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Influence of Data-Processing Strategies on Normalized Lipid Levels using an Open-Source LC-HRMS/MS Lipidomics Workflow

ABSTRACT: 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.

ORGANISM(S): Human Homo Sapiens

TISSUE(S): Blood

SUBMITTER: Allison Levy

PROVIDER: ST001027 | MetabolomicsWorkbench | Wed Jul 25 00:00:00 BST 2018

REPOSITORIES: MetabolomicsWorkbench

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Project description:BACKGROUND: Lipidomics, the comprehensive measurement of lipids within a biological system or substrate, is an emerging field with significant potential for improving clinical diagnosis and our understanding of health and disease. While lipids diverse biological roles contribute to their clinical utility, the diversity of lipid structure and concentrations prove to make lipidomics analytically challenging. Without internal standards to match each lipid species, researchers often apply individual internal standards to a broad range of related lipids. 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.RESULTS: LMN uses a ranking system (1-3) to assign lipid standards to target analytes. A ranking of 1 signifies that both the lipid class and adduct of the internal standard and target analyte match, while a ranking of 3 signifies that neither the adduct or class match. If multiple internal standards are provided for a lipid class, standards with the closest retention time to the target analyte will be chosen. The user can also signify which lipid classes an internal standard represents, for example indicating that ether-linked phosphatidylcholine can be semi-quantified using phosphatidylcholine. LMN is designed to work with any lipid identification software and feature finding software, and in this study is used to quantify lipids in NIST SRM 1950 human plasma annotated using LipidMatch and MZmine.CONCLUSIONS: LMN can be integrated into an open source workflow which completes all data processing steps including feature finding, annotation, and quantification for LC-MS/MS studies. Using LMN we determined that in certain cases the use of peak height versus peak area, certain adducts, and negative versus positive polarity data can have major effects on the final concentration obtained.

Project description:This dataset derives from experiments testing quantitation using label-free methods. We used the Sigma-Aldrich UPS1 and UPS2 protein standard sets, containing proteins of 6-83 kD; the UPS1 set includes 49 human proteins at a single molar concentration, while the UPS2 set includes six groups of eight human proteins spanning a six orders of magnitude in concentration. In both cases, dilutions of UPS1 or UPS2 proteins were made into an E. coli extract, which allows these experiments to mimic detection of low abundance proteins in a complex protein mixture. The UPS1/E. coli and UPS2/E. coli mixtures were run on three mass spectrometers, an Orbitrap Velos Elite and two ion-trap instruments (Velos and LTQ). Proteins were quantified using MS1 peak volume (Orbitrap) or MS2 intensities and spectral counts (Orbitrap, Velos, LTQ). The analyzed results show that the ion-trap instruments perform nearly as well for label-free quantitation as does the Orbitrap. In addition, the \"Top 3\" method for quantitation—measuring only the three most abundant peptides—performed no better than the \"iBAQ\" quantitation method. Data analysis: MS1 data were analyzed using MaxQuant. MaxQuant version 1.2.2.5 software was used for protein identification and quantitation. Using the search engine Andromeda, mass spectrometry data were searched against the database containing UPS and E. coli proteins. MaxQuant reports summed intensity for each protein, as well as its iBAQ value. In the iBAQ algorithm, the intensities of the precursor peptides that map to each protein are summed together and divided by the number of theoretically observable peptides, which is considered to be all tryptic peptides between 6 and 30 amino acids in length. This operation converts a measure that is expected to be proportional to mass (intensity) into one that is proportional to molar amount (iBAQ). To determine relative molar abundances for each E. coli and human protein using MS1 data, two methods were used. In the first, we determined each proteins relative iBAQ (riBAQ), a normalized measure of molar abundance. We removed from the analysis all contaminant proteins that entered our sample-preparation workflow, for example keratins and trypsin, and then divided each remaining proteins iBAQ value by the sum of all non-contaminant iBAQ values. We also measured the average intensity of the three (or fewer, if fewer peptides were detected) peptides with the highest intensity, Top3, for each protein detected9. Peptides were chosen separately for each experiment, so the same peptides were not necessarily used across the four experiments. We generated a normalized \"top three\" abundance factor by dividing the average intensity for the three most abundant peptides of an individual protein and by the sum of all Top3 values in an experiment. Peptide intensities were summed for all charge states and variable modifications. For MS2 label-free analysis with MS2 spectra, MS2 data were searched against the database described above using SEQUEST. Setting to 1% the peptide false-discovery rate, estimated using a decoy (reversed) database, proteins were identified using the PAW pipeline. Normalized molar intensity (im) was calculated by dividing i, the summed intensity for an individual protein, by its molecular mass; the i/mr value for each protein was divided by the sum of all i/mr values. Normalized molar counts (cm) were calculated similarly, except the summed spectral counts for a protein (c) was used instead.

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Influence of Data-Processing Strategies on Normalized Lipid Levels using an Open-Source LC-HRMS/MS Lipidomics Workflow

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