Literature DB >> 31561798

Comprehensive lipidomics of mouse plasma using class-specific surrogate calibrants and SWATH acquisition for large-scale lipid quantification in untargeted analysis.

Bernhard Drotleff1, Julia Illison2, Jörg Schlotterbeck1, Robert Lukowski2, Michael Lämmerhofer3.   

Abstract

Lipidomics has gained rising attention in recent years. Several strategies for lipidomic profiling have been developed, with targeted analysis of selected lipid species, typically utilized for lipid quantification by low-resolution triple quadrupole MS/MS, and untargeted analysis by high-resolution MS instruments, focusing on hypothesis generation for prognostic, diagnostic and/or disease-relevant biomarker discovery. The latter methodologies generally yield relative quantification data with limited inter-assay comparability. In this work we aimed to combine untargeted analysis and absolute quantification to enhance data quality and to obtain independent results for optimum comparability to previous studies or database entries. For the lipidomic analysis of mouse plasma, RP-UHPLC hyphenated to a high-resolution quadrupole TOF mass spectrometer in comprehensive data-independent SWATH acquisition mode was employed. This way, quantifiable data on the MS and the MS/MS level were recorded, which increases assay specificity and quantitative performance. Due to the lack of an appropriate blank matrix for untargeted lipidomics, we herein established a sophisticated strategy for lipid class-specific calibration with stable isotope labeled standards (surrogate calibrants). LLOQs were in the range between 10 and 50 ng mL-1 for LPC, LPE, PI, PS, PG, SM, PC, PE, DAG) or 100-700 ng mL-1 (MAG, TAG), except for cholesterol and CE (1-20 μg mL-1). Acceptable values for accuracy and precision well below ±15% bias were reached for the majority of surrogate calibrants. However, to achieve sufficient accuracy for target lipids, response factors to corresponding surrogate calibrants are required. An approach to estimate response factors via a standard reference material (NIST SRM 1950) was therefore conducted. Furthermore, a useful workflow for post-acquisition re-calibration, involving response factor determination and iteratively built libraries, is suggested. In comparison to single-point calibration, the presented surrogate calibrant method was shown to yield results with improved accuracy that are largely in accordance with standard addition. Quantitative results of real samples (high-fat diet vs control diet) were then compared to two previously published dietary mouse plasma studies that provided absolute lipid levels and showed similar trends.
Copyright © 2019 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Data-independent acquisition; Lipidomics; Obesity; SWATH; Surrogate calibration; Untargeted quantification

Mesh:

Substances:

Year:  2019        PMID: 31561798     DOI: 10.1016/j.aca.2019.08.030

Source DB:  PubMed          Journal:  Anal Chim Acta        ISSN: 0003-2670            Impact factor:   6.558


  8 in total

1.  Untargeted lipidomics reveals specific lipid profiles in COVID-19 patients with different severity from Campania region (Italy).

Authors:  Michele Ciccarelli; Fabrizio Merciai; Albino Carrizzo; Eduardo Sommella; Paola Di Pietro; Vicky Caponigro; Emanuela Salviati; Simona Musella; Veronica di Sarno; Mariarosaria Rusciano; Anna Laura Toni; Paola Iesu; Carmine Izzo; Gabriella Schettino; Valeria Conti; Eleonora Venturini; Carolina Vitale; Giuliana Scarpati; Domenico Bonadies; Antonella Rispoli; Benedetto Polverino; Sergio Poto; Pasquale Pagliano; Ornella Piazza; Danilo Licastro; Carmine Vecchione; Pietro Campiglia
Journal:  J Pharm Biomed Anal       Date:  2022-05-10       Impact factor: 3.571

2.  Developing a SWATH capillary LC-MS/MS method for simultaneous therapeutic drug monitoring and untargeted metabolomics analysis of neonatal plasma.

Authors:  Jingcheng Xiao; Jian Shi; Ruiting Li; Lucy Her; Xinwen Wang; Jiapeng Li; Matthew J Sorensen; Varsha Bhatt-Mehta; Hao-Jie Zhu
Journal:  J Chromatogr B Analyt Technol Biomed Life Sci       Date:  2021-07-27       Impact factor: 3.318

Review 3.  Reference materials for MS-based untargeted metabolomics and lipidomics: a review by the metabolomics quality assurance and quality control consortium (mQACC).

Authors:  Katrice A Lippa; Juan J Aristizabal-Henao; Richard D Beger; John A Bowden; Corey Broeckling; Chris Beecher; W Clay Davis; Warwick B Dunn; Roberto Flores; Royston Goodacre; Gonçalo J Gouveia; Amy C Harms; Thomas Hartung; Christina M Jones; Matthew R Lewis; Ioanna Ntai; Andrew J Percy; Dan Raftery; Tracey B Schock; Jinchun Sun; Georgios Theodoridis; Fariba Tayyari; Federico Torta; Candice Z Ulmer; Ian Wilson; Baljit K Ubhi
Journal:  Metabolomics       Date:  2022-04-09       Impact factor: 4.747

4.  Lipidomic profiling of non-mineralized dental plaque and biofilm by untargeted UHPLC-QTOF-MS/MS and SWATH acquisition.

Authors:  Bernhard Drotleff; Simon R Roth; Kerstin Henkel; Carlos Calderón; Jörg Schlotterbeck; Merja A Neukamm; Michael Lämmerhofer
Journal:  Anal Bioanal Chem       Date:  2020-01-15       Impact factor: 4.142

Review 5.  Sample Preparation Methods for Lipidomics Approaches Used in Studies of Obesity.

Authors:  Ivan Liakh; Tomasz Sledzinski; Lukasz Kaska; Paulina Mozolewska; Adriana Mika
Journal:  Molecules       Date:  2020-11-13       Impact factor: 4.411

Review 6.  Recent advances in analytical strategies for mass spectrometry-based lipidomics.

Authors:  Tianrun Xu; Chunxiu Hu; Qiuhui Xuan; Guowang Xu
Journal:  Anal Chim Acta       Date:  2020-09-30       Impact factor: 6.558

7.  Recurrent Topics in Mass Spectrometry-Based Metabolomics and Lipidomics-Standardization, Coverage, and Throughput.

Authors:  Evelyn Rampler; Yasin El Abiead; Harald Schoeny; Mate Rusz; Felina Hildebrand; Veronika Fitz; Gunda Koellensperger
Journal:  Anal Chem       Date:  2020-11-28       Impact factor: 6.986

Review 8.  The foundations and development of lipidomics.

Authors:  Xianlin Han; Richard W Gross
Journal:  J Lipid Res       Date:  2021-12-22       Impact factor: 5.922

  8 in total

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