Summary of Study ST001027
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 PR000685. The data can be accessed directly via it's Project DOI: 10.21228/M84H56 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 | ST001027 |
| Study Title | Influence of Data-Processing Strategies on Normalized Lipid Levels using an Open-Source LC-HRMS/MS Lipidomics Workflow |
| Study Summary | Lipidomics is an emerging field with significant potential for improving clinical diagnosis and our understanding of health and disease. While the diverse biological roles of lipids contribute to their clinical utility, the unavailability of lipid internal standards representing each species, make lipid quantitation analytically challenging. The common approach is to employ one or more internal standards for each lipid class examined and use a single point calibration for normalization (relative quantitation). To aid in standardizing and automating this relative quantitation process, we developed LipidMatch Normalizer (LMN) http://secim.ufl.edu/secim-tools/ which can be used in most open source lipidomics workflows. While the effect of lipid structure on relative quantitation has been investigated, applying LMN we show that data-processing can significantly affect lipid semi-quantitative amounts. Polarity and adduct choice had the greatest effect on normalized levels; when calculated using positive versus negative ion mode data, one fourth of lipids had greater than 50 % difference in normalized levels. Based on our study, sodium adducts should not be used for statistics when sodium is not added intentionally to the system, as lipid levels calculated using sodium adducts did not correlate with lipid levels calculated using any other adduct. Relative quantification using smoothing versus not smoothing, and peak area versus peak height, showed minimal differences, except when using peak area for overlapping isomers which were difficult to deconvolute. By characterizing sources or variation introduced during data-processing and introducing automated tools, this work helps increase through-put and improve data-quality for determining relative changes across groups. |
| Institute | University of Florida |
| Department | Chemistry |
| Laboratory | Richard Yost Laboratory |
| Last Name | Levy |
| First Name | Allison |
| Address | 214 Leigh Hall, PO Box 117200, Gainesville, Florida, 32611, USA |
| allisonjlevy@ufl.edu | |
| Phone | 3523920515 |
| Submit Date | 2018-07-25 |
| Raw Data Available | Yes |
| Raw Data File Type(s) | raw(Thermo) |
| Analysis Type Detail | LC-MS |
| Release Date | 2018-08-27 |
| Release Version | 1 |
Select appropriate tab below to view additional metadata details:
Project:
| Project ID: | PR000685 |
| Project DOI: | doi: 10.21228/M84H56 |
| Project Title: | Influence of Data-Processing Strategies on Normalized Lipid Levels using an Open-Source LC-HRMS/MS Lipidomics Workflow |
| Project Type: | MS Data Processing |
| Project Summary: | Lipidomics is an emerging field with significant potential for improving clinical diagnosis and our understanding of health and disease. While the diverse biological roles of lipids contribute to their clinical utility, the unavailability of lipid internal standards representing each species, make lipid quantitation analytically challenging. The common approach is to employ one or more internal standards for each lipid class examined and use a single point calibration for normalization (relative quantitation). To aid in standardizing and automating this relative quantitation process, we developed LipidMatch Normalizer (LMN) http://secim.ufl.edu/secim-tools/ which can be used in most open source lipidomics workflows. While the effect of lipid structure on relative quantitation has been investigated, applying LMN we show that data-processing can significantly affect lipid semi-quantitative amounts. Polarity and adduct choice had the greatest effect on normalized levels; when calculated using positive versus negative ion mode data, one fourth of lipids had greater than 50 % difference in normalized levels. Based on our study, sodium adducts should not be used for statistics when sodium is not added intentionally to the system, as lipid levels calculated using sodium adducts did not correlate with lipid levels calculated using any other adduct. Relative quantification using smoothing versus not smoothing, and peak area versus peak height, showed minimal differences, except when using peak area for overlapping isomers which were difficult to deconvolute. By characterizing sources or variation introduced during data-processing and introducing automated tools, this work helps increase through-put and improve data-quality for determining relative changes across groups. |
| Institute: | University of Florida |
| Department: | Chemistry |
| Laboratory: | Richard Yost Laboratory |
| Last Name: | Levy |
| First Name: | Allison |
| Address: | 214 Leigh Hall, PO Box 117200, Gainesville, Florida, 32611, USA |
| Email: | allisonjlevy@ufl.edu |
| Phone: | 352-392-0515 |
