Single-Cell Transcriptomics: A High-Resolution Avenue for Plant Functional Genomics.

cell types fluorescence-activated cell sorting plant/root body plan single-cell RNA-seq

Journal

Trends in plant science
ISSN: 1878-4372
Titre abrégé: Trends Plant Sci
Pays: England
ID NLM: 9890299

Informations de publication

Date de publication:
02 2020
Historique:
received: 03 06 2019
revised: 30 09 2019
accepted: 17 10 2019
pubmed: 30 11 2019
medline: 18 3 2020
entrez: 30 11 2019
Statut: ppublish

Résumé

Plant function is the result of the concerted action of single cells in different tissues. Advances in RNA-seq technologies and tissue processing allow us now to capture transcriptional changes at single-cell resolution. The incredible potential of single-cell RNA-seq lies in the novel ability to study and exploit regulatory processes in complex tissues based on the behaviour of single cells. Importantly, the independence from reporter lines allows the analysis of any given tissue in any plant. While there are challenges associated with the handling and analysis of complex datasets, the opportunities are unique to generate knowledge of tissue functions in unprecedented detail and to facilitate the application of such information by mapping cellular functions and interactions in a plant cell atlas.

Identifiants

pubmed: 31780334
pii: S1360-1385(19)30272-9
doi: 10.1016/j.tplants.2019.10.008
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

186-197

Subventions

Organisme : Biotechnology and Biological Sciences Research Council
ID : BB/M017982/1
Pays : United Kingdom

Informations de copyright

Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.

Auteurs

Charlotte Rich-Griffin (C)

School of Life Sciences, The University of Warwick, Coventry CV4 7AL, UK.

Annika Stechemesser (A)

Warwick Mathematics Institute, The University of Warwick, Coventry CV4 7AL, UK.

Jessica Finch (J)

School of Life Sciences, The University of Warwick, Coventry CV4 7AL, UK.

Emma Lucas (E)

Warwick Medical School, The University of Warwick, Coventry CV4 7AL, UK.

Sascha Ott (S)

Department of Computer Science, The University of Warwick, Coventry CV4 7AL, UK. Electronic address: S.Ott@warwick.ac.uk.

Patrick Schäfer (P)

School of Life Sciences, The University of Warwick, Coventry CV4 7AL, UK; Warwick Integrative Synthetic Biology Centre, The University of Warwick, Coventry CV4 7AL, UK. Electronic address: P.Schafer@warwick.ac.uk.

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Classifications MeSH