Methods appendix: OMNImet™•GUT (ME-200) enables at-home collection andambient temperature transport of fecal samples for metabolomics (PD-WP-00066)
Olle M. De Bruin1, Elizaveta Freinkman2, Héloïse Breton1, Anne M. Evans2, Luke A. D. Miller2, UyenThao Nguyen2,Klaus-Peter Adam2 and Evgueni Doukhanine1
1 DNA Genotek, Ottawa, Ontario, Canada
2 Metabolon Inc, Durham, North Carolina, USA

2020-06-16

Introduction
This methods appendix describes the study design,
wet lab procedure and data analysis process that led tothe findings and interpretations within PD-WP-00066.

Materials and methods
Study design
The main study design is shown in Table 1. A fresh
fecal sample was collected from 7 adult volunteers
and stored in n=3 technical replicates (3 cryovials forthe flash-frozen (FF) condition; 3 OMNImet™•GUT
devices for the remaining conditions) per donor.
For each donor, the same 3 OMNImet•GUT devices
were sampled at each of the indicated timepoints(immediately frozen (T0), room temperature for 1, 4,
or 7 days (T1, T4, T7)). For the Freeze-Thaw (FT)
condition, an aliquot from each OMNImet•GUT
device was held at -20°C for 7 days, followed by
4 hours at 30°C. After the indicated storage condition,
all samples were held at -80°C until analysis.
Collection
methodCryovial

OMNImet•GUTTimepoint

Technical replicatesper donor(donor 1 to 7)

T0 (FF)

n=3

T0

n=3

T1

n=3

T4

n=3

T7

n=3FT

n=3

Table 1: Study design for validation of fecal sample stability
in OMNImet•GUT. T0 (FF) represents flash-frozen samples; T0, T1,T4 and T7 represent samples stored in OMNImet•GUT tubes atroom temperature for 0, 1, 4, or 7 days; FT represents storageat -20°C for 7 days, followed by 4 hours at 30°C.

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In a second experiment, the performance ofOMNImet•GUT device in stabilizing short-chain
fatty acids (SCFAs) in fecal sample at room
temperature was evaluated relative to unstabilizedstorage. A fecal sample was collected from a different
group of 7 adult volunteers, pooled and mixed
thoroughly and stored in n=4 technical replicates percondition as shown in Table 2. In this experiment,
independent replicates (cryovials or OMNImet•GUTdevices) were used at each timepoint. After theindicated storage timepoint, all samples were heldat -80°C until analysis.
Collection
methodCryovialOMNImet•GUTTimepoint

Technical replicates(pooled sample)

T0 (FF)

n=4T1

n=4T4

n=4T0

n=4T1

n=4T4

n=4T7

n=4T13n=4

Table 2: Study design for evaluation of SCFA stability in fecal samples
stored at room temperature in OMNImet•GUT devices vs. unstabilized
samples. Timepoints are labeled as in Table 1.

Sample preparation, data acquisition anddata processing: Global metabolomics
To prepare fecal samples for metabolomic analysis,
unstabilized samples were lyophilized while
OMNImet•GUT samples were dried in a Genevac
evaporator. All dried samples were weighed and thenresuspended at a 50:1 (50 μL deionized water for
every 1 mg of fecal sample) ratio for homogenization1.

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The homogenates were subjected to automatedbiochemical extraction and analysis by liquid
chromatography and high-resolution tandem mass
spectrometry (LC-MS/MS) on Metabolon’s global
metabolomics platform2, 3. Raw data were extracted,
peak-identified and processed by Metabolon using
proprietary software4, 5, 6. In brief, compoundsof exogenous, human and microbial origin wereidentified by comparison to library entries of purified
standards or recurrent unknown entities. Metabolon
maintains a dynamic and proprietary biochemicalreference library of more than 4,500 known
metabolites (based on authenticated standards)
and more than 2,000 novel metabolites (without anidentified chemical structure); each library entry
contains the retention time/index (RI), mass to charge
ratio (m/z) and chromatographic data (including
MS/MS spectral data). Biochemical identifications
are based on three criteria: retention index within anarrow RI window of the proposed identification,accurate mass match to the library ±10 ppm andthe MS/MS forward and reverse scores between
the experimental data and authentic standards.
The MS/MS scores are based on a comparison of
the ions present in the experimental spectrum to theions present in the library spectrum. Integrated ion
peaks were quantified using area-under-the-curveand scaled to a median of 1 for each biochemical.
Missing values, if any, were imputed with theobserved minimum for that particular compound.
The data were natural log-transformed prior to
statistical analyses including Welch’s two-sample

t-test, principal component analysis (PCA) andhierarchical clustering analysis (HCA) using complete
clustering with Euclidean distance.

Sample preparation, data acquisition and dataprocessing: Short-chain fatty acid (SCFA) assay
For unstabilized samples, each fecal sample wasweighed and extracted in organic solvent in thepresence of isotopically labeled internal standards; theextracts were then derivatized with 2,4-difluorophenyl
hydrazine. For OMNImet•GUT samples, each tube
was centrifuged and an aliquot of the supernatant wasderivatized in the presence of the same internalstandards under the same conditions as the unstabilizedsamples. For both sample types, the derivatization
reaction mixture was diluted and an aliquot wasinjected onto an Agilent 1290/AB Sciex QTrap® 5500
LC-MS/MS system equipped with a C18 reversed phase
UHPLC column. The mass spectrometer was operated
in negative mode using electrospray ionization (ESI).
The peak area of each individual analyte product ion
was measured against the peak area of the product ionsof the corresponding internal standards. Quantitation
was performed using a weighted linear least squares
regression analysis generated from calibration
standards fortified with isotopically labeled internal
standards, prepared immediately prior to each run.
For unstabilized samples, SCFA concentrations werenormalized by wet weight. OMNImet•GUT samples
were normalized by the average wet weight collected
per tube as established in preliminary experiments.

References:
123
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Data Quantity and Quality as Compared to Unit Mass Resolution Mass Spectrometry in High-Throughput Profiling Metabolomics.
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Ford, L., Kennedy, A.D., Goodman, K.D., Pappan, K.L., Evans, A.M., Miller, L.A.D., Wulff, J.E., Wiggs, B.R., Lennon, J.J., Elsea, S.,et al. (2020). Precision of a Clinical Metabolomics Profiling Platform for Use in the Identification of Inborn Errors of Metabolism.
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Evans, A.M., DeHaven, C.D., Barrett, T., Mitchell, M., and Milgram, E. (2009). Integrated, nontargeted ultrahigh performanceliquid chromatography/electrospray ionization tandem mass spectrometry platform for the identification and relative quantificationof the small-molecule complement of biological systems. Anal Chem 81, 6656-6667. http://doi.org/10.1021/ac901536h
Evans, A.M., Mitchell, M.W., Dai, H., and DeHaven, C.D. (2012) Categorizing Ion Features in Liquid Chromatography/Mass
Spectrometry Metabolomics Data. Metabolomics 2:110. http://doi.org/:10.4172/2153-0769.1000110
Dehaven, C.D., Evans, A.M., Dai, H., and Lawton, K.A. (2010). Organization of GC/MS and LC/MS metabolomics data into
chemical libraries. J Cheminform 2, 9. http://doi.org/10.1186/1758-2946-2-9Some DNA Genotek products may not be available in all geographic regions.
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