Systematic assessment of tissue dissociation and storage biases in single-cell and single-nucleus RNA-seq workflows.
10x Genomics
RNA-seq
Single-cell transcriptomics
scRNA-seq
snRNA-seq
Journal
Genome biology
ISSN: 1474-760X
Titre abrégé: Genome Biol
Pays: England
ID NLM: 100960660
Informations de publication
Date de publication:
02 06 2020
02 06 2020
Historique:
received:
09
01
2020
accepted:
15
05
2020
entrez:
4
6
2020
pubmed:
4
6
2020
medline:
2
4
2021
Statut:
epublish
Résumé
Single-cell RNA sequencing has been widely adopted to estimate the cellular composition of heterogeneous tissues and obtain transcriptional profiles of individual cells. Multiple approaches for optimal sample dissociation and storage of single cells have been proposed as have single-nuclei profiling methods. What has been lacking is a systematic comparison of their relative biases and benefits. Here, we compare gene expression and cellular composition of single-cell suspensions prepared from adult mouse kidney using two tissue dissociation protocols. For each sample, we also compare fresh cells to cryopreserved and methanol-fixed cells. Lastly, we compare this single-cell data to that generated using three single-nucleus RNA sequencing workflows. Our data confirms prior reports that digestion on ice avoids the stress response observed with 37 °C dissociation. It also reveals cell types more abundant either in the cold or warm dissociations that may represent populations that require gentler or harsher conditions to be released intact. For cell storage, cryopreservation of dissociated cells results in a major loss of epithelial cell types; in contrast, methanol fixation maintains the cellular composition but suffers from ambient RNA leakage. Finally, cell type composition differences are observed between single-cell and single-nucleus RNA sequencing libraries. In particular, we note an underrepresentation of T, B, and NK lymphocytes in the single-nucleus libraries. Systematic comparison of recovered cell types and their transcriptional profiles across the workflows has highlighted protocol-specific biases and thus enables researchers starting single-cell experiments to make an informed choice.
Sections du résumé
BACKGROUND
Single-cell RNA sequencing has been widely adopted to estimate the cellular composition of heterogeneous tissues and obtain transcriptional profiles of individual cells. Multiple approaches for optimal sample dissociation and storage of single cells have been proposed as have single-nuclei profiling methods. What has been lacking is a systematic comparison of their relative biases and benefits.
RESULTS
Here, we compare gene expression and cellular composition of single-cell suspensions prepared from adult mouse kidney using two tissue dissociation protocols. For each sample, we also compare fresh cells to cryopreserved and methanol-fixed cells. Lastly, we compare this single-cell data to that generated using three single-nucleus RNA sequencing workflows. Our data confirms prior reports that digestion on ice avoids the stress response observed with 37 °C dissociation. It also reveals cell types more abundant either in the cold or warm dissociations that may represent populations that require gentler or harsher conditions to be released intact. For cell storage, cryopreservation of dissociated cells results in a major loss of epithelial cell types; in contrast, methanol fixation maintains the cellular composition but suffers from ambient RNA leakage. Finally, cell type composition differences are observed between single-cell and single-nucleus RNA sequencing libraries. In particular, we note an underrepresentation of T, B, and NK lymphocytes in the single-nucleus libraries.
CONCLUSIONS
Systematic comparison of recovered cell types and their transcriptional profiles across the workflows has highlighted protocol-specific biases and thus enables researchers starting single-cell experiments to make an informed choice.
Identifiants
pubmed: 32487174
doi: 10.1186/s13059-020-02048-6
pii: 10.1186/s13059-020-02048-6
pmc: PMC7265231
doi:
Types de publication
Comparative Study
Evaluation Study
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
130Références
Sci Rep. 2017 Jul 20;7(1):6031
pubmed: 28729663
Genome Biol. 2017 Mar 1;18(1):45
pubmed: 28249587
Cell Syst. 2016 Oct 26;3(4):346-360.e4
pubmed: 27667365
Development. 2017 Oct 1;144(19):3625-3632
pubmed: 28851704
Science. 2016 Jun 24;352(6293):1586-90
pubmed: 27339989
PLoS One. 2018 Dec 26;13(12):e0209648
pubmed: 30586455
Science. 2018 May 18;360(6390):758-763
pubmed: 29622724
Nat Biotechnol. 2018 Jun;36(5):411-420
pubmed: 29608179
Nat Genet. 2017 May;49(5):708-718
pubmed: 28319088
PLoS Biol. 2019 Feb 21;17(2):e3000152
pubmed: 30789893
Nat Methods. 2017 Sep 29;14(10):935-936
pubmed: 28960196
Nat Protoc. 2016 Mar;11(3):499-524
pubmed: 26890679
Nat Commun. 2017 Jan 16;8:14049
pubmed: 28091601
Nucleic Acids Res. 2009 Jul;37(Web Server issue):W305-11
pubmed: 19465376
BMC Bioinformatics. 2018 Jun 22;19(1):236
pubmed: 29929481
Kidney Int. 2003 Dec;64(6):2155-62
pubmed: 14633138
Bioinformatics. 2014 Apr 1;30(7):923-30
pubmed: 24227677
Cell. 2015 May 21;161(5):1202-1214
pubmed: 26000488
Sci Rep. 2019 Jul 23;9(1):10699
pubmed: 31337793
Am J Hum Genet. 2002 Aug;71(2):439-41
pubmed: 12111669
Nat Med. 2020 May;26(5):792-802
pubmed: 32405060
Nat Methods. 2019 Dec;16(12):1289-1296
pubmed: 31740819
Elife. 2018 Feb 16;7:
pubmed: 29451494
Kidney Int. 2019 Apr;95(4):787-796
pubmed: 30826016
Genome Biol. 2019 Mar 22;20(1):63
pubmed: 30902100
Development. 2019 Jun 12;146(12):
pubmed: 31118232
J Am Soc Nephrol. 2019 Apr;30(4):712-713
pubmed: 30867246
J Am Soc Nephrol. 2018 Aug;29(8):2060-2068
pubmed: 29794128
Bioinformatics. 2019 Nov 1;35(22):4688-4695
pubmed: 31028376
BMC Bioinformatics. 2015 Jan 28;16:24
pubmed: 25627334
Bioinformatics. 2010 Jan 1;26(1):139-40
pubmed: 19910308
BMC Biol. 2017 May 19;15(1):44
pubmed: 28526029
Bioinformatics. 2017 Apr 15;33(8):1179-1186
pubmed: 28088763
Bioinformatics. 2013 Jan 1;29(1):15-21
pubmed: 23104886
Nature. 2019 Feb;566(7745):496-502
pubmed: 30787437
Cell. 2017 Dec 14;171(7):1611-1624.e24
pubmed: 29198524
Cell. 2019 Sep 5;178(6):1493-1508.e20
pubmed: 31474370
Science. 2016 Apr 8;352(6282):189-96
pubmed: 27124452
EMBO J. 2019 Sep 16;38(18):e100811
pubmed: 31436334
Nature. 2018 Oct;562(7727):367-372
pubmed: 30283141
J Am Soc Nephrol. 2019 Jan;30(1):23-32
pubmed: 30510133
Genome Biol. 2020 Mar 5;21(1):57
pubmed: 32138770
Genome Biol. 2016 Apr 29;17:80
pubmed: 27139883
J Transl Med. 2018 Jul 17;16(1):198
pubmed: 30016977
Nature. 2019 Aug;572(7768):199-204
pubmed: 31292543
Cell. 2015 May 21;161(5):1187-1201
pubmed: 26000487
Nat Rev Nephrol. 2018 Aug;14(8):479-492
pubmed: 29789704