Lung Single-Cell Transcriptomics Reveal Diverging Pathobiology and Opportunities for Precision Targeting in Scleroderma-Associated Versus Idiopathic Pulmonary Arterial Hypertension

Abstract

Background: Pulmonary arterial hypertension (PAH) involves progressive cellular and molecular change within the pulmonary vasculature, leading to increased vascular resistance. Current therapies targeting nitric oxide, endothelin, and prostacyclin pathways yield variable treatment responses. Patients with systemic sclerosis-associated PAH (SSc-PAH) often experience worse outcomes than those with idiopathic PAH (IPAH). We hypothesized that distinct and overlapping gene expression patterns in SSc-PAH versus IPAH lung tissues could inform the investigation of precision-targeted therapies.

Methods: Lung tissue samples from 4 SSc-PAH, 4 IPAH, and 4 failed donor specimens were obtained from the Pulmonary Hypertension Breakthrough Initiative lung tissue bank. Single-cell RNA sequencing was performed using the 10X Genomics Chromium Flex platform. Data normalization, clustering, and differential expression analysis were conducted using Seurat. Additional analyses included gene set enrichment analysis, transcription factor activity analysis, and ligand-receptor signaling. Pharmacotranscriptomic screening was performed using the Connectivity Map.

Results: SSc-PAH samples showed a higher proportion of fibroblasts compared with failed donors and a higher proportion of dendritic cells/macrophages compared with IPAH. Gene set enrichment analysis revealed enriched pathways related to epithelial-to-mesenchymal transition, apoptosis, and vascular remodeling in SSc-PAH samples. There was pronounced differential gene expression across diverse pulmonary vascular cell types and in various epithelial cell types in both IPAH and SSc-PAH, with epithelial-to-endothelial cell signaling observed. Macrophage-to-endothelial cell signaling was particularly pronounced in SSc-PAH. Pharmacotranscriptomic screening identified TIE2, GSK-3, and PKC inhibitors, among other compounds, as potential drug candidates for reversing SSc-PAH gene expression signatures.

Conclusions: Overlapping and distinct gene expression patterns exist in SSc-PAH versus IPAH, with significant molecular differences suggesting unique pathogenic mechanisms in SSc-PAH. These findings highlight the potential for precision-targeted therapies to improve outcomes in patient with SSc-PAH. Future studies should validate these targets and explore their therapeutic efficacy.

Keywords: computational biology; lung; precision medicine; pulmonary arterial hypertension; scleroderma, systemic; transcriptome.

Figure 1.

Overview of clustering results. A) Uniform manifold approximation and projection (UMAP) of human lung scRNAseq from all cells of all samples analyzed, annotated and colored by cell cluster. B) Purple-yellow heatmap of top-most differentially expressed genes by cluster. Cells for a given cluster are compared to all other cells in the dataset. Yellow cells represent upregulated genes, while purple cells represent downregulated genes. C) Hierarchical dendrogram of clustered cell types.

Figure 2.

Differential expression analysis. A) Jitter plot showing differentially expressed genes for each cell type in IPAH vs failed donor controls (top) and SSc-PAH vs failed donor controls (bottom). Each dot represents the average log2FC of a gene. Dots indicating differentially expressed genes that surpass a false discovery corrected p-value <0.05 are colored by cell type. Gray dots indicate genes that did not surpass the threshold for statistical significance. B) Bar plot representing counts of differentially expressed genes (relative to donors) for IPAH and SSc-PAH cell clusters.

Figure 3.

Gene set enrichment analysis. Heatmaps showing cluster-specific gene set enrichment analysis of gene signatures from IPAH (A) and SSc-PAH (B) samples compared with donor controls using hallmark pathways from the Molecular Signatures Database. SSc-PAH vs IPAH GSEA is shown in (C). Gene sets are listed on the on the y-axis, and cell clusters are on the x-axis. The dot size corresponds to −log10(p-value), and color represents the normalized enrichment score (NES) from GSEA, indicating upregulation (pink) or downregulation (purple). NES is provided for enriched gene sets with FDR-adjusted p-value <0.05.

Figure 4.

Gene regulatory analysis of differential transcription factor activity. Heatmaps showing the fold-change differences in UCell scores by condition for the top 25 transcription factors (sorted by significance) that are most differentially active for each cluster for IPAH vs donors (A) and SSc-PAH vs donors (B). Dot color represents the FC difference between conditions, with purple indicating upregulation and yellow indicating downregulation of TF activity relative to donors. Dot size corresponds to −log10(p-value) of the FC difference.

Figure 5.

Global intercellular signaling patterns. Chord diagrams showing intercellular inferences via CellPhoneDB for IPAH (A) and SSc-PAH (B) cells. Each segment around the circle corresponds to a different cell cluster identified in the dataset. The length of colored segments indicates the relative abundance of each cell type. Chords connecting the segments represent ligand-receptor interactions, with the thickness of each chord proportional to the interaction strength. Colors are used to distinguish between different cell types and their interactions. Arrowheads indicate directionality from ligand to receptor. C) Heatplot of quantitative differences in ligand-receptor interaction scores in SSc-PAH versus IPAH.

Figure 6.

Summary of pharmacotranscriptomic screen. A) Heatmap of connectivity scores for the compounds opposing cluster-specific gene expression signatures from SSc-PAH cells when tested in human umbilical vein endothelial cell lines. Individual compound names are on the y-axis, and cell types are on the x-axis. Scores are depicted for compounds with FDR-corrected p-value <0.05. Dot color corresponds to connectivity score, with dark purple representing −1, and light blue representing −0.7. Dot size corresponds to −log10(p-value). B) Kernel density plots depicting the distribution of connectivity scores opposing SSc-PAH gene expression signatures (in purple) and IPAH gene expression signatures (in pink) for approved and commonly used pulmonary hypertension therapies. B)) Kernel density plots depicting the distribution of connectivity scores opposing SSc-PAH gene expression signatures (in purple) and IPAH gene expression signatures (in pink) for investigational pulmonary hypertension therapies.