Single-cell RNA-Seq analysis of diabetic wound macrophages in STZ-induced mice

F1 scRNA-seq based identification of STZ-induced diabetic mouse wounds immune cell populations

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 (supplementary1).

Fig. 1figure 1

scRNA-seq based identification of STZ-induced diabetic mouse wounds immune cell populations. (a) Experimental design. Single cell were collected from day1,day3,day5,day7,along wound healing (b) A t-distributed stochastic neighbour embedding (t-SNE) visualization of all cells displayed with different colours for samples (c) t-SNE visualization of 9240 single cells, colour-coded by assigned cell type (d) Heat map of all clusters top 20 upregulated marker gene. Shades of colour indicate high or low gene expression, with yellow being high expression and dark red being low expression

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. 1c). We mapped the heat map of major marker genes in all populations (Fig. 1d). 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, Atp6V0d2, which are noted in the literature as marker genes for osteoclasts (supplementary 2, Table 1).

Table 1 Summary of Major Cell Types in the Wounds Healing Process F2 scRNA-seq analysis reveals a dynamic immune landscape in STZ-induced diabetic mouse wounds

After obtaining the overall spectrum of immune cells, we further counted the number of each group of immune cells in the two groups of wounds according to different time points and combined it with their gene expression. Exploring the differences in immune status between two groups of wounds healing process and their possible underlying causes (Fig. 2; Table 2, Supplementary 3).

Fig. 2figure 2

scRNA-seq analysis eveals a dynamic immune landscape in STZ-induced diabetic mouse wounds. (a) Stacked bar plots showing the proportion of cells from each sample source among the different cell types (D-1: diabetes group day 1, D-3: diabetes group day 3, D-5: diabetes group day 5, D-7: diabetes group day 7, W-1: Wildtype control group day1, etc.) (b) Pie chart plots showing the proportion of various cells in different samples

Table 2 Summary of Cell Numbers in Differently Sampling Time

The population of neutrophils that responded earliest to wound healing also had the correspondingly highest proportion of total cell counts, and the proportion decreased over the healing process, but the proportion of neutrophils declined gently on early days 1, 3, and 5 in the diabetic group, whereas a steep decline occurred on day 5 in the control group. The change of subcluster Retnlg + Lcn2 + Wfdc21 + Mmp8 + neutrophils (cluster3) was noteworthy, with the initial cell count in the control group consistent with that of the diabetic group (day1, 1085 vs. 1191) and then dropping rapidly to low levels (day5, 312 vs. 7). Lcn2 promotes neutrophil recruitment and can contribute to inflammation through synergistic Th17 (Hau et al. 2016; Shashidharamurthy et al. 2013). It is also a marker of inflammation associated with obesity and insulin resistance (Wang et al. 2007). The enrichment of MMp8 in chronic inflammation and its ability to degrade the extracellular matrix suggest that this group of neutrophils may be a factor in the chronic healing of diabetic wounds (Diegelmann 2003).

At day1 monocytes were the second most abundant cell type and then began to decline, with the proportion of monocytes on day 5 in the control group decreasing dramatically and being significantly lower than in the diabetic group (1.83% vs. 11.28%). Monocytes were divided into two groups of subclusters, Arg1 + Pdpn + Ccl2 + Cxcl1 + Fn1 + monocytes (cluster6). The functions of cluster 6 c include Angiogenesis in addition to Inflammatory response, immune response. Although Arg1 has been reported to be elevated in ischaemic chronic wounds (Roy et al. 2009), the initial cluster6 cells counts we observed in both groups of wounds were consistent, so the differential decrease in this group of monocytes may not be due to a compensatory effect but may be due to a blocked conversion of monocytes to macrophages in diabetic wounds. In contrast, the other group of Plac8 + chil3 + Vcan + Ly6c2hi, CCR2hi monocytes (Cluster8) was consistent with the inflammatory monocytes reported previously (Shi and Pamer 2011). The number of cells in this group was significantly higher in the diabetic group than in the control group at the beginning (day1, 611 vs. 334) and reversed at the end (day5, 323 vs. 24). These results suggest that excessive inflammation in diabetic wounds in terms of monocytes may be the result of a combination of pro-inflammatory monocyte retention and impaired monocyte-macrophage transformation.

DC cells regulate and activate endogenous and adaptive immunity, further activating T cells through antigen presentation. We observed a gradual increase in the proportion of Dc cells in diabetic wounds from day1-day7, with a regression in the number of cells in the control wounds group from day5-day7. In addition to the classical Dc cells expressing major histocompatibility complex class II (cluster5, H2-Ab1 + H2-Aa + H2-Eb1+) there was also a group of cells expressing the Fscn1, Ccl22, Tbc1d4, Ccr7 marker gene, migration-related (CCR7, FSCN1), and encoding chemokine ligands (CCL22) suggest the function of recruit immune cells, mostly Tregs (Peng et al. 2022).The proportion of T cells (cluster7, Icos + Rora + Ets1+) followed the same trend as that of Dc cells.

Day5 was the cut-off point for the change in the proportion of numerous cells in both wounds group, and the differences in the immune profile between wounds in terms of the number and proportion of cells accumulated from day1 to day3, after which the differences in the degree of inflammation between the control and diabetic wounds groups became highly significant. The precise timing of interventions selected for different immune cell populations appears to be important in promoting diabetic wound healing.

F3 Gene expression characteristics and biological function analysis of cluster 11 and the gene expression differences compared with other macrophages

The steep increase in the numbers of cells expressing osteoclast marker gene (clusters11) in the control wounds group attracted our attention. 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 also applied cell cycle analysis, according to G2M.Score, only a small part of cluster 11 cells were enriched for cell cycle gene (Stmn1, Top2a). (Supplementary 4), The self-renewal and proliferation of this small number of cells indicates that this group of cells is actively involved in the healing process and suggests that our data are indicative of the dynamic characteristics of this group of cells during the healing process.

Fig. 3figure 3

Gene expression characteristics and biological function analysis of cluster 11 and the gene expression differences compared with other macrophages. (a) Violin plot view cluster11 top 20 marker gene demonstrating overall gene expression. The number of identity is the same of clusters (b) GO histogram analysis results of cluster11 marker gene: Biological Process (BP), Molecular Function (MF), Cellular Component (CC). Coordinate axis Y: Go-Term entry name,Coordinate axis X: -log10 (P-Value). Red for significant entries, blue for non-significant entries (c) Volcano plot view for the gene expression difference between cluster11 and other macrophages(cluster2,4,9).Coordinate axis Y: -log10(P-Value), axis X:avg_log2FC.X<-1 use pink color as down expression, X > 1 use blue color as up expression (d, e) GO analysis up regulated (d) and down regulated gene (e) of cluster11: Biological Process, Coordinate axis Y: Go-Term entry name, Coordinate axis X: Gene Ratio. Colors of the bubble represents P.adjust < 0.05 for significant entries. The size of the bubble indicates the number of genes enriched in this item

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). These results suggest that this group of cells is not primarily involved in the inflammatory process, instead may be involved in the wound healing process by balancing tissue homeostasis, tissue remodelling, and collagen metabolism in the extracellular matrix. This also explains the difference in their distribution between the two groups of wounds samples.

F4 Macrophage gene metabolism pattern analysis and cell-cell contact

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 highly 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). Macrophage function is dependent on different metabolic pathways, and the metabolism-related gene set of cluster11 is actively enriched, particularly in the tricarboxylic acid cycle and glycolysis-related genes. Tissue and vascular-related damage and hypoxia during the inflammatory phase have little effect on inflammatory macrophages, which are mainly dependent on glycolysis (Murdoch et al. 2005). The enrichment of anabolic metabolism is one of the key features of the tissue proliferation, repair and remodelling phase, and this population of macrophages, which peaks during the repair phase, achieves its repair-promoting function through active metabolism, while the high glucose environment of diabetic wounds and its induced production of ROS affects the activation and function of aerobic metabolic pathways of macrophages (Rendra et al. 2019). In particular, this population of cells is not active in metabolism with arginine and ornithine compared to the traditionally defined alternative activated macrophages, as a potential target for metabolism-related interventions that may avoid excessive scarring and fibrosis (Liu et al. 2017).

Fig. 4figure 4

Macrophage gene metabolism pattern analysis and cell-cell contact. (a) Heatmap of Qusage Analysis, shows the significance of enrichment between cluster in metabolism gene set.Axis Y: Gene set information,aixs X:clusters. The colour represents the significance of each cluster in each gene set, the closer the colour to red, the more significant it is; the closer the colour to blue, the less significant it is (b) Heatmap show number of potential ligand-receptor pairs between immune cell groups predicted by CellphoneDB

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 pro-inflammatory 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 (supplementary 5).

To further characterize cell-cell interactions, we inferred putative cell-cell interactions based on ligand-receptor signaling inferred from our scRNA-seq data using CellPhoneDB. fibroblasts and macrophages showed the most interactions (Fig. 4b). Further visualization of intercellular interactions revealed that hebp1/Fprs ligand-receptor pairs are widespread among neutrophil macrophages and mediate the recruitment of monocytes to play a reparative role (Birkl et al. 2019). The cell-cell contact between cluster11 and neutrophils is supported by Sema4d/ Cd72 (Supplementary 6), and the promotion of cluster11 production by neutrophils may be related to Sema4D inhibition of osteogenic activity and promotion of osteoclastogenesis (Shindo et al. 2022). Cluster2 pro-inflammatory macrophages communicate with monocytes (cluster8), macrophages (cluster4/9) as well as Dc cells (cluster13), fibroblasts (cluster17) via Grn/Sort1 ligand-receptor pairs (supplementary 7), and the multiple involvements of this immunomodulatory mechanism in our cutaneous trabecular immune cells include in Grn/Sort1 regulates the migration and division of fibroblasts for angiogenesis and the recruitment and activity of immune cells during wound repair (Terryn et al. 2021). According to our findings Grn can be used as one of the biological indicators of the intensity of the inflammatory response in skin wounds.

The group of fibroblasts (cluster 17, Pdgfrahigh, Acta2+, CD45+, Col12a1+) we identified while sorting myeloid cells had some similarities to fibroblasts that have been previously reported to be differentiated from trabecular myeloid cells (Haensel et al. 2020). Due to our prior cell sorting based on myeloid markers, fibroblasts were less numerous but showed strong auto cellular communication (Fig. 4b), which we observed in greater numbers in control group, and the expression of secreted cytokines such as VEGF supported their role in repair angiogenesis (supplementary3, 8). Other fibroblast populations could not be located in our samples, so the role of interfibroblast communication could not be clarified, but the role of these myeloid-derived fibroblasts on other myofibroblasts in the wounds was seen in earlier reports(Suga et al., 2014). These myeloid-derived intermediate cells are very easy to miss in the in vitro observation of fibroblasts and single-cell sequencing provides an excellent tool for analysis.

F5 Differences in the proportion of macrophages over time and the differences in gene expression between the diabetic wound group and the control group

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% versus 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% versus 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).

Fig. 5figure 5

Differences in the proportion of macrophages over time and the differences in gene expression between the diabetic wound group and the control group. (a) The proportion of cluster 11 cells in the control group on day 5 and day 7 was much higher than diabetic group. Coordinate axis Y: Proportion of specific cluster of cells in all single cells, Coordinate axis X: cell clusters, group and sampling time. Ex: D-1(Diabetic group -day 1), W-1(Wild type control group-day 1) (b) The proportion of cluster 2, cluster 4, and cluster 9 cells in the control group and diabetic group (c) 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 (d) GO enrichment analysis of the downregulated genes in cluster 11 on day 5 in the diabetes group and the biological functions of the differences (e) KEGG terms of control and diabetes two groups of cluster 11 cells on day 3 differentially expressed genes (f) KEGG terms of the cluster 11 cells on day 7 upregulated genes in the diabetes group (g) GO enrichment analysis of the upregulated genes in cluster 4 on day 5 in the diabetic group and the biological functions of the differences (h) KEGG enriched differential gene pathways on day 5 of cluster 4

It is well known that the immune environment of the diabetic group differs from that of the control 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 differences in the biological functions between the two groups with respect to cluster 11 on day 3. The number of cells in the two groups was very similar at day3, and the genes we found to be different included Acod1, Ccl3, Ctsk, vHsap5, Mmp9, and Fos, the functions of these genes were mainly enriched in immune system process, response to external stimulus, regulation of immune system process, regulation of neuron death, defense response to bacterium, and cellular response to oxidative stress (Fig. 5c). Differentially expressed genes CCl3, Hsap5, Mmp9, Fos, Ctsk corresponded to rheumatoid arthritis, lipid and atherosclerosis, and the Toll-like receptor signaling pathway (Fig. 5e). As the wound healing progressed we found a huge difference in the number of this group of cells on day5, the peak number of cluster11 osteoblast-like macrophages. Apoe, Ccl7, Cd36, and Fnip1 genes were downregulated in the diabetic group compared to the control group on day5, the biological functions of the downregulated genes 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, The downregulation of the Hmox1, Jun, Pf4 gene suggests that this group of cells Capacity of blood vessel morphogenesis has also been reduced (Fig. 5d). Then at day 7, the end of our observation H2-Aa, H2-Ab1, H2-DMa, H2-DMb1, H2-Eb1, Il1b genes were upregulated in the diabetes group corresponded to type I diabetes and Th17 cell differentiation (Fig. 5f).

As for the pro-inflammatory macrophage-cluster2, the expression level of Acod1, Il1a, Mt1, Retnlg, Osal1, Lyz1, Saa3, CD163, was different in day 3, functions of these genes are enriched 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. This enhanced immune response capacity is in line with our expectations. In parallel to the downregulated gene of pro-inflammatory macrophages in the diabetic group, mainly Lpl, which functions in cytokine production and regulation of cytokine production and with the healing process, in day5 we observed a decrease in Lpl, Cd36, Lipa genes which could suggest the possible involvement of cholesterol metabolism pathway in the progression of inflammatory cells. Inflammatory pathways suppress cholesterol metabolism and reverse cholesterol transport (RCT) which in turn enhances inflammatory responses, previously reported mainly in atherosclerosis-related studies, a similar mechanism can now be considered in diabetic skin damage (Groenen et al. 2021; Westerterp et al. 2018; Yvan-Charvet et al. 2008) (Supplementary 9).

On day 1 the cluster 4 macrophages differentially expressed genes Lgals3, Bcl2, Sod2, Hsph1 biological functions of these genes were enriched in positive regulation of developmental process, negative regulation of apoptotic signalling pathway, Ccl3, Tlr2, Tnfaip3 genes were enriched in interleukin-1 beta production, and interleukin-1 production. On day 3 differential genes Acod1, Cd209b, Cd209d, Clec4e were enriched in response to external stimulus, defence response, interspecies interaction between organisms, response to external biotic stimulus, response to other organism, andCd163, Cfh, Chil3 inflammatory response (Supplementary 10). The biological functions of the upregulated genes at day 5 in the diabetic group Acod1, H2-Aa, H2-Eb1, S100a8, A100a9, Slpi were enriched in response to external biotic stimulus, response to other organism, response to biotic stimulus, interspecies interaction between organisms, innate immune response, defense response to other organism, and response to lipopolysaccharide (Fig. 5g). The KEGG enriched differential gene H2-Aa, H2-Eb1 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 pathways (Fig. 5h).

The downregulated genes in cluster 9 in the diabetes group on day 1 including Arg1, Cxcl3, Plac8, Saa3 were enriched in response to external biotic stimulus, response to other organism, response to biotic stimulus, defence response (Supplementary 11). The downregulated genes at day 3 Ccr2, Egr1, Fos, and Jun 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 on day 5  including Ccl3, S100a8, S100a9, and Tnbs1, their functions were enriched in positive regulation of response to external stimulus, regulation of hydrolase activity, granulocyte chemotaxis, regulation of peptidase activity, granulocyte migration, myeloid leukocyte migration, and leukocyte chemotaxis (Supplementary 11). On day 7 the downregulated genes Egr1,Fos, and Jun 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 11).

The KEGG analysis showed that the downregulated genes at day 3 Cracr2b, Nr4a1, Fos, Jun, Vegfa were involved in the MAPK signalling pathway, Nr4a1, Fos, Jun were involved in the relaxin signalling pathway, chemical carcinogenesis-receptor activation, and rheumatoid arthritis. The downregulated genes on day 7 Fos, Hspa1a, Hspa1b, and Jun were enriched in the Estrogen 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 11). Taken together these suggest that the MAPK pathway may play a regulatory role in the proliferation and differentiation of this group of macrophage cells.

F6 Pseudotime analysis of macrophage populations and analysis of the differential gene expression patterns in the developmental branches

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 control wound groups could be classified into 11 states (Fig. 6a and b). According to the pseudotime analysis (Fig. 6c and d), 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 and f). 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 analyze the differential gene expression patterns of the two trajectory branches.

Fig. 6figure 6

Pseudotime analysis of macrophage populations and analysis of the differential gene expression patterns in the developmental branches. (a, b) Reconstruction of the monocyte/macrophage trajectory as 11 state. Diabetes group (a), Control group (b). (c, d) Reconstruction of the monocyte/macrophage trajectory in a pseudotime manner. Diabetes group (c), Control group (d). (e, f) Reconstruction of the monocyte/macrophage trajectory in cell clusters. Diabetes group (e), Control group (f). (g, h) Heatmap of branch 3 gene in diabetes group (g), branch 1 gene in control group (h), the pre-branch in the middle represents all the cells from the branch point to the root cell. Cell fate 1 corresponds to the state with small id while cell fate 2 corresponds to state with bigger id

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.

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