Thermal reactions can significantly alter the metabolomic and lipidomic content of biofluids and tissues during storage. In this study, we investigated the stability of polar metabolites and complex lipids in dry human serum and mouse liver extracts over a three-day period under various temperature conditions. Specifically, we tested temperatures of -80 °C (freezer), -24 °C (freezer), -0.5 °C (polystyrene box with gel-based ice packs), +5 °C (refrigerator), +23 °C (laboratory, room temperature), and +30 °C (thermostat) to simulate the time between sample extraction and analysis, shipping dry extracts to different labs as an alternative to dry ice, and document the impact of higher temperatures on sample integrity. The extracts were analyzed using five fast liquid chromatography-mass spectrometry (LC-MS) methods to screen polar metabolites and complex lipids, and over 600 metabolites were annotated in serum and liver extracts. We found that storing dry extracts at -24 °C and partially at -0.5 °C provided comparable results to -80 °C (reference condition). However, increasing the storage temperatures led to significant changes in oxidized triacylglycerols, phospholipids, and fatty acids within three days. Polar metabolites were mainly affected at storage temperatures of +23 °C and +30 °C.

Institute of Physiology of the Czech Academy of Sciences Videnska 1083 14200 Prague Czech Republic

West Coast Metabolomics Center University of California 451 Health Sciences Drive Davis CA 95616 USA

Zobrazit více v PubMed

Dettmer K., Aronov P.A., Hammock B.D. Mass spectrometry-based metabolomics. Mass Spectrom. Rev. 2007;26:51–78. doi: 10.1002/mas.20108. PubMed DOI PMC

Alseekh S., Aharoni A., Brotman Y., Contrepois K., D’auria J., Ewald J., Ewald J.C., Fraser P.D., Giavalisco P., Hall R.D., et al. Mass spectrometry-based metabolomics: A guide for annotation, quantification and best reporting practices. Nat. Methods. 2021;18:747–756. doi: 10.1038/s41592-021-01197-1. PubMed DOI PMC

Stevens V.L., Hoover E., Wang Y., Zanetti K.A. Pre-analytical factors that affect metabolite stability in human urine, plasma, and serum: A review. Metabolites. 2019;9:156. doi: 10.3390/metabo9080156. PubMed DOI PMC

Ang J.E., Revell V., Mann A., Mäntele S., Otway D.T., Johnston J.D., Thumser A.E., Skene D.J., Raynaud F. Identification of human plasma metabolites exhibiting time-of-day variation using an untargeted liquid chromatography–mass spectrometry metabolomic approach. Chronobiol. Int. 2012;29:868–881. doi: 10.3109/07420528.2012.699122. PubMed DOI PMC

Bervoets L., Louis E., Reekmans G., Mesotten L., Thomeer M., Adriaensens P., Linsen L. Influence of preanalytical sampling conditions on the 1H NMR metabolic profile of human blood plasma and introduction of the Standard PREanalytical Code used in biobanking. Metabolomics. 2015;11:1197–1207. doi: 10.1007/s11306-015-0774-y. DOI

Denery J.R., Nunes A.A.K., Dickerson T.J. Characterization of differences between blood sample matrices in untargeted metabolomics. Anal. Chem. 2011;83:1040–1047. doi: 10.1021/ac102806p. PubMed DOI

Carayol M., Licaj I., Achaintre D., Sacerdote C., Vineis P., Key T.J., Moret N.C.O., Scalbert A., Rinaldi S., Ferrari P. Reliability of serum metabolites over a two-year period: A targeted metabolomic approach in fasting and non-fasting samples from EPIC. PLoS ONE. 2015;10:e0135437. doi: 10.1371/journal.pone.0135437. PubMed DOI PMC

Ammerlaan W., Trezzi J.-P., Lescuyer P., Mathay C., Hiller K., Betsou F. Method validation for preparing serum and plasma samples from human blood for downstream proteomic, metabolomic, and circulating nucleic acid-based applications. Biopreserv. Biobank. 2014;12:269–280. doi: 10.1089/bio.2014.0003. PubMed DOI

Breier M., Wahl S., Prehn C., Fugmann M., Ferrari U., Weise M., Banning F., Seissler J., Grallert H., Adamski J., et al. Targeted metabolomics identifies reliable and stable metabolites in human serum and plasma samples. PLoS ONE. 2014;9:e89728. doi: 10.1371/journal.pone.0089728. PubMed DOI PMC

Anton G., Wilson R., Yu Z.-H., Prehn C., Zukunft S., Adamski J., Heier M., Meisinger C., Römisch-Margl W., Wang-Sattler R., et al. Pre-analytical sample quality: Metabolite ratios as an intrinsic marker for prolonged room temperature exposure of serum samples. PLoS ONE. 2015;10:e0121495. doi: 10.1371/journal.pone.0121495. PubMed DOI PMC

Chetwynd A.J., Abdul-Sada A., Holt S.G., Hill E.M. Use of a pre-analysis osmolality normalisation method to correct for variable urine concentrations and for improved metabolomic analyses. J. Chromatogr. A. 2016;1431:103–110. doi: 10.1016/j.chroma.2015.12.056. PubMed DOI

Bernini P., Bertini I., Luchinat C., Nincheri P., Staderini S., Turano P. Standard operating procedures for pre-analytical handling of blood and urine for metabolomic studies and biobanks. J. Biomol. NMR. 2011;49:231–243. doi: 10.1007/s10858-011-9489-1. PubMed DOI

Rakusanova S., Fiehn O., Cajka T. Toward building mass spectrometry-based metabolomics and lipidomics atlases for biological and clinical research. TrAC Trends Anal. Chem. 2023;158:116825. doi: 10.1016/j.trac.2022.116825. DOI

Saoi M., Britz-McKibbin P. New advances in tissue metabolomics: A review. Metabolites. 2021;11:672. doi: 10.3390/metabo11100672. PubMed DOI PMC

Fomenko M.V., Yanshole L.V., Tsentalovich Y.P. Stability of metabolomic content during sample preparation: Blood and brain tissues. Metabolites. 2022;12:811. doi: 10.3390/metabo12090811. PubMed DOI PMC

Lopes M., Brejchova K., Riecan M., Novakova M., Rossmeisl M., Cajka T., Kuda O. Metabolomics atlas of oral 13C-glucose tolerance test in mice. Cell Rep. 2021;37:109833. doi: 10.1016/j.celrep.2021.109833. PubMed DOI

Sistilli G., Kalendova V., Cajka T., Irodenko I., Bardova K., Oseeva M., Zacek P., Kroupova P., Horakova O., Lackner K., et al. Krill oil supplementation reduces exacerbated hepatic steatosis induced by thermoneutral housing in mice with diet-induced obesity. Nutrients. 2021;13:437. doi: 10.3390/nu13020437. PubMed DOI PMC

Cajka T., Hricko J., Rudl Kulhava L., Paucova M., Novakova M., Kuda O. Optimization of mobile phase modifiers for fast LC-MS-based untargeted metabolomics and lipidomics. Int. J. Mol. Sci. 2023;24:1987. doi: 10.3390/ijms24031987. PubMed DOI PMC

Chambers M.C., Maclean B., Burke R., Amodei D., Ruderman D.L., Neumann S., Gatto L., Fischer B., Pratt B., Egertson J., et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat. Biotechnol. 2012;30:918–920. doi: 10.1038/nbt.2377. PubMed DOI PMC

Koelmel J.P., Kroeger N.M., Gill E.L., Ulmer C.Z., Bowden J.A., Patterson R.E., Yost R.A., Garrett T.J. Expanding lipidome coverage using LC-MS/MS data-dependent acquisition with automated exclusion list generation. J. Am. Soc. Mass Spectrom. 2017;28:908–917. doi: 10.1007/s13361-017-1608-0. PubMed DOI PMC

Tsugawa H., Ikeda K., Takahashi M., Satoh A., Mori Y., Uchino H., Okahashi N., Yamada Y., Tada I., Bonini P., et al. A lipidome atlas in MS-DIAL 4. Nat. Biotechnol. 2020;38:1159–1163. doi: 10.1038/s41587-020-0531-2. PubMed DOI

Pang Z., Chong J., Zhou G., de Lima Morais D.A., Chang L., Barrette M., Gauthier C., Jacques P.-É., Li S., Xia J. MetaboAnalyst 5.0: Narrowing the gap between raw spectra and functional insights. Nucleic Acids Res. 2021;49:W388–W396. doi: 10.1093/nar/gkab382. PubMed DOI PMC

Vinaixa M., Samino S., Saez I., Duran J., Guinovart J.J., Yanes O. A guideline to univariate statistical analysis for LC/MS-based untargeted metabolomics-derived data. Metabolites. 2012;2:775–795. doi: 10.3390/metabo2040775. PubMed DOI PMC

Haid M., Muschet C., Wahl S., Römisch-Margl W., Prehn C., Möller G., Adamski J. Long-term stability of human plasma metabolites during storage at −80 °C. J. Proteome Res. 2018;17:203–211. doi: 10.1021/acs.jproteome.7b00518. PubMed DOI

Polson C., Sarkar P., Incledon B., Raguvaran V., Grant R. Optimization of protein precipitation based upon effectiveness of protein removal and ionization effect in liquid chromatography–tandem mass spectrometry. J. Chromatogr. B. 2003;785:263–275. doi: 10.1016/S1570-0232(02)00914-5. PubMed DOI

Wright H.T., Urry D.W. Nonenzymatic deamidation of asparaginyl and glutaminyl residues in protein. Crit. Rev. Biochem. Mol. Biol. 1991;26:1–52. doi: 10.3109/10409239109081719. PubMed DOI

Savino R.J., Kempisty B., Mozdziak P. The potential of a protein model synthesized absent of methionine. Molecules. 2022;27:3679. doi: 10.3390/molecules27123679. PubMed DOI PMC

Wyrzykowski D., Hebanowska E., Nowak-Wiczk G., Makowski M., Chmurzyński L. Thermal behaviour of citric acid and isomeric aconitic acids. J. Therm. Anal. Calorim. 2011;104:731–735. doi: 10.1007/s10973-010-1015-2. DOI

Morana A., Stiuso P., Colonna G., Lamberti M., Cartenì M., De Rosa M. Stabilization of S-adenosyl-l-methionine promoted by trehalose. BBA-Gen. Subj. 2002;1573:105–108. doi: 10.1016/S0304-4165(02)00333-1. PubMed DOI

Reis G.B., Rees J.C., Ivanova A.A., Kuklenyik Z., Drew N.M., Pirkle J.L., Barr J.R. Stability of lipids in plasma and serum: Effects of temperature-related storage conditions on the human lipidome. J. Mass Spectrom. Adv. Clin. Lab. 2021;22:34–42. doi: 10.1016/j.jmsacl.2021.10.002. PubMed DOI PMC

Liebisch G., Fahy E., Aoki J., Dennis E.A., Durand T., Ejsing C.S., Fedorova M., Feussner I., Griffiths W.J., Köfeler H., et al. Update on LIPID MAPS classification, nomenclature, and shorthand notation for MS-derived lipid structures. J. Lipid Res. 2020;61:1539–1555. doi: 10.1194/jlr.S120001025. PubMed DOI PMC

Ni Z., Angelidou G., Hoffmann R., Fedorova M. LPPtiger software for lipidome-specific prediction and identification of oxidized phospholipids from LC-MS datasets. Sci. Rep. 2017;7:15138. doi: 10.1038/s41598-017-15363-z. PubMed DOI PMC

Matsuoka Y., Takahashi M., Sugiura Y., Izumi Y., Nishiyama K., Nishida M., Suematsu M., Bamba T., Yamada K.-I. Structural library and visualization of endogenously oxidized phosphatidylcholines using mass spectrometry-based techniques. Nat. Commun. 2021;12:6339. doi: 10.1038/s41467-021-26633-w. PubMed DOI PMC

Ikeda K., Oike Y., Shimizu T., Taguchi R. Global analysis of triacylglycerols including oxidized molecular species by reverse-phase high resolution LC/ESI-QTOF MS/MS. J. Chromatogr. B. 2009;877:2639–2647. doi: 10.1016/j.jchromb.2009.03.047. PubMed DOI

Fabritius M., Yang B. Direct infusion and ultra-high-performance liquid chromatography/electrospray ionization tandem mass spectrometry analysis of phospholipid regioisomers. Rapid Commun. Mass Spectrom. 2021;35:e9151. doi: 10.1002/rcm.9151. PubMed DOI

Gladine C., Ostermann A.I., Newman J.W., Schebb N.H. MS-based targeted metabolomics of eicosanoids and other oxylipins: Analytical and inter-individual variabilities. Free Radic. Biol. Med. 2019;144:72–89. doi: 10.1016/j.freeradbiomed.2019.05.012. PubMed DOI

Chronodisruption that dampens output of the central clock abolishes rhythms in metabolome profiles and elevates acylcarnitine levels in the liver of female rats

Obesity alters adipose tissue response to fasting and refeeding in women: A study on lipolytic and endocrine dynamics and acute insulin resistance

Haplotype variability in mitochondrial rRNA predisposes to metabolic syndrome

Metabolomics and Lipidomics for Studying Metabolic Syndrome: Insights into Cardiovascular Diseases, Type 1 & 2 Diabetes, and Metabolic Dysfunction-Associated Steatotic Liver Disease

Exploring contrast-enhancing staining agents for studying adipose tissue through contrast-enhanced computed tomography

Hydrophilic Interaction Liquid Chromatography-Hydrogen/Deuterium Exchange-Mass Spectrometry (HILIC-HDX-MS) for Untargeted Metabolomics

Epitranscriptomic regulation in fasting hearts: implications for cardiac health

Exploring the Impact of Organic Solvent Quality and Unusual Adduct Formation during LC-MS-Based Lipidomic Profiling