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Single-cell transcriptomics reveals skewed cellular communication and phenotypic shift in pulmonary artery remodeling
Slaven Crnkovic, Francesco Valzano, Elisabeth Fließer, Jürgen Gindlhuber, Helene Thekkekara Puthenparampil, Maria Basil, Mike P. Morley, Jeremy Katzen, Elisabeth Gschwandtner, Walter Klepetko, Edward Cantu, Heimo Wolinski, Horst Olschewski, Jörg Lindenmann, You-Yang Zhao, Edward E. Morrisey, Leigh M. Marsh, Grazyna Kwapiszewska
Slaven Crnkovic, Francesco Valzano, Elisabeth Fließer, Jürgen Gindlhuber, Helene Thekkekara Puthenparampil, Maria Basil, Mike P. Morley, Jeremy Katzen, Elisabeth Gschwandtner, Walter Klepetko, Edward Cantu, Heimo Wolinski, Horst Olschewski, Jörg Lindenmann, You-Yang Zhao, Edward E. Morrisey, Leigh M. Marsh, Grazyna Kwapiszewska
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Research Article Pulmonology Vascular biology

Single-cell transcriptomics reveals skewed cellular communication and phenotypic shift in pulmonary artery remodeling

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Abstract

A central feature of progressive vascular remodeling is altered smooth muscle cell (SMC) homeostasis; however, the understanding of how different cell populations contribute to this process is limited. Here, we utilized single-cell RNA sequencing to provide insight into cellular composition changes within isolated pulmonary arteries (PAs) from pulmonary arterial hypertension and donor lungs. Our results revealed that remodeling skewed the balanced communication network between immune and structural cells, in particular SMCs. Comparative analysis with murine PAs showed that human PAs harbored heterogeneous SMC populations with an abundant intermediary cluster displaying a gradient transition between SMCs and adventitial fibroblasts. Transcriptionally distinct SMC populations were enriched in specific biological processes and could be differentiated into 4 major clusters: oxygen sensing (enriched in pericytes), contractile, synthetic, and fibroblast-like. End-stage remodeling was associated with phenotypic shift of preexisting SMC populations and accumulation of synthetic SMCs in neointima. Distinctly regulated genes in clusters built nonredundant regulatory hubs encompassing stress response and differentiation regulators. The current study provides a blueprint of cellular and molecular changes on a single-cell level that are defining the pathological vascular remodeling process.

Authors

Slaven Crnkovic, Francesco Valzano, Elisabeth Fließer, Jürgen Gindlhuber, Helene Thekkekara Puthenparampil, Maria Basil, Mike P. Morley, Jeremy Katzen, Elisabeth Gschwandtner, Walter Klepetko, Edward Cantu, Heimo Wolinski, Horst Olschewski, Jörg Lindenmann, You-Yang Zhao, Edward E. Morrisey, Leigh M. Marsh, Grazyna Kwapiszewska

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Figure 5

Pulmonary artery smooth muscle heterogeneity is shared between different human vascular beds but not conserved in murine pulmonary artery.

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Pulmonary artery smooth muscle heterogeneity is shared between different...
(A) Uniform manifold approximation and projection (UMAP) expression plots of markers RGS5, DCN, RGS16, and VCAN in the 4 smooth muscle cell (SMC) clusters. The color gradient for each marker 1 and marker 2 represents the average expression across the human pulmonary artery (PA) data set. (B) Representative immunofluorescence staining of the expression of different SMC population-designated markers in the medial layer of PAs (ACTA2 in cyan, RGS5 in green, RGS16 in yellow, DCN in magenta, VCAN in red, and DAPI as nuclear counterstain in blue). Scale bar = 50 μm (n = 5). (C) UMAP of the integration of the human PA single-cell RNA-sequencing (scRNA-Seq) data set and the human coronary artery (CA) scRNA-Seq data set. (D) Box plot representing the SMC cluster score in the 7 identified populations of the PA and CA integrated scRNA-Seq data set. SMC cluster scoring was inferred by calculating the average expression of the subpopulation-specific gene query (top 100 cluster-specific genes) and then subtracting the average expression of an equivalent set of randomly selected control genes across the data set. (E) UMAP expression plot of the SMC cluster scores in each of the data sets included in the PA and CA integrated scRNA-Seq data set. The color gradient for each score represents the average expression across the entire PA-CA data set. (F) UMAP of the integration of the human PA scRNA-Seq data set and the murine PA scRNA-Seq data set. (G) UMAP expression plot of the SMC cluster scores in each of the data sets included in the human and murine PA scRNA-Seq data set. The color gradient for each score represents the average expression across the entire human-murine PA data set.

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