An Optimization Method for Untargeted MS-based Isotopic Tracing Investigations of Metabolism

Overview

Journal Metabolomics

Publisher Springer

Specialty Endocrinology

Date 2022 Jun 17

PMID 35713733

Authors

Noemie Butin

Cecilia Berges

Jean-Charles Portais

Floriant Bellvert

Affiliations

Soon will be listed here.

Abstract

Introduction: Stable isotope tracer studies are increasingly applied to explore metabolism from the detailed analysis of tracer incorporation into metabolites. Untargeted LC/MS approaches have recently emerged and provide potent methods for expanding the dimension and complexity of the metabolic networks that can be investigated. A number of software tools have been developed to process the highly complex MS data collected in such studies; however, a method to optimize the extraction of valuable isotopic data is lacking.

Objectives: To develop and validate a method to optimize automated data processing for untargeted MS-based isotopic tracing investigations of metabolism.

Methods: The method is based on the application of a suitable reference material to rationally perform parameter optimization throughout the complete data processing workflow. It was applied in the context of C-labelling experiments and with two different software, namely geoRge and X13CMS. It was illustrated with the study of a E. coli mutant impaired for central metabolism.

Results: The optimization methodology provided significant gain in the number and quality of extracted isotopic data, independently of the software considered. Pascal triangle samples are well suited for such purpose since they allow both the identification of analytical issues and optimization of data processing at the same time.

Conclusion: The proposed method maximizes the biological value of untargeted MS-based isotopic tracing investigations by revealing the full metabolic information that is encoded in the labelling patterns of metabolites.

Citing Articles

Untargeted stable isotope-resolved metabolomics to assess the effect of PI3Kβ inhibition on metabolic pathway activities in a PTEN null breast cancer cell line.

Lackner M, Neef S, Winter S, Beer-Hammer S, Nurnberg B, Schwab M Front Mol Biosci. 2022; 9:1004602.

PMID: 36310598 PMC: 9614656. DOI: 10.3389/fmolb.2022.1004602.

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