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.
Anne O’Connor, Christopher J. Brasher, David A. Slatter, Sven W. Meckelmann, Jade I. Hawksworth, Stuart M. Allen, Valerie B. O’Donnell
Schematic of the LipidFinder data processing workflow (using SIEVE).
Data is first analyzed using UPLC/FTMS, and SIEVE is then fed into the LipidFinder workflow, which incorporates Optimiser, PeakFilter, Amalgamator, WebSearch, and FileProcessing. Data files that retain