bioRxivMolecular BiologyPreprint
A Single-Cell Atlas of PVOD Across the Lifespan Reveals Cellular and Molecular Therapeutic Targets
Prashant ChandrasekaraniD, Sylvia N. MichkiiD, Jennifer E. PogorileriD, Quinn Wright, MinQi Lu, Andrew R. PetersoniD, Hannah Hallquist, Edward CantuiD, Edward E. MorriseyiD, Lisa R. YoungiD, Maria Ciocca BasiliD, Susan M. LiniD, David B. FrankiD
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Abstract
Rationale Pulmonary veno-occlusive disease (PVOD) is a progressive and lethal disease cured only lung transplant. While genetic mutations in EIF2AK4 have been identified in some cases, 70% of PVOD patients have no known etiology. Objectives To construct a single-cell atlas to define the cellular and molecular constituents driving pathophysiology in PVOD. Methods Lung samples were collected from four idiopathic non-EIF2AK4 mutation pediatric and adult PVOD patients and age-matched controls. Analyses of single-cell RNA sequencing, Xenium spatial transcriptomics, and immunohistochemistry were performed to identify cell state changes and differential gene expression between controls and PVOD and subsequent transcriptome comparison between our PVOD and published pulmonary arterial hypertension (PAH) datasets. Measurement and Main Results The single-cell atlas reveals marked changes in the PVOD endothelium compared to controls. EndMT and ectopic COL15A1+ systemic venous shunts and pulmonary vein endothelium are hallmarks of PVOD. Molecular analyses reveal activation of the integrated stress response (ISR)/ATF4, TGF{beta} signaling, MAPK signaling, and BRD4 are potential key players in the pathophysiology of PVOD. While PVOD and PAH share both pathological and some molecular mechanisms, ISR and TGF{beta} signaling are more prominent in PVOD than PAH. Conclusions These data indicate that endothelial dysfunction is a primary etiology in the development of PVOD with activation of multiple pathways with druggable targets.
- organism: human
- assay: scRNA-seq
- assay: spatial transcriptomics
- research focus: cell type annotation
- research focus: differential expression
- approach: statistical model
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