Literature DB >> 33606265

Single-Cell Transcriptome Profiling.

Guy Shapira1, Noam Shomron2.   

Abstract

Over the last decade, single cell RNA sequencing (scRNAseq) became an increasingly viable solution for analyzing cellular heterogeneity and cell-specific expression differences. While not as mature or fully realized as bulk sequencing, newly developed computational methods offer a solution to the challenges of scRNAseq data analysis, providing previously inaccessible biological insight at unprecedented levels of detail. Here, we go over the inherent challenges of single-cell data analysis and the computational methods used to overcome them. We cover current and future applications of scRNAseq in research of cellular dynamics and as an integrative component of biological research.

Keywords:  Dimensionality reduction; Gene expression; Next-generation sequencing; R; Single-cell sequencing

Mesh:

Year:  2021        PMID: 33606265     DOI: 10.1007/978-1-0716-1103-6_16

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  13 in total

1.  Stochastic gene expression in a single cell.

Authors:  Michael B Elowitz; Arnold J Levine; Eric D Siggia; Peter S Swain
Journal:  Science       Date:  2002-08-16       Impact factor: 47.728

2.  Quantitative single-cell RNA-seq with unique molecular identifiers.

Authors:  Saiful Islam; Amit Zeisel; Simon Joost; Gioele La Manno; Pawel Zajac; Maria Kasper; Peter Lönnerberg; Sten Linnarsson
Journal:  Nat Methods       Date:  2013-12-22       Impact factor: 28.547

3.  Stochastic switching as a survival strategy in fluctuating environments.

Authors:  Murat Acar; Jerome T Mettetal; Alexander van Oudenaarden
Journal:  Nat Genet       Date:  2008-03-23       Impact factor: 38.330

Review 4.  Single-cell RNA sequencing to explore immune cell heterogeneity.

Authors:  Efthymia Papalexi; Rahul Satija
Journal:  Nat Rev Immunol       Date:  2017-08-07       Impact factor: 53.106

5.  Validation of noise models for single-cell transcriptomics.

Authors:  Dominic Grün; Lennart Kester; Alexander van Oudenaarden
Journal:  Nat Methods       Date:  2014-04-20       Impact factor: 28.547

6.  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

7.  Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types.

Authors:  Diego Adhemar Jaitin; Ephraim Kenigsberg; Hadas Keren-Shaul; Naama Elefant; Franziska Paul; Irina Zaretsky; Alexander Mildner; Nadav Cohen; Steffen Jung; Amos Tanay; Ido Amit
Journal:  Science       Date:  2014-02-14       Impact factor: 47.728

8.  Massively parallel single-nucleus RNA-seq with DroNc-seq.

Authors:  Naomi Habib; Inbal Avraham-Davidi; Anindita Basu; Tyler Burks; Karthik Shekhar; Matan Hofree; Sourav R Choudhury; François Aguet; Ellen Gelfand; Kristin Ardlie; David A Weitz; Orit Rozenblatt-Rosen; Feng Zhang; Aviv Regev
Journal:  Nat Methods       Date:  2017-08-28       Impact factor: 28.547

Review 9.  The single-cell sequencing: new developments and medical applications.

Authors:  Xiaoning Tang; Yongmei Huang; Jinli Lei; Hui Luo; Xiao Zhu
Journal:  Cell Biosci       Date:  2019-06-26       Impact factor: 7.133

10.  Dissecting cellular crosstalk by sequencing physically interacting cells.

Authors:  Amir Giladi; Merav Cohen; Chiara Medaglia; Yael Baran; Baoguo Li; Mor Zada; Pierre Bost; Ronnie Blecher-Gonen; Tomer-Meir Salame; Johannes U Mayer; Eyal David; Franca Ronchese; Amos Tanay; Ido Amit
Journal:  Nat Biotechnol       Date:  2020-03-09       Impact factor: 68.164

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