Nonlinear Data Alignment for UPLC-MS and HPLC-MS Based Metabolomics: Quantitative Analysis of Endogenous and Exogenous Metabolites in Human Serum

Overview

Journal Anal Chem

Specialty Chemistry

Date 2006 May 13

PMID 16689529

Citations 61

Authors

Anders Nordstrom

Grace OMaille

Chuan Qin

Gary Siuzdak

Affiliations

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Abstract

A nonlinear alignment strategy was examined for the quantitative analysis of serum metabolites. Two small-molecule mixtures with a difference in relative concentration of 20-100% for 10 of the compounds were added to human serum. The metabolomics protocol using UPLC and XCMS for LC-MS data alignment could readily identify 8 of 10 spiked differences among more than 2700 features detected. Normalization of data against a single factor obtained through averaging the XCMS integrated response areas of spiked standards increased the number of identified differences. The original data structure was well preserved using XCMS, but reintegration of identified differences in the original data reduced the number of false positives. Using UPLC for separation resulted in 20% more detected components compared to HPLC. The length of the chromatographic separation also proved to be a crucial parameter for a number of detected features. Moreover, UPLC displayed better retention time reproducibility and signal-to-noise ratios for spiked compounds over HPLC, making this technology more suitable for nontargeted metabolomics applications.

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References

Shen Y, Zhang R, Moore R, Kim J, Metz T, Hixson K . Automated 20 kpsi RPLC-MS and MS/MS with chromatographic peak capacities of 1000-1500 and capabilities in proteomics and metabolomics. Anal Chem. 2005; 77(10):3090-100. DOI: 10.1021/ac0483062. View

Fiehn O, Kopka J, Dormann P, Altmann T, Trethewey R, Willmitzer L . Metabolite profiling for plant functional genomics. Nat Biotechnol. 2000; 18(11):1157-61. DOI: 10.1038/81137. View

Jonsson P, Gullberg J, Nordstrom A, Kusano M, Kowalczyk M, Sjostrom M . A strategy for identifying differences in large series of metabolomic samples analyzed by GC/MS. Anal Chem. 2004; 76(6):1738-45. DOI: 10.1021/ac0352427. 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

Bylund D, Danielsson R, Malmquist G, Markides K . Chromatographic alignment by warping and dynamic programming as a pre-processing tool for PARAFAC modelling of liquid chromatography-mass spectrometry data. J Chromatogr A. 2002; 961(2):237-44. DOI: 10.1016/s0021-9673(02)00588-5. View

Wilson I, Nicholson J, Castro-Perez J, Granger J, Johnson K, Smith B . High resolution "ultra performance" liquid chromatography coupled to oa-TOF mass spectrometry as a tool for differential metabolic pathway profiling in functional genomic studies. J Proteome Res. 2005; 4(2):591-8. DOI: 10.1021/pr049769r. View

Andreev V, Rejtar T, Chen H, Moskovets E, Ivanov A, Karger B . A universal denoising and peak picking algorithm for LC-MS based on matched filtration in the chromatographic time domain. Anal Chem. 2003; 75(22):6314-26. DOI: 10.1021/ac0301806. View

Duran A, Yang J, Wang L, Sumner L . Metabolomics spectral formatting, alignment and conversion tools (MSFACTs). Bioinformatics. 2003; 19(17):2283-93. DOI: 10.1093/bioinformatics/btg315. View

Idborg H, Zamani L, Edlund P, Schuppe-Koistinen I, Jacobsson S . Metabolic fingerprinting of rat urine by LC/MS Part 2. Data pretreatment methods for handling of complex data. J Chromatogr B Analyt Technol Biomed Life Sci. 2005; 828(1-2):14-20. DOI: 10.1016/j.jchromb.2005.07.049. View

10.

Villas-Boas S, Mas S, Akesson M, Smedsgaard J, Nielsen J . Mass spectrometry in metabolome analysis. Mass Spectrom Rev. 2004; 24(5):613-46. DOI: 10.1002/mas.20032. View

11.

Boernsen K, Gatzek S, Imbert G . Controlled protein precipitation in combination with chip-based nanospray infusion mass spectrometry. An approach for metabolomics profiling of plasma. Anal Chem. 2005; 77(22):7255-64. DOI: 10.1021/ac0508604. View

12.

Want E, OMaille G, Smith C, Brandon T, Uritboonthai W, Qin C . Solvent-dependent metabolite distribution, clustering, and protein extraction for serum profiling with mass spectrometry. Anal Chem. 2006; 78(3):743-52. DOI: 10.1021/ac051312t. View

13.

Jonsson P, Bruce S, Moritz T, Trygg J, Sjostrom M, Plumb R . Extraction, interpretation and validation of information for comparing samples in metabolic LC/MS data sets. Analyst. 2005; 130(5):701-7. PMC: 6660143. DOI: 10.1039/b501890k. View

14.

Johnson K, Wright B, Jarman K, Synovec R . High-speed peak matching algorithm for retention time alignment of gas chromatographic data for chemometric analysis. J Chromatogr A. 2003; 996(1-2):141-55. DOI: 10.1016/s0021-9673(03)00616-2. View

15.

Wang W, Zhou H, Lin H, Roy S, Shaler T, Hill L . Quantification of proteins and metabolites by mass spectrometry without isotopic labeling or spiked standards. Anal Chem. 2003; 75(18):4818-26. DOI: 10.1021/ac026468x. View

16.

Plumb R, Castro-Perez J, Granger J, Beattie I, Joncour K, Wright A . Ultra-performance liquid chromatography coupled to quadrupole-orthogonal time-of-flight mass spectrometry. Rapid Commun Mass Spectrom. 2004; 18(19):2331-7. DOI: 10.1002/rcm.1627. View

17.

Radulovic D, Jelveh S, Ryu S, Hamilton T, Foss E, Mao Y . Informatics platform for global proteomic profiling and biomarker discovery using liquid chromatography-tandem mass spectrometry. Mol Cell Proteomics. 2004; 3(10):984-97. DOI: 10.1074/mcp.M400061-MCP200. View

18.

Idborg-Bjorkman H, Edlund P, Kvalheim O, Schuppe-Koistinen I, Jacobsson S . Screening of biomarkers in rat urine using LC/electrospray ionization-MS and two-way data analysis. Anal Chem. 2003; 75(18):4784-92. DOI: 10.1021/ac0341618. View

19.

Want E, Cravatt B, Siuzdak G . The expanding role of mass spectrometry in metabolite profiling and characterization. Chembiochem. 2005; 6(11):1941-51. DOI: 10.1002/cbic.200500151. View

20.

Katajamaa M, Oresic M . Processing methods for differential analysis of LC/MS profile data. BMC Bioinformatics. 2005; 6:179. PMC: 1187873. DOI: 10.1186/1471-2105-6-179. View