Objective:

To characterize cell states in complex vascular tissue using a label-free single-cell proteomics workflow, enhancing the understanding of disease-associated heterogeneity.

Key Findings:

• Smooth muscle cells were divided into seven distinct phenotypes with unique protein signatures, indicating diverse functional roles.
• Subsets enriched in Marfan tissue showed increased levels of LRP1 and PRSS2, suggesting altered phenotypes that may contribute to disease progression.
• Endothelial cells from Marfan mice exhibited reduced adhesion proteins and increased smooth muscle-associated proteins, indicating a shift towards a more mesenchymal state.

Interpretation:

The study reveals significant proteomic changes in vascular cells associated with Marfan syndrome, suggesting potential biomarkers for disease progression.

Limitations:

• Limited overlap in finer subpopulations when compared to single-cell RNA sequencing datasets, which may affect the robustness of the findings.
• The study primarily focused on a mouse model, which may not fully translate to human conditions, limiting the applicability of the results.

Conclusion:

Multi-omic approaches combining transcriptomic and proteomic data can enhance understanding of cellular states and their role in aneurysm progression, paving the way for future research into targeted therapies.