Summary of Study ST001357

This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench,, where it has been assigned Project ID PR000927. The data can be accessed directly via it's Project DOI: 10.21228/M8VT32 This work is supported by NIH grant, U2C- DK119886.


This study contains a large results data set and is not available in the mwTab file. It is only available for download via FTP as data file(s) here.

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Study IDST001357
Study TitleLongitudinal wastewater sampling and untargeted metabolomics of three buildings
Study SummaryDirect sampling of building wastewater has the potential to enable precision public health observations and interventions. Temporal sampling offers additional dynamic information that can be used to increase the informational content of individual metabolic “features”, but few studies have focused on high-resolution sampling. Here, we sampled three spatially close buildings, revealing individual metabolomics features, retention time (rt) and mass-to-charge ratio (mz) pairs, that often possess similar stationary statistical properties, as expected from aggregate sampling. However, the temporal profiles of features—providing orthogonal information to physicochemical properties—illustrate that many possess different feature temporal dynamics (fTDs) across buildings, with large and unpredictable single day deviations from the mean. Internal to a building, numerous and seemingly unrelated features, with mz and rt differences up to hundreds of Daltons and seconds, display highly correlated fTDs, suggesting non-obvious feature relationships. Data-driven building classification achieves high sensitivity and specificity, and extracts building-identifying features found to possess unique dynamics. Analysis of fTDs from many short-duration samples allows for tailored community monitoring with applicability in public health studies.
Massachusetts Institute of Technology
Last Nameethan
First Nameevans
Address77 Massachusetts Ave, Cambridge, MA, 02139, USA
Submit Date2020-03-20
Raw Data AvailableYes
Raw Data File Type(s)mzML
Analysis Type DetailLC-MS
Release Date2020-06-08
Release Version1
evans ethan evans ethan application/zip

Select appropriate tab below to view additional metadata details:


Project ID:PR000927
Project DOI:doi: 10.21228/M8VT32
Project Title:Longitudinal wastewater sampling and untargeted metabolomics of three buildings
Project Summary:Analyze the temporal dynamics of metabolites and assess the necessary temporal resolution of sampling to understand small, single building communities for target public health monitoring.
Institute:Massachusetts Institute of Technology
Last Name:Ethan
First Name:Evans
Address:77 Massachusetts Ave, Cambridge, MA, 02139, USA


Subject ID:SU001431
Subject Type:Other
Subject Species:Wastewater


Subject type: Other; Subject species: Wastewater (Factor headings shown in green)

mb_sample_id local_sample_id Sample type
SA098842mtab_alm_UW_072718_04alm mix in MQ neg
SA098843mtab_alm_UW_072718_81alm mix in MQ neg
SA098844mtab_alm_UW_072718_08alm mix in MQ neg
SA098845mtab_alm_UW_072718_03alm mix in MQ pos
SA098846mtab_alm_UW_072718_09alm mix in MQ pos
SA098847mtab_alm_UW_072718_80alm mix in MQ pos
SA098848mtab_alm_UW_072718_07alm mix in MQ pos (new vial)
SA098683mtab_alm_UW_072718_78MQ Blank neg
SA098684mtab_alm_UW_072718_151MQ Blank neg
SA098685mtab_alm_UW_072718_149MQ Blank neg
SA098686mtab_alm_UW_072718_76MQ Blank neg
SA098687mtab_alm_UW_072718_148MQ Blank neg
SA098688mtab_alm_UW_072718_79MQ Blank neg
SA098689mtab_alm_UW_072718_166MQ Blank neg
SA098690mtab_alm_UW_072718_165MQ Blank neg
SA098691mtab_alm_UW_072718_75MQ Blank neg
SA098692mtab_alm_UW_072718_150MQ Blank neg (07.31.18 rerun some samples, new aliquot)
SA098693mtab_alm_UW_072718_02MQ Blank neg (batch 1)
SA098694mtab_alm_UW_072718_77MQ Blank neg (batch 2, 07.30.18)
SA098695mtab_alm_UW_072718_01MQ Blank pos (HSST3#5, probe 3, 07.27.18)
SA098696mtab_alm_UW_072718_58UW D13B61 neg
SA098697mtab_alm_UW_072718_127UW D13B7 neg
SA098698mtab_alm_UW_072718_144UW D13B84 neg
SA098699mtab_alm_UW_072718_35UW D16B61 neg
SA098700mtab_alm_UW_072718_133UW D16B7 neg
SA098701mtab_alm_UW_072718_49UW D16B84 neg
SA098702mtab_alm_UW_072718_16UW D20B61 neg
SA098703mtab_alm_UW_072718_130UW D20B61 neg
SA098704mtab_alm_UW_072718_101UW D20B7 neg
SA098705mtab_alm_UW_072718_28UW D20B7 neg
SA098706mtab_alm_UW_072718_98UW D2B61 neg
SA098707mtab_alm_UW_072718_100UW D2B61 neg
SA098708mtab_alm_UW_072718_66UW D2B7 neg
SA098709mtab_alm_UW_072718_90UW D2B7 neg
SA098710mtab_alm_UW_072718_39UW D2B84 neg
SA098711mtab_alm_UW_072718_42UW D60B84 neg
SA098712mtab_alm_UW_072718_23UW D6B61 neg
SA098713mtab_alm_UW_072718_120UW D6B7 neg
SA098714mtab_alm_UW_072718_19UW D6B84 neg
SA098715mtab_alm_UW_072718_63UW D9B61 neg
SA098716mtab_alm_UW_072718_118UW D9B7 neg
SA098717mtab_alm_UW_072718_131UW D9B84 neg
SA098718mtab_alm_UW_072718_65UW N11B61 neg
SA098719mtab_alm_UW_072718_33UW N11B84 neg
SA098720mtab_alm_UW_072718_104UW N15B61-EIA neg
SA098721mtab_alm_UW_072718_159UW N15B61-EIA neg redo
SA098722mtab_alm_UW_072718_54UW N15B84 neg
SA098723mtab_alm_UW_072718_134UW N18B61 neg
SA098724mtab_alm_UW_072718_107UW N18B84 neg
SA098725mtab_alm_UW_072718_41UW N22B61 neg
SA098726mtab_alm_UW_072718_136UW N22B7 neg
SA098727mtab_alm_UW_072718_34UW N22B84 neg
SA098728mtab_alm_UW_072718_132UW N29B7 neg
SA098729mtab_alm_UW_072718_52UW N4B61 neg
SA098730mtab_alm_UW_072718_91UW N4B61 neg
SA098731mtab_alm_UW_072718_37UW N4B7 neg
SA098732mtab_alm_UW_072718_26UW N4B7 neg
SA098733mtab_alm_UW_072718_45UW N4B84 neg
SA098734mtab_alm_UW_072718_21UW N4B89 neg
SA098735mtab_alm_UW_072718_24UW N8B61 neg
SA098736mtab_alm_UW_072718_92UW N8B7 neg
SA098738mtab_alm_UW_072718_124UW O11B61-CTL neg
SA098739mtab_alm_UW_072718_161UW O11B61-CTL neg redo
SA098737mtab_alm_UW_072718_48UW O11B61 neg
SA098741mtab_alm_UW_072718_89UW O11B7-CTL neg
SA098742mtab_alm_UW_072718_158UW O11B7-CTL neg redo
SA098740mtab_alm_UW_072718_60UW O11B7 neg
SA098743mtab_alm_UW_072718_112UW O11B84 neg
SA098744mtab_alm_UW_072718_72UW O11B84-NO-CEN neg
SA098745mtab_alm_UW_072718_99UW O12B61neg
SA098746mtab_alm_UW_072718_70UW O13B61 neg
SA098747mtab_alm_UW_072718_61UW O13B7 neg
SA098748mtab_alm_UW_072718_59UW O14B61 CTL neg
SA098750mtab_alm_UW_072718_162UW O14B61-CTL neg redo
SA098749mtab_alm_UW_072718_38UW O14B61 neg
SA098752mtab_alm_UW_072718_106UW O14B7-CTL neg
SA098753mtab_alm_UW_072718_160UW O14B7-CTL neg redo
SA098751mtab_alm_UW_072718_111UW O14B7 neg
SA098754mtab_alm_UW_072718_121UW O16B7 neg
SA098755mtab_alm_UW_072718_67UW O16B84 neg
SA098756mtab_alm_UW_072718_126UW O18B61 neg
SA098757mtab_alm_UW_072718_115UW O18B61 neg
SA098758mtab_alm_UW_072718_103UW O18B7 neg
SA098759mtab_alm_UW_072718_114UW O18B7 neg
SA098762mtab_alm_UW_072718_117UW O18B84_2 neg
SA098760mtab_alm_UW_072718_55UW O18B84 CTL neg
SA098761mtab_alm_UW_072718_108UW O18B84 neg
SA098763mtab_alm_UW_072718_47UW O19B61 neg
SA098764mtab_alm_UW_072718_138UW O19B7 neg
SA098765mtab_alm_UW_072718_44UW O19B84 neg
SA098766mtab_alm_UW_072718_40UW O20B61 neg
SA098767mtab_alm_UW_072718_97UW O20B7 neg
SA098768mtab_alm_UW_072718_94UW O20B84 neg
SA098769mtab_alm_UW_072718_71UW O20B84 neg
SA098770mtab_alm_UW_072718_143UW O21B61 neg
SA098771mtab_alm_UW_072718_113UW O21B61 neg
SA098772mtab_alm_UW_072718_119UW O21B7 neg
SA098773mtab_alm_UW_072718_57UW O21B7 neg
SA098774mtab_alm_UW_072718_139UW O21B84 neg
SA098775mtab_alm_UW_072718_105UW O21B84 neg
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Collection ID:CO001426
Collection Summary:Samples were collected from street-level manholes located outside of three buildings: one multipurpose-use building (Building 7 = building 1), and two residential buildings (Buildings 61 and 84 = buildings 2 and 3). We used a commercial peristaltic pump (Boxer) to continuously collect wastewater samples for 3 hours starting from 9:00 AM for Building 1 and 8:00 AM for Buildings 2 and 3. The peristaltic pump was programmed to pump wastewater at a rate of 5.55 mL/min over a 3-hour period into a 1 L polycarbonate bottle (Thermo Scientific) stored on ice, for a total volume of 1 L of wastewater.
Sample Type:Wastewater


Treatment ID:TR001446
Treatment Summary:100 mL of each sample were filtered separately through a 0.2 μM PTFE membrane filter (Millipore) using a glass filtration apparatus (Glassco) to remove bacteria and debris. All filtration glassware and polycarbonate bottles were acid washed with hydrochloric acid and autoclaved prior to filtration.

Sample Preparation:

Sampleprep ID:SP001439
Sampleprep Summary:The filtrate from the previous step was collected in amber glass vials, the pH was adjusted to between 2 and 3, and stored at -80 ࿁C, all in less than 2 hours post sampling.

Combined analysis:

Analysis ID AN002260
Analysis type MS
Chromatography type Reversed phase
Chromatography system Thermo Vanquish
Column Waters Acquity BEH HSS T3 (100 x 2.1mm,1.8um)
MS instrument type Orbitrap
MS instrument name Thermo Fusion Orbitrap
Units peak area


Chromatography ID:CH001659
Instrument Name:Thermo Vanquish
Column Name:Waters Acquity BEH HSS T3 (100 x 2.1mm,1.8um)
Chromatography Type:Reversed phase


MS ID:MS002104
Analysis ID:AN002260
Instrument Name:Thermo Fusion Orbitrap
Instrument Type:Orbitrap
MS Comments:Data was collected in negative ionization mode with data-dependent secondary mass spectra (MS/MS) obtained via high-energy collisional dissociation (HCD, mass resolution 15,000 and collision energy of 35 arbitrary units, automatic gain control, AGT, of 5.0e4 and max injection time, IT, of 22 ms). The full MS resolution was 120,000 at 200 mz with an AGT target of 4.0e5 and a maximum IT of 50 ms. The quadrupole isolation width was set at 1.0 m/z. ESI was carried out at a source voltage of 2600 kV for negative ion mode with a capillary temperature of 350 ࿁C, vaporizer temperature of 400 ࿁C, and sheath, auxiliary, and sweep gases at 55, 20, and 1 arbitrary units, respectively. Python 3.6.5 with scikit-learn version 0.19.1 as well as R 3.5.1 were used for processing and analysis. Following data acquisition, all data files were converted to an open source file format (.mzML) via a custom wrapper ( of the program MSConvert in the ProteoWizard suite. All files were then processed as a single batch with a custom python wrapper script ( of both IPO and then subsequent XCMS processing. The parameters for XCMS were: CentWave (ppm=10, peakwidth=(5,15), snthresh=(100), prefilter=(4,10000), mzCenterFun=wMean, integrate=2, mzdiff=-0.005, noise=50,000), ObiwarpParam (binsize=0.1, response=1, distFun=cor_opt, gapInit=0.3, gapExtend=2.4, factorDiag=2, factorGap=1), PeakDensityParam (bw=10, minFraction=0.05, minSamples=1, binSize=0.002, maxFeatures=50), mode (negative). In addition to aligning and extracting peak information, this program automatically extracted all MS/MS spectra and saved as a separate .mgf file for use in the metabolite naming pipeline. Mentioned python scripts can be found at: