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HomeMetabolomics ResearchLC-MS Analysis of a Polar Metabolites QC Mix on a ZIC® HILIC Column

LC-MS Analysis of a Polar Metabolites QC Mix on a ZIC® HILIC Column

Alon Sayer1, Aviran Amir2
1R&D Scientist, Rehovot Israel
2R&D Department Manager, Rehovot Israel

Abstract

In a metabolomics analysis workflow, inclusion of a quality control (QC) sample at the beginning of each LC-MS batch is recommended to detect instrumental drift, including fluctuations in signal intensity, ion suppression, and retention time shifts. The ready-to-use Polar Metabolites QC Mix, containing eight compounds, is applied for effective monitoring of these effects. A hydrophilic interaction liquid chromatographic (HILIC) method example was developed using a SeQuant® ZIC®-cHILIC column, enabling analysis of the Polar Metabolite QC mix within a 25-minute run.

Section Overview

Introduction

Metabolomics is defined as the profiling of small molecules derived from biochemical processes and pathways,1,2 primarily characterized from samples such as stool,3 serum/plasma,4 urine,5 cerebrospinal fluid,6 and saliva.7 The field spans various research areas including microbiome studies,8 nutrition,9 disease study,10 and agriculture11. Metabolite analysis is generally performed through two main approaches, targeted and untargeted metabolomics.12-15 Targeted metabolomics focuses on analyzing specific groups of known metabolites, such as short-chain fatty acids,16 bile acids, lipids,17 and amino acids.18 In contrast, untargeted metabolomics examines all unknown chemical compounds present within a sample.

Liquid chromatography-mass spectrometry (LC-MS) is widely employed as the primary technique for metabolite profiling in metabolomics analysis.19,20 To detect variations in LC-MS metabolomics data, inclusion of an additional quality control (QC) sample at the start of each analytical batch is recommended, followed by repeated injections after every 4-10 samples within the workflow.19,21,22 Such a QC sample helps in identification of drifting effects, such as fluctuations in intensity values, ion suppression, or changes in the retention times of peaks. The ready-to-use Polar Metabolites QC Mix (SBR00055), suitable for LC-MS analysis, allows users to effectively monitor drifting and ion suppression phenomena. The QC mix contains eight compounds comprising polar metabolites like amino acids, vitamins, and nucleosides (Table 3). The mixture is prepared in ~9:1 acetonitrile / 10 mM ammonium formate in water and is therefore suitable for direct injection. The solution is packaged in a crimp-top amber vial with a silicone/PTFE liner to ensure compatibility with most LC-MS autosamplers. It is recommended to store the mixture at a temperature of 2-8 °C.

Experimental

The example LC-MS analysis was performed using a SeQuant® ZIC®-cHILIC column under HILIC conditions (Tabe 1). Mass spectrometric detection was conducted in both positive and negative electrospray (ESI) modes.

Results and Discussion

The mass spectrometry (MS) electrospray ionization positive [ESI(+)] and negative [ESI(-)] base peak chromatograms (BPC) for the Polar Metabolites QC Mix are shown in Figures 1 and 2. Distinct peaks labeled 1 to 8 were observed and were assigned to the corresponding polar metabolites in the mix (refer to the metabolites list in Tables 3 and 4). A stable baseline was observed, indicating good sensitivity and effective chromatographic separation, thereby providing a clear profile of the metabolites present in the QC mix, essential for subsequent analytical assessments.

Method A - ESI(+)

LC-MS ESI(+) base peak chromatogram showing eight labeled metabolite peaks of the Polar Metabolites QC Mix across retention time.

Figure 1.MS ESI(+) base peak chromatogram (BPC) of the Polar Metabolites QC Mix.

Method B - ESI(-)

LC-MS ESI(-) base peak chromatogram showing eight labeled metabolite peaks of the Polar Metabolites QC Mix across retention time.

Figure 2.MS ESI(-) base peak chromatogram (BPC) of the Polar Metabolites QC Mix.

Conclusion

The Polar Metabolites QC Mix is presented as a dedicated solution for LC-MS applications in metabolomics research. It enables a robust LC-MS methodology for the eight included compounds, allowing effective monitoring and evaluation of drift phenomena and supporting consistent results across metabolite profiling studies. The Mix is designed to be applicable and suitable for both ESI(+) and ESI(-) applications and comes conveniently packaged as ready-to-use solution.

Related Products

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References

1.
Oliver S. 1998. Systematic functional analysis of the yeast genome. Trends in Biotechnology. 16(9):373-378. https://doi.org/10.1016/s0167-7799(98)01214-1
2.
Cho K, Mahieu NG, Johnson SL, Patti GJ. 2014. After the feature presentation: technologies bridging untargeted metabolomics and biology. Current Opinion in Biotechnology. 28143-148. https://doi.org/10.1016/j.copbio.2014.04.006
3.
Karu N, Deng L, Slae M, Guo AC, Sajed T, Huynh H, Wine E, Wishart DS. 2018. A review on human fecal metabolomics: Methods, applications and the human fecal metabolome database. Analytica Chimica Acta. 10301-24. https://doi.org/10.1016/j.aca.2018.05.031
4.
Psychogios N, Hau DD, Peng J, Guo AC, Mandal R, Bouatra S, Sinelnikov I, Krishnamurthy R, Eisner R, Gautam B, et al. 2011. The Human Serum Metabolome. PLoS ONE. 6(2):e16957. https://doi.org/10.1371/journal.pone.0016957
5.
Bouatra S, Aziat F, Mandal R, Guo AC, Wilson MR, Knox C, Bjorndahl TC, Krishnamurthy R, Saleem F, Liu P, et al. 2013. The Human Urine Metabolome. PLoS ONE. 8(9):e73076. https://doi.org/10.1371/journal.pone.0073076
6.
Wishart DS, Lewis MJ, Morrissey JA, Flegel MD, Jeroncic K, Xiong Y, Cheng D, Eisner R, Gautam B, Tzur D, et al. 2008. The human cerebrospinal fluid metabolome. Journal of Chromatography B. 871(2):164-173. https://doi.org/10.1016/j.jchromb.2008.05.001
7.
Dame ZT, Aziat F, Mandal R, Krishnamurthy R, Bouatra S, Borzouie S, Guo AC, Sajed T, Deng L, Lin H, et al. 2015. The human saliva metabolome. Metabolomics. 11(6):1864-1883. https://doi.org/10.1007/s11306-015-0840-5
8.
Bauermeister A, Mannochio-Russo H, Costa-Lotufo LV, Jarmusch AK, Dorrestein PC. 2022. Mass spectrometry-based metabolomics in microbiome investigations. Nat Rev Microbiol. 20(3):143-160. https://doi.org/10.1038/s41579-021-00621-9
9.
Tebani A, Bekri S. 2019. Paving the Way to Precision Nutrition Through Metabolomics. Front. Nutr.. 641. https://doi.org/10.3389/fnut.2019.00041
10.
Tounta V, Liu Y, Cheyne A, Larrouy-Maumus G. 2021. Metabolomics in infectious diseases and drug discovery. Molecular Omics. 17(3):376-393. https://doi.org/10.1039/d1mo00017a
11.
Hong J, Yang L, Zhang D, Shi J. 2016. Plant Metabolomics: An Indispensable System Biology Tool for Plant Science. IJMS. 17(6):767. https://doi.org/10.3390/ijms17060767
12.
Nikolskiy I, Siuzdak G, Patti GJ. 2015. Discriminating precursors of common fragments for large-scale metabolite profiling by triple quadrupole mass spectrometry. Bioinformatics. 31(12):2017-2023. https://doi.org/10.1093/bioinformatics/btv085
13.
Patti GJ, Yanes O, Siuzdak G. 2012. Metabolomics: the apogee of the omics trilogy. Nat Rev Mol Cell Biol. 13(4):263-269. https://doi.org/10.1038/nrm3314
14.
Roberts LD, Souza AL, Gerszten RE, Clish CB. 2012. Targeted Metabolomics. CP Molecular Biology. 98(1): https://doi.org/10.1002/0471142727.mb3002s98
15.
Zheng X, Qiu Y, Zhong W, Baxter S, Su M, Li Q, Xie G, Ore BM, Qiao S, Spencer MD, et al. 2013. A targeted metabolomic protocol for short-chain fatty acids and branched-chain amino acids. Metabolomics. 9(4):818-827. https://doi.org/10.1007/s11306-013-0500-6
16.
Schrimpe-Rutledge AC, Codreanu SG, Sherrod SD, McLean JA. 2016. Untargeted Metabolomics Strategies—Challenges and Emerging Directions. J. Am. Soc. Mass Spectrom.. 27(12):1897-1905. https://doi.org/10.1007/s13361-016-1469-y
17.
Griffiths W, Koal T, Wang Y, Kohl M, Enot D, Deigner H. 2010. Targeted Metabolomics for Biomarker Discovery. Angew Chem Int Ed. 49(32):5426-5445. https://doi.org/10.1002/anie.200905579
18.
Klepacki J, Klawitter J, Klawitter J, Karimpour-fard A, Thurman J, Ingle G, Patel D, Christians U. 2016. Amino acids in a targeted versus a non-targeted metabolomics LC-MS/MS assay. Are the results consistent?. Clinical Biochemistry. 49(13-14):955-961. https://doi.org/10.1016/j.clinbiochem.2016.06.002
19.
Zhou B, Xiao JF, Tuli L, Ressom HW. 2011. LC-MS-based metabolomics. 8(2):470-481. https://doi.org/10.1039/c1mb05350g
20.
Theodoridis G, Gika HG, Wilson ID. 2008. LC-MS-based methodology for global metabolite profiling in metabonomics/metabolomics. TrAC Trends in Analytical Chemistry. 27(3):251-260. https://doi.org/10.1016/j.trac.2008.01.008
21.
Ivanisevic J, Want EJ. 2019. From Samples to Insights into Metabolism: Uncovering Biologically Relevant Information in LC-HRMS Metabolomics Data. Metabolites. 9(12):308. https://doi.org/10.3390/metabo9120308
22.
Sarvin B, Lagziel S, Sarvin N, Mukha D, Kumar P, Aizenshtein E, Shlomi T. Fast and sensitive flow-injection mass spectrometry metabolomics by analyzing sample-specific ion distributions. Nat Commun. 11(1):3186. https://doi.org/10.1038/s41467-020-17026-6
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