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Transcriptomic-based clustering of human atherosclerotic plaques identifies subgroups with different underlying biology and clinical presentation


Histopathological studies have revealed key processes of atherosclerotic plaque thrombosis. However, the diversity and complexity of lesion types highlight the need for improved subphenotyping. Here, we analyzed the gene expression profiles of 654 advanced human carotid plaques. The unsupervised, transcriptome-driven clustering revealed five dominant plaque types. These plaque phenotypes were associated with clinical presentation and showed differences in cellular compositions. Validation in coronary segments showed that the molecular signature of these plaques was linked to coronary ischemia. One of the plaque types with the most severe clinical symptoms pointed to both inflammatory and fibrotic cell lineages. Furthermore, we did a preliminary analysis of potential circulating biomarkers that mark the different plaque phenotypes. In conclusion, the definition of the plaque at risk for a thrombotic event can be fine-tuned by in-depth transcriptomic-based phenotyping. These differential plaque phenotypes prove clinically relevant for both carotid and coronary artery plaques and point to distinct underlying biology of symptomatic lesions.

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Fig. 1: Unsupervised clustering of plaques based on transcriptomics data.
Fig. 2: Unsupervised clustering of plaques and cell composition.
Fig. 3: Association of molecular plaque types with secondary risk and genetic risk.
Fig. 4: Unsupervised clustering of coronary artery samples based on transcriptomics data.
Fig. 5: Circulating biomarkers differ among transcriptome-based clusters.
Fig. 6: Graphical summary of the study.

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Data availability

Raw RNA-seq data from the Athero-Express cohort are not publicly available owing to research participant privacy and consent. Data can be accessed via DataverseNL at this address: There are restrictions on use by commercial parties and on sharing openly based on (inter)national laws and regulations and written informed consent. Therefore, these data (and additional clinical data) are available only upon discussion and upon signing a data sharing agreement (see Terms of Access in DataverseNL) and within a specially designed environment provided by UMC Utrecht.

The processed Athero-Express RNA-seq dataset can be accessed using the PlaqView portal (

Raw and processed bulk RNA-seq data from the coronary artery tissues will be made available on the Gene Expression Omnibus as well as on upon publication of the primary manuscript describing these data. In the meantime, requests to access these data can be addressed to C.L.M. (

Code availability

The core scripts used for the analysis can be found at


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This work was supported by the Dutch Heart Foundation (CVON2017-20: Generating the best evidence-based pharmaceutical targets and drugs for atherosclerosis (GENIUS II) to G.P., S.W.v.d.L. and M.P.J.d.W. and Targeting macrophages in atherosclerotic disease (T-MAD) to M.P.J.d.W.); Fondation Leducq (Transatlantic Network Grant PlaqOmics) to N.J.L., M.C., G.K.O., A.V.F., J.L.M.B., C.L.M. and G.P.; Transatlantic Network LEAN to M.P.J.d.W.; EU 755320 Taxinomisis grant to E.P., E.A., G.J.d.B., A.B. and G.P.; and the Dutch Research Council VENI grant (VI.VENI.212.196) to K.H.M.P. We acknowledge the European Research Area Network on Cardiovascular Diseases (grant no. 01KL1802 to F.W.A. and S.W.v.d.L.); the ERA-Endless Consortium (Dutch Heart Foundation, grant no. 2017/T099 to H.M.d.R. and G.P.); a European Research Council Consolidator Grant (grant no. 866478 UCARE to H.M.d.R.); and National Institutes of Health grant R01HL148239 to C.L.M. F.W.A. is supported by UCL Hospitals NIHR Biomedical Research Centre. The authors would like to thank the Utrecht Sequencing Facility for continuous support and patience.

Author information

Authors and Affiliations



M.M. and A.B. analyzed and integrated the data. S.W.v.d.L. and J.M. provided PRS calculations. A.B. and S.W.v.d.L. performed patient selection, randomization and sample handling. G.J.d.B. performed CEA procedures. N.A.M.v.d.D., N.L. and E.M. tested library preparation strategies and processed coronary samples for sequencing. M.A.C.D., K.H.M.P., L.S., M.P.J.W. and J.P. provided, analyzed and interpreted single-cell sequencing data. K.C. and G.B.-B. performed deconvolution of bulk RNA-seq data. J.M., N.T., F.W. and M.C.V. recruited the patients, coordinated by D.P.V.d.K. D.P.v.d.K. and E.S.G.S. provided and coordinated Olink measurements. L.S. and S.W.v.d.L. performed MAGMA analysis. A.B., N.T., F.W., D.P.v.d.K., H.M.d.R., F.W.A., J.L.M.B. and S.W.v.d.L. participated in conceptualization and data interpretation and provided critical feedback on the article. E.D.B., R.J.G.H., E.P., E.A., H.S., G.K.O., C.M., J.C.K., A.V.F., R.V., N.J.L. and M.C. participated in data interpretation and provided critical feedback on the article. A.W.T., M.D.K., C.J.H., J.C.K., J.L.M.B. and C.L.M. recruited, processed and analyzed coronary samples. E.A., F.W.A., S.W.v.d.L., C.L.M., M.M. and G.P. provided funding. M.M., C.L.M. and G.P. participated in the conceptualization and supervision of the project and the finalization of the article. M.M. prepared the figures. M.M. and G.P. drafted the manuscript. All authors provided feedback on the research, analyses and article.

Corresponding authors

Correspondence to Michal Mokry or Gerard Pasterkamp.

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Competing interests

C.L.M. has received funding support from AstraZeneca for work unrelated to this study. G.P. received funding support from Roche to partly cover the generation of biomarker data. The remaining authors declare no competing interests.

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Nature Cardiovascular Research thanks Muredach Reilly, Dennis Wolf and Anders Malarstig for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Unsupervised clustering of plaques based on transcriptomics data.

a) PCA plot and b) tSNE projection of the 654 plaque samples based on RNA-seq dataset. The color indicates the cluster corresponding to the plaque type cluster from the SNN modularity optimization based clustering algorithm. c) Distribution of non-ribosomal protein-coding genes with annotated HGNC name; reads mapping to mitochondrial genes and mean Pearson correlation of samples per cluster

Extended Data Fig. 2 Robustness of the clustering.

a) UMAP projection of the 654 plaque samples based on RNA-seq dataset. The color indicates the cluster corresponding to the plaque type cluster from the SNN modularity optimization-based clustering algorithm after batch and hospital correction. b) Heatmap depicts relative gene expression levels of selected plaque type enriched genes in individual samples and plaque clusters. c) Correspondence of clusters derived from batch corrected and original dataset. Numbers indicate sample counts in the intersection of the corresponding clusters. d) UMAP projection of the 654 plaque samples based on RNA-seq dataset. The color indicates the cluster corresponding to the plaque type cluster from the SNN modularity optimization-based clustering algorithm after batch and hospital correction using Harmony integration. e) Heatmap depicts relative gene expression levels of selected plaque type enriched genes in individual samples and plaque clusters. f) Correspondence of clusters derived from batch corrected (using Harmony) and original dataset. Numbers indicate sample counts in the intersection of the corresponding clusters. g) UMAP projection of the 654 plaque samples based on RNA-seq dataset. The color indicates the frequency of the sample being assigned in other than the original cluster in permutation analysis. h) Distribution of the frequencies of the sample being assigned in other than the original cluster in permutation analysis.

Extended Data Fig. 3 Integrative analysis of coronary and carotid dataset.

a) Heatmaps depicting relative gene expression levels of selected plaque type enriched genes in individual samples and plaque clusters (upper panel - combined coronary and carotid dataset, lower panel – original carotid dataset). b) UMAP projection of the coronary and carotid sample based on RNA-seq data. The color indicates the cluster corresponding to the plaque type cluster from the SNN modularity optimization based clustering algorithm

Extended Data Fig. 4 Plaque transcriptomics.

a) Bioanalyzer profiles of total RNA isolated from 10 random samples of advanced atherosclerotic lesions. b) Distribution of sequencing reads between samples using four different library preparation strategies. (n = 12 per library preparation method, in the case of SMARTer n = 6) c) Percentage of sequenced reads mapped to annotated genes using four different library preparation strategies. (n = 12 per library preparation method, in the case of SMARTer n = 6) Boxplot’s top, middle, and bottom lines in c) and d) represent values at 25th, 50th, and 75th percentile. Whiskers extend up to 1.5 times the interquartile range from the top (bottom) of the box to the furthest data point within that distance. d) Number of annotated genes identified per sample with at least one mapped read. Samples with less than 9000 genes were excluded from the analysis e) Number of non-ribosomal protein-coding genes with annotated HGNC name per sample used in the analysis.

Supplementary information

Reporting Summary

Supplementary Table 1

Baseline table

Supplementary Table 2

Cluster-specific genes

Supplementary Table 3

Pathway enrichment analysis

Supplementary Table 4

Scaled gene expression values per cluster

Supplementary Table 5

Histological parameters

Supplementary Table 6

Circulating biomarkers

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Mokry, M., Boltjes, A., Slenders, L. et al. Transcriptomic-based clustering of human atherosclerotic plaques identifies subgroups with different underlying biology and clinical presentation. Nat Cardiovasc Res 1, 1140–1155 (2022).

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