J Lipid Atheroscler.  2019 Sep;8(2):152-161. 10.12997/jla.2019.8.2.152.

Single Cell RNA-Sequencing for the Study of Atherosclerosis

• Hajkarim M
• Won KJ

• KMID: 2458380
• DOI: http://doi.org/10.12997/jla.2019.8.2.152

Abstract

Atherosclerosis is a major cause of coronary artery disease and stroke. A massive and new type of data has finally arrived in the field of atherosclerosis: single cell RNA sequencing (scRNAseq). Recently, scRNAseq has been successfully applied to the study of atherosclerosis to identify previously uncharacterized cell populations. scRNAseq is an effective approach to evaluate heterogeneous cell populations by measuring the transcriptomic profiles at the single cell level. Besides the studies of atherosclerosis, scRNAseq is being employed in various areas of biology, including cancer research and organ development. In order to analyze these new massive datasets, various analytic approaches have been developed. This review aims to enhance the understanding of this new technology by exploring how the single cell transcriptome has been applied to the study of atherosclerosis and further discuss potential analysis of using scRNAseq.

Keyword

Atherosclerosis; Single-cell analysis; Transcriptome

MeSH Terms

Atherosclerosis*
Biology
Coronary Artery Disease
Dataset
Sequence Analysis, RNA
Single-Cell Analysis
Stroke
Transcriptome

Figure

• 

  Fig. 1 The scRNAseq data processing for atherosclerosis. (A) scRNA procedure. The scRNAseq data are collected from each sample which are represented in a table. PCA and/or tSNE is applied to reduce the dimension for the sake of clustering. Genes expressed in each cluster are examined. (B) Clustering results using scRNAseq data from the study by Lin et al.16 scRNAseq, single cell RNA sequencing; PCA, principal components analysis; tSNE, t-distributed stochastic neighbor embedding; Ear2, V-erbA-related protein 2; Irf7, interferon regulatory factor 7.

• 

  Fig. 2 Cell decomposition using scRNAseq. When bulk RNAseq and scRNAseq are available, cell decomposition may be used to obtain the cell composition. scRNAseq, single cell RNA sequencing; RNAseq, RNA sequencing.

• 

  Fig. 3 Pseudo-time analysis using scRNAseq. The scRNAseq are obtained from cells during direct conversion to cardiomyocytes49 and reprocessed. Fibroblast cells are located on the left side and cardiomyocyte cells on the top right. Cells can be aligned in between based on their transcriptomic similarities. When aligned, pseudo-time analysis is applied. The expression level of Eln, a fibroblast marker, decreases along the pseudo-time. scRNAseq, single cell RNA sequencing; Eln, elastin; Dlk1, delta like non-canonical notch ligand 1; Tnni1, troponin I1, slow skeletal type; Tnni3, troponin I3, cardiac type.

Reference

1. Vengrenyuk Y, Nishi H, Long X, Ouimet M, Savji N, Martinez FO, 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. 2015; 35:535–546.

2. Rong JX, Shapiro M, Trogan E, Fisher EA. Transdifferentiation of mouse aortic smooth muscle cells to a macrophage-like state after cholesterol loading. Proc Natl Acad Sci U S A. 2003; 100:13531–13536.

3. Feil S, Fehrenbacher B, Lukowski R, Essmann F, Schulze-Osthoff K, Schaller M, et al. Transdifferentiation of vascular smooth muscle cells to macrophage-like cells during atherogenesis. Circ Res. 2014; 115:662–667.

4. Li Y, Lui KO, Zhou B. Reassessing endothelial-to-mesenchymal transition in cardiovascular diseases. Nat Rev Cardiol. 2018; 15:445–456.

5. Huang S. Non-genetic heterogeneity of cells in development: more than just noise. Development. 2009; 136:3853–3862.

6. Maamar H, Raj A, Dubnau D. Noise in gene expression determines cell fate in Bacillus subtilis. Science. 2007; 317:526–529.

7. Eldar A, Elowitz MB. Functional roles for noise in genetic circuits. Nature. 2010; 467:167–173.

8. Jiang L, Chen H, Pinello L, Yuan GC. GiniClust: detecting rare cell types from single-cell gene expression data with Gini index. Genome Biol. 2016; 17:144.

9. Trapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li S, Morse M, et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. 2014; 32:381–386.

10. Chen G, Ning B, Shi T. Single-cell RNA-seq technologies and related computational data analysis. Front Genet. 2019; 10:317.

11. Butcher MJ, Filipowicz AR, Waseem TC, McGary CM, Crow KJ, Magilnick N, et al. Atherosclerosis-driven Treg plasticity results in formation of a dysfunctional subset of plastic IFNγ+ Th1/Tregs. Circ Res. 2016; 119:1190–1203.

12. Cochain C, Vafadarnejad E, Arampatzi P, Pelisek J, Winkels H, Ley K, et al. Single-cell RNA-seq reveals the transcriptional landscape and heterogeneity of aortic macrophages in murine atherosclerosis. Circ Res. 2018; 122:1661–1674.

13. Winkels H, Ehinger E, Vassallo M, Buscher K, Dinh HQ, Kobiyama K, et al. Atlas of the immune cell repertoire in mouse atherosclerosis defined by single-cell RNA-sequencing and mass cytometry. Circ Res. 2018; 122:1675–1688.

14. Gu W, Ni Z, Tan YQ, Deng J, Zhang SJ, Lv ZC, et al. Adventitial cell atlas of wt (wild type) and ApoE (apolipoprotein E)-deficient mice defined by single-cell RNA sequencing. Arterioscler Thromb Vasc Biol. 2019; 39:1055–1071.

15. Kim K, Shim D, Lee JS, Zaitsev K, Williams JW, Kim KW, et al. Transcriptome analysis reveals nonfoamy rather than foamy plaque macrophages are proinflammatory in atherosclerotic murine models. Circ Res. 2018; 123:1127–1142.

16. Lin JD, Nishi H, Poles J, Niu X, Mccauley C, Rahman K, et al. Single-cell analysis of fate-mapped macrophages reveals heterogeneity, including stem-like properties, during atherosclerosis progression and regression. JCI Insight. 2019; 4:124574.

17. Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018; 36:411–420.

18. Valihrach L, Androvic P, Kubista M. Platforms for single-cell collection and analysis. Int J Mol Sci. 2018; 19:E807.

19. Wolock SL, Lopez R, Klein AM. Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Cell Syst. 2019; 8:281–291.e9.

20. McGinnis CS, Murrow LM, Gartner ZJ. DoubletFinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Cell Syst. 2019; 8:329–337.e4.

21. Büttner M, Miao Z, Wolf FA, Teichmann SA, Theis FJ. A test metric for assessing single-cell RNA-seq batch correction. Nat Methods. 2019; 16:43–49.

22. Lun AT, Bach K, Marioni JC. Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol. 2016; 17:75.

23. Lall S, Sinha D, Bandyopadhyay S, Sengupta D. Structure-aware principal component analysis for single-cell RNA-seq data. J Comput Biol. 2018; 25:1365–1373.

24. Kiselev VY, Kirschner K, Schaub MT, Andrews T, Yiu A, Chandra T, et al. SC3: consensus clustering of single-cell RNA-seq data. Nat Methods. 2017; 14:483–486.

25. Wang B, Ramazzotti D, De Sano L, Zhu J, Pierson E, Batzoglou S. SIMLR: a tool for large-scale genomic analyses by multi-kernel learning. Proteomics. 2018; 18:1700232.

26. Kim J, Stanescu DE, Won KJ. CellBIC: bimodality-based top-down clustering of single-cell RNA sequencing data reveals hierarchical structure of the cell type. Nucleic Acids Res. 2018; 46:e124.

27. Wan SJ, Kim J, Won KJ. SHARP: single-cell RNA-seq hyper-fast and accurate processing via ensemble random projection. bioRxivorg. 2018.

28. Shen-Orr SS, Gaujoux R. Computational deconvolution: extracting cell type-specific information from heterogeneous samples. Curr Opin Immunol. 2013; 25:571–578.

29. Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015; 12:453–457.

30. Ji Z, Ji H. TSCAN: pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis. Nucleic Acids Res. 2016; 44:e117.

31. Wolf FA, Hamey FK, Plass M, Solana J, Dahlin JS, Göttgens B, et al. PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells. Genome Biol. 2019; 20:59.

32. Tegner J, Yeung MK, Hasty J, Collins JJ. Reverse engineering gene networks: integrating genetic perturbations with dynamical modeling. Proc Natl Acad Sci U S A. 2003; 100:5944–5949.

33. Ståhl PL, Salmén F, Vickovic S, Lundmark A, Navarro JF, Magnusson J, et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science. 2016; 353:78–82.

34. Moffitt JR, Bambah-Mukku D, Eichhorn SW, Vaughn E, Shekhar K, Perez JD, et al. Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region. Science. 2018; 362:eaau5324.

35. Svensson V, Teichmann SA, Stegle O. SpatialDE: identification of spatially variable genes. Nat Methods. 2018; 15:343–346.

36. Ramilowski JA, Goldberg T, Harshbarger J, Kloppmann E, Lizio M, Satagopam VP, et al. A draft network of ligand-receptor-mediated multicellular signalling in human. Nat Commun. 2015; 6:7866.

37. Grün D, Lyubimova A, Kester L, Wiebrands K, Basak O, Sasaki N, et al. Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature. 2015; 525:251–255.

38. Patel AP, Tirosh I, Trombetta JJ, Shalek AK, Gillespie SM, Wakimoto H, et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science. 2014; 344:1396–1401.

39. Shalek AK, Satija R, Shuga J, Trombetta JJ, Gennert D, Lu D, et al. Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature. 2014; 510:363–369.

40. Pollen AA, Nowakowski TJ, Shuga J, Wang X, Leyrat AA, Lui JH, et al. Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nat Biotechnol. 2014; 32:1053–1058.

41. Proserpio V, Piccolo A, Haim-Vilmovsky L, Kar G, Lönnberg T, Svensson V, et al. Single-cell analysis of CD4+ T-cell differentiation reveals three major cell states and progressive acceleration of proliferation. Genome Biol. 2016; 17:103.

42. Kolodziejczyk AA, Kim JK, Svensson V, Marioni JC, Teichmann SA. The technology and biology of single-cell RNA sequencing. Mol Cell. 2015; 58:610–620.

43. Haque A, Engel J, Teichmann SA, Lönnberg T. A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications. Genome Med. 2017; 9:75.

44. Macaulay IC, Haerty W, Kumar P, Li YI, Hu TX, Teng MJ, et al. G&T-seq: parallel sequencing of single-cell genomes and transcriptomes. Nat Methods. 2015; 12:519–522.

45. Rodriguez-Meira A, Buck G, Clark SA, Povinelli BJ, Alcolea V, Louka E, et al. Unravelling intratumoral heterogeneity through high-sensitivity single-cell mutational analysis and parallel RNA sequencing. Mol Cell. 2019; 73:1292–1305.e8.

46. Han KY, Kim KT, Joung JG, Son DS, Kim YJ, Jo A, et al. SIDR: simultaneous isolation and parallel sequencing of genomic DNA and total RNA from single cells. Genome Res. 2018; 28:75–87.

47. Stoeckius M, Hafemeister C, Stephenson W, Houck-Loomis B, Chattopadhyay PK, Swerdlow H, et al. Simultaneous epitope and transcriptome measurement in single cells. Nat Methods. 2017; 14:865–868.

48. Peterson VM, Zhang KX, Kumar N, Wong J, Li L, Wilson DC, et al. Multiplexed quantification of proteins and transcripts in single cells. Nat Biotechnol. 2017; 35:936–939.

49. Liu Z, Wang L, Welch JD, Ma H, Zhou Y, Vaseghi HR, et al. Single-cell transcriptomics reconstructs fate conversion from fibroblast to cardiomyocyte. Nature. 2017; 551:100–104.