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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2020.1
- 수록면
- 393 - 399 (7page)
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초록· 키워드
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Recent advancements in the resolution and throughput of single-cell analyses, including single-cell RNA sequencing (scRNA-seq), have achieved significant progress in biomedical research in the last decade. These techniques have been used to understand cellular heterogeneity by identifying many rare and novel cell types and characterizing subpopulations of cells that make up organs and tissues. Analysis across various datasets can elucidate temporal patterning in gene expression and developmental cues and is also employed to examine the response of cells to acute injury, damage, or disruption. Specifically, scRNA-seq and spatially resolved transcriptomics have been used to describe the identity of novel or rare cell subpopulations and transcriptional variations that are related to normal and pathological conditions in mammalian models and human tissues. These applications have critically contributed to advance basic cardiovascular research in the past decade by identifying novel cell types implicated in development and disease. In this review, we describe current scRNA-seq technologies and how current scRNA-seq and spatial transcriptomic (ST) techniques have advanced our understanding of cardiovascular development and disease.
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참고문헌 (48)
참고문헌 신청
Lipsett DB / 2019 / Cardiomyocyte substructure reverts to an immature phenotype during heart failure / J Physiol 597:1833~1853
Eberwine J / 2014 / The promise of single-cell sequencing / Nat Methods 11:25~27
Olsen TK / 2018 / Introduction to single-cell RNA sequencing / Curr Protoc Mol Biol 122:1~14
Tang F / 2009 / mRNA-Seq whole-transcriptome analysis of a single cell / Nat Methods 6:377~382
Chen G / 2019 / Single-cell RNA-seq technologies and related computational data analysis / Front Genet 10:1~13
Eberwine J / 2014 / The promise of single-cell sequencing / Nat Methods 11:25~27
Olsen TK / 2018 / Introduction to single-cell RNA sequencing / Curr Protoc Mol Biol 122:1~14
Tang F / 2009 / mRNA-Seq whole-transcriptome analysis of a single cell / Nat Methods 6:377~382
Chen G / 2019 / Single-cell RNA-seq technologies and related computational data analysis / Front Genet 10:1~13
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