Optimization of XCMS Parameters for LC-MS Metabolomics: an Assessment of Automated Versus Manual Tuning and Its Effect on the Final Results

Overview

Journal Metabolomics

Publisher Springer

Specialty Endocrinology

Date 2020 Jan 12

PMID 31925557

Citations 18

Authors

Oihane E Alboniga

Oskar Gonzalez

Rosa M Alonso

Yun Xu

Royston Goodacre

Affiliations

Soon will be listed here.

Abstract

Introduction: Several software packages containing diverse algorithms are available for processing Liquid Chromatography-Mass Spectrometry (LC-MS) chromatographic data and within these deconvolution packages different parameters settings can lead to different outcomes. XCMS is the most widely used peak picking and deconvolution software for metabolomics, but the parameter selection can be hard for inexpert users. To solve this issue, the automatic optimization tools such as Isotopologue Parameters Optimization (IPO) can be extremely helpful.

Objectives: To evaluate the suitability of IPO as a tool for XCMS parameters optimization and compare the results with those manually obtained by an exhaustive examination of the LC-MS characteristics and performance.

Methods: Raw HPLC-TOF-MS data from two types of biological samples (liver and plasma) analysed in both positive and negative electrospray ionization modes from three groups of piglets were processed with XCMS using parameters optimized following two different approaches: IPO and Manual. The outcomes were compared to determine the advantages and disadvantages of using each method.

Results: IPO processing produced the higher number of repeatable (%RSD < 20) and significant features for all data sets and allowed the different piglet groups to be distinguished. Nevertheless, on multivariate level, similar clustering results were obtained by Principal Component Analysis (PCA) when applied to IPO and manual matrices.

Conclusion: IPO is a useful optimization tool that helps in choosing the appropriate parameters. It works well on data with a good LC-MS performance but the lack of such adequate data can result in unrealistic parameter settings, which might require further investigation and manual tuning. On the contrary, manual selection criteria requires deeper knowledge on LC-MS, programming language and XCMS parameter interpretation, but allows a better fine-tuning of the parameters, and thus more robust deconvolution.

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References

Tautenhahn R, Bottcher C, Neumann S . Highly sensitive feature detection for high resolution LC/MS. BMC Bioinformatics. 2008; 9:504. PMC: 2639432. DOI: 10.1186/1471-2105-9-504. View

Smith C, Want E, OMaille G, Abagyan R, Siuzdak G . XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem. 2006; 78(3):779-87. DOI: 10.1021/ac051437y. View

Dunn W, Broadhurst D, Begley P, Zelena E, Francis-McIntyre S, Anderson N . Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat Protoc. 2011; 6(7):1060-83. DOI: 10.1038/nprot.2011.335. View

Grimbs A, Shrestha A, Rezk A, Grimbs S, Said I, Schepker H . Bioactivity in : A Systemic Analysis of Antimicrobial and Cytotoxic Activities and Their Phylogenetic and Phytochemical Origins. Front Plant Sci. 2017; 8:551. PMC: 5390042. DOI: 10.3389/fpls.2017.00551. View

Myers O, Sumner S, Li S, Barnes S, Du X . Detailed Investigation and Comparison of the XCMS and MZmine 2 Chromatogram Construction and Chromatographic Peak Detection Methods for Preprocessing Mass Spectrometry Metabolomics Data. Anal Chem. 2017; 89(17):8689-8695. DOI: 10.1021/acs.analchem.7b01069. View

Stoessel D, Stellmann J, Willing A, Behrens B, Rosenkranz S, Hodecker S . Metabolomic Profiles for Primary Progressive Multiple Sclerosis Stratification and Disease Course Monitoring. Front Hum Neurosci. 2018; 12:226. PMC: 5994544. DOI: 10.3389/fnhum.2018.00226. View

Tautenhahn R, Patti G, Rinehart D, Siuzdak G . XCMS Online: a web-based platform to process untargeted metabolomic data. Anal Chem. 2012; 84(11):5035-9. PMC: 3703953. DOI: 10.1021/ac300698c. View

Harvey C, Tang M, Schlecht U, Horecka J, Fischer C, Lin H . HEx: A heterologous expression platform for the discovery of fungal natural products. Sci Adv. 2018; 4(4):eaar5459. PMC: 5895447. DOI: 10.1126/sciadv.aar5459. View

Broadhurst D, Goodacre R, Reinke S, Kuligowski J, Wilson I, Lewis M . Guidelines and considerations for the use of system suitability and quality control samples in mass spectrometry assays applied in untargeted clinical metabolomic studies. Metabolomics. 2018; 14(6):72. PMC: 5960010. DOI: 10.1007/s11306-018-1367-3. View

10.

Narath S, Mautner S, Svehlikova E, Schultes B, Pieber T, Sinner F . An Untargeted Metabolomics Approach to Characterize Short-Term and Long-Term Metabolic Changes after Bariatric Surgery. PLoS One. 2016; 11(9):e0161425. PMC: 5008721. DOI: 10.1371/journal.pone.0161425. View

11.

Stoessel D, Schulte C, Teixeira Dos Santos M, Scheller D, Rebollo-Mesa I, Deuschle C . Promising Metabolite Profiles in the Plasma and CSF of Early Clinical Parkinson's Disease. Front Aging Neurosci. 2018; 10:51. PMC: 5844983. DOI: 10.3389/fnagi.2018.00051. View

12.

Kirwan J, Broadhurst D, Davidson R, Viant M . Characterising and correcting batch variation in an automated direct infusion mass spectrometry (DIMS) metabolomics workflow. Anal Bioanal Chem. 2013; 405(15):5147-57. DOI: 10.1007/s00216-013-6856-7. View

13.

Spicer R, Salek R, Moreno P, Canueto D, Steinbeck C . Navigating freely-available software tools for metabolomics analysis. Metabolomics. 2017; 13(9):106. PMC: 5550549. DOI: 10.1007/s11306-017-1242-7. View

14.

Kuhl C, Tautenhahn R, Bottcher C, Larson T, Neumann S . CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets. Anal Chem. 2011; 84(1):283-9. PMC: 3658281. DOI: 10.1021/ac202450g. View

15.

Libiseller G, Dvorzak M, Kleb U, Gander E, Eisenberg T, Madeo F . IPO: a tool for automated optimization of XCMS parameters. BMC Bioinformatics. 2015; 16:118. PMC: 4404568. DOI: 10.1186/s12859-015-0562-8. View

16.

Weber R, Lawson T, Salek R, Ebbels T, Glen R, Goodacre R . Computational tools and workflows in metabolomics: An international survey highlights the opportunity for harmonisation through Galaxy. Metabolomics. 2017; 13(2):12. PMC: 5192046. DOI: 10.1007/s11306-016-1147-x. View

17.

Roszkowska A, Yu M, Bessonneau V, Bragg L, Servos M, Pawliszyn J . Tissue storage affects lipidome profiling in comparison to in vivo microsampling approach. Sci Rep. 2018; 8(1):6980. PMC: 5934459. DOI: 10.1038/s41598-018-25428-2. View

18.

Dudzik D, Barbas-Bernardos C, Garcia A, Barbas C . Quality assurance procedures for mass spectrometry untargeted metabolomics. a review. J Pharm Biomed Anal. 2017; 147:149-173. DOI: 10.1016/j.jpba.2017.07.044. View

19.

Prince J, Marcotte E . Chromatographic alignment of ESI-LC-MS proteomics data sets by ordered bijective interpolated warping. Anal Chem. 2006; 78(17):6140-52. DOI: 10.1021/ac0605344. View