JCI InsightBioinformaticsPeer-reviewed journal article
Single-cell multi-omic analysis of mesenchymal cells reveals molecular signatures and putative regulators of lung allograft fibrosis
Lu LuiD, A. Patrick McLindeniD, Natalie M. WalkeriD, Ragini VittaliD, Yichen WangiD, Fatemeh FattahiiD, Stephen T. RusselliD, Michael P. CombsiD, Joshua D. WelchiD, Vibha N. LamaiD
Abstract
Survival after lung transplantation is limited by chronic progressive graft failure, termed chronic lung allograft dysfunction (CLAD). Graft-resident mesenchymal cells (MCs) drive CLAD pathogenesis and exhibit stable, dysregulated signaling; however, the transcriptomic and epigenomic drivers behind this fibrogenic transformation remain elusive. Here, we utilize single-cell multi-omic technologies to study gene expression and chromatin accessibility of MCs from the lavage fluid of lung transplant recipients with and without CLAD, obtained either early post-transplantation or after disease onset. MCs obtained after CLAD onset (CLAD-MCs) demonstrated a unique transcriptomic signature compared to non-CLAD controls; a logistic regression model trained on these profiles classified the disease status of individual cells with > 98% accuracy using a set of signature genes. Chromatin accessibility and motif scan analysis identified the CCAAT-enhancer-binding proteins family of transcription factors, specifically CEBPD, as a key marker of the CLAD-enriched subtype. Footprint analysis of early time-point MCs revealed minimal differences in accessibility, suggesting that CEBPD-associated regulatory changes emerge over time after transplantation. Integration and unsupervised clustering identified 8 distinct cell states, and a compositional shift was noted uniquely in CLAD-MCs. Knocking down CEBPD with siRNA in CLAD-MCs partially reverted the CLAD transcriptomic signature, confirming its importance in the dysregulated molecular state of CLAD-associated MCs. scRNA-seq analysis on human lung CLAD tissue provided in situ validation of key genes and CEBPD expression changes noted in CLAD-MCs. Our results provide deeper insights into the transcriptomic and epigenomic changes in post-transplant MCs, nominating biomarkers and disease-associated factors with implications for future therapeutic efforts.
- organism: human
- assay: scRNA-seq
- assay: ATAC-seq
- research focus: differential expression
- research focus: cell type annotation
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 4
- FWCI
- 12.80
- Field percentile
- top 2%
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