期刊
JCI INSIGHT
卷 2, 期 7, 页码 -出版社
AMER SOC CLINICAL INVESTIGATION INC
DOI: 10.1172/jci.insight.91634
关键词
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资金
- European Research Council
- Wellcome Trust [094143/Z/10/Z]
- British Heart Foundation
- Cardiff University Research Opportunities Programme
- Wellcome Trust [094143/Z/10/Z] Funding Source: Wellcome Trust
- British Heart Foundation [FS/15/45/31603] Funding Source: researchfish
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.
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