TGFb signaling instructs a conserved fibrosis-associated cell state marked by LRRC15

作者：Justin A. Shyer, Fabien Wehbe, Christopher D. Davidson, Hannah Bender, Minh Thai, Christian Cox, Alsu Missarova, Alexander Arlantico, Ben Hall, Ruoyu Zhang, David Kim, Anthony Altieri, Shakir Hasan, Afshin Namdar, Tina Chen, Shaheed W. Hakim, Cynthia Guidos, Hans D. Brightbill, Tony Kuo, Graham Heimberg, Héctor Corrada Bravo, Rojo Ratsimandresy, Salil Uttarwar, Grace Teng, Omar Salem, Mehrdad Arjomandi, Mark S. Wilson, Spyros Darmanis, James Ziai, Alexis Scherl, Zora Modrusan, Paul J. Wolters, Matthew B. Buechler, Jason A. Vander Heiden, Shannon J. Turley · 发表于：Proceedings of the National Academy of Sciences · 年份：2026 · DOI：10.1073/pnas.2536550123 · 被引用次数：2 · 研究领域：Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis、Connective Tissue Growth Factor Research、Cardiac Fibrosis and Remodeling

Fibroblasts are key potentiators of chronic disease pathophysiology. Despite their established roles in promoting pathological inflammation and tissue remodeling, activated myofibroblasts are generally characterized as a single, homogeneous cell population, obscuring critical functional distinctions. Defining the cell states, their molecular regulators, and restricted markers is critical to developing effective therapies for the treatment of fibrosis. Here, using a human lung stromal cell atlas of idiopathic pulmonary fibrosis, we identify two myofibroblast transcriptional states associated with distinct predicted biological function, regulation, and cell surface marker expression. We identify fibroblast-specific TGFb signaling as the key regulator of the mechanistic switch from a wound healing-associated and proliferative to a profibrotic myofibroblast. Further, we elucidate conserved TGFb-dependent and suppressed gene expression programs that define these states. Our findings reveal that LRRC15 is highly restricted to myofibroblasts that primarily express an extracellular matrix-remodeling gene program and illuminate that this key cell state can differentiate in the absence of an obligate inflammatory precursor intermediate. Last, we apply machine learning using a human single-cell foundation model to demonstrate broad applicability of the biology described herein to human chronic disease.