Literature DB >> 34031612

Tutorial: guidelines for annotating single-cell transcriptomic maps using automated and manual methods.

Zoe A Clarke1,2, Tallulah S Andrews2,3,4, Jawairia Atif3,4, Delaram Pouyabahar1,2, Brendan T Innes1,2, Sonya A MacParland5,6,7, Gary D Bader8,9,10,11.   

Abstract

Single-cell transcriptomics can profile thousands of cells in a single experiment and identify novel cell types, states and dynamics in a wide variety of tissues and organisms. Standard experimental protocols and analysis workflows have been developed to create single-cell transcriptomic maps from tissues. This tutorial focuses on how to interpret these data to identify cell types, states and other biologically relevant patterns with the objective of creating an annotated map of cells. We recommend a three-step workflow including automatic cell annotation (wherever possible), manual cell annotation and verification. Frequently encountered challenges are discussed, as well as strategies to address them. Guiding principles and specific recommendations for software tools and resources that can be used for each step are covered, and an R notebook is included to help run the recommended workflow. Basic familiarity with computer software is assumed, and basic knowledge of programming (e.g., in the R language) is recommended.

Year:  2021        PMID: 34031612     DOI: 10.1038/s41596-021-00534-0

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


  96 in total

1.  Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets.

Authors:  Evan Z Macosko; Anindita Basu; Rahul Satija; James Nemesh; Karthik Shekhar; Melissa Goldman; Itay Tirosh; Allison R Bialas; Nolan Kamitaki; Emily M Martersteck; John J Trombetta; David A Weitz; Joshua R Sanes; Alex K Shalek; Aviv Regev; Steven A McCarroll
Journal:  Cell       Date:  2015-05-21       Impact factor: 41.582

Review 2.  A periodic table of cell types.

Authors:  Bo Xia; Itai Yanai
Journal:  Development       Date:  2019-06-27       Impact factor: 6.868

Review 3.  Strategies for Converting RNA to Amplifiable cDNA for Single-Cell RNA Sequencing Methods.

Authors:  Yohei Sasagawa; Tetsutaro Hayashi; Itoshi Nikaido
Journal:  Adv Exp Med Biol       Date:  2019       Impact factor: 2.622

4.  Comparative Analysis of Single-Cell RNA Sequencing Methods.

Authors:  Christoph Ziegenhain; Beate Vieth; Swati Parekh; Björn Reinius; Amy Guillaumet-Adkins; Martha Smets; Heinrich Leonhardt; Holger Heyn; Ines Hellmann; Wolfgang Enard
Journal:  Mol Cell       Date:  2017-02-16       Impact factor: 17.970

Review 5.  Tutorial: guidelines for the experimental design of single-cell RNA sequencing studies.

Authors:  Atefeh Lafzi; Catia Moutinho; Simone Picelli; Holger Heyn
Journal:  Nat Protoc       Date:  2018-12       Impact factor: 13.491

6.  Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming.

Authors:  Geoffrey Schiebinger; Jian Shu; Marcin Tabaka; Brian Cleary; Vidya Subramanian; Aryeh Solomon; Joshua Gould; Siyan Liu; Stacie Lin; Peter Berube; Lia Lee; Jenny Chen; Justin Brumbaugh; Philippe Rigollet; Konrad Hochedlinger; Rudolf Jaenisch; Aviv Regev; Eric S Lander
Journal:  Cell       Date:  2019-01-31       Impact factor: 41.582

7.  Single-cell messenger RNA sequencing reveals rare intestinal cell types.

Authors:  Dominic Grün; Anna Lyubimova; Lennart Kester; Kay Wiebrands; Onur Basak; Nobuo Sasaki; Hans Clevers; Alexander van Oudenaarden
Journal:  Nature       Date:  2015-08-19       Impact factor: 49.962

8.  Construction of a human cell landscape at single-cell level.

Authors:  Xiaoping Han; Ziming Zhou; Lijiang Fei; Huiyu Sun; Renying Wang; Yao Chen; Haide Chen; Jingjing Wang; Huanna Tang; Wenhao Ge; Yincong Zhou; Fang Ye; Mengmeng Jiang; Junqing Wu; Yanyu Xiao; Xiaoning Jia; Tingyue Zhang; Xiaojie Ma; Qi Zhang; Xueli Bai; Shujing Lai; Chengxuan Yu; Lijun Zhu; Rui Lin; Yuchi Gao; Min Wang; Yiqing Wu; Jianming Zhang; Renya Zhan; Saiyong Zhu; Hailan Hu; Changchun Wang; Ming Chen; He Huang; Tingbo Liang; Jianghua Chen; Weilin Wang; Dan Zhang; Guoji Guo
Journal:  Nature       Date:  2020-03-25       Impact factor: 49.962

9.  TWIK-1/TASK-3 heterodimeric channels contribute to the neurotensin-mediated excitation of hippocampal dentate gyrus granule cells.

Authors:  Jae Hyouk Choi; Oleg Yarishkin; Eunju Kim; Yeonju Bae; Ajung Kim; Seung-Chan Kim; Kanghyun Ryoo; Chang-Hoon Cho; Eun Mi Hwang; Jae-Yong Park
Journal:  Exp Mol Med       Date:  2018-11-12       Impact factor: 8.718

Review 10.  Current best practices in single-cell RNA-seq analysis: a tutorial.

Authors:  Malte D Luecken; Fabian J Theis
Journal:  Mol Syst Biol       Date:  2019-06-19       Impact factor: 11.429

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  8 in total

Review 1.  A cardioimmunologist's toolkit: genetic tools to dissect immune cells in cardiac disease.

Authors:  Anthony Wong; Homaira Hamidzada; Slava Epelman
Journal:  Nat Rev Cardiol       Date:  2022-05-06       Impact factor: 32.419

2.  Unsupervised cell functional annotation for single-cell RNA-seq.

Authors:  Dongshunyi Li; Jun Ding; Ziv Bar-Joseph
Journal:  Genome Res       Date:  2022-06-28       Impact factor: 9.438

Review 3.  Towards a definition of microglia heterogeneity.

Authors:  Luke M Healy; Sameera Zia; Jason R Plemel
Journal:  Commun Biol       Date:  2022-10-20

4.  Sincast: a computational framework to predict cell identities in single-cell transcriptomes using bulk atlases as references.

Authors:  Yidi Deng; Jarny Choi; Kim-Anh Lê Cao
Journal:  Brief Bioinform       Date:  2022-05-13       Impact factor: 13.994

Review 5.  Single-cell RNA sequencing technologies and applications: A brief overview.

Authors:  Dragomirka Jovic; Xue Liang; Hua Zeng; Lin Lin; Fengping Xu; Yonglun Luo
Journal:  Clin Transl Med       Date:  2022-03

Review 6.  Unraveling the Complexity of Liver Disease One Cell at a Time.

Authors:  Gary D Bader; Ian D McGilvray; Sonya A MacParland; Jawairia Atif; Cornelia Thoeni
Journal:  Semin Liver Dis       Date:  2022-08-25       Impact factor: 6.512

7.  scWizard: A web-based automated tool for classifying and annotating single cells and downstream analysis of single-cell RNA-seq data in cancers.

Authors:  Jinfen Wei; Qingsong Xie; Yimo Qu; Guanda Huang; Zixi Chen; Hongli Du
Journal:  Comput Struct Biotechnol J       Date:  2022-08-27       Impact factor: 6.155

8.  Single-cell analysis of gene expression in the substantia nigra pars compacta of a pesticide-induced mouse model of Parkinson's disease.

Authors:  Arshad H Khan; Lydia K Lee; Desmond J Smith
Journal:  Transl Neurosci       Date:  2022-09-01       Impact factor: 1.264

  8 in total

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