Summary of Study ST001357
This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, 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.
See: https://www.metabolomicsworkbench.org/about/howtocite.php
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.
| Study ID | ST001357 |
| Study Title | Longitudinal wastewater sampling and untargeted metabolomics of three buildings |
| Study Summary | Direct 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. |
| Institute | Massachusetts Institute of Technology |
| Last Name | ethan |
| First Name | evans |
| Address | 77 Massachusetts Ave, Cambridge, MA, 02139, USA |
| eevans@mit.edu | |
| Phone | 617-253-2726 |
| Submit Date | 2020-03-20 |
| Raw Data Available | Yes |
| Raw Data File Type(s) | mzML |
| Analysis Type Detail | LC-MS |
| Release Date | 2020-06-08 |
| Release Version | 1 |
Select appropriate tab below to view additional metadata details:
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 Type | ESI |
| MS instrument type | Orbitrap |
| MS instrument name | Thermo Fusion Orbitrap |
| Ion Mode | NEGATIVE |
| Units | peak area |
MS:
| MS ID: | MS002104 |
| Analysis ID: | AN002260 |
| Instrument Name: | Thermo Fusion Orbitrap |
| Instrument Type: | Orbitrap |
| MS Type: | ESI |
| 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 (msconvert_ee.py) of the program MSConvert in the ProteoWizard suite. All files were then processed as a single batch with a custom python wrapper script (full_ipo_xcms.py) 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: https://github.com/ethanev/Metabolite_lookup |
| Ion Mode: | NEGATIVE |
