Literature DB >> 20203668

RNA-Seq analysis to capture the transcriptome landscape of a single cell.

Fuchou Tang1, Catalin Barbacioru, Ellen Nordman, Bin Li, Nanlan Xu, Vladimir I Bashkirov, Kaiqin Lao, M Azim Surani.   

Abstract

We describe here a protocol for digital transcriptome analysis in a single mouse oocyte and blastomere using a deep-sequencing approach. In this method, individual cells are isolated and transferred into lysate buffer by mouth pipette, followed by reverse transcription carried out directly on the whole cell lysate. Free primers are removed by exonuclease I and a poly(A) tail is added to the 3' end of the first-strand cDNAs by terminal deoxynucleotidyl transferase. Single-cell cDNAs are then amplified by 20 + 9 cycles of PCR. The resulting 100-200 ng of amplified cDNAs are used to construct a sequencing library, which can be used for deep sequencing using the SOLiD system. Compared with cDNA microarray techniques, our assay can capture up to 75% more genes expressed in early embryos. This protocol can generate deep-sequencing libraries for 16 single-cell samples within 6 d.

Entities:  

Mesh:

Substances:

Year:  2010        PMID: 20203668      PMCID: PMC3847604          DOI: 10.1038/nprot.2009.236

Source DB:  PubMed          Journal:  Nat Protoc        ISSN: 1750-2799            Impact factor:   13.491


  44 in total

Review 1.  Applying genomics technologies to neural development.

Authors:  Seth Blackshaw; Rick Livesey
Journal:  Curr Opin Neurobiol       Date:  2002-02       Impact factor: 6.627

2.  Noise in eukaryotic gene expression.

Authors:  William J Blake; Mads KAErn; Charles R Cantor; J J Collins
Journal:  Nature       Date:  2003-04-10       Impact factor: 49.962

3.  Dynamics of global gene expression changes during mouse preimplantation development.

Authors:  Toshio Hamatani; Mark G Carter; Alexei A Sharov; Minoru S H Ko
Journal:  Dev Cell       Date:  2004-01       Impact factor: 12.270

4.  Representation is faithfully preserved in global cDNA amplified exponentially from sub-picogram quantities of mRNA.

Authors:  Norman N Iscove; Mary Barbara; Marie Gu; Meredith Gibson; Carolyn Modi; Neil Winegarden
Journal:  Nat Biotechnol       Date:  2002-08-12       Impact factor: 54.908

Review 5.  Microarrays and the gene expression profile of a single cell.

Authors:  Ernest S Kawasaki
Journal:  Ann N Y Acad Sci       Date:  2004-05       Impact factor: 5.691

6.  Transcript profiling during preimplantation mouse development.

Authors:  Fanyi Zeng; Don A Baldwin; Richard M Schultz
Journal:  Dev Biol       Date:  2004-08-15       Impact factor: 3.582

Review 7.  Strategies for microarray analysis of limiting amounts of RNA.

Authors:  F J Livesey
Journal:  Brief Funct Genomic Proteomic       Date:  2003-04

8.  Quantitative monitoring of gene expression patterns with a complementary DNA microarray.

Authors:  M Schena; D Shalon; R W Davis; P O Brown
Journal:  Science       Date:  1995-10-20       Impact factor: 47.728

9.  mRNA-Seq whole-transcriptome analysis of a single cell.

Authors:  Fuchou Tang; Catalin Barbacioru; Yangzhou Wang; Ellen Nordman; Clarence Lee; Nanlan Xu; Xiaohui Wang; John Bodeau; Brian B Tuch; Asim Siddiqui; Kaiqin Lao; M Azim Surani
Journal:  Nat Methods       Date:  2009-04-06       Impact factor: 28.547

10.  A genome-wide study of gene activity reveals developmental signaling pathways in the preimplantation mouse embryo.

Authors:  Q Tian Wang; Karolina Piotrowska; Maria Anna Ciemerych; Ljiljana Milenkovic; Matthew P Scott; Ronald W Davis; Magdalena Zernicka-Goetz
Journal:  Dev Cell       Date:  2004-01       Impact factor: 12.270

View more
  234 in total

1.  A multiplex RNA-seq strategy to profile poly(A+) RNA: application to analysis of transcription response and 3' end formation.

Authors:  Kristi Fox-Walsh; Jeremy Davis-Turak; Yu Zhou; Hairi Li; Xiang-Dong Fu
Journal:  Genomics       Date:  2011-04-15       Impact factor: 5.736

2.  A bias-reducing strategy in profiling small RNAs using Solexa.

Authors:  Guihua Sun; Xiwei Wu; Jinhui Wang; Haiqing Li; Xuejun Li; Hanlin Gao; John Rossi; Yun Yen
Journal:  RNA       Date:  2011-10-20       Impact factor: 4.942

3.  Dissecting cancer heterogeneity.

Authors:  David Dornan; Jeff Settleman
Journal:  Nat Biotechnol       Date:  2011-12-08       Impact factor: 54.908

Review 4.  The next-generation sequencing technology and application.

Authors:  Xiaoguang Zhou; Lufeng Ren; Qingshu Meng; Yuntao Li; Yude Yu; Jun Yu
Journal:  Protein Cell       Date:  2010-07-07       Impact factor: 14.870

5.  Direct metabolomics for plant cells by live single-cell mass spectrometry.

Authors:  Takashi Fujii; Shuichi Matsuda; Mónica Lorenzo Tejedor; Tsuyoshi Esaki; Iwao Sakane; Hajime Mizuno; Naohiro Tsuyama; Tsutomu Masujima
Journal:  Nat Protoc       Date:  2015-08-27       Impact factor: 13.491

Review 6.  The niche in single-cell technologies.

Authors:  Giacomo Donati
Journal:  Immunol Cell Biol       Date:  2015-12-22       Impact factor: 5.126

Review 7.  Single-cell analysis of the transcriptome and its application in the characterization of stem cells and early embryos.

Authors:  Na Liu; Lin Liu; Xinghua Pan
Journal:  Cell Mol Life Sci       Date:  2014-03-21       Impact factor: 9.261

8.  RNA Sequencing and Analysis.

Authors:  Kimberly R Kukurba; Stephen B Montgomery
Journal:  Cold Spring Harb Protoc       Date:  2015-04-13

9.  m6A modulates haematopoietic stem and progenitor cell specification.

Authors:  Chunxia Zhang; Yusheng Chen; Baofa Sun; Lu Wang; Ying Yang; Dongyuan Ma; Junhua Lv; Jian Heng; Yanyan Ding; Yuanyuan Xue; Xinyan Lu; Wen Xiao; Yun-Gui Yang; Feng Liu
Journal:  Nature       Date:  2017-09-06       Impact factor: 49.962

10.  Predicting the functional states of human iPSC-derived neurons with single-cell RNA-seq and electrophysiology.

Authors:  C Bardy; M van den Hurk; B Kakaradov; J A Erwin; B N Jaeger; R V Hernandez; T Eames; A A Paucar; M Gorris; C Marchand; R Jappelli; J Barron; A K Bryant; M Kellogg; R S Lasken; B P F Rutten; H W M Steinbusch; G W Yeo; F H Gage
Journal:  Mol Psychiatry       Date:  2016-10-04       Impact factor: 15.992

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.