DOI: 10.6084/m9.figshare.26997133.v1
title: Inflammation-Associated Stromal Reprogramming Can Initiate Metaplasia and Facilitate Dysplastic Progression via ECM

description: SUMMARYIn lungs, chronic exposure to tobacco smoke triggers chronic inflammation, fostering a fibrotic microenvironment and promoting the focal transition of columnar human bronchial epithelial cells (hBECs) to squamous metaplasia. These cells that change identity, metaplasias, can progress to squamous dysplasia and, ultimately, lead to squamous cell carcinoma. The cellular stress associated with chronic inflammation (e.g., oxidative stress, DNA damage) enhances TGF-β signaling and upregulates HSP47 expression in fibroblasts which results in increased collagen fiber alignment, elevated tissue stiffness, and subsequent activation of YAP-dependent mechanotransduction in hBECs. In air-liquid interface cultures, stressed fibroblasts alone were sufficient to induce epithelial metaplasia in hBECs. Furthermore, when tumor suppressor function was compromised in hBECs, stressed fibroblasts induced dysplastic phenotypes both <i>in vitro</i> and <i>in vivo</i>. Mechanistically, fibroblasts modulated epithelial cell identity via extracellular matrix (ECM)-dependent mechanotransduction. Notably, a stiff matrix alone could induce squamous metaplasia and dysplasia. Inhibition of HSP47-dependent collagen processing effectively prevented fibroblast-induced squamous metaplasia and dysplasia. Moreover, this intervention was capable of reversing fibroblast-induced metaplasia, restoring bronchial epithelial identity.The attached h5ad file represent the processed single cell RNA-seq data used in analysis.<br>Data dictionary for the .h5ad data file.The count matrix: AnnData.X=========X.Shape: (199271 cells, 35606 genes)<br>X contains raw counts data (non-logged).<br>X.__class__ == scipy.sparse._csr.csr_matrix<br>X.dtype == 'float32'<br>AnnData.obs<br>===========index - cell barcodes + sample_diagnosis<br>samplename - coded sample ID<br>n_genes - number of measured genes in the cell<br>n_molecules - number of molecules sequenced<br>doublet_score - whether the droplet contained two cells (scrublet)<br>percent_mito - percent of genes measured that are mitochondrial<br>leiden - cluster labels from leiden algorithm<br>louvain - cluster labels from the louvain algorithm<br>nobatch_leiden - non-batch corrected leiden cluster labels<br>nobatch_louvain - non-batch corrected louvain cluster labels<br>diagnosis - tissue diagnosis, N normal, M metaplasia, D dysplasia, T tumor<br>phase - cell cycle phase<br>sample_diagnosis - sample ID + tissue diagnosis<br>patient - patient ID<br>treatment - whether the patient recieved any treatment<br>procedure - how the sample was aquired<br>hcl_refined - human cell landscape refined cell type name<br>hcl_celltype - human cell landscape cell type best match<br>hcl_score - human cell landscape matching score<br>CLid - cell ontology ID<br>CL_name - cell ontology cell type name<br><br>AnnData.var<br>===========index - gene symbols<br>gene_ids - ensembl gene IDs<br>feature_types - type of the feature<br>genome - genome build<br>is_mito - whether the gene is mitochondrial<br>is_ribo - whether the gene is ribosomal<br>AnnData_embeddings:<br>========================PCA (obsm.X_pca)<br>UMAP (obsm.X_umap)<br>PCA_nobatch (obsm.X_pca_original)<br>UMAP_nobatch (obsm.X_umap_nobatch)<br>neighbors (AnnData.uns)<br><br>Marker Genes:<br>=============<br>AnnData.uns['rank_genes_groups_filtered'].keys()names - one list per leiden cluster<br>logfoldchanges - one cluster vs all othersscores - wilcoxon statistic<br>pvals - wilcoxon p-valuepvals_adj - BH adjusted p-valuesparams = {'corr_method': 'benjamini-hochberg', <br>'groupby': 'leiden',<br>'method': 'wilcoxon',<br>'reference': 'rest',<br>'use_raw': True}<br>