No abstract

Transforming growth factor–β (TGFβ) is a key driver of fibrogenesis. Three TGFβ isoforms (TGFβ1, TGFβ2, and TGFβ3) in mammals have distinct functions in embryonic development; however, the postnatal pathological roles and activation mechanisms of TGFβ2 and TGFβ3 have not been well characterized. Here, we show that the latent forms of TGFβ2 and TGFβ3 can be activated by integrin-independent mechanisms and have lower activation thresholds compared to TGFβ1. Unlike TGFB1, TGFB2 and TGFB3 expression is increased in human lung and liver fibrotic tissues compared to healthy control tissues. Thus, TGFβ2 and TGFβ3 may play a pathological role in fibrosis. Inducible conditional knockout mice and anti-TGFβ isoform-selective antibodies demonstrated that TGFβ2 and TGFβ3 are independently involved in mouse fibrosis models in vivo, and selective TGFβ2 and TGFβ3 inhibition does not lead to the increased inflammation observed with pan-TGFβ isoform inhibition. A cocrystal structure of a TGFβ2–anti-TGFβ2/3 antibody complex reveals an allosteric isoform-selective inhibitory mechanism. Therefore, inhibiting TGFβ2 and/or TGFβ3 while sparing TGFβ1 may alleviate fibrosis without toxicity concerns associated with pan-TGFβ blockade.

Summary Progressive lung fibrosis is a major cause of mortality in systemic sclerosis (SSc) patients, but the underlying mechanisms remain unclear. We demonstrate that immune complexes (ICs) activate human monocytes to promote lung fibroblast migration partly via osteopontin (OPN) secretion, which is amplified by autocrine monocyte colony stimulating factor (MCSF) and interleukin-6 (IL-6) activity. Bulk and single-cell RNA sequencing demonstrate that elevated OPN expression in SSc lung tissue is enriched in macrophages, partially overlapping with CCL18 expression. Serum OPN is elevated in SSc patients with interstitial lung disease (ILD) and prognosticates future lung function deterioration in SSc cohorts. Serum OPN levels decrease following tocilizumab (monoclonal anti-IL-6 receptor) treatment, confirming the connection between IL-6 and OPN in SSc patients. Collectively, these data suggest a plausible link between autoantibodies and lung fibrosis progression, where circulating OPN serves as a systemic proxy for IC-driven profibrotic macrophage activity, highlighting its potential as a promising biomarker in SSc ILD.

Compromised regenerative capacity of lung epithelial cells can lead to cellular senescence, which may precipitate fibrosis. While increased markers of senescence have been reported in idiopathic pulmonary fibrosis (IPF), the origin and identity of these senescent cells remain unclear, and tools to characterize context-specific cellular senescence in human lung are lacking. We observed that the senescent marker p16 is predominantly localized to bronchiolized epithelial structures in scarred regions of IPF and systemic sclerosis associated interstitial lung disease ILD (SSc-ILD) lung tissue, overlapping with the basal epithelial markers Keratin 5 and Keratin 17. Using in vitro models, we derived transcriptional signatures of senescence programming specific to different types of lung epithelial cells, and interrogated these signatures in a single-cell RNA-seq data set derived from control, IPF, and SSc-ILD lung tissue. We identified a population of basal epithelial cells defined by, and enriched for, markers of cellular senescence, and identified candidate markers specific to senescent basal epithelial cells in ILD that can enable future functional studies. Notably, gene expression of these cells significantly overlaps with terminally differentiating cells in stratified epithelia, where it is driven by p53 activation as part of the senescence program.

Single-cell RNA-seq (scRNA-seq) studies have profiled over 100 million human cells across diseases, developmental stages, and perturbations to date. A singular view of this vast and growing expression landscape could help reveal novel associations between cell states and diseases, discover cell states in unexpected tissue contexts, and relatein vivocells toin vitromodels. However, these require a common, scalable representation of cell profiles from across the body, a general measure of their similarity, and an efficient way to query these data. Here, we present SCimilarity, a metric learning framework to learn and search a unified and interpretable representation that annotates cell types and instantaneously queries for a cell state across tens of millions of profiles. We demonstrate SCimilarity on a 22.7 million cell corpus assembled across 399 published scRNA-seq studies, showing accurate integration, annotation and querying. We experimentally validated SCimilarity by querying across tissues for a macrophage subset originally identified in interstitial lung disease, and showing that cells with similar profiles are found in other fibrotic diseases, tissues, and a 3D hydrogel system, which we then repurposed to yield this cell statein vitro. SCimilarity serves as a foundational model for single cell gene expression data and enables researchers to query for similar cellular states across the entire human body, providing a powerful tool for generating novel biological insights from the growing Human Cell Atlas.