1. Department of Immunology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
2. Institute for the Advanced Study of Human Biology (ASHBi), Graduate School of Medicine, Kyoto University, Kyoto, Japan
3. Institute for Cardiogenetics, Universität zu Lübeck, Lübeck, Germany
4. DZHK (German Centre for Cardiovascular Research), Partner Site Hamburg/Kiel/Lübeck, University Heart Centre Lübeck, Lübeck, Germany
5. I.Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland

Atherosclerosis is a chronic inflammatory disease characterized by the accumulation of lipids, inflammatory cells, and fibrous elements in the large arteries (1). The progression of atherosclerosis involves various cell types, including endothelial cells, smooth muscle cells (SMCs), macrophages, and fibroblasts (2). Recent studies leveraging single-cell analysis and fate-mapping experiments further highlighted the heterogeneity and plasticity of atherosclerotic plaque cells, including the emerging concept that SMCs can undergo phenotypic modulation, with specific SMC-derived cell types contributing to plaque instability, while others promoting plaque stabilization. Additional large-scale single-cell analyses of human plaque are still very much needed to understand the overall landscape of various cell types in atherosclerotic lesions.

In their recently published work in the ATVB journal, Bashore et al. presented comprehensive scRNA-seq (n=15) and CITE-seq (n=6) data of vascular cells at the site of carotid artery atherosclerosis in humans (n=21, 13 of which were symptomatic) (3). They, in particular, highlighted macrophages and SMC heterogeneity identified by their multimodal analyses, which integrated data from both scRNA-seq and CITE-seq.  Among the various clusters in human carotid artery samples, they were able to identify distinct subpopulations of macrophages and SMCs. Studying these clusters can provide insight into the possible of these cells in mediating atherosclerosis.

Beyond comprehensive profiling, the study identified subpopulations of plaque vascular cells enriched in plaque samples from symptomatic patients. For macrophages, a total of 5 subpopulations were identified, including two proinflammatory subpopulations (macrophages 2 and 3), having a higher expression of either C1Q, a component of the complement system, or IL1B, a signature gene of inflammasome activation. The cluster Macrophage 1 shows an efferocytic signature. For SMCs, the initial analysis identified 4 subpopulations, including SMC 1, SMC 2, SMC 3, and modulated SMCs. In symptomatic patients, there was a decrease in macrophage 1 and modulated SMC, while an increase in the proinflammatory cluster SMC 3.  Gene scoring analyses showed an increase in senescence signature in macrophages and glycolysis signature in SMCs in these patients. The findings suggest the role of these specific cell subpopulations in the progression of atherosclerosis, making them potential targets for future studies.

Leveraging CITE-seq profiling, this study also identified new protein phenotypic markers for SMCs. CD29 and CD90, both of which are markers of fibroblasts mediating interactions with ECM, were identified as markers in SMCs. CD29 is expressed in the atherosclerotic lesions at all stages, while CD90 is expressed in advanced lesions, particularly in the modulated SMC cluster, and shows greater expression in symptomatic compared to asymptomatic plaques. Therefore, CD29 may serve as a marker of SMC in earlier stages of the disease, whereas CD90 is for more advanced stages. In accordance, an increase in CD90⁺ SMCs was observed in LDLr-/-Myh11-CreER^T2ROSA26^ZsGreen+/− mice during atherosclerosis development.

Another strength of this study is that the study design including both CITE-seq and scRNA-seq analyses enables the authors to assess how well mRNA and protein expressions are correlated in human plaque cells. Their findings demonstrate an overall strong correlation between mRNA and expression in atherosclerotic plaque cells, although there were exceptions where protein expression was essential in the identification of certain cell types.  It is noteworthy that in some pathological conditions, such as colon cancer, Copy Number Alterations (CNAs) often lead to changes in mRNA abundance without corresponding changes in protein levels (5). The observed strong correlation between gene and protein expression in Bashore et al.’s study compared to the contrasting lack of correlation in for instance colon cancers suggests the potential predictive power of transcriptomic signature profiling and the rationale for more focused attention on gene expression rather than post-transcriptional processes for mechanistic studies.

Last but not least, this study by Bashore et al. provides a rich dataset for additional analysis, e.g., exploration of cell-cell communications using computational tools, such as CellPhoneDB . Future studies may leverage these approaches to further enhance our understanding of possible macrophage and SMC crosstalk in atherosclerotic lesions, potentially revealing new mechanistic insights and therapeutic .

References:

1. Malekmohammad K, Bezsonov EE, Rafieian-Kopaei M. Role of lipid accumulation and inflammation in atherosclerosis: focus on molecular and cellular mechanisms. Frontiers in cardiovascular medicine. 2021;8:707529.
2. Grootaert MO, Bennett MR. Vascular smooth muscle cells in atherosclerosis: time for a re-assessment. Cardiovascular research. 2021;117(11):2326-39.
3. Bashore AC, Yan H, Xue C, Zhu LY, Kim E, Mawson T, et al. High-dimensional single-cell multimodal landscape of human carotid atherosclerosis. Arteriosclerosis, Thrombosis, and Vascular Biology. 2024;44(4):930-45.
4. Sridharan R, Cavanagh B, Cameron AR, Kelly DJ, O'Brien FJ. Material stiffness influences the polarization state, function and migration mode of macrophages. Acta biomaterialia. 2019;89:47-59.
5. Zhang B, Wang J, Wang X, Zhu J, Liu Q, Shi Z, et al. Proteogenomic characterization of human colon and rectal cancer. Nature. 2014;513(7518):382-7.
6. Betsholtz C. Toward a granular molecular-anatomic map of the blood vasculature–single-cell RNA sequencing makes the leap. Upsala Journal of Medical Sciences. 2022;127.
7. Efremova M, Vento-Tormo M, Teichmann SA, Vento-Tormo R. CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes. Nature protocols. 2020;15(4):1484-506.