Quantifying and Understanding Well-to-Well Contamination in Microbiome Research.

16S rRNA gene automation built environment contamination genomics low biomass metagenomics microbiome microbiota study design

Journal

mSystems
ISSN: 2379-5077
Titre abrégé: mSystems
Pays: United States
ID NLM: 101680636

Informations de publication

Date de publication:
25 Jun 2019
Historique:
entrez: 27 6 2019
pubmed: 27 6 2019
medline: 27 6 2019
Statut: epublish

Résumé

Microbial sequences inferred as belonging to one sample may not have originated from that sample. Such contamination may arise from laboratory or reagent sources or from physical exchange between samples. This study seeks to rigorously assess the behavior of this often-neglected between-sample contamination. Using unique bacteria, each assigned a particular well in a plate, we assess the frequency at which sequences from each source appear in other wells. We evaluate the effects of different DNA extraction methods performed in two laboratories using a consistent plate layout, including blanks and low-biomass and high-biomass samples. Well-to-well contamination occurred primarily during DNA extraction and, to a lesser extent, in library preparation, while barcode leakage was negligible. Laboratories differed in the levels of contamination. Extraction methods differed in their occurrences and levels of well-to-well contamination, with plate methods having more well-to-well contamination and single-tube methods having higher levels of background contaminants. Well-to-well contamination occurred primarily in neighboring samples, with rare events up to 10 wells apart. This effect was greatest in samples with lower biomass and negatively impacted metrics of alpha and beta diversity. Our work emphasizes that sample contamination is a combination of cross talk from nearby wells and background contaminants. To reduce well-to-well effects, samples should be randomized across plates, samples of similar biomasses should be processed together, and manual single-tube extractions or hybrid plate-based cleanups should be employed. Researchers should avoid simplistic removals of taxa or operational taxonomic units (OTUs) appearing in negative controls, as many will be microbes from other samples rather than reagent contaminants.

Identifiants

pubmed: 31239396
pii: 4/4/e00186-19
doi: 10.1128/mSystems.00186-19
pmc: PMC6593221
pii:
doi:

Types de publication

Journal Article

Langues

eng

Informations de copyright

Copyright © 2019 Minich et al.

Références

PLoS One. 2009 Jul 27;4(7):e6372
pubmed: 19633714
Nat Methods. 2010 May;7(5):335-6
pubmed: 20383131
Proc Natl Acad Sci U S A. 2011 Mar 15;108 Suppl 1:4516-22
pubmed: 20534432
J Clin Microbiol. 2010 Dec;48(12):4634-5
pubmed: 20962145
Nature. 2011 May 12;473(7346):174-80
pubmed: 21508958
Nat Methods. 2011 Jul 17;8(9):761-3
pubmed: 21765408
ISME J. 2012 Aug;6(8):1621-4
pubmed: 22402401
Nature. 2012 Jun 13;486(7402):207-14
pubmed: 22699609
BMC Genomics. 2014 Feb 07;15:110
pubmed: 24507442
BMC Genomics. 2014 Jun 06;15:443
pubmed: 24906487
BMC Biol. 2014 Nov 12;12:87
pubmed: 25387460
Genome Biol. 2014 Dec 17;15(12):564
pubmed: 25608874
PLoS One. 2015 Apr 10;10(4):e0120520
pubmed: 25860802
BMC Microbiol. 2015 Mar 21;15:66
pubmed: 25880246
Microbiome. 2015 May 05;3:19
pubmed: 25969736
Environ Microbiol. 2016 May;18(5):1403-14
pubmed: 26271760
J Lab Autom. 2016 Feb;21(1):37-48
pubmed: 26311060
Microbiome. 2015 Oct 13;3:49
pubmed: 26459172
Science. 2016 Apr 29;352(6285):560-4
pubmed: 27126039
mSystems. 2015 Dec 22;1(1):
pubmed: 27822518
mSystems. 2016 Apr 26;1(2):
pubmed: 27822524
mSystems. 2016 May 3;1(3):
pubmed: 27822526
mSystems. 2017 Jan 17;2(1):
pubmed: 28144630
mSystems. 2017 Mar 7;2(2):
pubmed: 28289731
Biotechniques. 2017 Jun 1;62(6):290-293
pubmed: 28625159
Nat Biotechnol. 2017 Nov;35(11):1077-1086
pubmed: 28967885
Nature. 2017 Nov 23;551(7681):457-463
pubmed: 29088705
Genome Biol. 2017 Nov 30;18(1):228
pubmed: 29187204
Adv Dent Res. 2018 Feb;29(1):71-77
pubmed: 29355422
Appl Environ Microbiol. 2018 Mar 19;84(7):
pubmed: 29427429
mSystems. 2018 Mar 13;3(2):null
pubmed: 29556537
Science. 2018 Mar 23;359(6382):1366-1370
pubmed: 29567708
mSystems. 2018 Mar 13;3(3):null
pubmed: 29577086
Nat Rev Microbiol. 2018 Jul;16(7):410-422
pubmed: 29795328
mSystems. 2018 May 15;3(3):null
pubmed: 29795809
Nat Microbiol. 2018 Aug;3(8):851-853
pubmed: 30046175
Nat Med. 2018 Oct;24(10):1532-1535
pubmed: 30150716
Nat Methods. 2018 Oct;15(10):796-798
pubmed: 30275573
mSystems. 2018 Nov 6;3(6):null
pubmed: 30417111
Trends Microbiol. 2019 Feb;27(2):105-117
pubmed: 30497919
Microbiome. 2018 Dec 17;6(1):226
pubmed: 30558668

Auteurs

Jeremiah J Minich (JJ)

Marine Biology Research Division, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California, USA.

Jon G Sanders (JG)

Department of Pediatrics, University of California, San Diego, La Jolla, California, USA.

Amnon Amir (A)

Department of Pediatrics, University of California, San Diego, La Jolla, California, USA.

Greg Humphrey (G)

Department of Pediatrics, University of California, San Diego, La Jolla, California, USA.

Jack A Gilbert (JA)

Department of Ecology and Evolution, University of Chicago, Chicago, Illinois, USA.
Department of Surgery, University of Chicago, Chicago, Illinois, USA.

Rob Knight (R)

Department of Pediatrics, University of California, San Diego, La Jolla, California, USA robknight@ucsd.edu.
Center for Microbiome Innovation, Jacobs School of Engineering, University of California, San Diego, La Jolla, California, USA.
Department of Computer Science and Engineering, University of California, San Diego, La Jolla, California, USA.
Department of Bioengineering, University of California, San Diego, La Jolla, California, USA.

Classifications MeSH