An optimized protocol for retina single-cell RNA sequencing.


Journal

Molecular vision
ISSN: 1090-0535
Titre abrégé: Mol Vis
Pays: United States
ID NLM: 9605351

Informations de publication

Date de publication:
2020
Historique:
received: 07 07 2020
accepted: 08 10 2020
entrez: 22 10 2020
pubmed: 23 10 2020
medline: 21 7 2021
Statut: epublish

Résumé

Single-cell RNA sequencing (scRNA-seq) is a powerful technique used to explore gene expression at the single cell level. However, appropriate preparation of samples is essential to obtain the most information out of this transformative technology. Generating high-quality single-cell suspensions from the retina is critical to preserve the native expression profile that will ensure meaningful transcriptome data analysis. We modified the conditions for rapid and optimal dissociation of retina sample preparation. We also included additional filtering steps in data analysis for retinal scRNA-seq. We report a gentle method for dissociation of the mouse retina that minimizes cell death and preserves cell morphology. This protocol also results in detection of higher transcriptional complexity. In addition, the modified computational pipeline leads to better-quality single-cell RNA-sequencing data in retina samples. We also demonstrate the advantages and limitations of using fresh versus frozen retinas to prepare cell or nuclei suspensions for scRNA-seq. We provide a simple yet robust and reproducible protocol for retinal scRNA-seq analysis, especially for comparative studies.

Identifiants

pubmed: 33088174
pii: 62
pmc: PMC7553720

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, N.I.H., Intramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

705-717

Subventions

Organisme : Intramural NIH HHS
ID : ZIA EY000450
Pays : United States
Organisme : Intramural NIH HHS
ID : ZIC DC000086
Pays : United States
Organisme : CCR NIH HHS
ID : HHSN261200800001C
Pays : United States
Organisme : Intramural NIH HHS
ID : ZIA EY000546
Pays : United States
Organisme : NCI NIH HHS
ID : HHSN261200800001E
Pays : United States

Informations de copyright

Copyright © 2020 Molecular Vision.

Références

Neuron. 2012 Oct 18;76(2):266-80
pubmed: 23083731
Proc Natl Acad Sci U S A. 2006 Mar 7;103(10):3890-5
pubmed: 16505381
Proc Natl Acad Sci U S A. 2019 Apr 30;116(18):9103-9114
pubmed: 30988181
Stem Cells. 2019 May;37(5):593-598
pubmed: 30548510
Development. 2017 Oct 1;144(19):3625-3632
pubmed: 28851704
Cell Rep. 2020 Jan 28;30(4):1246-1259.e6
pubmed: 31995762
Nature. 2009 Aug 20;460(7258):1016-20
pubmed: 19693082
Exp Eye Res. 2013 Jun;111:105-11
pubmed: 23500522
J Neurosci. 1998 Nov 1;18(21):8936-46
pubmed: 9786999
J Transl Med. 2018 Jul 17;16(1):198
pubmed: 30016977
Nat Commun. 2019 Oct 25;10(1):4902
pubmed: 31653841
Cell Rep. 2020 Feb 4;30(5):1644-1659.e4
pubmed: 32023475
Sci Rep. 2019 Mar 19;9(1):4858
pubmed: 30890724
Stem Cells. 2018 Mar;36(3):313-324
pubmed: 29230913
Cell. 2015 May 21;161(5):1202-1214
pubmed: 26000488
Eye Brain. 2010;2:99-116
pubmed: 23226947
Genome Biol. 2020 Jun 2;21(1):130
pubmed: 32487174
Genome Biol. 2019 Dec 23;20(1):296
pubmed: 31870423
J Comp Neurol. 2017 Jun 1;525(8):1759-1777
pubmed: 27997986
J Vis Exp. 2018 Oct 12;(140):
pubmed: 30371670
Nat Biotechnol. 2012 Aug;30(8):777-82
pubmed: 22820318
Cell. 2019 Feb 21;176(5):1222-1237.e22
pubmed: 30712875
Stem Cell Reports. 2019 Oct 8;13(4):747-760
pubmed: 31543471
Nat Biotechnol. 2018 Jun;36(5):411-420
pubmed: 29608179
Sci Data. 2018 Feb 13;5:180013
pubmed: 29437159
Genome Res. 2008 Sep;18(9):1509-17
pubmed: 18550803
Proc Natl Acad Sci U S A. 2019 May 28;116(22):10824-10833
pubmed: 31072937
Cell. 2016 Aug 25;166(5):1308-1323.e30
pubmed: 27565351
Nat Rev Genet. 2009 Jan;10(1):57-63
pubmed: 19015660
Nat Commun. 2018 Jul 17;9(1):2759
pubmed: 30018341
Nat Methods. 2008 Jul;5(7):621-8
pubmed: 18516045
Nat Methods. 2017 Sep 29;14(10):935-936
pubmed: 28960196
Cell. 2019 Jun 13;177(7):1888-1902.e21
pubmed: 31178118
Neuron. 2019 Jun 19;102(6):1111-1126.e5
pubmed: 31128945
Dev Dyn. 2009 Sep;238(9):2115-38
pubmed: 19582864
Sci Rep. 2020 Jun 17;10(1):9802
pubmed: 32555229
EMBO J. 2019 Sep 16;38(18):e100811
pubmed: 31436334

Auteurs

Benjamin R Fadl (BR)

Neurobiology, Neurodegeneration & Repair Laboratory, National Eye Institute, National Institutes of Health, Bethesda, MD.

Seth A Brodie (SA)

Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health & NCI-Frederick, Leidos Biomedical Research Inc., Frederick, MD.

Michael Malasky (M)

Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health & NCI-Frederick, Leidos Biomedical Research Inc., Frederick, MD.

Joseph F Boland (JF)

Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health & NCI-Frederick, Leidos Biomedical Research Inc., Frederick, MD.

Michael C Kelly (MC)

Single Cell Analysis Facility, Cancer Research Technology Program, Frederick National Laboratory, Bethesda, MD.

Matthew W Kelley (MW)

Laboratory of Cochlear Development, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD.

Erich Boger (E)

Genomics and Computational Biology Core, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD.

Robert Fariss (R)

Biological Imaging Core, National Eye Institute, National Institutes of Health, Bethesda, MD.

Anand Swaroop (A)

Neurobiology, Neurodegeneration & Repair Laboratory, National Eye Institute, National Institutes of Health, Bethesda, MD.

Laura Campello (L)

Neurobiology, Neurodegeneration & Repair Laboratory, National Eye Institute, National Institutes of Health, Bethesda, MD.

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Classifications MeSH