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.
Highlights
Lipids are a large heterogenous class of hydrophobic molecules important in health, development, and metabolic disorders, including cardiovascular disease, arthritis, and diabetes [1,2,3,4]
We previously reported that platelets acutely esterify 12-HETE to the highly abundant phosphoethanolamine (PE) and PC phospholipids, generating procoagulant species through changing the interaction of the headgroup with membrane-binding proteins [36]
Fundamental questions remain unanswered regarding the diversity and overall number of lipids in mammalian cells and tissues, and informatics tools to analyze datasets generated from lipidomics of healthy and diseased tissues are lacking
Summary
Lipids are a large heterogenous class of hydrophobic molecules important in health, development, and metabolic disorders, including cardiovascular disease, arthritis, and diabetes [1,2,3,4]. They account for 30% of most organs but 60% of the brain (w/w). While LipidSearch can be applied to high-resolution MS analysis as well as MS/MS, it is not designed to mine for novel lipids Both LipidView and LipidSearch are only available commercially and cannot be modified or improved by addition of code by subsequent users
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