Spatial transcriptome-based analysis method for improving diabetic skin injury by 4D printing stem cell vector

Abstract

The invention belongs to the technical field of bioinformatics, and particularly relates to a space transcriptome technology-based analysis method for improving diabetic skin injury by a 4D printing stem cell vector, which comprises the following steps of: firstly, respectively carrying out space transcriptome sequencing on tissues of a PBS treatment group and tissues of a combined treatment group, then, carrying out cell clustering and cell annotation, and finally, carrying out space transcriptome sequencing on the tissues of the PBS treatment group and the tissues of the combined treatment group; after main cell types are identified, spatial distribution of the two groups of cell types is visualized, the number proportion of various cell types is counted and quantified, finally, cell subpopulations with significant differential genes are subjected to enrichment analysis and verification, and through a combined treatment scheme of combining a 4D printing stem cell vector with a pRNA inhibitor and stem cells, the cell subpopulations with significant differential genes are obtained. The space distribution and dynamic change of the cells in the wound healing process are displayed, the accurate position and gene expression mode of the cells in the tissue are disclosed, the method is crucial for understanding the complex biological process of biological materials for promoting diabetic injury healing, and meanwhile, a new thought can be provided for subsequent drug development.

Description

Technical field:

[0001] The present invention belongs to the technical field of bioinformatics, and specifically relates to a method for analyzing diabetic skin damage improved by a 4D-printed stem cell carrier based on spatial transcriptome technology, providing a new perspective for the study of the healing mechanism of diabetic skin damage. Background technology:

[0002] Skin damage (foot ulcers and lower limb erosions, etc.) is one of the common complications caused by diabetes, characterized by slow healing and chronic inflammation. During the healing process, immune cells, cytokines and other components in the local microenvironment of the injury interact with each other, affecting the inflammation, proliferation and remodeling stages of wound healing. In addition, cell function and signal transduction in the high-sugar microenvironment will also be affected, making it difficult for the wound to heal.

[0003] 4D printed stem cell carriers are biomaterials prepared using chitosan and its derivatives. They have the characteristics of efficient transfer of stem cells and can effectively help diabetic skin injury wounds heal. Although good results have been achieved, there is still a lack of exploration of the mechanism by which they play a related role. At present, there is a lack of suitable research methods for the mechanism of biomaterials repairing diabetic skin injuries. They are often limited to the analysis of a single cell type or molecular signal, and cannot conduct a more in-depth study of the dynamic changes in the local microenvironment, which seriously limits the comprehensive understanding of the healing mechanism of diabetic skin injuries. In addition, the research methods in the existing technology have certain limitations in analyzing cell interactions and signal networks in the local microenvironment of the injury. Based on this, it is necessary to develop new technical means for further research to comprehensively analyze the cell composition, molecular signals and dynamic changes of the local microenvironment of diabetic skin injuries.

[0004] The application of spatial transcriptomics technology in the study of diabetic skin wound healing has become increasingly important. It can reveal the precise location of cells in tissues and the gene expression pattern, providing a new perspective for understanding the complex biological processes occurring in the local microenvironment of diabetic skin wounds. Spatial transcriptomics technology can be used to describe in detail the spatial distribution of cell types and the dynamic changes of gene expression during the healing process of diabetic skin wounds, and to analyze the cell-cell interactions and signal transduction networks during the healing process of diabetic skin wounds, which is crucial for studying the mechanism of slow healing of diabetic skin wounds and the role of chronic inflammation in the healing process.

[0005] Chinese patent 202210106899.X discloses an analysis method for improving myocardial fibrosis in atrial fibrillation using a Qibo Shengmai composition based on spatial transcriptome technology, comprising the following steps:

[0006] 1) The spatial transcriptome technology was used to perform spatial transcription sequencing on the myocardial tissue sections of the animals in the normal group, atrial fibrillation model group, and Qibo Shengmai administration group, and the spatial transcriptome data of the samples in the normal group, model group, and Qibo Shengmai administration group were obtained respectively;

[0007] 2) The sctransform (SCT) algorithm in the Seurat package of the R language was used to convert the spatial transcriptome data of each sample into expression matrix data for standardization; then the standardized data was screened for highly variable genes using the vst algorithm, and a gene set with differential expression changes was obtained;

[0008] 3) The sctransform (SCT) algorithm in the Seurat package of the R language was used to standardize the differentially expressed gene sets screened in each sample; then the PCA method was used to perform linear dimensionality reduction on the standardized data to obtain PCA reduced dimensionality data; then the T-SNE nonlinear dimensionality reduction algorithm was used to perform nonlinear dimensionality reduction on the PCA reduced dimensionality data to obtain T-SNE reduced dimensionality data;

[0009] 4) Based on the SNN clustering algorithm, cluster analysis was performed on the T-SNE dimension reduction data to obtain the differentially expressed gene cluster subgroups (Cluster) of the normal group, model group, and Qipo Shengmai administration group samples;

[0010] 5) The Wilcox rank sum test of Seurat software was used to compare the genes of each cluster subgroup of the normal group, model group, and Qipo Shengmai administration group with the genes of all other cluster subgroups of the corresponding group samples, and the cluster difference gene set of each cluster (i.e., the gene set with large expression difference in the cluster) was screened;

[0011] 6) The ClusterProfiler package of R language was used to perform differential gene enrichment analysis on the cluster differential gene sets of each cluster of samples in the normal group, model group, and Qipo Shengmai administration group, and the differential gene enrichment analysis results of the normal group were compared with those of the model group and Qipo Shengmai administration group, respectively, to screen and obtain the main differential groups in the model group and Qipo Shengmai administration group;

[0012] 7) Functional enrichment analysis of differentially expressed genes was performed on the characteristic differentially expressed genes of the main differentially expressed groups of samples in the Qipo Shengmai administration group and the main differentially expressed groups of samples in the model group, and the biological processes and signaling pathways related to the characteristic differentially expressed genes of the Qipo Shengmai composition that intervene and improve myocardial fibrosis in atrial fibrillation were obtained; it mainly targets myocardial fibrosis in atrial fibrillation, which is different from diabetic skin damage, and does not indicate the specific pathways and cell types that play a role.

[0013] Although spatial transcriptomics technology has been applied in atrial fibrillation-related research, there is a lack of systematic research methods and systematic analysis strategies in the field of diabetic skin injury wound healing. Therefore, it is necessary to further develop and optimize spatial transcriptomics technology and related bioinformatics analysis methods to fully and deeply understand the process of diabetic skin injury wound healing, so as to reveal the healing mechanism of biomaterials on diabetic skin injury. Summary of the invention:

[0014] The purpose of the present invention is to overcome the shortcomings of the prior art, develop and design a 4D printed stem cell carrier to improve the analysis method of diabetic skin damage, and reveal the spatial distribution and dynamic changes of cells in the process of diabetic skin damage wound healing.

[0015] In order to achieve the above-mentioned purpose, the specific process of the method for improving the analysis of diabetic skin damage by 4D printing stem cell carriers based on spatial transcriptome according to the present invention is as follows:

[0016] First, spatial transcriptome sequencing was performed on tissues in the PBS (phosphate buffered saline) treatment group and the combined treatment group (4D printed stem cell carrier combined with pRNA inhibitor and stem cell treatment regimen) to obtain two sets of data;

[0017] Then, cell clustering and cell annotation were performed. After the main cell types were identified, the spatial distribution of the two groups of cell types was visualized, the quantitative proportion of various cell types was statistically quantified, and a plot was drawn;

[0018] Finally, the cell subpopulations with significant differentially expressed genes were enriched and verified.

[0019] Compared with the prior art, the present invention provides a research method for studying the specific mechanism of biomaterials in the process of wound healing on the back of diabetic mice using spatial transcriptomics technology. By combining 4D printed stem cell carriers with pRNA inhibitors and stem cells for combined treatment, the spatial distribution and dynamic changes of cells in the wound healing process are demonstrated, revealing the precise position and gene expression pattern of cells in tissues, which is crucial for understanding the complex biological process of biomaterials in promoting the healing of diabetic injuries. At the same time, the Ptpn3-T cells related to injury healing identified by spatial transcriptomics technology can provide new ideas for subsequent drug development, have both economic and social benefits, and also provide a new perspective for the study of the healing mechanism of diabetic skin injuries. Description of the drawings:

[0020] Figure 1 This is a spatial distribution diagram of cell types in different treatment groups involved in the present invention.

[0021] Figure 2 It is a bar graph showing the proportion of cell types in different treatment groups involved in the present invention.

[0022] Figure 3 This is a heat map of differentially expressed genes in cells in different treatment groups involved in the present invention.

[0023] Figure 4 This is a spatial localization map of the T cell subtypes involved in the present invention.

[0024] Figure 5 This is a bar graph showing the proportion of cell types in the T cell subtypes involved in the present invention.

[0025] Figure 6 This is a violin plot of differential gene expression of T cell cluster 0 involved in the present invention.

[0026] Figure 7 This is a diagram of the GO analysis results involved in the present invention.

[0027] Figure 8 This is a diagram of the KEGG analysis results involved in the present invention. Specific implementation method:

[0028] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0029] Embodiment 1:

[0030] The present embodiment involves a spatial transcriptome-based 4D printed stem cell carrier to improve diabetic skin damage analysis method based on the back wound healing process of diabetic mice, and the specific process is as follows:

[0031] First, the tissues of the PBS-treated group and the combined treatment group were prepared. The tissues of the PBS-treated group were the skin tissues of diabetic mice, and the tissues of the combined treatment group were 4D-printed stem cell carriers + adipose-derived mesenchymal stem cells + piRNA-hsa-32182 inhibitors.

[0032] 10x Genomics spatial transcriptome sequencing analysis was used to perform spatial transcriptome sequencing on tissues in the PBS treatment group and tissues in the combined treatment group to obtain two sets of data;

[0033] Then, the UMAP (Uniform Manifold Approximation and Projection) method was used to cluster the meaningful cell subpopulations (such as T cells) and divide them into three different subpopulations, namely cluster 0,

[0034] cluster 1 and cluster 2;

[0035] According to references (Haensel D, Jin S, Sun P, Cinco R et al.

[0036] Defining Epidermal Basal Cell States during Skin Homeostasis and Wound Healing Using Single-Cell Transcriptomics. Cell Rep 2020Mar 17; 30(11): 3932-3947.e6.PMID: 32187560) and singR methods were used for cell annotation, and 8 major cell types including T cells, endothelial cells, fibroblasts, keratinocytes, macrophages, myofibroblasts, neutrophils and smooth muscle cells were identified;

[0037] Visualizing the spatial distribution of cell types in the two groups, the results are as follows Figure 1 As shown, the percentage of various cell types was statistically quantified, and the results were as follows Figure 2 As shown, it can be seen that the number distribution of the two groups of cell types has changed significantly, especially myofibroblasts, smooth muscle cells, macrophages, neutrophils and T cells. The synergistic effect of the above cell types ensures the effective progress of the wound healing process, thereby restoring the function and integrity of the tissue;

[0038] The Seurat package of R language was used for differential analysis to further screen out the top 10 differentially expressed genes in different cell types before and after treatment. The differentially expressed gene heat map is shown in Figure 2. Figure 3 As shown, the changes in the microenvironment before and after treatment were revealed;

[0039] Finally, cluster 0, cluster 1, and cluster 2 are located at the spatial level. The results are as follows: Figure 4 As shown, the proportion of cell subpopulations was quantified, and the results were as follows Figure 5 As shown, the number of cluster 0 T cells increased significantly in the combined treatment group, indicating that they played an active role in the wound healing process;

[0040] The gene expression profile of cluster 0 T cells was analyzed in depth using the Seurat method. Figure 6 As shown, the TOP 5 genes with the most significant differential expression were Ptpn3, Krt83, Dhcr24, Nsun5, and R3hdm2;

[0041] ClusterProfiler was used to deeply analyze the T cell subsets of Ptpn3 with significant differential genes, and the healing mechanism was verified by GO analysis (gene function analysis) and KEGG analysis (Kyoto Encyclopedia of Genes and Genomes). Figure 7 As shown in Figure 2, T cells with high expression of Ptpn3 are closely related to cell metabolism, indicating that they promote wound healing by regulating abnormal metabolic states; KEGG analysis results are shown in Figure 2 Figure 8 As shown, T cells with high expression of Ptpn3 were significantly associated with the Wnt signaling pathway.

[0042] The present embodiment involves a spatial transcriptome-based 4D printed stem cell carrier analysis method for improving diabetic skin injuries, which for the first time identified T cells with high expression of Ptpn3 associated with wound healing, and speculated that the healing-promoting effect of these cells activated the Wnt pathway; it provides new insights into the role of T cells with high expression of Ptpn3 in diabetic wound healing and provides potential targets for future treatment strategies.

Claims

1. A spatial transcriptome-based 4D printed stem cell carrier analysis method for improving diabetic skin damage, characterized in that: The specific process is as follows: First, spatial transcriptome sequencing was performed on tissues from the PBS-treated group and the combined treatment group; Then, cell clustering and cell annotation were performed. After the main cell types were identified, the spatial distribution of the two groups of cell types was visualized, the quantitative proportion of various cell types was statistically quantified, and a plot was drawn; Finally, the cell subpopulations with significant differentially expressed genes were enriched and verified.

2. The method for analyzing the improvement of diabetic skin damage by 4D printed stem cell carriers based on spatial transcriptome according to claim 1, characterized in that: The tissue in the PBS-treated group was the skin tissue of diabetic mice, and the tissue in the combined treatment group was 4D-printed stem cell carrier + adipose-derived mesenchymal stem cells + piRNA-hsa-32182 inhibitor.

3. The method for analyzing the improvement of diabetic skin damage by 4D printed stem cell carriers based on spatial transcriptome according to claim 1, characterized in that: Cell clustering by UMAP.

4. The method and application of 4D printed stem cell carriers for improving diabetic skin damage based on spatial transcriptome according to claim 1, characterized in that: Cell annotation was performed using references and the singR method to identify major cell types.

5. The method and application of 4D printed stem cell carriers for improving diabetic skin damage based on spatial transcriptome according to claim 1, characterized in that: Differential analysis was performed using the Seurat package in R language to screen the top 10 differentially expressed genes in different cell types before and after treatment.

6. The method for analyzing the improvement of diabetic skin damage by 4D printed stem cell carriers based on spatial transcriptome according to claim 4, characterized in that: The reference is Haensel D, Jin S, Sun P, Cinco R et al. Defining Epidermal Basal Cell States during Skin Homeostasis and Wound Healing Using Single-Cell Transcriptomics. Cell Rep 2020Mar17;30(11):3932-3947.e6.PMID:32187560.

7. The method for analyzing the improvement of diabetic skin damage by 4D printed stem cell carriers based on spatial transcriptome according to any one of claims 1 to 6, characterized in that: T cells with high expression of Ptpn3 associated with wound healing were identified, and their healing-promoting effects activated the Wnt pathway.

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