Bioorthogonal Click Chemistry in LC-MS lipidomics to trace lipid metabolism: from experimental data to high-throughput computational analysis

Bioorthogonal Click Chemistry in LC-MS lipidomics to trace lipid metabolism: from experimental data to high-throughput computational analysis

Metabolomics 2025. 22-26 June.

Stefano Bonciarelli1; Palina Nepachalovich2; Gabriele Lombardi Bendoula2; Jenny Desantis3; Michela Eleuteri3; Christoph Thiele4; Laura Goracci3; Maria Fedorova2

1Mass Analytica, Sant Cugat del Valles, Spain; 2Center of Membrane Biochemistry and Lipid Research, Faculty of Medicine Carl Gustav Carus, Technical University Dresden, Dresden, Germany; 3Department of Chemistry, Biology and Biotechnology, University of Perugia, Perugia, Italy; 4Life & Medical Sciences Institute, University of Bonn, Bonn, Germany

Abstract

Investigating lipid metabolism is crucial for understanding its role in health and disease. Recently, lipid metabolic tracing using bioorthogonal click chemistry has emerged as a powerful alternative to stable isotope tracers. [1,2] Thiele and colleagues showed that ω-alkyne-functionalized fatty acids, derivatized with azido-quaternary ammonium reporters, offer advantages like enhanced ionization and specific neutral loss, enabling high-throughput, sensitive lipid analysis. However, direct injection MS faces challenges, such as resolving isomeric species and possible occurrence of false positives from unaccounted in-source fragmentation. [3]

To address these limitations, we present an integrated LC-MS and bioinformatics platform for high-throughput lipid tracing using bioorthogonal click chemistry. [4] Synthetic standards and endogenously produced alkyne lipids were used to explore the LC-MS behavior of “clicked” lipid species with the C171 azido-quaternary ammonium reporter. Key factors like preferential adduct formation, in-source fragmentation (ISF), and MS/MS fragmentation patterns were annotated across 23 lipid subclasses. Fragmentation rules for each subclass were implemented in Lipostar2 software, enabling high-throughput annotation and quantification of 224,514 “clicked” lipids from 15 lipid classes.[4]

We validated the platform by tracing palmitic and oleic acids in HT1080 fibrosarcoma cells under fatty acid overload. High-throughput analysis identified 479 “clicked” lipids in palmitic acid-treated cells and 379 in oleic acid-treated cells, with species labeled singly, doubly, or triply. Distinct incorporation patterns were observed, including isomeric sphingolipid species, where the tracer incorporated into either the fatty acyl or sphingoid base. LC-MS revealed ISF products that would be indistinguishable from endogenous lipids by other methods. [4]

In conclusion, this integrated platform enables efficient, high-throughput analysis of “clicked” lipid tracers in LC-MS lipidomics.

 

[1] C. Thiele, C. Papan, D. Hoelper, K. Kusserow, A. Gaebler, M. Schoene, K. Piotrowitz, D. Lohmann, J. Spandl, A. Stevanovic, A. Shevchenko, L. Kuerschner, ACS Chem. Biol. 2012, 7, 2004–2011.

[2] C. Thiele, K. Wunderling, P. Leyendecker, Nat. Methods 2019, 16, 1123–1130.

[3] F.-F. Hsu, Anal Bioanal Chem 2018, 410, 6387–6409

[4] P. Nepachalovich, S. Bonciarelli, G. Lombardi Bendoula, J. Desantis, M. Eleuteri, C. Thiele, L. Goracci, M. Fedorova, Angew. Chem. Int. Ed. 2025, e202501884.

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Recent improvements in MARS for computer assisted-analysis in untargeted stable isotope labelling studies in metabolomics

Recent improvements in MARS for computer assisted-analysis in untargeted stable isotope labelling studies in metabolomics

2025 Metabolomics and Human Health (GRS) Gordon Research Seminar

S. Bonciarellia,b, L. Goraccic, P. Tiberib, I. Zamoraa, M. Piroddid, G. I. Passerib, and G. Crucianic

aMass Analytica, Rambla de celler 113, Sant Cugat del Vallés 08173, Spain; bMolecular Discovery Ltd., Centennial Park, Borehamwood, Hertfordshire WD6 4PJ, U.K.; cDepartment of Chemistry, Biology and Biotechnology, University of Perugia, Via Elce di Sotto 8, 06123 Perugia; dMolecular Horizon Srl, Via Montelino 30, 06084 Bettona, Italy

Abstract

Technological advances in mass spectrometry instrumentation have increased the resolution and sensitivity of measurements allowing the detection of less abundant species in matrices. At the same time, the coupling of liquid chromatography with mass spectrometry (LC-MS) has enabled the extra-dimensional separation of compounds, facilitating their detection in untargeted studies. Specifically designed software solutions have become increasing necessary for data analysis when working untargeted, due to the huge amounts of data generated. However, only a limited number of software cover all the steps required in untargeted metabolomics data analysis, also providing tailored computational tools for stable isotope labelling (SIL) analysis. Indeed, complexity increases when SIL studies are performed. Unlabeled and labelled compounds needs to be processed and annotated within the same dataset. In addition, the projection of the identified species in metabolic maps is crucial for the biological interpretation of results. Here, we describe the improvements of the computational workflow for SIL analysis already in place in the previous version of MARS [1] (https://mass-analytica.com/products/mars/) a software solution for untargeted LC-MS untargeted metabolomics analysis. MARS covers the study of uniformly and partially labelled species, considering the most common isotopes used for labelling (e.g. D, 13C, 15N, 34S, 37Cl). The annotation of labelled metabolites can be performed at MS and MS/MS level. Indeed, the MS/MS spectra of labelled metabolites are generated automatically from those of native species and stored in a database for identification purpose. In addition, optimization of the isotopic pattern clustering based on instrument resolution was recently added. Native and labelled compounds identified as different ionization adducts and at different level (MS or MS/MS) can be inspected together or separately for an easier investigation and for projection into the metabolic maps included in the package. Ready-to-use databases derived by commonly used tracers are available for download. In conclusion, we implemented user-friendly informatic tools in MARS to support and facilitate the data analysis in stable isotope labelling studies in metabolomics. The entire workflow and algorithms are elucidated using an appropriate showcase.

[1] Goracci L., Tiberi P., Di Bona S., Bonciarelli S., Passeri G. I., Piroddi M., Moretti S., Volpi C., Zamora I., Cruciani G.; MARS: a multi-purpose software for untargeted LC-MS-based metabolomics and exposomics. Anal. Chem., 2024, 96, 4, 1468–1477. https://doi.org/10.1021/acs.analchem.3c03620

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