Summary of project PR000685

This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench,, 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.


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) 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
Laboratory:Richard Yost Laboratory
Last Name:Levy
First Name:Allison
Address:214 Leigh Hall, PO Box 117200, Gainesville, Florida, 32611, USA

Summary of all studies in project PR000685

Study IDStudy TitleSpeciesInstituteAnalysis
(* : Contains Untargted data)
(* : Contains raw data)
ST001027 Influence of Data-Processing Strategies on Normalized Lipid Levels using an Open-Source LC-HRMS/MS Lipidomics Workflow Homo sapiens University of Florida MS* 2018-08-27 1 28 Uploaded data (5.6G)*