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Published on: March 16, 2022
Spatial transcriptomic modeling of vascular remodeling in aortic aneurysm using integrated single-cell RNA sequencing
Shaodong Xiong1, Ludong Liang1, Tao Liu2
1Department of Cardiothoracic Surgery, Baise People's Hospital, 533000, Baise City, Guangxi province, China; Department of Cardiothoracic Surgery, Affiliated Southwest Hospital of Youjiang Medical University for Nationalities, Baise, 533000, Guangxi province, China.
Background:
The phenotypic regulation of vascular smooth muscle cells (VSMCs) is a critical characteristic of aortic aneurysm formation, although its spatial and transcriptional dynamics remain incompletely understood.
Methods:
We developed a computational workflow integrating single-cell RNA sequencing (scRNA-seq) with pseudo-spatial transcriptomic inference to model vascular remodeling at cellular resolution. Using the publicly available SCP1361 dataset from the Broad Institute's Single Cell Portal (15,698 high-quality cells from ascending aorta of normal and high- fat diet mice), we employed Seurat v4.3.0 for quality control and clustering, Tangram v1.0 for pseudo-spatial projection onto synthetic tissue scaffolds, and Monocle3 for trajectory inference. Statistical analyses included chi-square tests for cell proportion differences, Wilcoxon rank-sum tests for differential expression, and Moran's I for spatial variability (α = 0.05, adjusted for multiple testing).
Results:
We identified 25 transcriptionally distinct cell populations including 5 VSMC subtypes and 4 fibroblast subtypes showing region-specific localization patterns. High-fat diet significantly increased synthetic VSMC_2 populations (+13.1 %, p = 0.008) and monocyte infiltration (+16.3 %, p = 0.042) while decreasing contractile VSMC_3 (-8.2 %, p = 0.041). Pseudo-spatial reconstruction revealed anatomically compartmentalized cell states with contractile VSMCs localizing to the media and synthetic/inflammatory phenotypes enriching in adventitial regions. Trajectory analysis identified 847 genes with pseudotemporal dynamics (q < 0.05) associated with VSMC phenotypic transitions.
Conclusions:
This framework demonstrates how publicly available scRNA-seq data can be leveraged for hypothesis-generating spatial modeling of vascular disease. The approach reveals cell-type-specific transcriptional programs and phenotypic transitions that warrant experimental validation through immunohistochemistry and true spatial transcriptomics.
