Table 2.
Metabolomics Metadata Checklist
| Checklist Version | 1.0 (ref: Kolker, 2014) |
|---|---|
| Experiment information | Description |
| Lab Name | Snyder Lab, Department of Genetics, Stanford University |
| Date | October 24, 2013 |
| Author Information | George Mias, Somallee Datta |
| Title of Experiment | Integrated Personal Omics Profiling |
| Project | Integrated Personal Omics Profiling |
| Funding | Stanford University, NIH training grant, NIH/NLM training grant T15-LM007033, NIH/NIGMS R24-GM61374; the Spanish Ministry of Science and Innovation Projects SAF2008-05384 and CSD2007-00017; European Union FP7 Projects 2007-A-201630 (GENICA) and 2007-A-200950 (TELOMARKER); European Research Council Advanced Grant GA232854, the Körber Foundation, the Fundación Marcelino Botín, and Fundación Lilly (España); NIH/NHLBI training grant T32 HL094274; NIH/NHLBI KO8 HL083914; NIH New Investigator DP2 Award D004613; the Breetwor Family Foundation. G.M.'s research is supported by the National Human Genome Research Institute of the National Institutes of Health under award number K99HG007065, and previously T32HG000044. NSF/DBI award 0969929, NIH/NIDDK awards U01-DK-089571 and U01-DK-072473, The Robert B. McMillen Foundation, The Gordon and Betty Moore Foundation, and Seattle Children's Research Institute. |
| Digital ID | 2_2013; Open Science Data Cloud keyservice.opensciencedatacloud.org/ark:/31807/synder-002 |
| Abstract | This study presented an integrative personal omics profile that combined genomic, transcriptomic, proteomic, metabolomic, and autoantibody profiles from a single individual over a 14-month period. The study revealed various medical risks and uncovered extensive, dynamic changes in diverse molecular components and biological pathways across healthy and diseased conditions. The current checklist provides the metadata for the metabolomics part of the study. |
| Experimental design | |
| Organism | Human |
| OMICS type(s) utilized | Metabolomics |
| Reference | Cell 148(6), 1293-1307, 2012, (PMID 22424236) (Chen, 2012) |
| Experimental design | Longitudinal data collected on a single subject |
| Sample description | Samples were taken on a single individual over a 14 month period |
| Tissue/cell type ID | Blood serum |
| Localization ID | Cell |
| Condition ID | Healthy state, RSV and HRV infections (time specific) |
| Experimental methods | |
| Sample prep description | About 100 μL of the serum sample was used for the metabolomics study. Metabolites were extracted by adding four times volume of equal-volume mixture of methanol, acetonitrile, and acetone that were prechilled at −20°C. To maximize metabolite extraction, samples were vortex at 4°C for 15 min at 2 min intervals. Proteins were precipitated by incubating the sample at −20°C for 2 hr. Samples were then centrifuged at 10,000 rpm at 4°C for 10 min. The supernatant was collected and dried for metabolomics analysis. For each time point, three of the 100 μL samples were analyzed in triplicate. |
| Platform type | LC-MS and LC-MS/MS |
| Instrument name | Agilent 1260 LC system and Agilent 6538 Q-TOF MS |
| Instrument details | Coupled in-line with Agilent 6538 Q-TOF MS with electrospray ionization. |
| Instrument protocol | The LC mobile phases consisted of 0.2% acetic acid in water (solvent A) and 0.2% acetic acid in methanol (solvent B). The extract was resuspended in 50% methanol and sonicated for 5 min. The sample was loaded to an Agilent SB-aq 1.8 μm, 2.1×50 mm analytical column with a SB-C8 3.5 μM, 2.1×30 mm guard column in front. Columns were heated to 60°C with a flow rate of 0.6 mL/min. A linear gradient from 2% to 98% solvent B in 13 min was used for metabolites separation. To assure the mass accuracy of the recorded ions, continuous internal calibration ions were infused in-line through the dual electrospray ionization (ESI) source using an isocratic pump at flow rate of 0.05 mL/min. Internal calibrants at m/z 121.0509 and 922.0098 were used in positive ion mode and m/z of 119.0362 and 980.0164 were used in negative ion mode. The Q-TOF was operated at a source condition of 3.75 kV with drying gas 9 L/min and nebulizer gas 45 psi at 300°C. The instrument was run at extended mass range to 1,700 m/z. The fragmentor voltage was 125 V and skimmer at 47 V. The data were acquired at a scan rate of 1.5 spectra/sec for MS. MS/MS was run at targeted mode at a scan rate of 3 spec/sec with 10 spec/sec for MS. Collision energy of 20 V, a fixed isolation window of 4 m/z, and retention time window of 0.25 min. Each sample was run at MS mode first at both positive and negative modes, and the differentially expressed metabolites were selected for MS/MS experiment. |
| Data processing | |
| Processing/normalization methods/software | The Molecular Feature Extractor in QA was used to search for features that have common elution profile and groups ions into one or more compounds containing m/z values that are related. For the chromatography alignment, only ions with intensity above 5,000 counts and retention time window within 0.2 min were selected. Ions not present in all files were filtered out. For samples from the same time point, the median value was used. |
| Sequence/annotation database | METLIN human metabolites |
| ID method/software | MassHunter Workstation software (Agilent Technologies), including Qualitative Analysis (QA v3.01) and Mass Profiler Professional (vB.02); Mass tolerance=10 ppm |
| ID/expression measures | Spectra from profiling at each time point were obtained with three technical replicates and aligned for mass and retention time. The aligned spectra were filtered for a minimum of 2/3 time points being present for each identified mass. Data with CV<0.4 were retained. |
| Data analysis method/software | Clustering, pathway analysis, custom R, Mathematica, Python scripts. |
| I/O data file formats | Compound Exchange Format (CEF) |
| Additional Information | None |
ID, identification.