteristic gene display, the present invention screens for dominant functional cluster Hr-MSCs with the function of promoting wound healing. Based on the expression status of marker genes in each cluster, the functional enrichment status of each cluster, and the expression status of transcription factors in each cluster, a comprehensive preliminary screening is carried out in combination with databases such as Cellmarker, PanglaoDB, and EBI. Among them, the present invention is the first to apply single-cell multi-omics ATAC+GEX technology and spatial transcriptomics technology to the spatial heterogeneity study of human umbilical cord Wharton's jelly mesenchymal stem cells. The preliminary screening includes the following: Localization: intracellular localization of cluster marker genes and tissue localization of immunofluorescence. Quantification: horizontal comparison of cluster ratios of different generations, different individuals, and different genders.

[0049] As long as it does not violate the common knowledge in the art, the above-mentioned preferable conditions can be arbitrarily combined to obtain each preferable embodiment of the present invention.

[0050] The reagents and materials used in the present invention are commercially available.

[0051] Positive advances of the present invention: The wound repair dominant functional cluster (Hr-MSC) of the present invention can efficiently promote the healing of wounds (including various types of acute wounds, chronic refractory wounds, etc.), can be used to promote the clinical healing of various types of acute and chronic wounds, and can intervene in each stage of wound healing to achieve the goal of promoting scarless wound healing. It also provides sufficient data support and technical support for the research and development of innovative stem cell drugs, promotes the clinical use of innovative stem cell drugs to shorten the course of diseases and facilitate the customized development of "innovative stem cell drugs" that improve the quality of wound healing, and finally builds a scientific and rational stem cell application standardization system.

[0052] [Brief description of the drawings] [Figure 1] Schematic diagram of umbilical cord mesenchymal stem cell cluster results.

[0053] [Figure 2A] to [Figure 2D] Schematic diagram of heat map results of marker genes of Hr-MSC.

[0054] [Figure 3A] and [Figure 3B] are schematic diagrams showing the expression levels of marker genes in Hr-MSCs.

[0055] [Figure 4] Functional enrichment analysis and gene heatmap of 13 clusters.

[0056] [Figure 5] UMAP clustering plot of four different WJ-MSCs clusters.

[0057] FIG. 6 is a schematic diagram of the clustering results of umbilical cord stem cells.

[0058] [Figure 7] CD29 + / CD142 + / S100A9 + FIG. 1 is a schematic diagram of the classification results of Hr-MSCs.

[0059] FIG. 8 is a schematic diagram showing the wound healing efficiency results of Example 6.

[0060] [Figure 9] CD29 in HDF and HUCV + / CD142 + / S100A9 + FIG. 1 is a schematic diagram showing the migration capacity of Hr-MSCs.

[0061] [Figure 10] Schematic diagram of skin wound healing in zebrafish.

[0062] [Figure 11] A system (21) for identifying wound repair dominant functional cell clusters comprising a typing module (11) and a gene display module (21).

[0063] [Mode for carrying out the invention] The present invention will be further described below with reference to examples, but the present invention is not limited to the scope of the examples. In the following examples, the experimental methods without specific conditions are selected according to the usual methods and conditions or product instructions.

[0064] Example 1: Umbilical Cord Stem Cell Clustering In this example, single-cell transcriptomics was used to sequence and analyze umbilical cord mesenchymal stem cells, dividing them into multiple clusters at the single-cell level, and bioinformatics analysis was used to perform functional annotation for each cluster and determine candidate target clusters.

[0065] 1. Sample: human umbilical cord Wharton's jelly tissue; Pregnant women who delivered at 37–40 weeks of gestation were enrolled, and the newborns' umbilical cords were selected for the experiment and used as a source of umbilical cord mesenchymal stem cell tissue for the subsequent P0 and P3 generations (number: PUM-A1-000152).

[0066] Cells from six samples (huc_1P0, huc_1P3, huc_2P0, huc_2P3, huc_3P0 and huc_3P3) were selected for downstream analysis.

[0067] 2, Equipment: 10 x Chromium, 3, Parameters: (1) Eight channels were provided for each operation, and each channel could collect 100 to 10,000 cells.

[0068] (2) The cell capture efficiency was approximately 65%.

[0069] (3) The doublet ratio of the sequencing results was 0.9% / 1000 cells.

[0070] (4) No limit was set on cell size.

[0071] 4. Sequencing and subsequent data analysis were performed using the Illumina NovaSeq sequencing platform. Barcodes, UMIs, and RNA sequences were extracted from the original sequencing data, and the human reference genome was selected for alignment to obtain a feature-barcode matrix. Subsequent analysis was as follows.

[0072] (1) Data were filtered for gene counts, UMI counts, and mitochondria, and a normalized negative binomial model of gene expression was constructed using sctransform for data normalization, after which PCA dimensionality reduction was performed and PCA counts were determined according to the scree plot.

[0073] (2) By converting the bioinformatics results obtained by sequencing methods using dimensionality reduction algorithms such as t-SNE and UMAP into biological results, we determined the cell type heterogeneity in various individuals and various regions, performed visualization, determined the cell type heterogeneity in various individuals, and obtained typing results, resulting in 13 clusters (cluster 0, cluster 1, cluster 2, cluster 3, cluster 4, cluster 5, cluster 6, cluster 7, cluster 8, cluster 9, cluster 10, cluster 11 and cluster 12).

[0074] (3) Using the FindClusters function, differential expression analysis of genes in the clustering results was performed, and the characteristic marker genes of the clusters were displayed in various forms. Based on the results, cluster 6 and cluster 9 (i.e., Hr-MCS) that may have similar functions were screened.

[0075] (4) The differentially expressed genes in each cluster of Hr-MCSs were subjected to analyses including GO and KEGG enrichment analysis, protein interaction network, and transcription factor annotation to confirm the signaling pathways affected by the enrichment of up-regulated genes in Hr-MSCs and infer their possible functions.

[0076] As shown in Figure 1A and B, it has been preliminary determined that umbilical cord mesenchymal stem cells show limited heterogeneity, that the cell composition of different individuals, different generations and different sexes is consistent, that the cell ratios of different individuals, different generations and different sexes are different, and that the genes expressed by cells of different individuals, different generations and different sexes are different.

[0077] Example 2: Marker gene analysis of Hr-MSCs In this example, a gene expression heat map was created for each marker gene of the umbilical cord stem cell cluster obtained in Example 1, and the results are shown in Figures 2A to 2D.

[0078] The complexes of ranges 2A~2D are UBE2C, TOP2A, CDK1, CENPF, KPNA2, CKS2, PLK1, and HMGB2 、AURKA、ARL6IP1、GTSE1、MKI67、ASPM、CENPE、NUSAP1、SGO2、AURKB、TPX2、U BE2S, SMC4, NEAT1, ACTA2, MXD4, TIMP1, LMCD1, TAGLN, DCN, IGFBP4, TGM2, P., et al APPA, ANGPTL4, COL8A1, KCNE4, INHBA, MEG3, SAT1, TUBA1A, CDKN1A, SELENOM SAA1, CDC20, CCNB1, NCL, PNN, HSP90AA1, HSPA8, DDX21, SFPQ, SLBP, CCNB2 SRSF7, TUBB4B, ODC1, PTTG1, BIRC5, HIST1H4C, HIST1H1A, HIST1H1D, HIST1 H1B, ANKRD1, CLSPN, CYR61, GINS2, PTX3, FEN1, MCM7, ATAD2, FBXO5, GMNN, P CNA, HIST1H1C, MCM3, HIST1H1E, TYMS, LUM, SERPINF1, HTRA3, COL3A1, FOS, P.S NRC1, HTRA1, APOE, TGFBI, COL1A2, CDKN1C, COL1A1, KRT18, GYPC, TK1, CENP X, GNG11, LSM4, S100A16, SRM, SNRPB, CDKN3, MYL9, PFN1, RRM2, RPS27L, EXOS C8, LRRC75A, SET, GOLGA4, DST, KPNB1, B4GALT1, FLNA, HNRNPH1, ANKRD11, Z FP36L1, HEG1, HDLBP, PEG10, ATP2B1, PAK2, CDV3, COL5A1, HBZ, NUDC, GAS6, S. A., et al LC20A1, MALAT1, BOP1, SERBP1, LRRC59, HNRNPU, IL6ST, HNRNPA3, CAPN2, PF KP, EBNA1BP2, CTGF, HMGN2, TPM1, ACTB, CYCS, RBM8A, STMN1, TPM2, SMS, TMSB 4X, ACTG1, FTL, SNRPG, MARCKSL1, RANBP1, HMOX1, SQSTM1, SLC3A2, GADD45A NUPR1, DDIT3, FTH1, CXCL3, HLA-B, GDF15, TSC22D3, ZFAS1, CXCL1, S100A4PPP1R15A, LGALS3, IL32, S100A10, KRT7, PDLIM1, CAV1, RTN4, TM4SF1, FAH, CRIP2, PDPN, CD9, SPON2, LMNA, PRSS23, S100A6, MAOA, CYBA, ABHD5, EZR, MT-ND3, FN1, MT-ND6, MT -Includes ATP6, THBS1, MT-ND4, MT-ND1, MT-CYB, MT-ND2, SLC38A2, FBN1, COL4A1, COL5A2, RAB13, POSTN, LDHA, ACTG2, CTSC, COTL1, POLR3K, ANXA2, MT2A, PLAC9, NPW and TUBA1B. ,

[0079] The classification of each cluster and the top four or top five marker genes are shown in Table 1.

[0080] [Table 1]

[0081] Example 3: Expression levels of marker genes in Hr-MSCs The expression levels of some marker genes of Hr-MSCs listed in Example 2 were measured to obtain the expression status of the marker genes of each cluster, as shown in Figures 3A and 3B.

[0082] Taking the marker gene DCN as an example, DCN was expressed in all clusters, but showed the highest expression in Cluster 4. The analysis of each gene in the other figures was the same as in the past. This revealed that there were differences in the expression levels of the marker genes.

[0083] As shown in Figure 4, after a series of standard quality control and data preprocessing in Seurat, 13 cell clusters (clusters 0-12) were obtained based on the characteristic genes of cells, and further analysis was performed on the characteristic genes and functional enrichment of each cell cluster to confirm the cell identity. The results showed that clusters 0, 2, 3, and 7 expressed more proliferation-related genes such as UBE2C, TOP2A, HMGB2, CDC20, PCNA, and HIF1A compared with other cell clusters, which suggested that these clusters had strong proliferation ability. The results of GO enrichment analysis showed that these three clusters were also significantly enriched during cell nuclear division, DNA replication, and stem cell proliferation. Clusters 1, 4 and 11 feature ACTA2, TGM2, FOS, TGFB1, FLNA and COL3A1 as signature genes, and these genes are mainly enriched in biological processes such as glucocorticoid response, pericyte differentiation, putrescine degradation and extracellular matrix organization; cluster 10 is characterized by high expression of S100A10, PDLIM1 and CAV1; clusters 5, 8 and 12 are characterized by high expression of UBE2S, TAGLN, TPM1 and TMSB4X; and the key genes in clusters 6 and 9 are mainly involved in immune regulation and wound repair-related pathways, and these genes include B4GALT1, HEG1, CXCL3, CXCL1 and HMOX1.

[0084] Finally, based on gene expression profiles, functional similarities and literature review, we merged similar clusters and named them proliferative_MSCs (composed of clusters 0, 2, 3 and 7), niche-supporting_MSCs (composed of clusters 1, 4 and 11), metabolic_related_MSCs (composed of clusters 5, 8 and 12) and biofunctional_MSCs (composed of functional stem cells; clusters 6 and 9), i.e., CD29 in this application. + / CD142 + / S100A9 +We named this cluster Hr-MSC (shown in Figure 5). Among them, cluster 10 had a low weight and could not be well classified into other clusters due to GO enrichment, so it was not included in the subsequent analysis.

[0085] Combining the above results with the single-cell data, CD29, CD142, and CYR61 of biofunctional MSCs were selected as candidate markers for Hr-MSCs, and CD29, CD142, and S100A9 of biofunctional MSCs were selected as candidate markers for Hr-MSCs for subsequent experiments. The positive expression ratios of the above candidate markers in each cluster of Hr-MSCs were at least 90%.

[0086] Example 4: CD29 + / CD142 + / CYR61 + Classification of Hr-MSCs Referring to Example 1, in this example, single-cell transcriptomics sequence analysis was used to obtain marker genes of clusters with target therapeutic function, and combined with flow cytometry sorting technology, a target cluster was selected from primary mixed umbilical cord mesenchymal stem cells, as shown in Figure 6, and the cluster accounted for about 5.310% of all cells. The cluster was identified by flow cytometry technology using positive markers CD29+, CD142+, CYR61+. The target cluster that was all positive for these three indicators was P6, which accounted for 5.3% of all stem cells, and was the target cluster that was then used for amplification.

[0087] Example 5: CD29 + / CD142 + / S100A9 + Classification of Hr-MSCs Referring to Example 1, in this example, single cell transcriptomics sequence analysis is used to obtain marker genes of clusters with target therapeutic function, and combined with flow cytometry sorting technology, target clusters are selected from primary mixed umbilical cord mesenchymal stem cells. The clusters are identified by flow cytometry technology using positive markers CD29+, CD142+, S100A9+. As shown in Figure 7, the target clusters that are all positive for these three indicators account for 9.53% of the total stem cells, and are the target clusters that are then used for amplification.

[0088] Example 6: Example of effect In this example, a defect was created in the full-thickness skin of an adult zebrafish, and the healing time, i.e., healing efficiency, of various administrations (i.e., the groups described below) to the defect wound of an adult zebrafish was evaluated and the effect was verified (for model construction and validation, see Kennard Andrew S et al., Theriot Julie A. (2021). Wounding Zebrafish Larval Epidermis by Laceration. Bio Protoc, 11(24), e4260; Mhlongo Fikile et al., Evaluation of the wound healing properties of South African medicinal plants using zebrafish and in vitro bioassays. [J]. J Ethnopharmacol, 2022, 286: 114867).

[0089] Adult zebrafish were maintained in an automated fish rearing system at 28 °C with a 14-h light / 10-h dark cycle. They were fed with freshly hatched Artemia once daily. Zebrafish were divided into a control group (PBS, n = 6), a positively sorted MSC group (CD29 + / CD142 + / CYR61 + MSC, n=6) and negatively classified MSC group (CD29 - / CD142 - / CYR61 -MSC, n=6).

[0090] As shown in FIG. 8, the preliminary validation results showed that both the positively classified MSC group and the negatively classified MSC group showed significant differences in wound healing efficiency compared to the control group (p<0.001), and the wound healing efficiency of the positively classified MSC group was significantly different from that of the negatively classified MSC group.

[0091] Example 7: Example of effect In this example, a defect was created in the full-thickness skin of an adult zebrafish, and the healing time, i.e., healing efficiency, was evaluated and the effect was verified by various administrations (i.e., the groups described below) to the defect wound of the adult zebrafish (model construction and validation are described in Kennard Andrew S et al., Theriot Julie A. (2021). Wounding Zebrafish Larval Epidermis by Laceration. Bio Protoc, 11(24), e4260; Mhlongo Fikile et al., Evaluation of the wound healing properties of South African medicinal plants using zebrafish and in vitro bioassays. [J] .J Ethnopharmacol, 2022, 286: 114867). Details are as follows.

[0092] Adult zebrafish were maintained in an automated fish rearing system at 28 °C with a 14-h light / 10-h dark cycle. They were fed with freshly hatched Artemia once a day. Zebrafish were divided into a control group (PBS, n = 6), an unsorted MSC group (Wharton's jelly tissue MSC, n = 6), a positively sorted MSC group (CD29 + / CD142 + / S100A9 + MSC, n=6) and negatively classified MSC group (CD29 - / CD142 - / S100A9 -MSCs, n = 6). Then, the zebrafish were anesthetized using 0.03% triazine (Aladdin Biochemical Technology Co., Ltd., China). After anesthesia, a full-thickness wound (diameter approximately 3 mm) was made on the left side of the back of the zebrafish using small ophthalmic scissors. Then, 10 μL of the corresponding experimental solution (1 × 10 4 Cells (1000 cells) were injected subcutaneously around the wound. After injection, the needle was carefully held in place for 5 minutes and then the fish was placed back into the water of the circulatory system. If any leakage of injection fluid was observed at the injection site, the fish was euthanized and excluded from further experiments. At 0, 5, 15 and 30 days post-wounding (dpw), the fish were anesthetized and then photographed. Wound area was measured using ImageJ software.

[0093] The results are shown in Figures 9 and 10. On the 5th day after wounding, a small amount of epithelial tissue was observed on the wound surface, which gradually increased with time (Figure 10). On the 30th day after wounding, the wound in the control group was still significant, with thin epithelial cell tissue, and various degrees of wound healing were observed in the unclassified group, positive classification group, and negative classification group. In particular, the positive classification group showed more significant epithelialization, with good reconstruction of the silver stripe pattern, and melanin spots formed by melanocytes around the wound extended to the wound site. Specifically, the wound healing efficiency of the positive classification group reached 0.857 ± 0.050, while the wound healing efficiencies of the control group, negative classification MSC group, and unclassified MSC group were 0.575 ± 0.110, 0.648 ± 0.129, and 0.646 ± 0.129, respectively, and the positive classification group showed statistical differences in wound healing efficiency, with CD29 + / CD142 + / S100A9 + This indicates that MSCs can better promote wound healing.

[0094] Example 8: A system for identifying wound repair-dominant functional cell clusters This embodiment provides a system (21) for identifying wound repair dominant functional cell clusters, which includes a typing module (11) and a gene display module (21), as shown in FIG.

[0095] Among them, the typing module (11) performed molecular typing on test cells based on single-cell multimodal omics technology to obtain target candidate cell clusters.

[0096] The gene display module (21) performed characteristic gene display and classification on the target candidate cell clusters obtained in the typing module (11), and obtained cell clusters containing characteristic genes, which were wound repair dominant functional cell clusters.

[0097] Characteristic genes are found in one or more of the following marker combinations:

[0098] (1) CD29, CD142 and CYR61; (2) DCN, TGFB1, COL3A1, COLIA2, and COLIA1; (3) NEAT1, TMP1, GFBP4, SAT1, TGM2, LUM, SERPNF1, MXD4, FOS, ACTA2, HTRA3, HTRA1, PNRC1, APOE, and CDKN1C.

[0099] Although the specific embodiments of the present invention have been described above, those skilled in the art will understand that these are merely illustrative examples, and that various changes and modifications can be made to these embodiments without departing from the principles and substance of the present invention. Therefore, the scope of protection of the present invention is limited by the appended claims. [Brief description of the drawings]

[0100] [Figure 1] FIG. 1 is a schematic diagram of umbilical cord mesenchymal stem cell cluster results. [Figure 2A] FIG. 1 is a schematic diagram of the heat map results of marker genes for Hr-MSCs. [Figure 2B] FIG. 1 is a schematic diagram of the heat map results of marker genes for Hr-MSCs. [Figure 2C] FIG. 1 is a schematic diagram of the heat map results of marker genes for Hr-MSCs. [Figure 2D] FIG. 1 is a schematic diagram of the heat map results of marker genes for Hr-MSCs. [Figure 3A] FIG. 1 is a schematic diagram of the expression levels of marker genes in Hr-MSCs. [Figure 3B] FIG. 1 is a schematic diagram of the expression levels of marker genes in Hr-MSCs. [Figure 4] Functional enrichment analysis and gene heatmap of the 13 clusters are shown. [Diagram 5] UMAP clustering plot of four different WJ-MSCs clusters. [Figure 6] FIG. 1 is a schematic diagram of umbilical cord stem cell clustering results. [Figure 7] FIG. 1 is a schematic diagram of the sorting results of CD29+ / CD142+ / S100A9+ Hr-MSCs. [Figure 8] FIG. 1 is a schematic diagram of wound healing efficiency results of Example 6. [Figure 9] FIG. 1 is a schematic diagram showing the migration capacity of CD29+ / CD142+ / S100A9+ Hr-MSCs in HDFs and HUCV. [Figure 10] FIG. 1 is a schematic diagram of zebrafish skin wound healing. [Figure 11] A system (21) for identifying wound repair dominant functional cell clusters comprising a typing module (11) and a gene display module (21).

Claims

1. A combination of biomarkers of wound repair dominant functional cells, characterized by including CD29, CD142 and CYR61.

2. A combination of biomarkers of wound repair dominant functional cells, characterized by including CD29, CD142 and S100A9.

3. The combination of biomarkers further comprises one or more selected from DCN, TGFB1, COL3A1, COLIA2, and COLIA1; Preferably, the combination of biomarkers according to claim 1 or 2, characterized in that the combination of biomarkers comprises one or more selected from NEAT1, TMP1, GFBP4, SAT1, TGM2, LUM, SERPNF1, MXD4, FOS, ACTA2, HTRA3, HTRA1, PNRC1, APOE and CDKN1C.

4. A wound repair-dominant functional cell preparation, characterized in that it contains CD29, CD142 and CYR61 positive stem cells.

5. A wound repair-dominant functional cell preparation, characterized in that it contains CD29, CD142 and S100A9 positive stem cells.

6. The stem cells are mesenchymal stem cells, And / or the wound repair dominant functional cell preparation of claim 4 or 5, characterized in that the stem cells express one or more biomarkers selected from DCN, TGFB1, COL3A1, COLIA2 and COLIA1.

7. The wound repair dominant functional cell preparation of claim 6, characterized in that the stem cells express one or more biomarkers selected from NEAT1, TMP1, GFBP4, SAT1, TGM2, LUM, SERPNF1, MXD4, FOS, ACTA2, HTRA3, HTRA1, PNRC1, APOE and CDKN1C.

8. comprising CD29, CD142 and CYR61 positive stem cells; A cell cluster, characterized in that the proportion of stem cells in the cell cluster is at least 90%.

9. CD29, CD142 and S100A9 positive stem cells; A cell cluster, characterized in that the proportion of stem cells in the cell cluster is at least 90%.

10. The stem cells are mesenchymal stem cells, And / or the stem cells express one or more selected from DCN, TGFB1, COL3A1, COLIA2 and COLIA1, characterized in that the stem cells express one or more of the following: DCN, TGFB1, COL3A1, COLIA2 and COLIA1.

11. The cell cluster of claim 10, wherein the stem cells express one or more selected from NEAT1, TMP1, GFBP4, SAT1, TGM2, LUM, SERPNF1, MXD4, FOS, ACTA2, HTRA3, HTRA1, PNRC1, APOE and CDKN1C.

12. A pharmaceutical composition comprising the stem cells according to any one of claims 4 to 7 and a pharma- ceutically acceptable carrier and / or excipient.

13. a first therapeutic agent and a second therapeutic agent, the first therapeutic agent comprising the pharmaceutical composition of claim 12; Preferably, the second therapeutic agent is selected from one or more of an immunosuppressant, an analgesic and an anti-infective agent.

14. A method for promoting wound repair comprising administering to a subject in need thereof a wound repair dominant functional cell preparation according to any one of claims 4 to 7, a cell cluster according to any one of claims 8 to 11, a pharmaceutical composition according to claim 12 or a pharmaceutical combination according to claim 13.

15. (1) performing molecular typing on cells of a test sample based on single-cell multimodal omics technology to obtain target candidate cell clusters; (2) performing signature gene display and selection on the target candidate cell cluster obtained in (1) to obtain a cell cluster containing signature genes, and the cell cluster is a wound repair dominant functional cell cluster; Including, A method for identifying wound repair dominant functional cell clusters, characterized in that said signature genes code for a combination of biomarkers according to any one of claims 1 to 3.

16. The method of claim 15, further comprising, after (1), (1-1): obtaining localization information of the target candidate cell clusters through spatial transcriptomics, wherein the localization information includes enrichment status and localization status.

17. a typing module and a gene display module; Wherein, the typing module performs molecular typing on the test cells by single cell multimodal omics technology to obtain target candidate cell clusters; the gene display module performs characteristic gene display and classification on the target candidate cell clusters obtained by the typing module to obtain cells containing characteristic genes; and the cell clusters are wound repair dominant functional cluster cells; A system for identifying wound repair dominant functional cell clusters, characterized in that said characteristic genes code for a combination of biomarkers according to any one of claims 1 to 3.

18. A kit for identifying wound repair dominant functional cell clusters comprising reagents for detecting the combination of biomarkers according to any one of claims 1 to 3.

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