Biomarker related to senescence of mesenchymal stem cells and application of biomarker

Abstract

The invention discloses a biomarker related to senescence of mesenchymal stem cells and application of the biomarker, and belongs to the field of biomarkers. The biomarker comprises a characteristic gene set consisting of 22 genes, wherein the characteristic gene set comprises MCF2L, PSG5, IGFBP5, LMNTD1, ANKRD29, F3, SCN9A, IL6, CDKN3, TENM2, TGFB2, DUSP1, TMEM47, IGFBP3, EEF1E1, SCN3A, ANKRD1, NGF, VCAM1, PDGFRL, EGR1 and RUNX2. When the biomarker provided by the invention is used for constructing an MSC aging prediction model, not only can sequential aging caused by in-vitro artificial amplification of the MSC be evaluated, but also the aging state of MSCs from different donors or tissues can be predicted, and an effective tool is provided for quality control and screening of preclinical MSCs.

Description

Technical Field

[0001] The present invention relates to the field of biomarkers, and in particular to a biomarker associated with mesenchymal stem cell aging and applications thereof. Background Art

[0002] Mesenchymal stem cells (MSCs) are a type of pluripotent stem cell with the potential for self-renewal and multidirectional differentiation. They are widely used in tissue engineering, regenerative medicine, and immunomodulation. However, with aging or long-term in vitro culture, MSCs gradually undergo senescence, manifested by decreased proliferation, weakened differentiation potential, cell cycle arrest, and altered secretory properties. These changes not only affect the clinical efficacy of MSCs but may also reduce their therapeutic safety. Therefore, accurately predicting the senescence status of MSCs is crucial to improving their clinical value.

[0003] Furthermore, MSCs are highly heterogeneous, and different donor sources, culture conditions, and passage methods can significantly affect their biological properties and aging process. Long-term passage culture often leads to functional degradation of MSCs, including increased secretion of inflammatory factors, abnormal gene expression profiles, and enhanced aging-related secretory phenotypes. These changes may weaken their immunomodulatory capacity and increase the risk of fibrosis. During clinical infusion, aging MSCs may reduce therapeutic efficacy and even trigger adverse immune reactions. Therefore, before the clinical application of MSCs, it is crucial to establish a set of reliable molecular biological indicators to assess their aging status.

[0004] At present, the aging status of MSCs cultured in vitro is mostly assessed by indicators such as the number of passages, β-gal staining, or telomere length detection. However, these methods have the following limitations: (1) Low sensitivity and lack of early prediction ability. Traditional aging indicators can often only be detected after MSCs have entered the obvious aging stage, and lack the ability to identify early latent aging states. (2) Lack of unified standards and difficulty in quantification. Different laboratories or institutions have differences in passage standards and detection methods, and lack objective, stable, and repeatable quantitative tools. (3) Unable to take into account heterogeneity and molecular mechanisms. Traditional methods mostly rely on phenotypic or functional changes, which makes it difficult to reflect the aging mechanism at the molecular level within MSCs, and it is also difficult to adapt to the heterogeneity of MSCs from different donor sources. (4) Unable to predict the quality risks of MSC clinical infusion: Aged MSCs may trigger immune responses and inflammatory activation after clinical infusion.

[0005] Although some recent studies have attempted to identify molecular markers of MSC aging from transcriptomic and epigenomic perspectives, molecular tools for preemptive identification of "problem MSCs" are still lacking. A systematic, stable, and diverse MSC-sourced aging prediction gene set is urgently needed for application in clinical quality control processes. Some aging-related gene markers in existing literature are derived from specific experimental conditions and lack cross-sample validation and clinical adaptability, making them inadequate for dynamic monitoring and quality control during MSC preparation. Therefore, developing a gene set that can stably predict MSC aging and applying it to processes such as MSC quality control has significant scientific and clinical value. Summary of the Invention

[0006] The present invention aims to provide a biomarker associated with mesenchymal stem cell aging and its application to address the problems of the prior art described above. By using the biomarkers provided by the present invention to construct a MSC aging prediction model, the functional status of MSCs can be accurately assessed. This model can not only assess the temporal aging caused by artificial expansion of MSCs in vitro, but also predict the aging status of MSCs derived from different donors (adult or perinatal) and different tissues (fat or umbilical cord). This provides an effective tool for preclinical MSC quality control and screening, and is expected to improve the clinical application of MSCs.

[0007] To achieve the above object, the present invention provides the following solutions:

[0008] The present invention provides a biomarker associated with mesenchymal stem cell aging, comprising a characteristic gene set consisting of 22 genes, wherein the characteristic gene set is MCF2L, PSG5, IGFBP5, LMNTD1, ANKRD29, F3, SCN9A, IL6, CDKN3, TENM2, TGFB2, DUSP1, TMEM47, IGFBP3, EEF1E1, SCN3A, ANKRD1, NGF, VCAM1, PDGFRL, EGR1 and RUNX2.

[0009] The present invention also provides the use of a reagent for detecting the expression level of the biomarker in preparing a detection product for predicting mesenchymal stem cell aging.

[0010] The present invention also provides a detection product for predicting mesenchymal stem cell aging, comprising a reagent for detecting the expression level of the biomarker.

[0011] The present invention also provides the use of the biomarker as a detection target in predicting mesenchymal stem cell aging, and the use of the detection product in predicting mesenchymal stem cell aging. By detecting the expression level of the biomarker, the aging tendency score of the mesenchymal stem cell is calculated. The higher the aging tendency score, the more serious the aging of the mesenchymal stem cell.

[0012] The aging tendency score=1.584×MCF2L expression level+1.349×PSG5 expression level+0.357×IGFBP5 expression level+1.304×LMNTD1 expression level+1.509×ANKRD29 expression level+0.37×F3 expression level+0.485×SCN9A expression level+0.162×IL6 expression level+0.853×CDKN3 expression level+0.236×TENM2 expression level-0.016×TGFB2 expression level level + 0.116 × DUSP1 expression level + 0.037 × TMEM47 expression level + 0.26 × IGFBP3 expression level + 0.611 × EEF1E1 expression level + 0.835 × SCN3A expression level + 0.193 × ANKRD1 expression level + 0.316 × NGF expression level + 0.107 × VCAM1 expression level + 0.125 × PDGFRL expression level - 0.335 × EGR1 expression level - 0.158 × RUNX2 expression level.

[0013] The present invention also provides the use of the detection product in quality monitoring during the in vitro expansion of mesenchymal stem cells. The detection product is used to detect the expression level of the biomarker, and the degree of aging of the mesenchymal stem cells is predicted based on the expression level of the biomarker. Aged mesenchymal stem cells with functional degeneration are eliminated, and non-aging mesenchymal stem cells with healthy functions are retained for further culture or preservation.

[0014] Furthermore, the aging tendency score of the mesenchymal stem cells is calculated according to the expression level of the biomarker. The higher the aging tendency score, the more serious the aging degree of the mesenchymal stem cells.

[0015] The present invention discloses the following technical effects:

[0016] The present invention uses MSC single-cell sequencing data to construct a set of characteristic genes that can predict the aging status and function of MSCs. The characteristic gene set is used to construct an MSC aging prediction model. The constructed prediction model can accurately evaluate the functional status of MSCs using the expression characteristics of a small number of genes. It can not only evaluate the temporal aging caused by artificial expansion of MSCs in vitro, but also predict the aging status of MSCs derived from different donors (adult or perinatal) and different tissues (fat or umbilical cord), providing an effective tool for preclinical MSC quality control and screening, and is expected to improve the clinical application effect of MSCs. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 Receiver operating curve for internal validation of the signature gene set of the present invention in a self-constructed single-cell transcriptome sequencing dataset;

[0019] Figure 2 The receiver operating curve for the signature gene set of the present invention validated in third-party independent data;

[0020] Figure 3 To verify the predictive effect of the characteristic gene set of the present invention in third-party independent data;

[0021] Figure 4 Receiver operating curve for the validation of the signature gene set of the present invention in the public data GSE200161;

[0022] Figure 5 To verify the prediction effect of the characteristic gene set of the present invention in the public data GSE200161;

[0023] exist Figure 1 、 Figure 2 and Figure 4 In the data set, Gene Set 1 is the characteristic gene set of the present invention, and Gene Set 2 is the publicly published data set of "StemCellRes Ther. 2022Jul 26; 13(1):358." DETAILED DESCRIPTION

[0024] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as limiting the present invention, but rather as a more detailed description of certain aspects, features, and embodiments of the present invention.

[0025] It should be understood that the terms described herein are intended only to describe particular embodiments and are not intended to limit the present invention. In addition, for numerical ranges herein, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. The intermediate value within any stated value or stated range, and each smaller range between any other stated value or intermediate value within the stated range, is also encompassed within the present invention. The upper and lower limits of these smaller ranges may be independently included or excluded within the scope.

[0026] Unless otherwise indicated, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art. Although only preferred methods and materials are described herein, any methods and materials similar or equivalent to those described herein may also be used in the practice or testing of the present invention. All documents mentioned in this specification are incorporated by reference to disclose and describe the methods and / or materials associated with the documents. In the event of any conflict with any incorporated document, the contents of this specification shall prevail.

[0027] It will be apparent to those skilled in the art that various modifications and variations may be made to the specific embodiments described herein without departing from the scope or spirit of the invention. Other embodiments will be apparent to those skilled in the art from the description of the invention. The description and examples are intended to be exemplary only.

[0028] The words “include,” “including,” “have,” “contain,” etc. used in this document are open-ended terms, meaning including but not limited to.

[0029] The present invention expands MSCs derived from clinical samples in vitro to passage 5 (P5) and passage 15 (P15), combines single-cell transcriptome sequencing technology to reveal the heterogeneity within the MSC population, and constructs a set of characteristic genes that can predict the aging status of MSCs. The characteristic gene set consists of 22 genes, namely MCF2L, PSG5, IGFBP5, LMNTD1, ANKRD29, F3, SCN9A, IL6, CDKN3, TENM2, TGFB2, DUSP1, TMEM47, IGFBP3, EEF1E1, SCN3A, ANKRD1, NGF, VCAM1, PDGFRL, EGR1 and RUNX2.

[0030] In a specific embodiment of the present invention, MSCs were divided into three subpopulations based on UMAP cluster analysis: Biofunctional MSCs (strong immunomodulatory function), Active proliferation MSCs (high proliferation), and Multipotent progenitor MSCs (maintaining stemness). Further comparison of P15 and P5 revealed that the proportion of the Biofunctional MSC population decreased significantly in P15, suggesting that this population contained differentially expressed genes related to aging. Differential gene analysis of this population revealed 453 upregulated genes and 763 downregulated genes in P15. Based on the average logFoldChange>1 and gene function annotation, the above 22 genes were selected from the 453 upregulated genes to form a characteristic gene set for aging MSCs, which was used to develop a functional prediction model for MSCs.

[0031] Specifically, the MSC function prediction model developed from the characteristic gene set consisting of the above 22 genes is used to predict the aging propensity score. The aging propensity score is calculated as follows:

[0032] Senescence tendency score = 1.584 × MCF2L expression level + 1.349 × PSG5 expression level + 0.357 × IGFBP5 expression level + 1.304 × LMNTD1 expression level + 1.509 × ANKRD29 expression level + 0.37 × F3 expression level + 0.485 × SCN9A expression level + 0.162 × IL6 expression level + 0.853 × CDKN3 expression level + 0.236 × TENM2 expression level - 0.016 × TGFB2 expression level Average + 0.116×DUSP1 expression level + 0.037×TMEM47 expression level + 0.26×IGFBP3 expression level + 0.611×EEF1E1 expression level + 0.835×SCN3A expression level + 0.193×ANKRD1 expression level + 0.316×NGF expression level + 0.107×VCAM1 expression level + 0.125×PDGFRL expression level - 0.335×EGR1 expression level - 0.158×RUNX2 expression level.

[0033] In specific examples of the present invention, the performance of the aforementioned signature gene set in predicting MSC aging was evaluated using receiver operating characteristic (ROI) curves and compared with existing MSC aging-related gene sets. This performance evaluation was validated using three different datasets: the single-cell transcriptome sequencing dataset of the present invention, the GSE200161 public dataset, and an independent third-party dataset from the China Food and Drug Administration. The gene set of the present invention demonstrated superior predictive performance compared to the publicly available gene sets.

[0034] In a specific embodiment of the present invention, the inclusion criteria, quality control parameters and quality control processes of the three different data sets used for the performance evaluation of the above-mentioned feature gene set are consistent, and the data have been uniformly standardized, and all data sets are representative of the samples.

[0035] Example

[0036] 1. Acquisition and culture of MSC samples

[0037] First, umbilical cord MSCs were obtained from healthy donors from clinical sources. Primary cells were obtained using collagenase digestion. MSCs were then expanded in vitro using DMEM / DF-12 medium containing 10% fetal bovine serum (FBS). The culture conditions were controlled at a temperature of 37°C, a pH of 7.2-7.4, and a CO2 concentration of 5%. When the cell confluence reached 80%-90%, digestion and passage were performed. The cells were cultured to the 5th generation (P5, representing the early stage) and the 15th generation (P15, representing long-term expansion) for subsequent experiments.

[0038] 2. Single-cell transcriptome sequencing

[0039] MSCs at passages P5 and P15 were isolated and single-cell suspensions were prepared. High-throughput sequencing was performed using single-cell transcriptomics technology. After data quality control and removal of low-quality cells, a total of 52,153 high-quality cell samples were obtained. Single-cell clustering was performed using the Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction analysis method, which classified these cells into three subpopulations: biofunctional MSCs, active proliferation MSCs, and multipotent progenitor MSCs.

[0040] 3. Constructing a signature gene set

[0041] Comparison of biofunctional MSC populations between P5 and P15 generations revealed 453 upregulated genes and 763 downregulated genes in P15 based on differential gene analysis. Based on average logFoldChange > 1 and gene function annotation, 22 genes (MCF2L, PSG5, IGFBP5, LMNTD1, ANKRD29, F3, SCN9A, IL6, CDKN3, TENM2, TGFB2, DUSP1, TMEM47, IGFBP3, EEF1E1, SCN3A, ANKRD1, NGF, VCAM1, PDGFRL, EGR1, and RUNX2) were selected from the 453 upregulated genes to form a signature gene set for aging MSCs. A multivariate Cox regression model was used to weight the 22 genes using the Cox coefficient to generate an aging prediction model. This aging prediction model was used to derive a aging propensity score. The calculation formula is:

[0042] Senescence tendency score = 1.584 × MCF2L expression level + 1.349 × PSG5 expression level + 0.357 × IGFBP5 expression level + 1.304 × LMNTD1 expression level + 1.509 × ANKRD29 expression level + 0.37 × F3 expression level + 0.485 × SCN9A expression level + 0.162 × IL6 expression level + 0.853 × CDKN3 expression level + 0.236 × TENM2 expression level - 0.016 × TGFB2 expression level Average + 0.116×DUSP1 expression level + 0.037×TMEM47 expression level + 0.26×IGFBP3 expression level + 0.611×EEF1E1 expression level + 0.835×SCN3A expression level + 0.193×ANKRD1 expression level + 0.316×NGF expression level + 0.107×VCAM1 expression level + 0.125×PDGFRL expression level - 0.335×EGR1 expression level - 0.158×RUNX2 expression level.

[0043] The aging tendency score is calculated according to the above formula. This score is a relative distribution indicator that does not rely on an absolute threshold. By comparing the scores between samples, cells with a higher aging tendency can be identified. For example, in a sample set, cells with higher scores (in the top quarter) can be judged to have an aging tendency, while cells with lower scores (in the bottom quarter) can be considered to be in a young state. The higher the aging tendency score, the more severe the aging of the mesenchymal stem cells.

[0044] Subsequently, the screened characteristic gene set of aging MSCs was compared with the published MSC aging-related gene set (StemCell Res Ther. 2022 Jul 26; 13(1): 358.) in this single-cell sequencing data to perform receiver operating curve verification. The results are as follows Figure 1 The characteristic gene set of the above-mentioned aging MSCs screened by the present invention showed a better prediction effect.

[0045] 4. Third-party independent data verification

[0046] Next, the present invention obtained a batch of MSC single-cell transcriptome sequencing data from the China Food and Drug Administration, and obtained 52,742 cells after strict quality control and pretreatment. This batch of MSCs are cells of different generations from the same source (umbilical cord). The characteristic gene set of the above-screened aging MSCs was compared with the published MSC aging-related gene set (StemCell Res Ther. 2022Jul 26;13(1):358.) Receiver operating curve validation was performed in this third-party independent data, and the published MSC aging-related gene set was: "AKT1", "ANGPT1", "ANKRD1", "CCL13", "CCL16", "CCL20", "CCL3", "CCL8", "CDKN1A", "CDKN2A", "CHPF2", "CLIC3", "COL13A1", "CXCL1", "CXCL12", "CXCL2", "CXCL3", "CYBB", "DKK1", " DPP4", "EEF1E1", "ESM1", "F3", "FOXE1", "FST", "GALNT5", "HAS3", "HSP90B1", "ICAM1", "IFNG", "IGFBP2", "IGFBP3", "IG FBP4", "IGFBP5", "IGFBP6", "IGFBP7", "IL13", "IL15", "IL1A", "IL1B", "IL6", "IL7", "IL8", "ITGB8", "JAK2", "KDELR3", "KDM6B", "KRT19", "KRT34", "KRTAP1-5", "L1CAM", "LMCD1", "LMNA", "MIF", "NTN4", "OXTR", "PDGFRL", "PIM1", "PLAT", "P LK1", "PLOD1", "PML", "PPBP", "PRNP", "PSG5", "PSG9", "RAP1A", "RB1", "RBL2", "RPE65", "RPS6KB1", "RXRA", "S100B", "S AA1", "SCN3A", "SCUBE3", "SERPINB2", "SERPINB7", "SERPINE1", "SLC1A1", "SLC9A7", "SMARCA4", "SPP1", "SSR3", "STAT3 ","STMN2","SYNM","SYNPO2","TAT","TFE3","TGFB1","THBS1","TMEM159","TMEM59","TNF","TP53","VCAM1","WFDC1".

[0047] Verification results are as follows Figure 2 and Figure 3 As shown, the gene set and prediction model screened by the present invention can well distinguish low-passage MSCs from high-passage MSCs, and have better discrimination performance than the gene set in the prior art.

[0048] 5. Public Data Verification

[0049] In addition, in order to explore whether the characteristic gene set screened by the present invention has a judgment effect on the status of MSCs derived from different donor tissues, the present invention obtained the original single-cell sequencing data of MSCs from an article published in "Nature Communication" in 2023 (GSE200161), which contains four types of MSCs from adipose tissue (AD), bone marrow (BM), placental villi (PM) and umbilical cord (UC). According to the conclusion of the article, MSCs derived from adult tissues such as AD and BM show a higher level of aging than MSCs derived from perinatal tissues such as PM and UC. The AD and BM-derived MSCs were labeled "old", and the PM and UC-derived MSCs were labeled "young", and the binary classification performance of the prediction model of the present invention was evaluated by the receiver operating curve. The results are as follows. Figure 4 and Figure 5 As shown, the characteristic gene set screened by the present invention also has a good predictive effect on the aging level of MSCs from different tissue sources.

[0050] The embodiments described above are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by persons skilled in the art should fall within the scope of protection defined by the claims of the present invention.

Claims

1. A biomarker associated with mesenchymal stem cell aging, characterized in that: A signature gene set consisting of 22 genes is included, namely MCF2L, PSG5, IGFBP5, LMNTD1, ANKRD29, F3, SCN9A, IL6, CDKN3, TENM2, TGFB2, DUSP1, TMEM47, IGFBP3, EEF1E1, SCN3A, ANKRD1, NGF, VCAM1, PDGFRL, EGR1 and RUNX2.

2. The use of the biomarker according to claim 1 as a detection target in the prediction of mesenchymal stem cell aging, characterized in that: Calculating the aging tendency score of the mesenchymal stem cells by detecting the expression level of the biomarker, wherein the higher the aging tendency score, the more serious the aging degree of the mesenchymal stem cells; The aging tendency score=1.584×MCF2L expression level+1.349×PSG5 expression level+0.357×IGFBP5 expression level+1.304×LMNTD1 expression level+1.509×ANKRD29 expression level+0.37×F3 expression level+0.485×SCN9A expression level+0.162×IL6 expression level+0.853×CDKN3 expression level+0.236×TENM2 expression level-0.016×TGFB2 expression level level + 0.116 × DUSP1 expression level + 0.037 × TMEM47 expression level + 0.26 × IGFBP3 expression level + 0.611 × EEF1E1 expression level + 0.835 × SCN3A expression level + 0.193 × ANKRD1 expression level + 0.316 × NGF expression level + 0.107 × VCAM1 expression level + 0.125 × PDGFRL expression level - 0.335 × EGR1 expression level - 0.158 × RUNX2 expression level.

3. Use of a reagent for detecting the expression level of the biomarker according to claim 1 in the preparation of a detection product for predicting mesenchymal stem cell aging.

4. A detection product for predicting mesenchymal stem cell aging, characterized in that: Comprising a reagent for detecting the expression level of the biomarker according to claim 1.

5. Use of the detection product according to claim 4 in predicting mesenchymal stem cell aging, characterized in that: Calculating the aging tendency score of the mesenchymal stem cells by detecting the expression level of the biomarker, wherein the higher the aging tendency score, the more serious the aging degree of the mesenchymal stem cells; The aging tendency score=1.584×MCF2L expression level+1.349×PSG5 expression level+0.357×IGFBP5 expression level+1.304×LMNTD1 expression level+1.509×ANKRD29 expression level+0.37×F3 expression level+0.485×SCN9A expression level+0.162×IL6 expression level+0.853×CDKN3 expression level+0.236×TENM2 expression level-0.016×TGFB2 expression level level + 0.116 × DUSP1 expression level + 0.037 × TMEM47 expression level + 0.26 × IGFBP3 expression level + 0.611 × EEF1E1 expression level + 0.835 × SCN3A expression level + 0.193 × ANKRD1 expression level + 0.316 × NGF expression level + 0.107 × VCAM1 expression level + 0.125 × PDGFRL expression level - 0.335 × EGR1 expression level - 0.158 × RUNX2 expression level.

6. Use of the detection product according to claim 4 in quality monitoring during in vitro expansion of mesenchymal stem cells, characterized in that: The detection product according to claim 4 is used to detect the expression level of the biomarker, and the aging degree of the mesenchymal stem cells is predicted according to the expression level of the biomarker, and the aged mesenchymal stem cells with functional degeneration are eliminated, and the non-aged mesenchymal stem cells with healthy functions are retained for further culture or preservation.

7. The use according to claim 6, characterized in that The aging tendency score of the mesenchymal stem cells is calculated according to the expression level of the biomarker. The higher the aging tendency score, the more serious the aging degree of the mesenchymal stem cells.

8. The use according to claim 7, characterized in that The aging tendency score=1.584×MCF2L expression level+1.349×PSG5 expression level+0.357×IGFBP5 expression level+1.304×LMNTD1 expression level+1.509×ANKRD29 expression level+0.37×F3 expression level+0.485×SCN9A expression level+0.162×IL6 expression level+0.853×CDKN3 expression level+0.236×TENM2 expression level-0.016×TGFB2 expression level level + 0.116 × DUSP1 expression level + 0.037 × TMEM47 expression level + 0.26 × IGFBP3 expression level + 0.611 × EEF1E1 expression level + 0.835 × SCN3A expression level + 0.193 × ANKRD1 expression level + 0.316 × NGF expression level + 0.107 × VCAM1 expression level + 0.125 × PDGFRL expression level - 0.335 × EGR1 expression level - 0.158 × RUNX2 expression level.

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