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Merciai F, Basilicata MG, La Gioia D, Salviati E, Caponigro V, Ciaglia T, Musella S, Crescenzi C, Sommella E, Campiglia P. Sub-5-min RP-UHPLC-TIMS for high-throughput untargeted lipidomics and its application to multiple matrices. Anal Bioanal Chem 2024; 416:959-970. [PMID: 38078946 DOI: 10.1007/s00216-023-05084-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 11/10/2023] [Accepted: 11/27/2023] [Indexed: 01/23/2024]
Abstract
Untargeted lipidomics, with its ability to take a snapshot of the lipidome landscape, is an important tool to highlight lipid changes in pathology or drug treatment models. One of the shortcomings of most untargeted lipidomics based on UHPLC-HRMS is the low throughput, which is not compatible with large-scale screening. In this contribution, we evaluate the application of a sub-5-min high-throughput four-dimensional trapped ion mobility mass spectrometry (HT-4D-TIMS) platform for the fast profiling of multiple complex biological matrices. Human AC-16 cells and mouse brain, liver, sclera, and feces were used as samples. By using a fast 4-min RP gradient, the implementation of TIMS allows us to differentiate coeluting isomeric and isobaric lipids, with correct precursor ion isolation, avoiding co-fragmentation and chimeric MS/MS spectra. Globally, the HT-4D-TIMS allowed us to annotate 1910 different lipid species, 1308 at the molecular level and 602 at the sum composition level, covering 58 lipid subclasses, together with quantitation capability covering more than three orders of magnitude. Notably, TIMS values were highly comparable with respect to longer LC gradients (CV% = 0.39%). These results highlight how HT-4D-TIMS-based untargeted lipidomics possess high coverage and accuracy, halving the analysis time with respect to conventional UHPLC methods, and can be used for fast and accurate untargeted analysis of complex matrices to rapidly evaluate changes of lipid metabolism in disease models or drug discovery campaigns.
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Affiliation(s)
- Fabrizio Merciai
- Department of Pharmacy, University of Salerno, Via Giovanni Paolo II, 13284084, Fisciano, SA, Italy
| | | | - Danila La Gioia
- Department of Pharmacy, University of Salerno, Via Giovanni Paolo II, 13284084, Fisciano, SA, Italy
- PhD Program in Drug Discovery and Development, University of Salerno, Fisciano, SA, Italy
| | - Emanuela Salviati
- Department of Pharmacy, University of Salerno, Via Giovanni Paolo II, 13284084, Fisciano, SA, Italy
| | - Vicky Caponigro
- Department of Pharmacy, University of Salerno, Via Giovanni Paolo II, 13284084, Fisciano, SA, Italy
| | - Tania Ciaglia
- Department of Pharmacy, University of Salerno, Via Giovanni Paolo II, 13284084, Fisciano, SA, Italy
| | - Simona Musella
- Department of Pharmacy, University of Salerno, Via Giovanni Paolo II, 13284084, Fisciano, SA, Italy
| | - Carlo Crescenzi
- Department of Pharmacy, University of Salerno, Via Giovanni Paolo II, 13284084, Fisciano, SA, Italy
| | - Eduardo Sommella
- Department of Pharmacy, University of Salerno, Via Giovanni Paolo II, 13284084, Fisciano, SA, Italy.
| | - Pietro Campiglia
- Department of Pharmacy, University of Salerno, Via Giovanni Paolo II, 13284084, Fisciano, SA, Italy
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Lerner R, Baker D, Schwitter C, Neuhaus S, Hauptmann T, Post JM, Kramer S, Bindila L. Four-dimensional trapped ion mobility spectrometry lipidomics for high throughput clinical profiling of human blood samples. Nat Commun 2023; 14:937. [PMID: 36806650 PMCID: PMC9941096 DOI: 10.1038/s41467-023-36520-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 02/03/2023] [Indexed: 02/22/2023] Open
Abstract
Lipidomics encompassing automated lipid extraction, a four-dimensional (4D) feature selection strategy for confident lipid annotation as well as reproducible and cross-validated quantification can expedite clinical profiling. Here, we determine 4D descriptors (mass to charge, retention time, collision cross section, and fragmentation spectra) of 200 lipid standards and 493 lipids from reference plasma via trapped ion mobility mass spectrometry to enable the implementation of stringent criteria for lipid annotation. We use 4D lipidomics to confidently annotate 370 lipids in reference plasma samples and 364 lipids in serum samples, and reproducibly quantify 359 lipids using level-3 internal standards. We show the utility of our 4D lipidomics workflow for high-throughput applications by reliable profiling of intra-individual lipidome phenotypes in plasma, serum, whole blood, venous and finger-prick dried blood spots.
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Affiliation(s)
- Raissa Lerner
- Clinical Lipidomics Unit, Institute of Physiological Chemistry, University Medical Center, Duesbergweg 6, 55128, Mainz, Germany
| | - Dhanwin Baker
- Clinical Lipidomics Unit, Institute of Physiological Chemistry, University Medical Center, Duesbergweg 6, 55128, Mainz, Germany
| | - Claudia Schwitter
- Clinical Lipidomics Unit, Institute of Physiological Chemistry, University Medical Center, Duesbergweg 6, 55128, Mainz, Germany
| | - Sarah Neuhaus
- Clinical Lipidomics Unit, Institute of Physiological Chemistry, University Medical Center, Duesbergweg 6, 55128, Mainz, Germany
| | - Tony Hauptmann
- Data Mining, Institute of Computer Science, Johannes Gutenberg University Mainz, Staudingerweg 9, 55128, Mainz, Germany
| | - Julia M Post
- Clinical Lipidomics Unit, Institute of Physiological Chemistry, University Medical Center, Duesbergweg 6, 55128, Mainz, Germany
| | - Stefan Kramer
- Data Mining, Institute of Computer Science, Johannes Gutenberg University Mainz, Staudingerweg 9, 55128, Mainz, Germany
| | - Laura Bindila
- Clinical Lipidomics Unit, Institute of Physiological Chemistry, University Medical Center, Duesbergweg 6, 55128, Mainz, Germany.
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Medina J, Borreggine R, Teav T, Gao L, Ji S, Carrard J, Jones C, Blomberg N, Jech M, Atkins A, Martins C, Schmidt-Trucksass A, Giera M, Cazenave-Gassiot A, Gallart-Ayala H, Ivanisevic J. Omic-Scale High-Throughput Quantitative LC-MS/MS Approach for Circulatory Lipid Phenotyping in Clinical Research. Anal Chem 2023; 95:3168-3179. [PMID: 36716250 DOI: 10.1021/acs.analchem.2c02598] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Lipid analysis at the molecular species level represents a valuable opportunity for clinical applications due to the essential roles that lipids play in metabolic health. However, a comprehensive and high-throughput lipid profiling remains challenging given the lipid structural complexity and exceptional diversity. Herein, we present an 'omic-scale targeted LC-MS/MS approach for the straightforward and high-throughput quantification of a broad panel of complex lipid species across 26 lipid (sub)classes. The workflow involves an automated single-step extraction with 2-propanol, followed by lipid analysis using hydrophilic interaction liquid chromatography in a dual-column setup coupled to tandem mass spectrometry with data acquisition in the timed-selective reaction monitoring mode (12 min total run time). The analysis pipeline consists of an initial screen of 1903 lipid species, followed by high-throughput quantification of robustly detected species. Lipid quantification is achieved by a single-point calibration with 75 isotopically labeled standards representative of different lipid classes, covering lipid species with diverse acyl/alkyl chain lengths and unsaturation degrees. When applied to human plasma, 795 lipid species were measured with median intra- and inter-day precisions of 8.5 and 10.9%, respectively, evaluated within a single and across multiple batches. The concentration ranges measured in NIST plasma were in accordance with the consensus intervals determined in previous ring-trials. Finally, to benchmark our workflow, we characterized NIST plasma materials with different clinical and ethnic backgrounds and analyzed a sub-set of sera (n = 81) from a clinically healthy elderly population. Our quantitative lipidomic platform allowed for a clear distinction between different NIST materials and revealed the sex-specificity of the serum lipidome, highlighting numerous statistically significant sex differences.
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Affiliation(s)
- Jessica Medina
- Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, Quartier UNIL-CHUV, Rue du Bugnon 19, Lausanne CH-1005, Switzerland
| | - Rebecca Borreggine
- Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, Quartier UNIL-CHUV, Rue du Bugnon 19, Lausanne CH-1005, Switzerland
| | - Tony Teav
- Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, Quartier UNIL-CHUV, Rue du Bugnon 19, Lausanne CH-1005, Switzerland
| | - Liang Gao
- Department of Biochemistry and Precision Medicine TRP, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore.,Singapore Lipidomics Incubator, Life Sciences Institute, National University of Singapore, Singapore 117456, Singapore
| | - Shanshan Ji
- Singapore Lipidomics Incubator, Life Sciences Institute, National University of Singapore, Singapore 117456, Singapore
| | - Justin Carrard
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Birsstrasse 320B, Basel CH-4052, Switzerland
| | - Christina Jones
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States
| | - Niek Blomberg
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden 2333ZA, Netherlands
| | - Martin Jech
- Thermo Fisher Scientific, 355 River Oaks Pkwy, San Jose, California 95134, United States
| | - Alan Atkins
- Thermo Fisher Scientific, 355 River Oaks Pkwy, San Jose, California 95134, United States
| | - Claudia Martins
- Thermo Fisher Scientific, 355 River Oaks Pkwy, San Jose, California 95134, United States
| | - Arno Schmidt-Trucksass
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Birsstrasse 320B, Basel CH-4052, Switzerland
| | - Martin Giera
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden 2333ZA, Netherlands
| | - Amaury Cazenave-Gassiot
- Department of Biochemistry and Precision Medicine TRP, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore.,Singapore Lipidomics Incubator, Life Sciences Institute, National University of Singapore, Singapore 117456, Singapore
| | - Hector Gallart-Ayala
- Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, Quartier UNIL-CHUV, Rue du Bugnon 19, Lausanne CH-1005, Switzerland
| | - Julijana Ivanisevic
- Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, Quartier UNIL-CHUV, Rue du Bugnon 19, Lausanne CH-1005, Switzerland
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