John A. Bowden, Alan Heckert, Candice Z. Ulmer, Christina M. Jones, Jeremy P. Koelmel, Laila Abdullah, Linda Ahonen, Yazen Alnouti, Aaron M. Armando, John M. Asara, Takeshi Bamba, John R. Barr, Jonas Bergquist, Christoph H. Borchers, Joost Brandsma, Susanne B. Breitkopf, Tomas Cajka, Amaury Cazenave-Gassiot, Antonio Checa, Michelle A. Cinel, Romain A. Colas, Serge Cremers, Edward A. Dennis, James E. Evans, Alexander Fauland, Oliver Fiehn, Michael S. Gardner, Timothy J. Garrett, Katherine H. Gotlinger, Jun Han, Yingying Huang, Aveline Huipeng Neo, Tuulia Hyötyläinen, Yoshihiro Izumi, Hongfeng Jiang, Houli Jiang, Jiang Jiang, Maureen Kachman, Reiko Kiyonami, Kristaps Klavins, Christian Klose, Harald C. Köfeler, Johan Kolmert, Therese Koal, Grielof Koster, Zsuzsanna Kuklenyik, Irwin J. Kurland, Michael Leadley, Karen Lin, Krishna Rao Maddipati, Danielle McDougall, Peter J. Meikle, Natalie A. Mellett, Cian Monnin, M. Arthur Moseley, Renu Nandakumar, Matej Oresic, Rainey Patterson, David Peake, Jason S. Pierce, Martin Post, Anthony D. Postle, Rebecca Pugh, Yunping Qiu, Oswald Quehenberger, Parsram Ramrup, Jon Rees, Barbara Rembiesa, Denis Reynaud, Mary R. Roth, Susanne Sales, Kai Schuhmann, Michal Laniado Schwartzman, Charles N. Serhan, Andrej Shevchenko, Stephen E. Somerville, Lisa St. John-Williams, Michal A. Surma, Hiroaki Takeda, Rhishikesh Thakare, J. Will Thompson, Federico Torta, Alexander Triebl, Martin Trötzmüller, S. J. Kumari Ubhayasekera, Dajana Vuckovic, Jacquelyn M. Weir, Ruth Welti, Markus R. Wenk, Craig E. Wheelock, Libin Yao, Min Yuan, Xueqing Heather Zhao, Senlin Zhou

Lipids are ubiquitous and serve numerous biological functions; thus lipids have been shown to have great potential as candidates for elucidating biomarkers and pathway perturbations associated with disease. Methods expanding coverage of the lipidome increase the likelihood of biomarker discovery and could lead to more comprehensive understanding of disease etiology. We introduce LipidMatch, an R-based tool for lipid identification for liquid chromatography tandem mass spectrometry workflows. LipidMatch currently has over 250,000 lipid species spanning 56 lipid types contained in in silico fragmentation libraries. Unique fragmentation libraries, compared to other open source software, include oxidized lipids, bile acids, sphingosines, and previously uncharacterized adducts, including ammoniated cardiolipins. LipidMatch uses rule-based identification. For each lipid type, the user can select which fragments must be observed for identification. Rule-based identification allows for correct annotation of lipids based on the fragments observed, unlike typical identification based solely on spectral similarity scores, where over-reporting structural details that are not conferred by fragmentation data is common. Another unique feature of LipidMatch is ranking lipid identifications for a given feature by the sum of fragment intensities. For each lipid candidate, the intensities of experimental fragments with exact mass matches to expected in silico fragments are summed. The lipid identifications with the greatest summed intensity using this ranking algorithm were comparable to other lipid identification software annotations, MS-DIAL and Greazy. For example, for features with identifications from all 3 software, 92% of LipidMatch identifications by fatty acyl constituents were corroborated by at least one other software in positive mode and 98% in negative ion mode. LipidMatch allows users to annotate lipids across a wide range of high resolution tandem mass spectrometry experiments, including imaging experiments, direct infusion experiments, and experiments employing liquid chromatography. LipidMatch leverages the most extensive in silico fragmentation libraries of freely available software. When integrated into a larger lipidomics workflow, LipidMatch may increase the probability of finding lipid-based biomarkers and determining etiology of disease by covering a greater portion of the lipidome and using annotation which does not over-report biologically relevant structural details of identified lipid molecules.

In the modern "omics" era, measurement of the human exposome is a critical missing link between genetic drivers and disease outcomes. High-resolution mass spectrometry (HRMS), routinely used in proteomics and metabolomics, has emerged as a leading technology to broadly profile chemical exposure agents and related biomolecules for accurate mass measurement, high sensitivity, rapid data acquisition, and increased resolution of chemical space. Non-targeted approaches are increasingly accessible, supporting a shift from conventional hypothesis-driven, quantitation-centric targeted analyses toward data-driven, hypothesis-generating chemical exposome-wide profiling. However, HRMS-based exposomics encounters unique challenges. New analytical and computational infrastructures are needed to expand the analysis coverage through streamlined, scalable, and harmonized workflows and data pipelines that permit longitudinal chemical exposome tracking, retrospective validation, and multi-omics integration for meaningful health-oriented inferences. In this article, we survey the literature on state-of-the-art HRMS-based technologies, review current analytical workflows and informatic pipelines, and provide an up-to-date reference on exposomic approaches for chemists, toxicologists, epidemiologists, care providers, and stakeholders in health sciences and medicine. We propose efforts to benchmark fit-for-purpose platforms for expanding coverage of chemical space, including gas/liquid chromatography-HRMS (GC-HRMS and LC-HRMS), and discuss opportunities, challenges, and strategies to advance the burgeoning field of the exposome.

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.