GSE99999
Increased dermal collagen bundle alignment in Systemic Sclerosis is associated with a cell migration signature and role of Arhgdib in directed fibroblast migration on aligned ECMs [ECMs]
Systemic sclerosis (SSc) is a devastating disease affecting the skin and internal organs. Dermal fibrosis manifests early and Modified Rodnan Skin Scores (MRSS) correlate with disease progression. Transcriptomics of SSc skin biopsies suggest the role of the in vivo microenvironment in maintaining the pathological myofibroblasts. Therefore, defining the structural changes in dermal collagen in SSc patients could inform our understanding of fibrosis pathogenesis. Here, we report a method for quantitative whole-slide image analysis of dermal collagen from SSc patients, and our findings of more aligned dermal collagen bundles in diffuse cutaneous SSc (dcSSc) patients. Using the bleomycin-induced mouse model of SSc, we identified a distinct high dermal collagen bundle alignment gene signature, characterized by a concerted upregulation in cell migration, adhesion, and guidance pathways, and downregulation of spindle, replication, and cytokinesis pathways. Furthermore, increased bundle alignment induced a cell migration gene signature in fibroblasts in vitro, and these cells demonstrated increased directed migration on aligned ECM fibers that is dependent on expression of Arhgdib (Rho GDP-dissociation inhibitor 2).  Our results indicate that increased cell migration is a cellular response to the increased collagen bundle alignment featured in fibrotic skin. Moreover, many of the cell migration genes identified in our study are shared with human SSc skin and may be new targets for therapeutic intervention. Overall design: This experiment is to perform RNASeq on human fibroblasts cultured on engineered extracellular matrices (ECMs) with patterned surfaces. Dermal fibroblasts were cultured for 48 hours, on collagen-adsorbed aligned nanofibers vs. randomly oriented nanofibers coated with type I collagen. Total RNA was extracted, and analyzed by RNASeq.