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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2018
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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. |
format | Online Article Text |
id | pubmed-6205970 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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