Abstract
Lipidomics has great promise in various applications; however, a major bottleneck in lipidomics is the accurate and comprehensive annotation of high-resolution tandem mass spectral data. While the number of available lipidomics software has drastically increased over the past five years, the reduction of false positives and the realization of obtaining structurally accurate annotations remains a significant challenge. We introduce Lipid Annotator, which is a user-friendly software for lipidomic analysis of data collected by liquid chromatography high-resolution tandem mass spectrometry (LC-HRMS/MS). We validate annotation accuracy against lipid standards and other lipidomics software. Lipid Annotator was integrated into a workflow applying an iterative exclusion MS/MS acquisition strategy to National Institute of Standards and Technology (NIST) SRM 1950 Metabolites in Frozen Human Plasma using reverse phase LC-HRMS/MS. Lipid Annotator, LipidMatch, and MS-DIAL produced consensus annotations at the level of lipid class for 98% and 96% of features detected in positive and negative mode, respectively. Lipid Annotator provides percentages of fatty acyl constituent species and employs scoring algorithms based on probability theory, which is less subjective than the tolerance and weighted match scores commonly used by available software. Lipid Annotator enables analysis of large sample cohorts and improves data-processing throughput as compared to previous lipidomics software.
Highlights
Lipids are an incredibly complex class of non-polar small molecules with a vast diversity in the number of known lipid species and their biological roles
Lipid Annotator can be used as a standalone tool for the rapid peak picking and annotation of lipids within a given sample, or it can be integrated into a larger LC-HRMS/mass spectrometric (MS) workflow covering all steps, including peak picking, annotation, normalization to lipid internal standards, and statistics
Lipid Annotator can be used on large datasets for rapid annotation, relative quantification, and statistics
Summary
Lipids are an incredibly complex class of non-polar small molecules with a vast diversity in the number of known lipid species and their biological roles. The entire range of lipids in a given substrate are called the lipidome. The structural and functional diversity of lipids explains the recent spike and continually expanding interest in lipidomics (comprehensive measurement of the lipidome) and includes application in clinical [1,2,3], material [4,5,6], agricultural [7,8], environmental sciences, and many other domains. While new lipids are discovered almost monthly, the complete diversity of lipids is still unknown, even within humans [9]. By increasing the coverage and accuracy of lipid identifications, scientists can better determine biological effects and lipid-based diagnostic markers of disease and other biological perturbations, as well as discover new lipids for novel materials. Though untargeted data-acquisition using liquid chromatography high-resolution tandem mass spectrometry (LC-HR-MS/MS) currently provides a wealth of information on lipids, processing the immense mass spectral data to provide accurate lipid annotations and corresponding relative lipid concentrations remains a challenge
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