Summary of Study ST001402

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 PR000962. The data can be accessed directly via it's Project DOI: 10.21228/M8BM3B 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	ST001402
Study Title	Ontogeny related changes in the pediatric liver metabolome
Study Summary	A major challenge in implementing personalized medicine in pediatrics is identifying appropriate drug dosages for children. The majority of drug dosing studies have been based on adult populations, often with modification of the dosing for children based on size and weight. However, the growth and development experienced by children between birth and adulthood represents a dynamically changing biological system, with implications for effective drug dosing, efficacy as well as potential drug toxicity. The purpose of this study was to apply a metabolomics approach to gain preliminary insights into the ontogeny of liver function from newborn to adolescent.
Institute	Moffitt Cancer Center
Last Name	Fridley
First Name	Brooke
Address	12902 USF Magnolia Drive
Email	brooke.fridley@moffitt.org
Phone	813-745-1461
Submit Date	2020-05-27
Analysis Type Detail	LC-MS
Release Date	2020-09-10
Release Version	1

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Sample Preparation:

Sampleprep ID:	SP001484
Sampleprep Summary:	Experiment 1 The first experiment was completed using the first set of samples (N = 48) (referred to as “batch 1”). Metabolite extraction and detection were performed at Metabolon Inc. (Durham, NC, USA) as previously described (Evans et al., 2009). Briefly, liver samples were extracted through the automated MicroLab STAR® system (Hamilton Company, UT, USA), centrifuged, and the resulting supernatants were analyzed by UPLC-MS/MS in a positive and negative ion mode (UPLC: Waters, Milford, MA; mass spectrometer: Thermo-Finnigan LTQ, Thermo Fisher Scientific, Waltham, MA, scan range, 80–1000 m/z) and by GC-MS (Thermo-Finnigan Trace DSQ fast-scanning single-quadrupole mass spectrometer, scan range 50–750 m/z). The final experiment 1 metabolomic dataset comprised a total of 751 biochemicals, 478 scompounds of known identity (named biochemicals) and 273 compounds of unknown structural identity. As initial statistical analysis revealed an age-dependent effect that could not be distinguished from a tissue source-related effect, a replication set of group 1 samples was obtained through collaboration with Erasmus Medical Center/Sophia Children’s Hospital. Experiment 2 Given that the metabolomic platform changed between the first analysis and the sample set containing the replication samples, the second experiment examined the entire set of 98 samples. The same 48 samples previously processed in Experiment 1 and designated as “batch 1” above were re-analyzed on the new platform, with the results designated “batch 2”. The replication samples from Erasmus/Sophia Children’s Hospital and additional samples from CMH (N=50) are designated as “batch 3”. Following the sample extraction, the resulting extract was analyzed using a Waters ACQUITY ultra-performance liquid chromatography (UPLC) and a Thermo Scientific Q-Exactive high resolution/accurate mass spectrometer interfaced with a heated electrospray ionization (HESI-II) source and Orbitrap mass analyzer operated at 35,000 mass resolution (Evans et al., 2014). Four methods were utilized: two separate reverse phase (RP)/UPLC-MS/MS methods with positive ion mode electrospray ionization (ESI), RP/UPLC-MS/MS with negative ion mode ESI, and HILIC/UPLC-MS/MS with negative ion mode ESI. The MS analysis alternated between MS and data-dependent MSn scans using dynamic exclusion. The scan range varied slighted between methods but covered 70-1000 m/z. The final experiment 2 metabolomic dataset comprised a total of 971 biochemicals, 779 compounds of known identity (named biochemicals) and 192 compounds of unknown structural identity.

Sampleprep ID:

SP001484

Sampleprep Summary:

Experiment 1 The first experiment was completed using the first set of samples (N = 48) (referred to as “batch 1”). Metabolite extraction and detection were performed at Metabolon Inc. (Durham, NC, USA) as previously described (Evans et al., 2009). Briefly, liver samples were extracted through the automated MicroLab STAR® system (Hamilton Company, UT, USA), centrifuged, and the resulting supernatants were analyzed by UPLC-MS/MS in a positive and negative ion mode (UPLC: Waters, Milford, MA; mass spectrometer: Thermo-Finnigan LTQ, Thermo Fisher Scientific, Waltham, MA, scan range, 80–1000 m/z) and by GC-MS (Thermo-Finnigan Trace DSQ fast-scanning single-quadrupole mass spectrometer, scan range 50–750 m/z). The final experiment 1 metabolomic dataset comprised a total of 751 biochemicals, 478 scompounds of known identity (named biochemicals) and 273 compounds of unknown structural identity. As initial statistical analysis revealed an age-dependent effect that could not be distinguished from a tissue source-related effect, a replication set of group 1 samples was obtained through collaboration with Erasmus Medical Center/Sophia Children’s Hospital. Experiment 2 Given that the metabolomic platform changed between the first analysis and the sample set containing the replication samples, the second experiment examined the entire set of 98 samples. The same 48 samples previously processed in Experiment 1 and designated as “batch 1” above were re-analyzed on the new platform, with the results designated “batch 2”. The replication samples from Erasmus/Sophia Children’s Hospital and additional samples from CMH (N=50) are designated as “batch 3”. Following the sample extraction, the resulting extract was analyzed using a Waters ACQUITY ultra-performance liquid chromatography (UPLC) and a Thermo Scientific Q-Exactive high resolution/accurate mass spectrometer interfaced with a heated electrospray ionization (HESI-II) source and Orbitrap mass analyzer operated at 35,000 mass resolution (Evans et al., 2014). Four methods were utilized: two separate reverse phase (RP)/UPLC-MS/MS methods with positive ion mode electrospray ionization (ESI), RP/UPLC-MS/MS with negative ion mode ESI, and HILIC/UPLC-MS/MS with negative ion mode ESI. The MS analysis alternated between MS and data-dependent MSn scans using dynamic exclusion. The scan range varied slighted between methods but covered 70-1000 m/z. The final experiment 2 metabolomic dataset comprised a total of 971 biochemicals, 779 compounds of known identity (named biochemicals) and 192 compounds of unknown structural identity.