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.
