Summary of Study ST002046
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 PR001293. The data can be accessed directly via it's Project DOI: 10.21228/M8KH6M 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 | ST002046 |
| Study Title | A Taguchi Design of Experiments Approach for Untargeted Metabolomics Sample Preparation Optimization |
| Study Type | Design of Experiments - Extraction Optimization |
| Study Summary | Metabolomics commonly uses analytical techniques such as nuclear magnetic resonance (NMR) and liquid chromatography coupled to mass spectrometry (LC-MS) to quantify and identify metabolites associated with biological variation. Metabolome coverage from non-targeted LC-MS studies relies heavily on the pre-analytical protocols (e.g., homogenization and extraction) used. Chosen protocols impact which metabolites are successfully measured, which in turn impacts biological conclusions. Different homogenization and extraction methods produce significant variability in metabolome coverage, sample reproducibility, and extraction efficiency. Herein we describe an efficient Taguchi method design of experiments (DOE) approach to optimize the extraction solvent and volume, extraction time, and LC reconstitution solvent for a sequential non-polar and polar Caenorhabditis elegans extraction. DOE is rarely used in metabolomics yet provides a systematic approach for optimizing sample preparation while simultaneously decreasing the number of experiments required to obtain high-quality data. |
| Institute | University of Georgia |
| Last Name | Garcia |
| First Name | Brianna |
| Address | 315 Riverbend Road |
| brianna.garcia@uga.edu | |
| Phone | 6269059945 |
| Submit Date | 2021-12-16 |
| Num Groups | 10 |
| Total Subjects | 30 |
| Raw Data Available | Yes |
| Raw Data File Type(s) | mzML |
| Analysis Type Detail | LC-MS |
| Release Date | 2022-12-20 |
| Release Version | 1 |
Select appropriate tab below to view additional metadata details:
Sample Preparation:
| Sampleprep ID: | SP002134 |
| Sampleprep Summary: | Samples were extracted according to the L9 orthogonal array described in the manuscript. Details are shown in the attached methods, but for full context refer to the manuscript. |
