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Single-cell dissection of chronic lung allograft dysfunction reveals convergent and distinct fibrotic mechanisms
Yuanqing Yan, … , G.R. Scott Budinger, Ankit Bharat
Yuanqing Yan, … , G.R. Scott Budinger, Ankit Bharat
Published October 22, 2025
Citation Information: JCI Insight. 2025;10(20):e197579. https://doi.org/10.1172/jci.insight.197579.
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Research Article Immunology Pulmonology

Single-cell dissection of chronic lung allograft dysfunction reveals convergent and distinct fibrotic mechanisms

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Abstract

Chronic lung allograft dysfunction (CLAD) is the leading cause of mortality after lung transplantation, yet its molecular mechanisms remain poorly understood. To elucidate the pathogenesis of CLAD, we conducted a comprehensive single-cell transcriptomic analysis of CLAD lungs, integrating our generated datasets with approximately 1.6 million cells from 15 published studies of other fibrotic lung diseases. By applying pseudo-bulk approaches to mitigate batch effects, we identified molecular signatures specific to CLAD and those shared with idiopathic pulmonary fibrosis, COVID-19, and other fibrotic conditions. Our analysis revealed CLAD-specific cellular subsets including Fibro.AT2 cells, exhausted CD8+ T cells, and superactivated macrophages while suggesting that pathogenic keratin 17–positive, keratin 5–negative (KRT17+KRT5−) cells represent a common fibrotic mechanism across fibrotic lung diseases. Additionally, we performed donor-recipient cell deconvolution in lung allografts, uncovering distinct transcriptional programs and intercellular crosstalk between donor- and recipient-derived cells that drive allograft fibrosis. Recipient-derived stromal and immune cells showed enhanced pro-fibrotic and allograft rejection pathways compared with their donor counterparts. By leveraging insights from other fibrotic diseases to elucidate CLAD-specific mechanisms, our study provides a molecular framework for understanding CLAD pathogenesis and identifies potential therapeutic targets for this treatment-refractory condition.

Authors

Yuanqing Yan, Taisuke Kaihou, Emilia Lecuona, Xin Wu, Masahiko Shigemura, Haiying Sun, Chitaru Kurihara, Ruli Gao, Felix L. Nunez-Santana, G.R. Scott Budinger, Ankit Bharat

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Figure 3

Transcriptomic divergence across disease etiologies.

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Transcriptomic divergence across disease etiologies.
(A) Heatmap illustr...
(A) Heatmap illustrating the number of differentially upregulated DUGs identified in each disease state, categorized by cell type. NA indicates that no analysis was performed due to insufficient sample size. (B) Box plots depicting the expression levels of selected DUGs across epithelial (AT2), endothelial, stromal (AlvF and AdvF), and immune (CD4T) compartments. Each dot represents an individual, with colors indicating the same dataset. Box plots show the interquartile range, median (line), and minimum and maximum (whiskers). CPM, counts per million. (C) Protein-protein interaction (PPI) network for DUGs in AT2 cells across IPF, COVID-19, and COPD. Genes involved in KRAS signaling in IPF are highlighted in red, genes in the G2M checkpoint pathway in COVID-19 in green, and genes in the allograft rejection pathway in COPD in orange. Genes without interactions or not part of signaling pathways are excluded. (D) Violin plot comparing expression levels of 3 representative genes across disease etiologies in pairwise comparisons. Violin plots show the full distribution of the data, with the width representing the density of values.

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