LipidFinder: A computational workflow for discovery of lipids identifies eicosanoid-phosphoinositides in platelets
Anne O’Connor, Christopher J. Brasher, David A. Slatter, Sven W. Meckelmann, Jade I. Hawksworth, Stuart M. Allen, Valerie B. O’Donnell
Anne O’Connor, Christopher J. Brasher, David A. Slatter, Sven W. Meckelmann, Jade I. Hawksworth, Stuart M. Allen, Valerie B. O’Donnell
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Resource and Technical Advance Inflammation Metabolism

LipidFinder: A computational workflow for discovery of lipids identifies eicosanoid-phosphoinositides in platelets

Abstract

Accurate and high-quality curation of lipidomic datasets generated from plasma, cells, or tissues is becoming essential for cell biology investigations and biomarker discovery for personalized medicine. However, a major challenge lies in removing artifacts otherwise mistakenly interpreted as real lipids from large mass spectrometry files (>60 K features), while retaining genuine ions in the dataset. This requires powerful informatics tools; however, available workflows have not been tailored specifically for lipidomics, particularly discovery research. We designed LipidFinder, an open-source Python workflow. An algorithm is included that optimizes analysis based on users’ own data, and outputs are screened against online databases and categorized into LIPID MAPS classes. LipidFinder outperformed three widely used metabolomics packages using data from human platelets. We show a family of three 12-hydroxyeicosatetraenoic acid phosphoinositides (16:0/, 18:1/, 18:0/12-HETE-PI) generated by thrombin-activated platelets, indicating crosstalk between eicosanoid and phosphoinositide pathways in human cells. The software is available on GitHub (https://github.com/cjbrasher/LipidFinder), with full userguides.

Authors

Anne O’Connor, Christopher J. Brasher, David A. Slatter, Sven W. Meckelmann, Jade I. Hawksworth, Stuart M. Allen, Valerie B. O’Donnell

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Figure 5

Putative identification of LipidFinder results and comparison of database searches.

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Putative identification of LipidFinder results and comparison of databas...
(A) Bar charts showing predominant lipid molecular species in platelets are phospholipids. Each ion was classed using FileProcessing according to the most prevalent hits from three databases into LIPID MAPS categories. (B) Scatter diagrams of the LipidFinder output showing elution of lipids from polar or nonpolar columns, in either negative or positive ionization mode and color-coded by lipid category. (C) Venn diagrams showing the utility of using several databases for putative identifications. Distribution of hits across three different databases found using WebSearch is shown. The number in yellow represents the number of lipids not given any putative match. Computational and curated data from all databases was used.