Genetic vulnerability to Crohn's disease reveals a spatially resolved epithelial restitution program

Abstract

Effective tissue repair requires coordinated intercellular communication to sense damage, remodel the tissue, and restore function. Here, we dissected the healing response in the intestinal mucosa by mapping intercellular communication at single-cell resolution and integrating with spatial transcriptomics. We demonstrated that a risk variant for Crohn's disease, hepatocyte growth factor activator (HGFAC) Arg⁵⁰⁹His (R509H), disrupted a damage-sensing pathway connecting the coagulation cascade to growth factors that drive the differentiation of wound-associated epithelial (WAE) cells and production of a localized retinoic acid (RA) gradient to promote fibroblast-mediated tissue remodeling. Specifically, we showed that HGFAC R509H was activated by thrombin protease activity but exhibited impaired proteolytic activation of the growth factor macrophage-stimulating protein (MSP). In Hgfac R509H mice, reduced MSP activation in response to wounding of the colon resulted in impaired WAE cell induction and delayed healing. Through integration of single-cell transcriptomics and spatial transcriptomics, we demonstrated that WAE cells generated RA in a spatially restricted region of the wound site and that mucosal fibroblasts responded to this signal by producing extracellular matrix and growth factors. We further dissected this WAE cell-fibroblast signaling circuit in vitro using a genetically tractable organoid coculture model. Collectively, these studies exploited a genetic perturbation associated with human disease to disrupt a fundamental biological process and then reconstructed a spatially resolved mechanistic model of tissue healing.

Figure 1.. HGFAC R509H missense variant impairs proMSP processing and leads to delayed wound healing in vivo.

(A) Schematic diagram of the HGFAC system and coagulation cascade. Tissue damage initiates the coagulation cascade, which activates the HGFAC system via thrombin-mediated cleavage of proHGFAC. HGFAC activates key growth factors MSP and HGF. Asterisks indicate gene associations with IBD listed in Table 1. (B) Domain structure of the HGFAC protein (51). R509H is located in the catalytic protease domain of the HGFAC protein. SP: Signal peptide, FNI & II: Fibronectin type I & II. Red triangle indicates the thrombin cleavage site, black triangle indicates the kallikrein cleavage site. (C) ProMSP processing by HGFAC R509 and HGFAC R509H. Percentage of proMSP processing activity was measured at indicated time points and pooled across four independent experiments; *p<0.05, **p<0.01 (unpaired student’s t-test). (D) (upper panel) Experimental scheme of colonic endoscopic-guided wound model. Using a miniature video endoscope and biopsy forceps, wounds were created in the dorsal side of the distal colon at day 0 and imaged by endoscopy on days 1 and 3. (lower panels) Representative endoscopic images. (left) Forceps grasping tissue to make a wound. (right) A fresh wound. (E) Representative endoscopic images of Hgfac WT, R509H, and KO wounds on day 1 (left panels) and day 3 (right panels); the yellow dotted line indicates an unhealed area. (F) Quantification of wound closure in colonic mucosal wounds on day 3 relative to day 1 (n=9–10 wounds per genotype; n=6 mice per genotype in two independent experiments). ****p<0.0001, ns: not significant (one-way ANOVA with Tukey’s multiple comparison test). (G) Serum proMSP processing at baseline and day 1 post wounding across three genotypes (n=10–11 mice per condition). *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001, ns: not significant (one-way ANOVA with Tukey’s multiple comparison test). (H) (upper panel) Schematic diagram detailing endoscopic procedure and MSP enema treatment. (lower panel) Delayed wound healing is improved by MSP treatment in Hgfac WT, R509H, and KO mice (n=5–8 wounds per condition, n=3 mice per condition). Wound closure was analyzed on days 1 and 3 and expressed as a percentage of the size of the day 1 wound area. **p<0.01, ****p<0.0001, ns: not significant (one-way ANOVA with Tukey’s multiple comparisons). Data represents mean +/− SEM.

Figure 2.. A single-cell transcriptome atlas of colonic wounds.

(A) Schematic diagram of the experimental design for colonic endoscopic-guided wound scRNA-seq. Hgfac WT mice (n=3 for intact tissue, n=3 for wounds) and Hgfac KO mice (n=3 for intact tissue, n=2 for wounds) were used. To minimize variation, three wounds or three intact tissues per mouse were collected by a 2mm skin biopsy punch tool and pooled as a single sample. (B) UMAP visualization of all epithelial, stromal, myeloid, and lymphoid cell clusters colored by cluster identity (n=6 and 5 for intact tissues and wounds, respectively). (C-E) Fraction of each epithelial (C), stromal (D), and immune (E) cell subpopulation per condition. Subpopulations that were significantly different in wounds (compared to intact tissues) are marked with arrows (up/down represents enriched/depleted in wound). Statistical significance was determined using a hierarchical Bayesian model with false discovery rate<0.05 (Methods). (F) Dot plot showing expression of cytokines or chemokines (left) and their corresponding receptors (right) across five cell types differentially abundant between wounds and intact tissues. Ligand-receptor pairs are linked by lines. Interactions between Osm and Osmr/Il-6st are highlighted in bold.

Figure 3.. Characterization of murine wound-associated epithelial (WAE) cells.

(A) Top 50 differentially expressed genes (DEGs, two-sided Wilcoxon test) in WAE cells vs other epithelial cells (ranked by absolute average log₂(fold change), p_adj<0.05 for all genes) obtained from all day 2 samples. (B) KEGG functional enrichment analysis (one-sided Fisher’s exact test) of DEGs in WAE cells and other epithelial cell types obtained from all day 2 samples. The top 20 curated pathways are shown. (C) Immunohistochemistry (Krt14, Cldn4, Aldh1a3) and RNAscope (Clu) of WAE cell (green) and epithelial (EpCAM or ß-catenin, red) markers in WT day 2 wounds. W.B. indicates wound bed. Right images are magnifications of left images. Scale bars: 100 μm (left) and 50 μm (right). Data are representative of two independent experiments. Images were acquired with identical parameters as fig. S6B. (D) RNA velocity estimates of WT WAE cells projected on the UMAP plot. (E) Volcano plot depicting DEGs (two-sided Wilcoxon test) in WT WAE cells compared to KO WAE cells (p_adj<0.05, enriched in WT: average log₂(fold change)>0.5, enriched in KO: average log₂(fold change)<−0.5). Genes overlapping with WAE cell markers are labeled (p_adj<0.05, average log₂(fold change)>0).

Figure 4.. MSP can induce WAE cell transcriptional changes to promote epithelial restitution in vitro.

(A) (left) Schematic diagram of the murine colonic spheroid bulk RNA-seq experiment. Day 2 colonic spheroids were cultured in 0%L-WRN CM with or without recombinant mouse MSP (100 ng/ml) for 0, 2, 4, or 6hrs. Three biological replicates were included. (right) Volcano plot showing DEGs (two-sided negative binomial test) in MSP-treated relative to untreated colonic spheroids at 4 hrs. DEGs upregulated by MSP treatment that overlap with WAE cell markers are labeled (p_adj<0.05, average log₂(fold change)>0.5 for both). (B) Heat map showing WAE cell gene signature of colonic spheroids treated with or without MSP at indicated time points. (C) Venn diagram showing 58 of 207 WAE cell marker genes (p_adj<0.05, average log₂(fold change)>0.5) in scRNA-seq data that overlap with colonic spheroid bulk RNA-seq data at 4hr with MSP treatment (p_adj <0.05, average log₂(fold change)>0.5; union of all DEGs at three time points). (D) Immunoblot analysis of day 2 colonic spheroids cultured in 50% or 0% L-WRN CM treated with or without recombinant mouse MSP (100 ng/ml) for 24hr. (top) Representative blot image for Aldh1a3, Cldn4, Lamc2, and Gapdh. Three biological replicates were included. (bottom) Quantitative analysis of blot images (n=6 per condition). Expression was first normalized to Gapdh, then calculated relative to the PBS/50% L-WRN-treated condition. *p<0.05, ***p<0.001, ****p<0.0001 (two-way ANOVA with Sidak’s multiple comparison test). (E) Estimated proportion of WAE cells based on bulk RNA-seq data in colonic spheroids treated with or without MSP at indicated time points. (F-G) Cell migration assay. Colonic spheroids established from C57BL/6J mice were used. (F) Representative digital phase-contrast images of colonic monolayer. Yellow dotted lines show the margin of migrated cells at 42hr. Scale bars: 1mm. (G) Quantification of cell migration at 42 hr. Data are representative of three independent experiments. **p<0.01 (unpaired student’s t-test). Data represent mean ± SEM.

Figure 5.. Spatial transcriptomics reveal co-localization of WAE cells and proliferating MAFs in wounds in mice.

(A) (left) H&E images of day 2 wounds from 3 individual WT mice used for spatial transcriptomic analysis. Yellow squares show wound beds (W.B.). (right) Tangram mapping of WAE cells on the Visium spatial data. Color gradient depicts the percentage of mapped WAE cells. Number of nuclei per spot is indicated. The distance between the centers of each circle is 100 μm. (B) Tangram mapping of WAE cells and proliferating MAFs (pMAFs) on the Visium spatial data. Color gradient depicts the percentage of mapped WAE cells (yellow) and proliferating MAFs (purple). WAE cell spots are shown as bold squares. Spatial plot showing WAE cells and proliferating MAFs are overlapping or located in adjacent spaces. The distance between the centers of each square is 100 μm. (C) Distance (μm) to nearest WAE cell spot across all cell types. Center lines are the medians, box limits are the upper and lower quartiles, and whiskers are 1.5 times the interquartile range. Number of samples included is indicated in parentheses.

Figure 6.. WAE cells produce retinoic acid (RA) locally to coordinate fibroblasts during wound healing.

(A) RA response signature visualized on the spatial transcriptomic data of wounds from 3 individual mice. Color gradients in each square depict RA transcriptional response (blue to yellow) and percentage of mapped proliferating MAFs (pMAFs, purple). RA transcriptional response was derived from the average expression of the top 30 DEGs 16hrs after RA treatment of fibroblast culture (fig. S10). Bold squares show the location of WAE cells mapped by Tangram (shown in Fig. 5B). Density plots depict the distribution of spots with high RA response score (greater than the upper quartile) in the wound beds (approximately three spots away from the wound), highlighting the concentrated area of RA-induced transcripts near WAE cells. The distance between the centers of each square is 100 μm. (B) Immunohistochemistry of WAE cells and proliferating MAFs. (left) WAE cells (Krt14) and MAFs (Pdpn) with DAPI staining. Scale bar: 100 μm. (right) Magnified image of yellow dotted square in left image. Scale bar: 50 μm. (C) Immunohistochemistry of MAFs (Pdpn) and immune cells (CD45). Scale bar: 100 μm. (D) RNAscope showing spatial distribution of WAE cells (probed with Clu) and proliferating MAFs (probed with Il-11/Lox) in WT day 2 wound. (left) Lower-magnification image. Scale bar: 100μm. (right) Magnified view of yellow dotted square in left image. Scale bar: 50 μm. Images in B-D are from serial sections; W.B. indicates wound bed. (E) Expression of RA transcriptional response genes (defined in A) with high gradient effect (negative correlation with distance to nearest WAE cell spot on the Visium slide). Hgfac WT and KO day 2 wound samples were analyzed. Expression of these genes was higher near WAE cells in WT compared to KO samples. *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001, ns: not significant. (F) Bar plot showing the gradient effect (negated Spearman correlation between expression and distance to nearest WAE cell spot; Spearman correlation<0, FDR<0.05) of DEGs induced by RA in fibroblast culture (log(fold change) >1). (G-H) RNAscope showing spatial distribution of Rbp1, a canonical RA-responsive gene, in relation to WAE cells, pMAFs, and immune cells in Hgfac WT and KO day 2 wounds. (G) Lower-magnification images. Scale bars: 100 μm. (H) Magnified views of yellow dotted squares in left images. In addition to Rbp1, Clu was used as a probe for WAE cells, Il-11/Lox were used for proliferating MAFs, and Ptprc was used for immune cells. Scale bars: 50 μm. (I) Semi-quantitative analysis of Rbp1 expression in Hgfac WT and KO day 2 wounds. Two fields of view per wound and two mice per genotype were used. **p<0.01, ***p<0.001, ****p<0.0001, ns: not significant (one-way ANOVA with Tukey’s multiple comparisons). Data represents mean +/− SEM.

Figure 7.. Ald1a3 is involved in generation of WAE cell-derived RA to promote fibroblast-mediated tissue repair in vitro.

(A) RA production from WAE cell monolayers. RA concentration was measured by ELISA (n=3–4 per condition). *p<0.05, ns: not significant (two-way ANOVA with Sidak’s multiple comparison test). Two independent experiments showed similar results. (B) (top left) Schematic diagram of WAE cell-fibroblast co-culture system, with a WAE cell monolayer on the top surface of a transwell and fibroblasts on the bottom surface. (top right) Representative z-stack image of WAE cell-fibroblast co-culture. WAE cells were stained with wheat germ agglutinin (WGA). Fibroblasts were stained with Vimentin. Nuclei are visualized with DAPI. Scale bar: 20 μm. (bottom) Representative confocal images of the WAE cell monolayer and fibroblasts. Scale bars: 20 μm. (C) qPCR analysis of RA-responsive genes in fibroblasts co-cultured with WT or Aldh1a3-deficient WAE cell monolayers. Fibroblasts were collected after 1 μM retinol (ROL) or DMSO treatment for 24 hr. Representative qPCR data (n=4 per condition). Three independent experiments showed similar results. *p<0.05, **p<0.01, ****p<0.0001, ns: not significant (two-way ANOVA with Sidak’s multiple comparison test). Fig. 7B was created in part using BioRender.com.