CRISPR/Cas9-based depletion of 16S ribosomal RNA improves library complexity of single-cell RNA-sequencing in planarians.
CRISPR/Cas9
DASH
Planarians
Ribodepletion
scRNA-seq
Journal
BMC genomics
ISSN: 1471-2164
Titre abrégé: BMC Genomics
Pays: England
ID NLM: 100965258
Informations de publication
Date de publication:
20 Oct 2023
20 Oct 2023
Historique:
received:
28
06
2023
accepted:
08
10
2023
medline:
30
10
2023
pubmed:
21
10
2023
entrez:
20
10
2023
Statut:
epublish
Résumé
Single-cell RNA-sequencing (scRNA-seq) relies on PCR amplification to retrieve information from vanishingly small amounts of starting material. To selectively enrich mRNA from abundant non-polyadenylated transcripts, poly(A) selection is a key step during library preparation. However, some transcripts, such as mitochondrial genes, can escape this elimination and overwhelm libraries. Often, these transcripts are removed in silico, but whether physical depletion improves detection of rare transcripts in single cells is unclear. We find that a single 16S ribosomal RNA is widely enriched in planarian scRNA-seq datasets, independent of the library preparation method. To deplete this transcript from scRNA-seq libraries, we design 30 single-guide RNAs spanning its length. To evaluate the effects of depletion, we perform a side-by-side comparison of the effects of eliminating the 16S transcript and find a substantial increase in the number of genes detected per cell, coupled with virtually complete loss of the 16S RNA. Moreover, we systematically determine that library complexity increases with a limited number of PCR cycles following CRISPR treatment. When compared to in silico depletion of 16S, physically removing it reduces dropout rates, retrieves more clusters, and reveals more differentially expressed genes. Our results show that abundant transcripts reduce the retrieval of informative transcripts in scRNA-seq and distort the analysis. Physical removal of these contaminants enables the detection of rare transcripts at lower sequencing depth, and also outperforms in silico depletion. Importantly, this method can be easily customized to deplete any abundant transcript from scRNA-seq libraries.
Sections du résumé
BACKGROUND
BACKGROUND
Single-cell RNA-sequencing (scRNA-seq) relies on PCR amplification to retrieve information from vanishingly small amounts of starting material. To selectively enrich mRNA from abundant non-polyadenylated transcripts, poly(A) selection is a key step during library preparation. However, some transcripts, such as mitochondrial genes, can escape this elimination and overwhelm libraries. Often, these transcripts are removed in silico, but whether physical depletion improves detection of rare transcripts in single cells is unclear.
RESULTS
RESULTS
We find that a single 16S ribosomal RNA is widely enriched in planarian scRNA-seq datasets, independent of the library preparation method. To deplete this transcript from scRNA-seq libraries, we design 30 single-guide RNAs spanning its length. To evaluate the effects of depletion, we perform a side-by-side comparison of the effects of eliminating the 16S transcript and find a substantial increase in the number of genes detected per cell, coupled with virtually complete loss of the 16S RNA. Moreover, we systematically determine that library complexity increases with a limited number of PCR cycles following CRISPR treatment. When compared to in silico depletion of 16S, physically removing it reduces dropout rates, retrieves more clusters, and reveals more differentially expressed genes.
CONCLUSIONS
CONCLUSIONS
Our results show that abundant transcripts reduce the retrieval of informative transcripts in scRNA-seq and distort the analysis. Physical removal of these contaminants enables the detection of rare transcripts at lower sequencing depth, and also outperforms in silico depletion. Importantly, this method can be easily customized to deplete any abundant transcript from scRNA-seq libraries.
Identifiants
pubmed: 37864134
doi: 10.1186/s12864-023-09724-4
pii: 10.1186/s12864-023-09724-4
pmc: PMC10588366
doi:
Substances chimiques
RNA, Ribosomal, 16S
0
RNA
63231-63-0
RNA, Messenger
0
RNA, Ribosomal
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
625Subventions
Organisme : NIGMS NIH HHS
ID : R01 GM139933
Pays : United States
Organisme : NIH HHS
ID : GM139933
Pays : United States
Commentaires et corrections
Type : UpdateOf
Informations de copyright
© 2023. BioMed Central Ltd., part of Springer Nature.
Références
Genome Biol. 2016 Mar 04;17:41
pubmed: 26944702
Nat Protoc. 2014 Jan;9(1):171-81
pubmed: 24385147
Genome Biol. 2014;15(12):550
pubmed: 25516281
Elife. 2016 Jul 21;5:
pubmed: 27441386
Nat Methods. 2019 Dec;16(12):1289-1296
pubmed: 31740819
Bioinformatics. 2021 May 17;37(7):963-967
pubmed: 32840568
Nucleic Acids Res. 2019 Aug 22;47(14):e84
pubmed: 31165880
Nat Cell Biol. 2021 Sep;23(9):939-952
pubmed: 34475533
Genome Biol. 2020 Mar 5;21(1):57
pubmed: 32138770
Genome Biol. 2020 Feb 7;21(1):31
pubmed: 32033589
Mol Cell Biol. 2005 Aug;25(15):6427-35
pubmed: 16024781
Nature. 2018 Feb 1;554(7690):56-61
pubmed: 29364871
PeerJ. 2021 Jan 15;9:e10717
pubmed: 33520469
Cell. 2015 May 21;161(5):1202-1214
pubmed: 26000488
Nat Methods. 2017 Mar;14(3):267-270
pubmed: 28092691
Nature. 2022 Jun;606(7913):329-334
pubmed: 35650439
Nat Methods. 2009 Sep;6(9):647-9
pubmed: 19668204
Nat Commun. 2018 Feb 12;9(1):619
pubmed: 29434199
Science. 2018 May 25;360(6391):
pubmed: 29674431
Genome Biol. 2019 Mar 22;20(1):63
pubmed: 30902100
Nat Biotechnol. 2023 Apr;41(4):513-520
pubmed: 36329320
Gigascience. 2020 Dec 26;9(12):
pubmed: 33367645
Nat Commun. 2022 May 18;13(1):2726
pubmed: 35585061
Science. 2018 May 25;360(6391):
pubmed: 29674432
PLoS Genet. 2016 May 13;12(5):e1006028
pubmed: 27176048
G3 (Bethesda). 2016 May 03;6(5):1191-200
pubmed: 26921295
Genome Med. 2017 Aug 18;9(1):75
pubmed: 28821273
Nat Biotechnol. 2022 Dec;40(12):1780-1793
pubmed: 35760914
Genome Biol. 2016 Apr 27;17:87
pubmed: 27150006
Genome Biol. 2016 Feb 17;17:29
pubmed: 26887813
Nat Commun. 2021 Apr 12;12(1):2158
pubmed: 33846360
Bioinformatics. 2013 Jan 1;29(1):15-21
pubmed: 23104886
Cell. 2019 Jun 13;177(7):1888-1902.e21
pubmed: 31178118
Proc Natl Acad Sci U S A. 2021 Dec 21;118(51):
pubmed: 34911763
Cell. 2018 Jun 14;173(7):1593-1608.e20
pubmed: 29906446
BMC Genomics. 2019 Nov 29;20(1):909
pubmed: 31783730
Methods Mol Biol. 2018;1774:241-258
pubmed: 29916158
Genome Biol. 2021 Apr 8;22(1):89
pubmed: 33827654
Exp Mol Med. 2018 Aug 7;50(8):1-14
pubmed: 30089861
Methods Mol Biol. 2018;1774:445-454
pubmed: 29916170
Nat Commun. 2017 Jan 16;8:14049
pubmed: 28091601
Nat Methods. 2017 Jun;14(6):565-571
pubmed: 28504683
Biochim Biophys Acta. 2012 Sep-Oct;1819(9-10):992-7
pubmed: 22172994