Fibrosis is a hallmark of the tumor microenvironment in many solid cancers, driving tumor progression, immune evasion, and treatment resistance; however, the molecular and cellular mechanisms underlying fibrogenesis—particularly stromal–immune crosstalk across organs—remain incompletely understood, compounded by organ-specific heterogeneity and a lack of reliable immune-related biomarkers. To address this, we performed an integrative single-cell RNA sequencing (scRNA-seq) analysis of fibrotic tissues from four major organs—liver, lung, heart, and kidney—alongside non-fibrotic controls, applying unsupervised clustering, trajectory inference, cell–cell communication modeling, and gene set variation analysis (GSVA) to map the fibro-immune landscape. Our analysis revealed both conserved and organ-specific features: fibroblasts were the dominant extracellular matrix (ECM)-producing cells in liver and lung, whereas endothelial-derived stromal populations prevailed in heart and kidney. Immune profiling uncovered distinct infiltration patterns—macrophages displayed organ-specific polarization states; T cells were enriched for tissue-resident subsets in lung and mucosal-associated invariant T (MAIT) cells in liver; and B cells exhibited marked heterogeneity, including a pathogenic interferon-responsive subset prominent in pulmonary fibrosis. GSVA further identified divergent signaling programs across organs and lineages, including TGF-β/TNF-α in the heart, NOTCH/mTOR in the kidney, glycolysis/ROS in the lung, and KRAS/interferon pathways in the liver. Cell–cell communication analysis highlighted robust crosstalk between macrophages, T/B cells, and stromal cells mediated by collagen, laminin, and CXCL signaling axes. Together, this cross-organ atlas delineates a highly heterogeneous fibro-immune ecosystem in human fibrotic diseases, revealing shared mechanisms alongside organ-specific regulatory networks, with immediate translational implications for precision anti-fibrotic therapy, immunomodulatory drug repurposing, and the development of context-specific biomarkers for clinical stratification and therapeutic monitoring.
Deng et al. (Fri,) studied this question.