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A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications.

作者信息

Haque Ashraful, Engel Jessica, Teichmann Sarah A, Lönnberg Tapio

机构信息

QIMR Berghofer Medical Research Institute, Herston, Brisbane, Queensland, 4006, Australia.

Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.

出版信息

Genome Med. 2017 Aug 18;9(1):75. doi: 10.1186/s13073-017-0467-4.


DOI:10.1186/s13073-017-0467-4
PMID:28821273
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5561556/
Abstract

RNA sequencing (RNA-seq) is a genomic approach for the detection and quantitative analysis of messenger RNA molecules in a biological sample and is useful for studying cellular responses. RNA-seq has fueled much discovery and innovation in medicine over recent years. For practical reasons, the technique is usually conducted on samples comprising thousands to millions of cells. However, this has hindered direct assessment of the fundamental unit of biology-the cell. Since the first single-cell RNA-sequencing (scRNA-seq) study was published in 2009, many more have been conducted, mostly by specialist laboratories with unique skills in wet-lab single-cell genomics, bioinformatics, and computation. However, with the increasing commercial availability of scRNA-seq platforms, and the rapid ongoing maturation of bioinformatics approaches, a point has been reached where any biomedical researcher or clinician can use scRNA-seq to make exciting discoveries. In this review, we present a practical guide to help researchers design their first scRNA-seq studies, including introductory information on experimental hardware, protocol choice, quality control, data analysis and biological interpretation.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1844/5561556/fe6ebb48050f/13073_2017_467_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1844/5561556/fe6ebb48050f/13073_2017_467_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1844/5561556/fe6ebb48050f/13073_2017_467_Fig1_HTML.jpg

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本文引用的文献

[1]
Granatum: a graphical single-cell RNA-Seq analysis pipeline for genomics scientists.

Genome Med. 2017-12-5

[2]
powsimR: power analysis for bulk and single cell RNA-seq experiments.

Bioinformatics. 2017-11-1

[3]
Comprehensive single-cell transcriptional profiling of a multicellular organism.

Science. 2017-8-18

[4]
Flipping between Polycomb repressed and active transcriptional states introduces noise in gene expression.

Nat Commun. 2017-6-26

[5]
Cell fixation and preservation for droplet-based single-cell transcriptomics.

BMC Biol. 2017-5-19

[6]
Computational approaches for interpreting scRNA-seq data.

FEBS Lett. 2017-8

[7]
Mapping the human DC lineage through the integration of high-dimensional techniques.

Science. 2017-6-9

[8]
JingleBells: A Repository of Immune-Related Single-Cell RNA-Sequencing Datasets.

J Immunol. 2017-5-1

[9]
Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq.

Science. 2017-3-31

[10]
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Nat Methods. 2017-5

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