Multiple freeze-thaw cycles lead to a loss of consistency in poly(A)-enriched RNA sequencing.
Differential expression
Freeze-thaw
Quality control
RNA-Seq
Sample preparation
Journal
BMC genomics
ISSN: 1471-2164
Titre abrégé: BMC Genomics
Pays: England
ID NLM: 100965258
Informations de publication
Date de publication:
21 Jan 2021
21 Jan 2021
Historique:
received:
25
08
2020
accepted:
08
01
2021
entrez:
22
1
2021
pubmed:
23
1
2021
medline:
15
5
2021
Statut:
epublish
Résumé
Both RNA-Seq and sample freeze-thaw are ubiquitous. However, knowledge about the impact of freeze-thaw on downstream analyses is limited. The lack of common quality metrics that are sufficiently sensitive to freeze-thaw and RNA degradation, e.g. the RNA Integrity Score, makes such assessments challenging. Here we quantify the impact of repeated freeze-thaw cycles on the reliability of RNA-Seq by examining poly(A)-enriched and ribosomal RNA depleted RNA-seq from frozen leukocytes drawn from a toddler Autism cohort. To do so, we estimate the relative noise, or percentage of random counts, separating technical replicates. Using this approach we measured noise associated with RIN and freeze-thaw cycles. As expected, RIN does not fully capture sample degradation due to freeze-thaw. We further examined differential expression results and found that three freeze-thaws should extinguish the differential expression reproducibility of similar experiments. Freeze-thaw also resulted in a 3' shift in the read coverage distribution along the gene body of poly(A)-enriched samples compared to ribosomal RNA depleted samples, suggesting that library preparation may exacerbate freeze-thaw-induced sample degradation. The use of poly(A)-enrichment for RNA sequencing is pervasive in library preparation of frozen tissue, and thus, it is important during experimental design and data analysis to consider the impact of repeated freeze-thaw cycles on reproducibility.
Sections du résumé
BACKGROUND
BACKGROUND
Both RNA-Seq and sample freeze-thaw are ubiquitous. However, knowledge about the impact of freeze-thaw on downstream analyses is limited. The lack of common quality metrics that are sufficiently sensitive to freeze-thaw and RNA degradation, e.g. the RNA Integrity Score, makes such assessments challenging.
RESULTS
RESULTS
Here we quantify the impact of repeated freeze-thaw cycles on the reliability of RNA-Seq by examining poly(A)-enriched and ribosomal RNA depleted RNA-seq from frozen leukocytes drawn from a toddler Autism cohort. To do so, we estimate the relative noise, or percentage of random counts, separating technical replicates. Using this approach we measured noise associated with RIN and freeze-thaw cycles. As expected, RIN does not fully capture sample degradation due to freeze-thaw. We further examined differential expression results and found that three freeze-thaws should extinguish the differential expression reproducibility of similar experiments. Freeze-thaw also resulted in a 3' shift in the read coverage distribution along the gene body of poly(A)-enriched samples compared to ribosomal RNA depleted samples, suggesting that library preparation may exacerbate freeze-thaw-induced sample degradation.
CONCLUSION
CONCLUSIONS
The use of poly(A)-enrichment for RNA sequencing is pervasive in library preparation of frozen tissue, and thus, it is important during experimental design and data analysis to consider the impact of repeated freeze-thaw cycles on reproducibility.
Identifiants
pubmed: 33478392
doi: 10.1186/s12864-021-07381-z
pii: 10.1186/s12864-021-07381-z
pmc: PMC7818915
doi:
Substances chimiques
RNA
63231-63-0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
69Subventions
Organisme : NIMH NIH HHS
ID : R01 MH110558
Pays : United States
Organisme : NIDCD NIH HHS
ID : T32GM008806
Pays : United States
Organisme : NIMH NIH HHS
ID : R01-MH110558
Pays : United States
Organisme : NIDCD NIH HHS
ID : R01-DC016385
Pays : United States
Organisme : NIGMS NIH HHS
ID : R35 GM119850
Pays : United States
Organisme : R35
ID : GM119850
Organisme : Novo Nordisk Fonden (DK)
ID : NNF10CC1016517
Références
Bioinformatics. 2015 Jan 15;31(2):166-9
pubmed: 25260700
J Clin Microbiol. 2010 Nov;48(11):4260-2
pubmed: 20810770
Proc Natl Acad Sci U S A. 2017 Jul 3;114(27):7130-7135
pubmed: 28634288
Cell Tissue Bank. 2017 Sep;18(3):433-440
pubmed: 28573389
Genome Biol. 2010;11(5):R50
pubmed: 20459815
BMC Genomics. 2011 Jun 06;12:293
pubmed: 21645359
Lab Invest. 2006 Feb;86(2):202-11
pubmed: 16402036
Genome Res. 2003 Aug;13(8):1863-72
pubmed: 12902380
Nat Methods. 2017 Apr;14(4):381-387
pubmed: 28263961
Genome Biol. 2018 Nov 28;19(1):208
pubmed: 30486838
BMC Biol. 2014 May 30;12:42
pubmed: 24885439
PLoS Genet. 2007 Sep;3(9):1724-35
pubmed: 17907809
Bioinformatics. 2012 Aug 15;28(16):2184-5
pubmed: 22743226
Bioinformatics. 2012 Mar 15;28(6):882-3
pubmed: 22257669
Cryobiology. 1988 Jun;25(3):178-85
pubmed: 3396384
Biostatistics. 2007 Jan;8(1):118-27
pubmed: 16632515
Nat Neurosci. 2019 Oct;22(10):1624-1634
pubmed: 31551593
Genome Biol. 2016 Aug 26;17(1):181
pubmed: 27565134
Nat Commun. 2015 Aug 03;6:7816
pubmed: 26234653
Ann Surg Oncol. 2013 May;20(5):1737-44
pubmed: 22711177
Nat Biotechnol. 2014 Sep;32(9):915-925
pubmed: 25150835
Bioinformatics. 2014 Aug 1;30(15):2114-20
pubmed: 24695404
Genome Biol. 2014;15(12):550
pubmed: 25516281
Nat Biotechnol. 2016 May;34(5):525-7
pubmed: 27043002
Nat Protoc. 2012 Mar 01;7(3):562-78
pubmed: 22383036
Transl Oncol. 2018 Jun;11(3):800-807
pubmed: 29705629
J Neurochem. 2016 Jul;138(1):53-9
pubmed: 27062510
Nat Biotechnol. 2013 Nov;31(11):1015-22
pubmed: 24037425
Onco Targets Ther. 2018 Jun 20;11:3573-3581
pubmed: 29950862
Recent Results Cancer Res. 2015;199:85-93
pubmed: 25636432
PLoS One. 2014 Aug 07;9(8):e104283
pubmed: 25101803
BMC Bioinformatics. 2016 Feb 03;17:58
pubmed: 26842848
J Biotechnol. 2007 Jan 20;127(4):549-59
pubmed: 16945445
Bioinformatics. 2010 Jan 1;26(1):139-40
pubmed: 19910308
Biopreserv Biobank. 2012 Feb;10(1):4-11
pubmed: 24849748
Bioinformatics. 2013 Jan 1;29(1):15-21
pubmed: 23104886
BMC Biotechnol. 2007 Sep 13;7:57
pubmed: 17854504
Nat Methods. 2008 Jul;5(7):621-8
pubmed: 18516045
Biopreserv Biobank. 2015 Oct;13(5):335-47
pubmed: 26484573
FASEB J. 2017 Aug;31(8):3298-3308
pubmed: 28446590
Nat Methods. 2013 Jul;10(7):623-9
pubmed: 23685885
Proc Natl Acad Sci U S A. 2002 Apr 30;99(9):5860-5
pubmed: 11972065
JAMA Psychiatry. 2015 Apr;72(4):386-94
pubmed: 25739104
F1000Res. 2019 Apr 23;8:532
pubmed: 31114675
Biostatistics. 2012 Apr;13(2):204-16
pubmed: 22285995
Bioinformatics. 2009 Aug 15;25(16):2078-9
pubmed: 19505943
Mol Ecol Resour. 2019 Mar;19(2):456-464
pubmed: 30447171
Nat Methods. 2017 Apr;14(4):417-419
pubmed: 28263959
Nat Biotechnol. 2017 Apr 11;35(4):319-321
pubmed: 28398307
Nat Biotechnol. 2014 Sep;32(9):896-902
pubmed: 25150836
Plant Cell. 2007 Nov;19(11):3418-36
pubmed: 18024567
Genome Res. 2019 Nov;29(11):1816-1825
pubmed: 31519740
BMC Genomics. 2014 Jun 02;15:419
pubmed: 24888378
Bioinformatics. 2008 Dec 1;24(23):2798-800
pubmed: 18842599
BMC Mol Biol. 2006 Jan 31;7:3
pubmed: 16448564
Genome Biol. 2019 Apr 16;20(1):75
pubmed: 30992037