본문 바로가기
KERD
논문KCI 등재

Single Cell RNA-Sequencing for the Study of Atherosclerosis

1

조회

0

다운로드

1

인용

0

참고문헌

키워드

초록

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.

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.

참고문헌 (49)

  1. Vengrenyuk Y, "Cholesterol loading reprograms the microRNA-143", 145-myocardin axis to convert aortic smooth muscle cells to a dysfunctional macrophage-like phenotype, 2015, pp.35, 535~546
  2. Rong JX, "Transdifferentiation of mouse aortic smooth muscle cells to a macrophage-like state after cholesterol loading", Proc Natl Acad Sci U S A, 100, pp.13531~13536 (2003)
  3. Feil S, "Transdifferentiation of vascular smooth muscle cells to macrophage-like cells during atherogenesis", Circ Res, 115, pp.662~667 (2014)
  4. Li Y, "Reassessing endothelial-to-mesenchymal transition in cardiovascular diseases", Nat Rev Cardiol, 15, pp.445~456 (2018)
  5. Huang S, "Non-genetic heterogeneity of cells in development: more than just noise", Development, 136, pp.3853~3862 (2009)
  6. Maamar H, "Noise in gene expression determines cell fate in Bacillus subtilis", Science, 317, pp.526~529 (2007)
  7. Eldar A, "Functional roles for noise in genetic circuits", Nature, 467, pp.167~173 (2010)
  8. Jiang L, "GiniClust: detecting rare cell types from single-cell gene expression data with Gini index", Genome Biol, 17, pp.144~ (2016)
  9. Trapnell C, "The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells", Nat Biotechnol, 32, pp.381~386 (2014)
  10. Chen G, "Single-cell RNA-seq technologies and related computational data analysis", Front Genet, 10, pp.317~ (2019)
  11. Butcher MJ, "Atherosclerosisdriven Treg plasticity results in formation of a dysfunctional subset of plastic IFNγ+ Th1", Tregs, 2016, pp.119, 1190~1203
  12. Cochain C, "Single-cell RNA-seq reveals the transcriptional landscape and heterogeneity of aortic macrophages in murine atherosclerosis", Circ Res, 122, pp.1661~1674 (2018)
  13. Winkels H, "Atlas of the immune cell repertoire in mouse atherosclerosis defined by single-cell RNA-sequencing and mass cytometry", Circ Res, 122, pp.1675~1688 (2018)
  14. Gu W, "Adventitial cell atlas of wt (wild type) and ApoE (apolipoprotein E)-deficient mice defined by single-cell RNA sequencing", Arterioscler Thromb Vasc Biol, 39, pp.1055~1071 (2019)
  15. Kim K, "Transcriptome analysis reveals nonfoamy rather than foamy plaque macrophages are proinflammatory in atherosclerotic murine models", Circ Res, 123, pp.1127~1142 (2018)
  16. Lin JD, "Single-cell analysis of fate-mapped macrophages reveals heterogeneity, including stem-like properties, during atherosclerosis progression and regression", JCI Insight, 4, pp.124574~ (2019)
  17. Butler A, "Integrating single-cell transcriptomic data across different conditions, technologies, and species", Nat Biotechnol, 36, pp.411~420 (2018)
  18. Valihrach L, "Platforms for single-cell collection and analysis", Int J Mol Sci, 19, pp.E807~ (2018)
  19. Wolock SL, "Scrublet: computational identification of cell doublets in single-cell transcriptomic data", Cell Syst, 8, pp.281~291.e9 (2019)
  20. McGinnis CS, "DoubletFinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors", Cell Syst, 8, pp.329~337.e4 (2019)
  21. Büttner M, "A test metric for assessing single-cell RNA-seq batch correction", Nat Methods, 16, pp.43~49 (2019)
  22. Lun AT, "Pooling across cells to normalize single-cell RNA sequencing data with many zero counts", Genome Biol, 17, pp.75~ (2016)
  23. Lall S, "Structure-aware principal component analysis for singlecell RNA-seq data", J Comput Biol, 25, pp.1365~1373 (2018)
  24. Kiselev VY, "SC3: consensus clustering of single-cell RNA-seq data", Nat Methods, 14, pp.483~486 (2017)
  25. Wang B, "SIMLR: a tool for large-scale genomic analyses by multi-kernel learning", Proteomics, 18, pp.1700232~ (2018)
  26. Kim J, "CellBIC: bimodality-based top-down clustering of single-cell RNA sequencing data reveals hierarchical structure of the cell type", Nucleic Acids Res, 46, pp.e124~ (2018)
  27. Wan SJ, "SHARP: single-cell RNA-seq hyper-fast and accurate processing via ensemble random projection", bioRxivorg (2018)
  28. Shen-Orr SS, "Computational deconvolution: extracting cell type-specific information from heterogeneous samples", Curr Opin Immunol, 25, pp.571~578 (2013)
  29. Newman AM, "Robust enumeration of cell subsets from tissue expression profiles", Nat Methods, 12, pp.453~457 (2015)
  30. Ji Z, "TSCAN: pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis", Nucleic Acids Res, 44, pp.e117~ (2016)
  31. Wolf FA, "PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells", Genome Biol, 20, pp.59~ (2019)
  32. Tegner J, "Reverse engineering gene networks: integrating genetic perturbations with dynamical modeling", Proc Natl Acad Sci U S A, 100, pp.5944~5949 (2003)
  33. Ståhl PL, "Visualization and analysis of gene expression in tissue sections by spatial transcriptomics", Science, 353, pp.78~82 (2016)
  34. Moffitt JR, "Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region", Science, 362, pp.eaau5324~ (2018)
  35. Svensson V, "SpatialDE: identification of spatially variable genes", Nat Methods, 15, pp.343~346 (2018)
  36. Ramilowski JA, "A draft network of ligand-receptor-mediated multicellular signalling in human", Nat Commun, 6, pp.7866~ (2015)
  37. Grün D, "Single-cell messenger RNA sequencing reveals rare intestinal cell types", Nature, 525, pp.251~255 (2015)
  38. Patel AP, "Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma", Science, 344, pp.1396~1401 (2014)
  39. Shalek AK, "Single-cell RNA-seq reveals dynamic paracrine control of cellular variation", Nature, 510, pp.363~369 (2014)
  40. Pollen AA, "Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex", Nat Biotechnol, 32, pp.1053~1058 (2014)
  41. Proserpio V, "Single-cell analysis of CD4+ T-cell differentiation reveals three major cell states and progressive acceleration of proliferation", Genome Biol, 17, pp.103~ (2016)
  42. Kolodziejczyk AA, "The technology and biology of singlecell RNA sequencing", Mol Cell, 58, pp.610~620 (2015)
  43. Haque A, "A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications", Genome Med, 9, pp.75~ (2017)
  44. Macaulay IC, "G&T-seq: parallel sequencing of single-cell genomes and transcriptomes", Nat Methods, 12, pp.519~522 (2015)
  45. Rodriguez-Meira A, "Unravelling intratumoral heterogeneity through high-sensitivity single-cell mutational analysis and parallel RNA sequencing", Mol Cell, 73, pp.1292~1305.e8 (2019)
  46. Han KY, "SIDR: simultaneous isolation and parallel sequencing of genomic DNA and total RNA from single cells", Genome Res, 28, pp.75~87 (2018)
  47. Stoeckius M, "Simultaneous epitope and transcriptome measurement in single cells", Nat Methods, 14, pp.865~868 (2017)
  48. Peterson VM, "Multiplexed quantification of proteins and transcripts in single cells", Nat Biotechnol, 35, pp.936~939 (2017)
  49. Liu Z, "Single-cell transcriptomics reconstructs fate conversion from fibroblast to cardiomyocyte", Nature, 551, pp.100~104 (2017)

관련 논문AI추천

이 간행물의 다른 논문 (16건)