A label-free single-cell proteomics workflow may enable more detailed characterization of cell states in complex vascular tissue, where protein-level measurements help resolve disease-associated heterogeneity.
In the study, researchers at Cedars-Sinai Medical Center applied a liquid chromatography–mass spectrometry–based approach to cells isolated from mouse aortic tissue in a model of Marfan syndrome, profiling nearly 5,000 individual cells and quantifying an average of around 500 proteins per cell.
To achieve this, the researchers used a direct, label-free workflow using nano-scale liquid chromatography and high-sensitivity mass spectrometry. By avoiding multiplexed labeling strategies, the method reduces ratio compression and improves quantitative accuracy for low-abundance proteins in single cells.
Clustering of the proteomic data identified major aortic cell types, including smooth muscle cells, endothelial cells, fibroblasts, and immune cells, alongside multiple subpopulations within these groups. Smooth muscle cells – which dominated the dataset – were further divided into seven distinct phenotypes spanning contractile and more modified states.
Several of these smooth muscle subtypes showed distinct protein signatures and were differentially represented in Marfan tissue. In particular, subsets enriched in the disease model exhibited increased abundance of proteins such as low-density lipoprotein receptor–related protein 1 (LRP1) and protease serine 2 (PRSS2), suggesting shifts toward altered or proliferative phenotypes.
The analysis also revealed cell-type–specific changes associated with disease. Endothelial cells from Marfan mice showed reduced expression of adhesion proteins alongside increased levels of smooth muscle–associated proteins, consistent with endothelial-to-mesenchymal transition. Spatial proteomics experiments confirmed both the presence and anatomical distribution of key markers identified in the single-cell data.
Comparison with published single-cell RNA sequencing datasets showed agreement at the level of major cell types, but limited overlap in finer subpopulations such as smooth muscle subtypes. Integrating transcriptomic and proteomic data produced additional clusters, indicating that protein-level measurements capture complementary aspects of cellular state.
The authors highlight targets including ACE, TPM4, LRP1, and PRSS2 as potential markers of disease-associated cell states, and suggest that multi-omic approaches could help clarify how these phenotypic shifts contribute to aneurysm progression.
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