Integrated Skin Transcriptomics and Serum Multiplex Assays Reveal Novel Mechanisms of Wound Healing in Diabetic Foot Ulcers

Abstract

Nonhealing diabetic foot ulcers (DFUs) are characterized by low-grade chronic inflammation, both locally and systemically. We prospectively followed a group of patients who either healed or developed nonhealing chronic DFUs. Serum and forearm skin analysis, both at the protein expression and the transcriptomic level, indicated that increased expression of factors such as interferon-γ (IFN-γ), vascular endothelial growth factor, and soluble vascular cell adhesion molecule-1 were associated with DFU healing. Furthermore, foot skin single-cell RNA sequencing analysis showed multiple fibroblast cell clusters and increased inflammation in the dorsal skin of patients with diabetes mellitus (DM) and DFU specimens compared with control subjects. In addition, in myeloid cell DM and DFU upstream regulator analysis, we observed inhibition of interleukin-13 and IFN-γ and dysregulation of biological processes that included cell movement of monocytes, migration of dendritic cells, and chemotaxis of antigen-presenting cells pointing to an impaired migratory profile of immune cells in DM skin. The SLCO2A1 and CYP1A1 genes, which were upregulated at the forearm of nonhealers, were mainly expressed by the vascular endothelial cell cluster almost exclusively in DFU, indicating a potential important role in wound healing. These results from integrated protein and transcriptome analyses identified individual genes and pathways that can potentially be targeted for enhancing DFU healing.

Figure 1

Study design, experimental approach, and analysis plan. A: Flowchart highlighting the prospective cohort studied: 112 total subjects, comprising 89 (79.5%) patients with DM and 23 (20.5%) control subjects without DM. Thirty-nine of the patients with DM had a DFU (43.8%), 15 (38.5%) of whom went on to heal their ulcer (healers). B: Schematic overview of the experimental procedures used in the study. Blood was collected at the baseline visit for all subjects and at the exit visit (either 12 weeks following baseline or 4 weeks posthealing for healers before completion of study) for patients with DFU. Serum cytokines, chemokines, and growth factors were quantitated with a Luminex apparatus or ELISA. Two 2-mm biopsies were obtained from the volar aspect of the forearm at the baseline visit and used for histology, immunofluorescence, and bulk RNA-seq experiments. Discarded skin specimens from foot surgeries were mechanically and enzymatically digested to create single-cell suspensions for capture with the 10× Genomics Chromium system for single-cell RNA-seq. C: Illustrative diagram of various analysis strategies. The results from multiplex arrays and transcriptome studies were used to generate the cellular landscape of foot skin and healers signature.

Figure 2

Serum profiles of selected inflammatory cytokines, chemokines, and biomarkers of endothelial function for healers and nonhealers at baseline and exit. A–C: Time to complete healing according to baseline serum levels for substance P (A), IFN-γ (B), and VEGF (C) quantitated with ELISA or Luminex assay and applied to a Cox regression model adjusted on age with P values shown. D–G: Baseline and exit levels between healers and nonhealers for IL-8 (D), TNF-α (E), sICAM-1 (F), and sVCAM-1 (G), measured with multiplex cytokine kits on the Luminex platform. Data are mean ± SD (n = 14–24 patients); comparisons and significances are displayed and calculated with two-way repeated-measures ANOVA with Fisher post hoc test. NS, not significant.

Figure 3

Bulk transcriptomic analysis of the forearm skin of patients with DM. A: Venn diagram of significantly differentially expressed genes (DEGs) among the different comparisons of healers (H) vs. DM, nonhealers (Non_H) vs. DM, and Non_H vs. H computed with a false discovery rate <0.05 and log-twofold change >0.5. Patients who healed (n = 5) or did not heal (n = 4) their ulcers and patients with DM without ulcers (n = 4) were analyzed. The sum of the numbers for each circle corresponds to the number of DEGs, while the overlapping numbers represent the mutual DEGs between different comparisons. B: Top five enriched IPA canonical pathways ranked according to P value (one-tailed Fisher exact test) for Non_H vs. H (top), H vs. DM (middle), and Non_H vs. DM (bottom) comparisons. C–E: IPA for prediction of upstream transcriptional regulators in Non_H vs. H (C), H vs. DM (D), and Non_H vs. DM (E) comparisons reveals IFNG, Notch, and IL13 (C); DAP3, TP53, ESRRA, and DNMT3B (D); and IL13, IL15, IL4R, and VEGFA (E) as the top implicated regulators. F: Detailed legend explaining relationships and activation/inhibition in IPA upstream regulator results. BER, base excision repair; GTPase, guanosine triphosphatase; LXR/RXR, liver X receptor/retinoid X receptor.

Figure 4

Single-cell RNA-seq analysis of lower-extremity skin samples reveals the cellular landscape of DM wound healing. A–C: Two-dimensional plots derived in Seurat from nonlinear dimensional reduction by t-SNE technique of the single-cell transcriptomes of 3,865 cells from foot skin of four subjects without DM (A), 3,691 cells from foot skin of four patients with DM without DFUs (B), and 2,322 cells from active DFUs of four patients (C). Each dot represents a single cell. Clusters were colored according to the unsupervised clustering in Seurat and identified and annotated by using canonical marker genes for cell types enriched in each cluster. D–F: Violin plots of representative marker genes showing their normalized expression (y-axis) across all identified clusters (x-axis) per condition for subjects without DM (D), patients with DM (E), and subjects with DFU (F). The colors of the violin plots are the same as their respective clusters in panels A–C. GAPDH shown as a housekeeping gene. FN1, COL1A1, and apolipoprotein D (APOD) selected as fibroblast markers; ACTA2 and TAGLN as SMC markers; IL1B and LYZ as Mono/Macro markers; CD3D and PTPRC as T-cell markers; SELE and atypical chemokine receptor 1 (ACKR1) as VEC markers; trefoil factor 3 (TFF3) and LYVE1 as LEC markers; NKG7 as NK-cell marker; and CD79A B-cell marker and TPSAB1 as mast cell markers. G: Heat map depicting the top 10 (with the top 3 highlighted) differentially expressed genes in each cluster for subjects without DM. Each column represents a single cell, and each row represents an individual gene. Red corresponds to maximum relative gene expression. H: Composition of identified cell types per condition. Data are mean ± SD (n = 4).

Figure 5

Serum cytokine and chemokine markers at single-cell level. A: Serum markers associated with poor healing (CX3CL1, CCL4, CCL3, CXCL8, and TNF, from left to right on x-axis) and their expression in single-cell RNA-seq data on a split dot plot. B: Serum markers associated with enhanced healing (IL-13, IL-10, MMP2, TAC1, VCAM1, and ICAM1, from left to right on x-axis) and their expression in single-cell RNA-seq data on a split dot plot. As outlined in the legend, the size of the dot encodes the percentage of cells within a class expressing the gene (pct.exp), different colors represent the conditions, and color intensity in the scale correlates with higher expression levels across all cells within a class. The y-axes in panels A and B list all the clusters computed from CCA per condition. C: Detailed legend explaining IPA results. D: DFU vs. non-DM DE IPA for activated upstream regulators in myeloid cell cluster with EGR1, HIF1A, mir-155, and NOS2 as central regulators. E: DFU vs. non-DM DE IPA for inhibited upstream regulators in myeloid cells with HIC1, IL13, RAD21, and SMC3 as central regulators. F: DFU vs. DM DE IPA of inhibited upstream regulators in myeloid cells with CBX5, IL-13, and SPDEF as central regulators.

Figure 6

A: IPA of the VEC cluster. DM vs. non-DM DE IPA results for inhibited upstream regulators in VEC with IFNA2, IFNG, IFNL1, and NF-κB (complex) as central regulators. B: DFU vs non-DM DE IPA analysis results for inhibited upstream regulators in VEC cluster with IFNA1, IFNB, IFNG, IFNL1, IL27, and STAT1 as central regulators. C: DFU vs. DM DE IPA results for inhibited upstream regulators in VEC cluster with IFNA2, IFNB1, IFNG, and IFNL1 as central regulators. D: Detailed legend explaining IPA results.

Figure 7

Differentially expressed genes (DEGs) upregulated in forearm skin healers’ bulk RNA-seq samples at single-cell level. A: Feature heat maps depicting the enrichment score generated from the list of all DEGs upregulated in forearm skin of patients who healed their ulcers. t-SNE plots with cells from each condition (DFU, DM, and non-DM). Each dot represents one cell, with purple denoting high enrichment score and intensity of color corresponding to increased scores, according to the scale shown. B and C: Representative selected DEGs expressed in the single-cell RNA-seq data. t-SNE plots of F13A1 expression (B) and TNXB (C) across the different conditions (DFU, DM, non-DM). All the cells are also shown together in the same plot. Each dot represents one cell, with yellow denoting no expression and green denoting high expression and intensity of color corresponding to increased expression levels, according to the scale shown. Gray dots correspond to nonpresent cells for the particular condition.