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.

Per- and polyfluoroalkyl substances (PFASs) are often present in complex mixtures at trace levels in environmental samples, posing difficulties for analytical chemists. Ion mobility offers highly replicable identifiers, enabling the use of community-based libraries for PFAS annotation in nontargeted analysis. Currently, limited software exists to leverage the capabilities of liquid chromatography ion mobility high-resolution mass spectrometry (LC-IM-HRMS) for nontargeted analysis. FluoroMatch IM is a free vendor-neutral open-source tool for rapid annotation of PFASs in LC-IM-HRMS datasets. Annotation algorithms include collision cross-section (CCS) matching, formula prediction, homologous series detection, mass defect filtering, and accurate mass matching with a database of 194 PFAS ions that can be continuously expanded by the community. Results from FluoroMatch IM were compared to a targeted approach with a laboratory-prepared mixture of 63 PFASs and real wastewater samples. A nontarget workflow incorporating FluoroMatch IM revealed additional likely PFASs (n = 16) while confirming most targeted annotations (11/12) in wastewater samples. Validation of the standard mix showed a low false negative rate of 5% and a 5% false positive rate for features included in the CCS library, with a 0% false positive rate for features assigned confident scores. This study demonstrates the promise of FluoroMatch IM for streamlining PFAS analysis workflows.