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A Single-Cell Sequencing Guide for Immunologists

In recent years there has been a rapid increase in the use of single-cell sequencing (scRNA-seq) approaches in the field of immunology. With the wide range of technologies available, it is becoming harder for users to select the best scRNA-seq protocol/platform to address their biological questions...

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Detalles Bibliográficos
Autores principales: See, Peter, Lum, Josephine, Chen, Jinmiao, Ginhoux, Florent
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6205970/
https://www.ncbi.nlm.nih.gov/pubmed/30405621
http://dx.doi.org/10.3389/fimmu.2018.02425
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author See, Peter
Lum, Josephine
Chen, Jinmiao
Ginhoux, Florent
author_facet See, Peter
Lum, Josephine
Chen, Jinmiao
Ginhoux, Florent
author_sort See, Peter
collection PubMed
description In recent years there has been a rapid increase in the use of single-cell sequencing (scRNA-seq) approaches in the field of immunology. With the wide range of technologies available, it is becoming harder for users to select the best scRNA-seq protocol/platform to address their biological questions of interest. Here, we compared the advantages and limitations of four commonly used scRNA-seq platforms in order to clarify their suitability for different experimental applications. We also address how the datasets generated by different scRNA-seq platforms can be integrated, and how to identify unknown populations of single cells using unbiased bioinformatics methods.
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spelling pubmed-62059702018-11-07 A Single-Cell Sequencing Guide for Immunologists See, Peter Lum, Josephine Chen, Jinmiao Ginhoux, Florent Front Immunol Immunology In recent years there has been a rapid increase in the use of single-cell sequencing (scRNA-seq) approaches in the field of immunology. With the wide range of technologies available, it is becoming harder for users to select the best scRNA-seq protocol/platform to address their biological questions of interest. Here, we compared the advantages and limitations of four commonly used scRNA-seq platforms in order to clarify their suitability for different experimental applications. We also address how the datasets generated by different scRNA-seq platforms can be integrated, and how to identify unknown populations of single cells using unbiased bioinformatics methods. Frontiers Media S.A. 2018-10-23 /pmc/articles/PMC6205970/ /pubmed/30405621 http://dx.doi.org/10.3389/fimmu.2018.02425 Text en Copyright © 2018 See, Lum, Chen and Ginhoux. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Immunology
See, Peter
Lum, Josephine
Chen, Jinmiao
Ginhoux, Florent
A Single-Cell Sequencing Guide for Immunologists
title A Single-Cell Sequencing Guide for Immunologists
title_full A Single-Cell Sequencing Guide for Immunologists
title_fullStr A Single-Cell Sequencing Guide for Immunologists
title_full_unstemmed A Single-Cell Sequencing Guide for Immunologists
title_short A Single-Cell Sequencing Guide for Immunologists
title_sort single-cell sequencing guide for immunologists
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6205970/
https://www.ncbi.nlm.nih.gov/pubmed/30405621
http://dx.doi.org/10.3389/fimmu.2018.02425
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