Sharps, M. C. | bioRxiv Science

bioRxiv2025

Spatial transcriptomic profiling reveals distinct signatures in acute versus chronic wounds through hypergraph modelling and transcriptomic entropy analysis.

IntroductionThe financial burden of wounds to healthcare systems continues to increase, with little progression on advanced treatment options. Single cell RNA sequencing studies have predominantly focused on phenotypically different cell types and are generally descriptive in their analyses. Coordination within the transcriptome can be modelled using hypergraphs, which quantify higher order interactions (coordination between two or more genes) in transcriptomic data. Hypergraph modelling and analysis of transcriptomic entropy of wound samples will further elucidate differences between healing and non-healing wounds. MethodsSpatial transcriptomic analysis (Visium Spatial Gene Expression) was performed on three acute wounds, three chronic wounds and two healthy, unwounded skin samples. Standard transcriptomic analyses were performed in Python using the packages Scanpy and Squidpy. Hypergraph modelling and transcriptomic entropy analyses were performed in R. ResultsAcross the samples, twenty-nine Leiden clusters were defined, with keratinocytes, fibroblasts and adipocytes subclusters present. Fifteen marker genes for acute and chronic wounds were identified, with pathway analysis identifying immune responses and cellular homeostasis present in acute wounds, whilst in chronic wounds, pathways included immune responses and extracellular matrix structure. Spatial transcriptomics allowed for a descriptive analysis of the Leiden clusters within the samples, with each tissue sample being divided up into upper, middle and lower layer for this purpose. Hypergraph analyses of the whole transcriptome from the pathology groups and the independent transcriptomic Leiden clustering, revealed differences in underlying transcriptomic coordination. The top 1000 highly connected genes from each pathology group were analysed by Over Representation Analysis, with integrated stress response signalling, maintenance of cell number and myeloid and mononuclear cell differentiation pathways present in acute samples (all FDR <0.05) compared to regulation of vasculature development, mononuclear cell differentiation and epithelial cell proliferation (all FDR <0.05) in the chronic samples. Analysis of transcriptomic entropy identified further differences between acute and chronic wounds, with decreasing entropy from control to acute to chronic samples (all p-adjusted <0.001). When comparing Leiden clusters that were present in at least two sample groups, there was a statistically significant difference in entropy, although Leiden clusters varied in high or low entropy between acute and chronic wound samples (all p< 2.2 x 10-16). By mapping high and low entropy values to spatial images of each sample, lower transcriptomic entropy within the wound bed was identified compared to the rest of the tissue sample. ConclusionsSpatial transcriptomic analysis allowed for distinct spatial patterning to be observed which delineated acute and chronic wounds. Analysis of higher order interactions within the wound sample transcriptome identified key pathways that were not identified using traditional analyses. Transcriptomic entropy analysis further highlighted the differences between the wound samples. This work has implications for the future development of biomarkers of chronic wounds.

physiology↗

bioRxiv2024

Single Cell Transcriptomic Modelling of the Fallopian Tube Epithelium Identifies Cellular Specialisation, Novel Differentiation Trajectories, and Gene Network Associations with Ectopic Pregnancy

STUDY QUESTIONCan network modelling of single cell transcriptomic data identify cellular developmental trajectories of fallopian tube (FT) epithelium and reveal functional and pathological divergence from the endometrium? SUMMARY ANSWERA bidirectional secretory and ciliated differentiation trajectory was apparent from a novel OVGP1+ progenitor population of FT epithelial cells. A causal network model of whole transcriptome action in the FT and endometrium revealed specific functional divergence between secretory cells of these tissues. The network model reflected the latest ectopic pregnancy genome wide association study (GWAS), invoking MUC1 and other candidate genes in mature secretory cells for ectopic and eutopic implantation. WHAT IS KNOWN ALREADYThe fallopian tube forms the in vivo peri-conceptual environment, which has a significant impact on programming offspring health. The fallopian tube epithelium establishes this environment, however the epithelial cell types are poorly characterised in health and disease. STUDY DESIGN, SIZE, DURATIONPublicly available benign FT single cell RNA sequencing (scRNA-seq) samples from thirteen women across three studies were combined. Endometrial scRNA-seq samples from thirteen women from one study were used to demonstrate transcriptomic differences between the epithelia of the two tissues. Network models of transcriptomic action were constructed with hypergraphs. PARTICIPANTS/MATERIALS, SETTING, METHODSA meta-analysis of FT scRNA-seq samples was performed to identify epithelial populations. Differential gene expression assessed differences between fallopian tube and endometrial epithelial scRNA-seq data. Functional differences between secretory cells in the tissues were characterised using hypergraph models. To identify associations with ectopic pregnancy, expression quantitative trait loci (eQTLs) from a recent GWAS were mapped onto the network models. MAIN RESULTS AND THE ROLE OF CHANCEEpithelial cells (n=14,360) were clustered into 8 secretory and ciliated epithelial populations in the meta-analysis of 3 scRNA-seq datasets. A novel OVGP1+ epithelial progenitor cell was also identified, and its bi-directional differentiation to mature secretory or mature ciliated populations was mapped by RNA velocity analysis. This progenitor exhibited a high velocity magnitude (12.47) and low confidence (0.69), a combination strongly indicative of multipotent progenitor status. Comparing FT epithelial cells with endometrial epithelial cells revealed 5.3-fold fewer shared genes between FT and endometrial glandular secretory cells than between FT and endometrial ciliated cells, suggesting functional divergence of secretory cells along the reproductive tract. Hypergraphs were used to identify highly coordinated regions of the transcriptome robustly associated with functional gene networks. In the FT secretory cells, these networks were enriched for lipid (FDR<0.002) and immune (FDR<0.00007) related pathways. We mapped eQTLs from a GWAS meta-analysis of 7070 women with ectopic pregnancy over a range of significance (P = 1.68 x 10-21- 5.8 x 10-4) to the hypergraphs of FT and endometrium. Of the 22 genes present in the hypergraphs, 13 of these clustered as highly coordinated genes. This demonstrated the functional importance of MUC1 in the FT and endometrium, (GWAS Study P = 5.32x10-9) and identified additional genes (SLC7A2, CLDN1, GLS, PEX6, PLXNA4, NR2F1, CLGN, PGGHG, ANKRD36) implicated in ectopic pregnancy and eutopic pregnancy. LIMITATIONS, REASONS FOR CAUTIONThe sample size of reproductive age women was limited in previous studies, and though causal network modelling was used and previous mechanistic data supports candidate gene involvement, no in vitro or in vivo validation of candidate was performed. WIDER IMPLICATIONS OF THE FINDINGSThese findings consolidate the existing single cell transcriptomic datasets of the FT to provide a comprehensive understanding of epithelial populations and define functionally distinct secretory cells that contribute to the peri-conceptual environment of the FT. We further implicate the role of MUC1 and secretory cells in ectopic pregnancy and suggest future targets for investigating embryo implantation in the FT and endometrium.

developmental biology↗