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Research Article | Novel Systems Biology Techniques

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

Jeremiah J. Minich, Jon G. Sanders, Amnon Amir, Greg Humphrey, Jack A. Gilbert, Rob Knight
Nicola Segata, Editor
Jeremiah J. Minich
Marine Biology Research Division, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California, USA
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Jon G. Sanders
Department of Pediatrics, University of California, San Diego, La Jolla, California, USA
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Amnon Amir
Department of Pediatrics, University of California, San Diego, La Jolla, California, USA
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Greg Humphrey
Department of Pediatrics, University of California, San Diego, La Jolla, California, USA
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Jack A. Gilbert
Department of Ecology and Evolution, University of Chicago, Chicago, Illinois, USADepartment of Surgery, University of Chicago, Chicago, Illinois, USA
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Rob Knight
Department of Pediatrics, University of California, San Diego, La Jolla, California, USACenter for Microbiome Innovation, Jacobs School of Engineering, University of California, San Diego, La Jolla, California, USADepartment of Computer Science and Engineering, University of California, San Diego, La Jolla, California, USADepartment of Bioengineering, University of California, San Diego, La Jolla, California, USA
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Nicola Segata
University of Trento
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DOI: 10.1128/mSystems.00186-19
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  • FIG 1
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    FIG 1

    Plate design and experimental design. (a) NTC, sink, and source samples are distributed in a checkboard pattern across the plate. (b and c) Antifoam A is added to first half (b) and second half (c) of the 96-well plates processed with the robot in order to test whether antifoam A reduces foaming during bead beating and thereby well-to-well contamination. The manual samples did not receive antifoam A. Each unique DNA extraction plate is processed in duplicate PCR plates.

  • FIG 2
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    FIG 2

    Example of plates with cross-contamination. Each panel depicts a 96-well plate, with source, sink, and blank wells denoted by “O,” “X,” and empty squares, respectively. Colors indicate the number of reads from a specific bacterium (Psychrobacter species, present in well E5). Panels a and b, c and d, and e and f correspond to two PCR replicates of robotic extractions 1 and 2 and manual extraction, respectively.

  • FIG 3
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    FIG 3

    Distance-decay relationship of source samples contaminating surrounding samples. The distance (in units of wells) between “contaminant” observations of each sOTU and its source well was calculated. Histograms plot the number of inferred contamination events for each distance range for all 16 source microbes across the various DNA extraction plates and PCR replicate plate types from UCSD. Panels a and b, c and d, and e and f correspond to two PCR replicates of manual extraction and robotic extractions 1 and 2, respectively.

  • FIG 4
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    FIG 4

    Summary statistics of sample fraction compositions of well-to-well contaminants compared across extraction types (blanks [pink], sink [blue], and source [purple]) and across extraction methods (tube versus plate). The y axis has a maximum value of 1 (corresponding to 100%). Sample types (NTC, sink, or source) were assigned an estimated input biomass of 0 to 100 cells, 1e5 cells, or 1e7 cells, respectively. For UCSD tube extractions, samples from both PCR replicate plates (PCRA and PCRB) were included. For UCSD robot plate extractions, samples from both PCR replicate plates and both DNA extraction plates were combined and organized by sample type. Argonne processed samples included one extraction plate and one PCR replicate plate. Samples processed at UCSD are indicated by circles with no outline, and samples processed at Argonne are indicated by circles with a dark border. All samples with zero well-to-well contamination occurrences are given a count of 0.00001 to enable visualization on the graph (labeled 0 counts). Medians and interquartile ranges are displayed in black lines over the data points. ****, P < 0.001; ns, not significant.

  • FIG 5
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    FIG 5

    Well-to-well effect size. Shown are proportions of samples containing well-to-well contaminants organized by sample type (NTC, sink, and source) (a) and extraction method (b). The y axis has a maximum value of 1 (corresponding to 100%). Statistical analyses of data within bars were performed using Kruskal-Wallis nonparametric testing and indicate differences in contaminant fractions across extraction types (a) and among sample types (b). IQR, interquartile range.

Tables

  • Figures
  • Supplemental Material
  • TABLE 1

    Impact of contamination (well to well and background) on NTC, low-biomass, and high-biomass sample typesa

    Sample type
    (no. of samples)b
    LocationcExtraction
    methodd
    Well to wellBackground kit
    composition (%)
    Mean
    prevalence
    (%)e
    RichnessComposition (%)MeanMedianMax
    Avg no. of
    total unique
    reads
    W2W%fMeanMedianMax
    NTC
        61UCSDm_tube47.54204.124.640.0056.0095.36100.0100.0
        32Argonnem_tube53.131651.560.850.038.2399.1599.97100.0
        28Argonnem_plate10.7184.233.140.0075.1796.86100.0100.0
        116UCSDRobot95.691527.7963.7974.78100.036.2125.22100.0
    Sink
        93UCSDm_tube15.05200.960.050.002.783.351.6898.73
        48Argonnem_tube50.001891.672.310.0059.3478.0883.8298.78
        46Argonnem_plate32.61166.6113.990.0098.7158.4662.67100.0
        187UCSDRobot67.381512.700.700.0815.610.930.2540.51
    Source
        31UCSDm_tube61.29186.510.130.012.998.300.29100.0
        16Argonnem_tube87.502113.780.020.020.0711.540.4199.98
        16Argonnem_plate81.251716.792.370.0136.4013.130.3299.99
        64UCSDRobot70.311213.760.940.0450.677.320.16100.0
    • ↵a Composition refers to the mean, median, or maximum frequency of sOTU contaminants that are due to well-to-well contamination or background kits.

    • ↵b Refers to the total samples or well which had enough sequencing data for analysis.

    • ↵c Location refers to the two laboratories which processed samples, either UCSD or Argonne.

    • ↵d m_, manual (non-robotic-based extraction); Robot, robot-based DNA cleanup.

    • ↵e Prevalence is calculated as the number of samples with any well-to-well contamination/total number of samples.

    • ↵f W2W% is the percentage of total richness that is a result of well-to-well events, calculated as the number of unique well-to-well contaminants/total number of sOTUs (mean).

Supplemental Material

  • Figures
  • Tables
  • FIG S1

    Plate map descriptions of experimental design (96-well format). Sample wells are color coordinated by sample type (orange, blank/NTC; blue, sink/A. fischeri; purple, 1 of 16 source microbes). The top plate map was used for robot extractions. The second plate map was used for manual extractions. The third was used for mock community and sOTU taxon assignment. The fourth was used for barcode jumping testing. Download FIG S1, TIF file, 1.2 MB.

    Copyright © 2019 Minich et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

  • TABLE S1

    Raw counts and summaries of contaminant profiles across samples, including metadata. Download Table S1, CSV file, 0.2 MB.

    Copyright © 2019 Minich et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

  • FIG S2

    Use of antifoam (antifoam = 1) does not reduce well-to-well contamination. Download FIG S2, TIF file, 0.4 MB.

    Copyright © 2019 Minich et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

  • FIG S3

    Sources of contamination (well-to-well and background contaminants) across manual and robot extraction plates and PCR replicate plates (red, all background contaminant sOTUs; purple, all A. fischeri “sink” sOTUs”; black, all source sOTUs). Shown is a summary of the compositionality of NTCs (n = 48) versus sink microbes (n = 32) versus source microbes (n = 16) processed in two facilities across five DNA extraction plates: UCSD tube extraction (a), UCSD plate extraction 1 (b), UCSD plate extraction 2 (c), Argonne tube extraction (d), and Argonne plate extraction (e). UCSD DNA extractions were each processed twice and thus had two PCRs per plate (PCRA and PCRB). Download FIG S3, TIF file, 2.1 MB.

    Copyright © 2019 Minich et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

  • TABLE S2

    Summary statistics on well-to-well contamination, including DNA extraction and PCR replicate plates from UCSD and Argonne. Download Table S2, CSV file, 0.00 MB.

    Copyright © 2019 Minich et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

  • FIG S4

    Determining the origin of well-to-well contamination and its impact on distance metrics from 96 unique well_IDs across three DNA extraction plates and six PCR plates. (a) Summary comparison of the use of compositional (Bray-Curtis) or presence-absence (binary Jaccard) metrics to describe microbial communities from NTCs (red), sink microbes (lower biomass), or source microbes (higher biomass). (b) Determining the effects of well-to-well contamination from PCR processing only (PCR replicates) compared to the entire process of DNA extraction and PCR (DNA extraction replicates). The statistical tests were performed for dark colors only, while lightly shaded bars indicate the replicates for robot extraction plates. Download FIG S4, TIF file, 1.0 MB.

    Copyright © 2019 Minich et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

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Quantifying and Understanding Well-to-Well Contamination in Microbiome Research
Jeremiah J. Minich, Jon G. Sanders, Amnon Amir, Greg Humphrey, Jack A. Gilbert, Rob Knight
mSystems Jun 2019, 4 (4) e00186-19; DOI: 10.1128/mSystems.00186-19

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Quantifying and Understanding Well-to-Well Contamination in Microbiome Research
Jeremiah J. Minich, Jon G. Sanders, Amnon Amir, Greg Humphrey, Jack A. Gilbert, Rob Knight
mSystems Jun 2019, 4 (4) e00186-19; DOI: 10.1128/mSystems.00186-19
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KEYWORDS

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

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