Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Cholesterol lowering depletes atherosclerotic lesions of smooth muscle cell-derived fibromyocytes and chondromyocytes

Nature Cardiovascular Research volume 3, pages 203–220 (2024)Cite this article

Subjects

Abstract

Drugs that lower plasma apolipoprotein B (ApoB)-containing lipoproteins are central to treating advanced atherosclerosis and provide partial protection against clinical events. Previous research showed that lowering ApoB-containing lipoproteins stops plaque inflammation, but how these drugs affect the heterogeneous population of plaque cells derived from smooth muscle cells (SMCs) is unknown. SMC-derived cells are the main cellular component of atherosclerotic lesions and the source of structural components that determine the size of plaques and their propensity to rupture and trigger thrombosis, the proximate cause of heart attack and stroke. Using lineage tracing and single-cell techniques to investigate the full SMC-derived cellular compartment in progressing and regressing plaques in mice, here we show that lowering ApoB-containing lipoproteins reduces nuclear factor kappa-light-chain-enhancer of activated B cells signaling in SMC-derived fibromyocytes and chondromyocytes and leads to depletion of these abundant cell types from plaques. These results uncover an important mechanism through which cholesterol-lowering drugs can achieve plaque regression.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Effects of 4 weeks of ApoB lipoprotein lowering on plaque morphology and inflammation.
Fig. 2: Phenotypic diversity of SMC-derived cells shown using scRNA-seq.
Fig. 3: Effects of 4 weeks of ApoB lipoprotein lowering on SMC-derived cell subtypes.
Fig. 4: Effects of 12 weeks of ApoB lipoprotein lowering on SMC-derived cell subtypes.
Fig. 5: Consistent gene expression changes in fibromyocytes and chondromyocytes during plaque regression.
Fig. 6: Validation of the regulation of top-regulated genes.
Fig. 7: Potential mechanisms governing the shifts in SMC-derived gene expression and phenotype.
Fig. 8: DEGs are controlled by NF-κB signaling and enriched for causal mouse and human atherosclerosis genes.

Similar content being viewed by others

Data availability

Raw and processed scRNA-seq and RNA-seq data generated in this study have been deposited in BioStudies (ArrayExpress) with accession nos. E-MTAB-12019 and E-MTAB-13416, respectively. The deposited scRNA-data for SMC-derived cells can be loaded and explored interactively through the Single Cell Data Visualization Platform (scDAVIS at https://bioinfo.cnic.es/scdavis/ with search input "E-MTAB-12019"). All other data supporting the findings in this study are included in the main article and associated source files. We used raw data deposited at the NCBI SRA from published studies PRJNA716327 (accession no. SRP311911) and PRJNA733106 (accession no. SRP321576), preprocessed data from the GEO (accession nos. GSE131780, GSE150644 and GSE131776) and the reference genome assemblies mm10 (Ensembl gene build 84, 10x reference build 2020-A based on GENCODE vM23/Ensembl 98), GRCm38 (AWS iGenomes Ensembl reference build) and GRCh38 (10x reference build 2020-A based on GENCODE v32/Ensembl 98).

Code availability

The code used for the enrichment analysis of DEGs for lists of mouse genes and human homologs is available at https://zenodo.org/doi/10.5281/zenodo.10145655.

References

  1. Borén, J. & Williams, K. J. The central role of arterial retention of cholesterol-rich apolipoprotein-B-containing lipoproteins in the pathogenesis of atherosclerosis: a triumph of simplicity. Curr. Opin. Lipidol. 27, 473–483 (2016).

    Article  PubMed  Google Scholar 

  2. Bartels, E. D., Christoffersen, C., Lindholm, M. W. & Nielsen, L. B. Altered metabolism of LDL in the arterial wall precedes atherosclerosis regression. Circ. Res. 117, 933–942 (2015).

    Article  CAS  PubMed  Google Scholar 

  3. Feig, J. E. et al. Reversal of hyperlipidemia with a genetic switch favorably affects the content and inflammatory state of macrophages in atherosclerotic plaques. Circulation 123, 989–998 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Peled, M. et al. A wild-type mouse-based model for the regression of inflammation in atherosclerosis. PLoS ONE 12, e0173975 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Lin, J.-D. et al. Single-cell analysis of fate-mapped macrophages reveals heterogeneity, including stem-like properties, during atherosclerosis progression and regression. JCI Insight 4, e124574 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Sharma, M. et al. Regulatory T cells license macrophage pro-resolving functions during atherosclerosis regression. Circ. Res. 127, 335–353 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Ridker, P. M. et al. Antiinflammatory therapy with canakinumab for atherosclerotic disease. N. Engl. J. Med. 377, 1119–1131 (2017).

    Article  CAS  PubMed  Google Scholar 

  8. Tardif, J.-C. et al. Efficacy and safety of low-dose colchicine after myocardial infarction. N. Engl. J. Med. 381, 2497–2505 (2019).

    Article  CAS  PubMed  Google Scholar 

  9. Feil, S., Hofmann, F. & Feil, R. SM22α modulates vascular smooth muscle cell phenotype during atherogenesis. Circ. Res. 94, 863–865 (2004).

    Article  CAS  PubMed  Google Scholar 

  10. Shankman, L. S. et al. KLF4-dependent phenotypic modulation of smooth muscle cells has a key role in atherosclerotic plaque pathogenesis. Nat. Med. 21, 628–637 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Jacobsen, K. et al. Diverse cellular architecture of atherosclerotic plaque derives from clonal expansion of a few medial SMCs. JCI Insight 2, e95890 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Wirka, R. C. et al. Atheroprotective roles of smooth muscle cell phenotypic modulation and the TCF21 disease gene as revealed by single-cell analysis. Nat. Med. 25, 1280–1289 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Kim, J. B. et al. Environment-sensing aryl hydrocarbon receptor inhibits the chondrogenic fate of modulated smooth muscle cells in atherosclerotic lesions. Circulation 142, 575–590 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Pan, H. et al. Single-cell genomics reveals a novel cell state during smooth muscle cell phenotypic switching and potential therapeutic targets for atherosclerosis in mouse and human. Circulation 142, 2060–2075 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Wang, Y. et al. Smooth muscle cells contribute the majority of foam cells in ApoE (apolipoprotein E)-deficient mouse atherosclerosis. Arterioscler. Thromb. Vasc. Biol. 39, 876–887 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Miano, J. M., Fisher, E. A. & Majesky, M. W. Fate and state of vascular smooth muscle cells in atherosclerosis. Circulation 143, 2110–2116 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Stegemann, C. et al. Comparative lipidomics profiling of human atherosclerotic plaques. Circ. Cardiovasc. Genet. 4, 232–242 (2011).

    Article  CAS  PubMed  Google Scholar 

  18. Dang, D. et al. Computational approach to identifying universal macrophage biomarkers. Front. Physiol. 11, 275 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Gayoso, A. & Shor, J. JonathanShor/DoubletDetection: doubletdetection v.4.2. Zenodo https://doi.org/10.5281/ZENODO.6349517 (2022).

  20. van den Brink, S. C. et al. Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations. Nat. Methods 14, 935–936 (2017).

    Article  CAS  PubMed  Google Scholar 

  21. Dobnikar, L. et al. Disease-relevant transcriptional signatures identified in individual smooth muscle cells from healthy mouse vessels. Nat. Commun. 9, 4567 (2018).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  22. Alencar, G. F. et al. Stem cell pluripotency genes Klf4 and Oct4 regulate complex SMC phenotypic changes critical in late-stage atherosclerotic lesion pathogenesis. Circulation 142, 2045–2059 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083–1086 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Newman, A. A. C. et al. Multiple cell types contribute to the atherosclerotic lesion fibrous cap by PDGFRβ and bioenergetic mechanisms. Nat. Metab. 3, 166–181 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Björkegren, J. L. M. et al. Plasma cholesterol-induced lesion networks activated before regression of early, mature, and advanced atherosclerosis. PLoS Genet. 10, e1004201 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Wang, Z. et al. Myocardin and ternary complex factors compete for SRF to control smooth muscle gene expression. Nature 428, 185–189 (2004).

    Article  ADS  CAS  PubMed  Google Scholar 

  27. Liu, Y. et al. Kruppel-like factor 4 abrogates myocardin-induced activation of smooth muscle gene expression. J. Biol. Chem. 280, 9719–9727 (2005).

    Article  CAS  PubMed  Google Scholar 

  28. Tang, R.-H. et al. Myocardin inhibits cellular proliferation by inhibiting NF-κB(p65)-dependent cell cycle progression. Proc. Natl Acad. Sci. USA 105, 3362–3367 (2008).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  29. Vengrenyuk, Y. et al. Cholesterol loading reprograms the microRNA-143/145-myocardin axis to convert aortic smooth muscle cells to a dysfunctional macrophage-like phenotype. Arterioscler. Thromb. Vasc. Biol. 35, 535–546 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Wang, Y. et al. Dynamic changes in chromatin accessibility are associated with the atherogenic transitioning of vascular smooth muscle cells. Cardiovasc. Res. 118, 2792–2804 (2022).

    Article  CAS  PubMed  Google Scholar 

  31. Turner, A. W. et al. Single-nucleus chromatin accessibility profiling highlights regulatory mechanisms of coronary artery disease risk. Nat. Genet. 54, 804–816 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Cartwright, T., Perkins, N. D. & L Wilson, C. L. NFKB1: a suppressor of inflammation, ageing and cancer. FEBS J. 283, 1812–1822 (2016).

    Article  CAS  PubMed  Google Scholar 

  33. Rong, J. X., Shapiro, M., Trogan, E. & Fisher, E. A. Transdifferentiation of mouse aortic smooth muscle cells to a macrophage-like state after cholesterol loading. Proc. Natl Acad. Sci. USA 100, 13531–13536 (2003).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  34. Feig, J. E. et al. Regression of atherosclerosis is characterized by broad changes in the plaque macrophage transcriptome. PLoS ONE 7, e39790 (2012).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  35. von Scheidt, M. et al. Applications and limitations of mouse models for understanding human atherosclerosis. Cell Metab. 25, 248–261 (2017).

    Article  CAS  PubMed  Google Scholar 

  36. Aragam, K. G. et al. Discovery and systematic characterization of risk variants and genes for coronary artery disease in over a million participants. Nat. Genet. 54, 1803–1815 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Räber, L. et al. Effect of alirocumab added to high-intensity statin therapy on coronary atherosclerosis in patients with acute myocardial infarction: the PACMAN-AMI randomized clinical trial. JAMA 327, 1771–1781 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Schwartz, G. G. et al. Alirocumab and cardiovascular outcomes after acute coronary syndrome. N. Engl. J. Med. 379, 2097–2107 (2018).

    Article  CAS  PubMed  Google Scholar 

  39. Martos-Rodríguez, C. J. et al. Fibrous caps in atherosclerosis form by Notch-dependent mechanisms common to arterial media development. Arterioscler. Thromb. Vasc. Biol. 41, e427–e439 (2021).

    Article  PubMed  Google Scholar 

  40. Alonso-Herranz, L., Albarrán-Juárez, J. & Bentzon, J. F. Mechanisms of fibrous cap formation in atherosclerosis. Front. Cardiovasc. Med. 10, 1254114 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Bennett, M. R., Sinha, S. & Owens, G. K. Vascular smooth muscle cells in atherosclerosis. Circ. Res. 118, 692–702 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Gomez, D. et al. Interleukin-1β has atheroprotective effects in advanced atherosclerotic lesions of mice. Nat. Med. 24, 1418–1429 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Brignall, R., Moody, A. T., Mathew, S. & Gaudet, S. Considering abundance, affinity, and binding site availability in the NF-κB target selection puzzle. Front. Immunol. 10, 609 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Evrard, S. M. et al. Endothelial to mesenchymal transition is common in atherosclerotic lesions and is associated with plaque instability. Nat. Commun. 7, 11853 (2016).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  45. Sui, Y. et al. IKKβ links vascular inflammation to obesity and atherosclerosis. J. Exp. Med. 211, 869–886 (2014).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  46. Otsuka, F., Sakakura, K., Yahagi, K., Joner, M. & Virmani, R. Has our understanding of calcification in human coronary atherosclerosis progressed? Arterioscler. Thromb. Vasc. Biol. 34, 724–736 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Kessler, T. et al. Functional characterization of the GUCY1A3 coronary artery disease risk locus. Circulation 136, 476–489 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Bizet, A. A. et al. CD109-mediated degradation of TGF-β receptors and inhibition of TGF-β responses involve regulation of SMAD7 and Smurf2 localization and function. J. Cell. Biochem. 113, 238–246 (2012).

    Article  CAS  PubMed  Google Scholar 

  49. Bindesbøll, C. et al. NBEAL1 controls SREBP2 processing and cholesterol metabolism and is a susceptibility locus for coronary artery disease. Sci. Rep. 10, 4528 (2020).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  50. Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    Article  CAS  PubMed  Google Scholar 

  51. McCarthy, D. J., Campbell, K. R., Lun, A. T. L. & Wills, Q. F. Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R. Bioinformatics 33, 1179–1186 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Stuart, T., Srivastava, A., Madad, S., Lareau, C. A. & Satija, R. Single-cell chromatin state analysis with Signac. Nat. Methods 18, 1333–1341 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Granja, J. M. et al. ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis. Nat. Genet. 53, 403–411 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Fornes, O. et al. JASPAR 2020: update of the open-access database of transcription factor binding profiles. Nucleic Acids Res. 48, D87–D92 (2020).

    CAS  PubMed  Google Scholar 

  56. Schep, A. N., Wu, B., Buenrostro, J. D. & Greenleaf, W. J. chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data. Nat. Methods 14, 975–978 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Ewels, P. A. et al. The nf-core framework for community-curated bioinformatics pipelines. Nat. Biotechnol. 38, 276–278 (2020).

    Article  CAS  PubMed  Google Scholar 

  58. Soneson, C., Love, M. I. & Robinson, M. D. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Res. 4, 1521 (2016).

    Article  PubMed Central  Google Scholar 

  59. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank members of the CNIC Viral Vectors Unit, Microscopy Unit, Genomics Unit, Bioinformatics Unit, Cellomics Unit and Animal Facility for excellent technical help, and S. Bartlett, CNIC, for English editing. This study was supported by grants from the European Research Council under the European Union’s Horizon 2020 research and innovation program (grant no. 866240 to J.F.B.), the Novo Nordisk Foundation (nos. NNF18OC0030688 and NNF20SA0061466 to J.F.B.), the Ministerio de Ciencia e Innovación with cofunding from the European Regional Development Fund (no. PID2019-108568RB-I00 to J.F.B.), the Ministerio de Ciencia e Innovación (no. BES-2016-076633 to P.N. and no. IJC2020-044971-I to D.M.-C.) and the Aarhus University Research Foundation (starting grant no. AUFF-E-201 9-723 to J.A.-J.). Microscopy was conducted at the Microscopy & Dynamic Imaging Unit, CNIC. CNIC is supported by the Instituto de Salud Carlos III, the Ministerio de Ciencia e Innovación and the Pro CNIC Foundation, and is a Severo Ochoa Center of Excellence (grant no. CEX2020-001041-S, funded by MICIN/AEI/10.13039/501100011033). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Centro Nacional de Investigaciones Cardiovasculares, Madrid, Spain

    Laura Carramolino, Esther Hernández-SanMiguel, Vanessa Cumbicus, Daniel Morales-Cano, Verónica Labrador-Cantarero, Paula Nogales, Alberto Benguria, Ana Dopazo, Fátima Sanchez-Cabo, Carlos Torroja & Jacob F. Bentzon

  2. Atherosclerosis Research Unit, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark

    Julián Albarrán-Juárez, Anton Markov, Diana Sharysh, Daniel Morales-Cano & Jacob F. Bentzon

  3. Department of Biomedicine, Aarhus University, Aarhus, Denmark

    Peter Loof Møller

  4. Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares, Madrid, Spain

    Ana Dopazo

  5. Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark

    Jacob F. Bentzon

  6. Department of Cardiology, Aarhus University Hospital, Aarhus, Denmark

    Jacob F. Bentzon

Authors
  1. Laura Carramolino
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Julián Albarrán-Juárez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anton Markov
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Esther Hernández-SanMiguel
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Diana Sharysh
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Vanessa Cumbicus
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Daniel Morales-Cano
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Verónica Labrador-Cantarero
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Peter Loof Møller
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Paula Nogales
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Alberto Benguria
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Ana Dopazo
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Fátima Sanchez-Cabo
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Carlos Torroja
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Jacob F. Bentzon
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

L.C., J.A.-J. and J.F.B. designed the experiments. L.C., E.H.-S., V.C., D.M.-C. and P.N. performed the animal experiments and analyzed the plaques. J.A.-J. performed the in vitro experiments. L.C., D.M.-C., A.B. and A.D. generated the scRNA-seq data. C.T., D.S. and F.S.-C. performed the scRNA-seq bioinformatics analysis. A.M. performed the scATAC–seq and RNA-seq analysis. D.S., P.L.M. and J.F.B. performed the enrichment analysis. V.L.C. designed the image analysis tools. L.C., J.A.-J. and J.F.B. interpreted the data and drafted the manuscript with important contributions from A.M., D.S., C.T., D.M.-C., A.B., A.D., F.S.-C. and P.N. All authors assisted with the manuscript revisions.

Corresponding author

Correspondence to Jacob F. Bentzon.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Cardiovascular Research thanks Stefano Bellosta, Gerard Pasterkamp and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Additional data for the 4-week regression experiment.

a, Plasma total and HDL cholesterol concentrations were measured in serial blood samples during the study. Plasma triglyceride concentrations were measured at the endpoint. b, Representative examples of aortic root sections stained with Oil Red O for neutral lipids (mainly cholesteryl esters) and Sirius Red for collagen-rich tissue. Data are presented as mean ± SEM for n = 9 HF, n = 13 4wLF, and n = 14 4wLF/ASO mice. P values were calculated with repeated-measures ANOVA for the intervention period with Tukey’s post-test (Total and HDL cholesterol) or ANOVA with Tukey’s post-test (Triglycerides). Scale bars, 200 μm.

Source data

Extended Data Fig. 2 Cluster identification and comparison to public scRNA-seq data.

a, UMAP visualization of cell clusters in our scRNA-seq data from plaques and adjacent media. b-e, Expression of Cdh5, Cd68, Tyrobp, and Myh11 to identify endothelial cells (Cdh5 + ), macrophages (Cd68+ and Tyrobp + ), and contractile SMCs (Myh11 + ). f, Comparison of SMC-derived cells in our data set with other published data sets. SMC-derived cells were isolated and integrated with similar data from Alencar et al.22 and Wirka et al.12 by reciprocal principal component analysis. There is high consistency in the detected subgroups of SMCs and SMC-derived cells. Cells were obtained from plaque alone or combined plaque and media in the aortic arch, ascending aorta including the aortic root, or brachiocephalic artery (BCA) of Apoe knockout (Apoe-/-) or rAAV-PCSK9-induced mice as indicated in the labels.

Extended Data Fig. 3 Plasma lipids in the 12-week regression study and tdT-negative cap SMCs.

a, Plasma total and HDL cholesterol concentrations were measured in serial blood samples during the study. Plasma triglyceride concentrations were measured at the endpoint. Data are presented as mean ± SEM for n = 8 HF and n = 9 12wLF/ASO mice. P values were calculated with repeated-measures ANOVA for the intervention period with Tukey’s post-test (Total and HDL cholesterol) or two-sided unpaired t-test with Welch’s correction (Triglycerides). b, Counts of tdT-negative ACTA2+ cells in aortic root sections from the 4-week and 12-week regression experiments. Data are presented as mean ± SEM for n = 9 HF, n = 13 4wLF, and n = 13 4wLF/ASO mice from the 4-week regression experiment, and n = 7 HF and n = 8 12wLF/ASO mice from the 12-week regression experiment (missing data points due to technical issues). Differences are not statistically significant.

Source data

Extended Data Fig. 4 Endothelial-derived intraplaque cells are reduced in regressing plaque.

a, Lineage-tracing of endothelial cells in Cdh5-CreERT2 mice subjected to 4 weeks of plaque regression (4wLF/ASO group) or maintained on HF diet (HF group). Endothelial cell-derived cells are observed within plaques (not part of the endothelial lining, examples marked by arrows). The analysis excluded cells that were part of the coronary artery orifices (marked by *). The image is a representative example from the HF group. b-d, Endothelial-derived cells were both observed as layers parallel to the surface (b), at the bottom of invaginations (c), and as isolated cells (d). Detectable ACTA2 at the level of other cap cells was rare. Representative examples from the HF group are shown. e, Total and CD31+ tdT+ cells, but not the small population of CD31- tdT+ cells, were significantly reduced in regressing plaque. Data are presented as median and interquartile range of n = 10 HF and n = 11 4wLF/ASO mice. P values were calculated by two-sided Mann-Whitney tests. L, Lumen. P, plaque. M, media. Scale bars, 200 μm (solid), 100 μm (dashed).

Source data

Extended Data Fig. 5 Gene expression changes in SMC-derived cells after 4 weeks of plaque regression.

Differentially expressed genes (DEGs) were analyzed in SMC-derived cell clusters (C0-C4). Volcano plots show DEGs in each cluster. The top 10 downregulated and upregulated genes after 4 weeks of ApoB-LP lowering are shown in cyan and ochre, respectively. Analysis was performed using the MAST method in Seurat with P values adjusted for multiple testing by the Bonferroni method.

Extended Data Fig. 6 Gene expression changes in SMC-derived cells after 12 weeks of plaque regression.

Differentially expressed genes (DEGs) were analyzed in SMC-derived cell clusters (C0-C4). Volcano plots show DEGs in each cluster. The top 10 downregulated and upregulated genes upon 12 weeks of ApoB-LP lowering are shown in cyan and ochre, respectively. Analysis was performed using the MAST method in Seurat with P values adjusted for multiple testing by the Bonferroni method.

Extended Data Fig. 7 Validation experiment without diet change.

a, Experimental design. Male and female Ldlr-/- mice were maintained on high-fat (HF) diet and treated for 4 weeks with either Apob antisense oligonucleotide (4wHF/ASO group) or a control ASO (HF group). b, Measurements of the concentration of total, ApoB-LP, and HDL cholesterol in plasma in each group. Data are presented as mean ± SEM for n = 4 HF females, n = 5 HF males, n = 5 4wHF/ASO females, n = 5 4wHF/ASO males. Comparisons between groups within each sex are shown. P values are calculated by a mixed-effects analysis with Šidák post-test comparing values during the intervention period (time points 27w-30w). c-d, C3 expression is restricted to SMC-derived plaque cells. C3 expression, tdT expression, and clustering in the 4-week (c) and 12-week (d) regression experiments are shown. Cells are from atherosclerotic aortic arches after removal of the adventitia, but a small group of fibroblasts is present in 12-week regression experiment data. Within plaque (i.e., not considering fibroblasts), C3 is selectively expressed in modulated SMC-derived cells. Color coding within the SMC-derived cells corresponds to the main text (light blue = SMC0, dark blue = fibromyocytes, light brown = chondromyocytes, light green = SMC1/cap, light red = SMC2). The cells shown are after doublet removal.

Source data

Extended Data Fig. 8 Expression of genes encoding the TNF and IL1β receptors in SMC-derived cells.

a-b, Expression of, encoding the TNF receptor 1, and Il1r1, encoding the IL1β receptor, in clusters C0-C4 in the 4-week (a) and 12-week (b) regression experiments. Tnfrsf1a and Il1r1 are expressed in SMC-derived cells with the highest expression in fibromyocytes and chondromyocytes. There is a trend towards lower expression of Tnfrsf1a after ApoB-LP lowering, which, however, did not reach genome-wide significance.

Extended Data Fig. 9 Validation of the RNA sequencing experiment.

a-b, To validate the RNA sequencing experiment, we performed independent quantitative real-time PCR experiments in primary mouse aortic SMCs treated with cholesterol loading (chol, 10 μg/ml, 72 h) (a) or tumor necrosis factor (TNF, 10 ng/ml, 24 h) (b). Additionally, cells were treated with either a control siRNA (siCtr) or a Ikbkb siRNA (siIkbkb). The expression levels of C3, Lcn2, Vcam1, and Il6 genes are shown. We used three independent replicates per condition. Data are presented as mean±SEM. P values calculated by one-way ANOVA followed by Šidák post-test.

Source data

Extended Data Fig. 10 Graphical summary.

In this study, we combined smooth muscle cell (SMC) lineage-tracing, mouse models of atherosclerosis, techniques to manipulate lipoprotein metabolism, and single-cell expression profiling. We found that lowering ApoB-containing lipoproteins (ApoB-LPs) leads to early reversal of NFκB signaling in SMC-derived fibromyocytes and chondromyocytes, followed by their depletion from plaques. ASO, antisense oligonucleotides. rAAV-PCSK9, recombinant adeno-associated virus encoding proprotein convertase subtilisin/kexin type 9.

Supplementary information

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Fig. 6

Statistical source data.

Source Data Fig. 7

Statistical source data.

Source Data Fig. 8

Statistical source data.

Source Data Extended Data Fig. 1

Statistical source data.

Source Data Extended Data Fig. 3

Statistical source data.

Source Data Extended Data Fig. 4

Statistical source data.

Source Data Extended Data Fig. 7

Statistical source data.

Source Data Extended Data Fig. 9

Statistical source data.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Carramolino, L., Albarrán-Juárez, J., Markov, A. et al. Cholesterol lowering depletes atherosclerotic lesions of smooth muscle cell-derived fibromyocytes and chondromyocytes. Nat Cardiovasc Res 3, 203–220 (2024). https://doi.org/10.1038/s44161-023-00412-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s44161-023-00412-w

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing