We performed scRNA-seq on CD45+ cells gathered from wound tissue obtained from wild-type and STZ-induced diabetic C57BL/6J mice (Fig. 1a). Four time points were selected for sampling (1, 3, 5, and 7 days). The single-cell data of the obtained samples were normalized by excluding low-quality cells to eliminate batch effects, and data from a total of 9240 cells were obtained. Principal component analysis (PCA) was performed, and the results were plotted with t-stochastic neighbour embedding (t-SNE) downscaled to show the distribution of cells from different sample sources in the overall data (Fig. 1b), along with the gene expression level of all single cells and the number of their UMI expressed (Fig. 1c).
QC cell data were unbiased using the Seraut package, and gene expression data from cells extracted from both conditions were aligned and projected in a 2D space through t-SNE to allow identification of overlapping and diabetic wound-associated immune cell populations. A total of 17 cell clusters were obtained, except for low-quality cells, which have a high preponderance of mitochondrial genes (Fig. 2a). We mapped the heat map of major marker genes in all populations. (Fig. 2b) The cell populations obtained were 4 clusters of neutrophils (cluster 0, cluster 1, cluster 3 and cluster 12, with marker genes Ptprc, S100a8, s100a9, Csf3r, Cxcr2, and Lrg1); 2 clusters of monocytes (cluster 6 and cluster 8, with marker genes Ly6c2, Vcan, and Fn1); 3 clusters of macrophages (cluster 2, cluster 4, and cluster 9, with marker genes C1qa and Mrc1); 2 clusters of DC cells (cluster 5 and cluster 13, with marker genes Ccr7, Mgl2, Ccl22 Cd209a, and Fscn1), 1 cluster of NK cells (cluster 14, with marker genes Cd3d-, Xcl1, and Ncr1); 1 cluster of T cells (cluster 7, with the main marker genes Cd3d, Cd3e, Cd3g, and Trac); 1 cluster of mast cells (cluster 16, with the main marker genes Ms4a2, Cpa3, Gata2, and Tpsb2); 1 cluster of fibroblasts (cluster 17, with the main marker genes Col1a1 and Dcn); and 1 cluster of cells not previously described (cluster 11), with the main marker genes Acp5, Ctsk, Mmp9, Top2a, and Mki67, which are noted in the literature as marker genes for osteoclasts (Fig. 2c).
To characterize cluster 11 as a specific group of immune cells, we mapped the top 20 marker genes on a violin plot (Fig. 3a) and performed GO functional enrichment analysis of the marker genes. The genes that were highly expressed were the osteoclast-associated genes Ctsk and Acp5; the adipose tissue-associated genes Hmgn1, Ranbp1 and Lpl; and the macrophage-associated genes Tsc22d1 and Banf1. The cycling basal cell-related genes Stmn1, Top2a, Ube2c, Pclaf, and Birc5 suggest that this group of cells may be a previously undescribed type of skin-resident macrophage. The GO functional enrichment analysis results showed that the gene functions were mainly related to translation, RNA splicing, mRNA processing, rRNA processing, oxidation-reduction process, translational initiation tricarboxylic acid cycle, cell cycle, protein folding, transport, etc. (Fig. 3b)
We further compared the gene expression differences between cluster 11 and all other macrophages (cluster 2, cluster 4, and cluster 9). A total of 230 genes were upregulated and 205 genes were downregulated in cluster 11 compared to the other macrophage populations (Fig. 3c). GO enrichment of the differential genes showed that upregulated genes were enriched in tissue remodeling, skeletal system development, multicellular organismal homeostasis, cation transmembrane transport, cation transport, collagen metabolic process, bone resorption, bone remodeling, porton transmembrane transport, and tissue homeostasis (Fig. 3d). Biological functions of the downregulated genes are enriched in defense response, immune response, inflammatory response, response to bacterium, leukocyte migration, myeloid leukocyte migration, cell chemotaxis, granulocyte migration, neutrophil migration, and granulocyte chemotaxis (Fig. 3e).
We observed that the differentially expressed genes in cluster 11 were enriched in multiple metabolic pathways, and we generated a metabolism heatmap for all cell populations. The gene metabolism patterns of cluster 11 were high enriched in one-carbon pool by folate, vitamin B6 metabolism, lipoic acid metabolism, synthesis and degradation of ketone bodies, citrate cycle, oxidative phosphorylation, 2-oxocarboxylic acid metabolism, carbon metabolism, pyruvate metabolism, fatty acid biosynthesis, and cysteine and methionine metabolism. Among the remaining macrophage populations, cluster 4 and cluster 9 showed some similarity in gene metabolism patterns and differed significantly from cluster 2. The similarities between cluster 4 and cluster 9 were mainly enriched in caffeine metabolism, glycosphingolipid biosynthesis – globo and isoglobo series, sphingolipid metabolism, other glycan degradation, glycosaminoglycan degradation, ascorbate and aldarate metabolism, and glycosphingolipid biosynthesis – ganglio series (Fig. 4a).
The violin plots for the marker genes expressed in cluster 2, cluster 4, and cluster 9 showed that cluster 4 expressed genes that were similar to those previously defined as “M2 macrophages” (Mrc1 and cd163). Cluster 2 had more proinflammatory genes, and the genes cd74, tnsf9, tnsf12, and tnsf12a were highly expressed.Gene expression of Gpnmb,Pf4,Lpl,Cd36,Apoe were found more significant in cluster9.（Fig. 4b,4c,4d）
The phenotypic changes and overall proportional changes in macrophages in the two different subgroups are also an important part of our understanding of their mechanisms. Thus, we counted the proportional changes in the macrophage populations in the two experimental groups at different sampling times, and the proportion of the cluster 11 cell population increased in the early stage (day 1–day 3) in both the diabetic wound group and the control group, but unlike the diabetic wound group, the proportion of this cell population in the control group increased consistently (1.26%) on day 5 and was much higher than that in the diabetic wound group (0.08%) and decreased (0.28%) on day 7, but the proportion was still higher than that of the diabetic group (0.08%) (Fig. 5A). The proportion of Cluster 2 cells was higher in diabetic groups on day 1(0.85% verses 0.41%), with similar trends in cell proportions within both groups. After day 3 the proportion of cluster2 cells was higher in the control wound group than in the diabetic wound group (1.42% verses 0.73%). Cluster 4 showed a gradual increase in the proportion of cells in the diabetic wound group, except on day 5. In the control group, however, a much higher increase was observed on day 5 (3.14%) and day 7 (8.09%) than that in the diabetic group. A peak in the proportion of cluster 9 was observed in the diabetic group (1.07%) at an earlier time point (on day 3) than in the control group (1.22% on day 5) (Fig. 5B).
It is well known that the immune environment of the diabetic group differs from that of the normal group, so the specific differences in the macrophage population at different time points are of interest to us. In the next step, we performed GO enrichment analysis of the differential genes and found that the differences in the biological functions between the two groups with respect to cluster 11 on day 3 were mainly enriched in immune system process, response to external stimulus, regulation of immune system process, regulation of neuron death, defence response to bacterium, and cellular response to oxidative stress (Fig. 5C). The biological functions of the downregulated genes in cluster 11 on day 5 in the diabetes group were enriched in positive regulation of protein modification process, positive regulation of cell communication, positive regulation of protein phosphorylation, positive regulation of phosphorus metabolic process, negative regulation of cell death, and blood vessel morphogenesis (Fig. 5D).
KEGG analysis of the day 3 differentially expressed genes corresponded to rheumatoid arthritis, lipid and atherosclerosis, and the Toll-like receptor signalling pathway (Fig. 5E). The day 7 upregulated genes in the diabetes group corresponded to type I diabetes and Th17 cell differentiation (Fig. 5F).
GO analysis of the differential genes in cluster 2 at day 3 in two wound groups showed enrichment of biological functions in response to stress, defence response, response to external stimulus, response to external biotic stimulus, response to other organism, response to biotic stimulus, interspecies interaction between organisms, response to bacterium, immune response, inflammatory response, and defence response to other organism. Downregulated gene functions in the diabetic group were enriched in cytokine production and regulation of cytokine production (Supplementary 1).
KEGG analysis showed differential gene enrichment in the cholesterol metabolism pathway, which was downregulated by day 5 in the diabetes group (Supplementary 1).
The biological functions of the cluster 4 differentially expressed genes on day 1 were enriched in positive regulation of developmental process, regulation of apoptotic signalling pathway, negative regulation of apoptotic signalling pathway, interleukin-1 beta production, and interleukin-1 production. The day 3 differential genes were enriched in response to external stimulus, defence response, interspecies interaction between organisms, response to external biotic stimulus, response to other organism, and inflammatory response (Supplementary 2). The biological functions of the upregulated genes at day 5 in the diabetic group were enriched in response to external biotic stimulus, response to other organism, response to biotic stimulus, interspecies interaction between organisms, innate immune response, defence response to other organism, and response to lipopolysaccharide (Fig. 5G).
The KEGG enriched differential gene pathways on day 5 were autoimmune thyroid disease, allograft rejection, graft-versus-host disease, type 1 diabetes mellitus, antigen processing and presentation, systemic lupus erythematosus, Staphylococcus aureus infection, and viral myocarditis (Fig. 5H).
The downregulated genes in cluster 9 in the diabetes group on day 1 were enriched in response to external biotic stimulus, response to other organism, response to biotic stimulus, defence response (Supplementary 3). The downregulated genes at day 3 were functionally enriched in tissue development, leukocyte differentiation, blood vessel development, vasculature development, cellular response to growth factor stimulus, and response to growth factor positive regulation of endothelial cell proliferation. The upregulated gene functions on day 5 were enriched in positive regulation of response to external stimulus, regulation of hydrolase activity, granulocyte chemotaxis, granulocyte migration, regulation of peptidase activity, myeloid leukocyte migration, and leukocyte chemotaxis (Supplementary 3). The downregulated genes at day 7 were enriched in biological functions including response to abiotic stimulus, cellular response to stress, positive regulation of pri-miRNA transcription by RNA polymerase II, and positive regulation of neuron death (Supplementary 3).
The KEGG analysis showed that the downregulated genes at day 3 were involved in the MAPK signalling pathway, relaxin signalling pathway, chemical carcinogenesis-receptor activation, and rheumatoid arthritis. The downregulated genes at day 7 were enriched in the oestrogen signalling pathway, measles, MAPK signalling pathway, lipid and atherosclerosis, prion disease, human T-cell leukaemia virus 1 infection, endocrine resistance, and antigen processing and presentation (Supplementary 3).
Monocytes can differentiate into macrophages during the immune process, and macrophages have rich phenotypic diversity and perform different functions at different times during wound healing. We performed a chronological analysis of the observed mononuclear macrophage population and the cells in the diabetic and normal trauma groups could be classified into 11 states (Fig. 6A, 6B). According to the pseudotime analysis (Fig. 6C, 6D), cluster 6 and cluster 8 were predominantly found in the early states, followed by cluster 2, and cluster 4 and cluster 9 were found in large numbers at later time points. In the diabetic wound group, a large number of cluster 4 cells were observed in only one state, whereas in the control group, cluster 4 cell aggregates were observed in several states. In contrast, in the diabetic group, cluster 2 was observed within multiple stages of differentiation (Fig. 6E, 6F). This finding suggests that within the diabetic group, cell differentiation was more towards cluster 2, whereas in the control group, more branches were differentiated into cluster 4, and the greatest number of cluster 4 aggregates could be seen in the diabetic group with a branch point of 3 compared to 1 in the control group, leading us to more closely analyse the differential gene expression patterns of the two trajectory branches.
In branch 1 of the control group and branch 3 of the diabetic group, a pattern of differential expression consisting of the grouping of genes with a reduction in the differentiation pathway towards cluster 4 and an elevation of the differentiation pathway towards cluster 2 can be observed, with such a pattern seen in branch3 of the diabetic group for Acod1, Slc7a11, il1a, spp1, Ccdc71l, Tnbs1, F10, Ptgs2, Chil3, Met, and Cxcl3 (Fig.6G). In the control group, there were Tgfbi, cd52, plac8, Ifi2712a, plbd1, lmnb1, gpr132, lsp1, ly6a, ccr2, and cytip in branch1 (Fig.6H). No crossover genes were found, suggesting that the polarization patterns of macrophages in the control and diabetic group may be quite different.