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Single-cell transcriptome sequencing: recent advances and remaining challenges

Single-cell RNA-sequencing methods are now robust and economically practical and are becoming a powerful tool for high-throughput, high-resolution transcriptomic analysis of cell states and dynamics. Single-cell approaches circumvent the averaging artifacts associated with traditional bulk populatio...

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Detalles Bibliográficos
Autores principales: Liu, Serena, Trapnell, Cole
Formato: Online Artículo Texto
Lenguaje:English
Publicado: F1000Research 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4758375/
https://www.ncbi.nlm.nih.gov/pubmed/26949524
http://dx.doi.org/10.12688/f1000research.7223.1
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author Liu, Serena
Trapnell, Cole
author_facet Liu, Serena
Trapnell, Cole
author_sort Liu, Serena
collection PubMed
description Single-cell RNA-sequencing methods are now robust and economically practical and are becoming a powerful tool for high-throughput, high-resolution transcriptomic analysis of cell states and dynamics. Single-cell approaches circumvent the averaging artifacts associated with traditional bulk population data, yielding new insights into the cellular diversity underlying superficially homogeneous populations. Thus far, single-cell RNA-sequencing has already shown great effectiveness in unraveling complex cell populations, reconstructing developmental trajectories, and modeling transcriptional dynamics. Ongoing technical improvements to single-cell RNA-sequencing throughput and sensitivity, the development of more sophisticated analytical frameworks for single-cell data, and an increasing array of complementary single-cell assays all promise to expand the usefulness and potential applications of single-cell transcriptomic profiling.
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spelling pubmed-47583752016-03-03 Single-cell transcriptome sequencing: recent advances and remaining challenges Liu, Serena Trapnell, Cole F1000Res Review Single-cell RNA-sequencing methods are now robust and economically practical and are becoming a powerful tool for high-throughput, high-resolution transcriptomic analysis of cell states and dynamics. Single-cell approaches circumvent the averaging artifacts associated with traditional bulk population data, yielding new insights into the cellular diversity underlying superficially homogeneous populations. Thus far, single-cell RNA-sequencing has already shown great effectiveness in unraveling complex cell populations, reconstructing developmental trajectories, and modeling transcriptional dynamics. Ongoing technical improvements to single-cell RNA-sequencing throughput and sensitivity, the development of more sophisticated analytical frameworks for single-cell data, and an increasing array of complementary single-cell assays all promise to expand the usefulness and potential applications of single-cell transcriptomic profiling. F1000Research 2016-02-17 /pmc/articles/PMC4758375/ /pubmed/26949524 http://dx.doi.org/10.12688/f1000research.7223.1 Text en Copyright: © 2016 Liu S and Trapnell C http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Review
Liu, Serena
Trapnell, Cole
Single-cell transcriptome sequencing: recent advances and remaining challenges
title Single-cell transcriptome sequencing: recent advances and remaining challenges
title_full Single-cell transcriptome sequencing: recent advances and remaining challenges
title_fullStr Single-cell transcriptome sequencing: recent advances and remaining challenges
title_full_unstemmed Single-cell transcriptome sequencing: recent advances and remaining challenges
title_short Single-cell transcriptome sequencing: recent advances and remaining challenges
title_sort single-cell transcriptome sequencing: recent advances and remaining challenges
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4758375/
https://www.ncbi.nlm.nih.gov/pubmed/26949524
http://dx.doi.org/10.12688/f1000research.7223.1
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