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A Scalable Strand-Specific Protocol Enabling Full-Length Total RNA Sequencing From Single Cells

RNA sequencing (RNAseq) has been widely used to generate bulk gene expression measurements collected from pools of cells. Only relatively recently have single-cell RNAseq (scRNAseq) methods provided opportunities for gene expression analyses at the single-cell level, allowing researchers to study he...

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Autores principales: Haile, Simon, Corbett, Richard D., LeBlanc, Veronique G., Wei, Lisa, Pleasance, Stephen, Bilobram, Steve, Nip, Ka Ming, Brown, Kirstin, Trinh, Eva, Smith, Jillian, Trinh, Diane L., Bala, Miruna, Chuah, Eric, Coope, Robin J. N., Moore, Richard A., Mungall, Andrew J., Mungall, Karen L., Zhao, Yongjun, Hirst, Martin, Aparicio, Samuel, Birol, Inanc, Jones, Steven J. M., Marra, Marco A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8209500/
https://www.ncbi.nlm.nih.gov/pubmed/34149808
http://dx.doi.org/10.3389/fgene.2021.665888
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author Haile, Simon
Corbett, Richard D.
LeBlanc, Veronique G.
Wei, Lisa
Pleasance, Stephen
Bilobram, Steve
Nip, Ka Ming
Brown, Kirstin
Trinh, Eva
Smith, Jillian
Trinh, Diane L.
Bala, Miruna
Chuah, Eric
Coope, Robin J. N.
Moore, Richard A.
Mungall, Andrew J.
Mungall, Karen L.
Zhao, Yongjun
Hirst, Martin
Aparicio, Samuel
Birol, Inanc
Jones, Steven J. M.
Marra, Marco A.
author_facet Haile, Simon
Corbett, Richard D.
LeBlanc, Veronique G.
Wei, Lisa
Pleasance, Stephen
Bilobram, Steve
Nip, Ka Ming
Brown, Kirstin
Trinh, Eva
Smith, Jillian
Trinh, Diane L.
Bala, Miruna
Chuah, Eric
Coope, Robin J. N.
Moore, Richard A.
Mungall, Andrew J.
Mungall, Karen L.
Zhao, Yongjun
Hirst, Martin
Aparicio, Samuel
Birol, Inanc
Jones, Steven J. M.
Marra, Marco A.
author_sort Haile, Simon
collection PubMed
description RNA sequencing (RNAseq) has been widely used to generate bulk gene expression measurements collected from pools of cells. Only relatively recently have single-cell RNAseq (scRNAseq) methods provided opportunities for gene expression analyses at the single-cell level, allowing researchers to study heterogeneous mixtures of cells at unprecedented resolution. Tumors tend to be composed of heterogeneous cellular mixtures and are frequently the subjects of such analyses. Extensive method developments have led to several protocols for scRNAseq but, owing to the small amounts of RNA in single cells, technical constraints have required compromises. For example, the majority of scRNAseq methods are limited to sequencing only the 3′ or 5′ termini of transcripts. Other protocols that facilitate full-length transcript profiling tend to capture only polyadenylated mRNAs and are generally limited to processing only 96 cells at a time. Here, we address these limitations and present a novel protocol that allows for the high-throughput sequencing of full-length, total RNA at single-cell resolution. We demonstrate that our method produced strand-specific sequencing data for both polyadenylated and non-polyadenylated transcripts, enabled the profiling of transcript regions beyond only transcript termini, and yielded data rich enough to allow identification of cell types from heterogeneous biological samples.
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spelling pubmed-82095002021-06-18 A Scalable Strand-Specific Protocol Enabling Full-Length Total RNA Sequencing From Single Cells Haile, Simon Corbett, Richard D. LeBlanc, Veronique G. Wei, Lisa Pleasance, Stephen Bilobram, Steve Nip, Ka Ming Brown, Kirstin Trinh, Eva Smith, Jillian Trinh, Diane L. Bala, Miruna Chuah, Eric Coope, Robin J. N. Moore, Richard A. Mungall, Andrew J. Mungall, Karen L. Zhao, Yongjun Hirst, Martin Aparicio, Samuel Birol, Inanc Jones, Steven J. M. Marra, Marco A. Front Genet Genetics RNA sequencing (RNAseq) has been widely used to generate bulk gene expression measurements collected from pools of cells. Only relatively recently have single-cell RNAseq (scRNAseq) methods provided opportunities for gene expression analyses at the single-cell level, allowing researchers to study heterogeneous mixtures of cells at unprecedented resolution. Tumors tend to be composed of heterogeneous cellular mixtures and are frequently the subjects of such analyses. Extensive method developments have led to several protocols for scRNAseq but, owing to the small amounts of RNA in single cells, technical constraints have required compromises. For example, the majority of scRNAseq methods are limited to sequencing only the 3′ or 5′ termini of transcripts. Other protocols that facilitate full-length transcript profiling tend to capture only polyadenylated mRNAs and are generally limited to processing only 96 cells at a time. Here, we address these limitations and present a novel protocol that allows for the high-throughput sequencing of full-length, total RNA at single-cell resolution. We demonstrate that our method produced strand-specific sequencing data for both polyadenylated and non-polyadenylated transcripts, enabled the profiling of transcript regions beyond only transcript termini, and yielded data rich enough to allow identification of cell types from heterogeneous biological samples. Frontiers Media S.A. 2021-06-03 /pmc/articles/PMC8209500/ /pubmed/34149808 http://dx.doi.org/10.3389/fgene.2021.665888 Text en Copyright © 2021 Haile, Corbett, LeBlanc, Wei, Pleasance, Bilobram, Nip, Brown, Trinh, Smith, Trinh, Bala, Chuah, Coope, Moore, Mungall, Mungall, Zhao, Hirst, Aparicio, Birol, Jones and Marra. https://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 Genetics
Haile, Simon
Corbett, Richard D.
LeBlanc, Veronique G.
Wei, Lisa
Pleasance, Stephen
Bilobram, Steve
Nip, Ka Ming
Brown, Kirstin
Trinh, Eva
Smith, Jillian
Trinh, Diane L.
Bala, Miruna
Chuah, Eric
Coope, Robin J. N.
Moore, Richard A.
Mungall, Andrew J.
Mungall, Karen L.
Zhao, Yongjun
Hirst, Martin
Aparicio, Samuel
Birol, Inanc
Jones, Steven J. M.
Marra, Marco A.
A Scalable Strand-Specific Protocol Enabling Full-Length Total RNA Sequencing From Single Cells
title A Scalable Strand-Specific Protocol Enabling Full-Length Total RNA Sequencing From Single Cells
title_full A Scalable Strand-Specific Protocol Enabling Full-Length Total RNA Sequencing From Single Cells
title_fullStr A Scalable Strand-Specific Protocol Enabling Full-Length Total RNA Sequencing From Single Cells
title_full_unstemmed A Scalable Strand-Specific Protocol Enabling Full-Length Total RNA Sequencing From Single Cells
title_short A Scalable Strand-Specific Protocol Enabling Full-Length Total RNA Sequencing From Single Cells
title_sort scalable strand-specific protocol enabling full-length total rna sequencing from single cells
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8209500/
https://www.ncbi.nlm.nih.gov/pubmed/34149808
http://dx.doi.org/10.3389/fgene.2021.665888
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