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Comprehensive lipidomic profiling by plasma separation cards

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Abstract

Large-scale lipidomic analyses have been limited by the cost and accessibility of traditional venipuncture sampling. Microsampling techniques offer a less-invasive and more accessible alternative. From a single drop of blood, plasma separation cards (PSC) deliver two volumetric dried plasma samples which are studied here for profiling endogenous blood lipids. Six lots of EDTA-treated human whole blood were used to compare PSC, dried blood spot analyses (DBS), and classic wet plasma extractions. Six replicate extractions were performed for each lot. Nontargeted lipidomics was performed by liquid chromatography-high resolution tandem mass spectrometry. Lipids were annotated by accurate mass/retention time matching and MS/MS spectral library matching using peak intensities for quantitation. Four hundred ninety-eight compounds covering 24 lipid subclasses were annotated. Inter-lot repeatability was evaluated by the percent relative standard deviation (%RSD) for each lot, giving median %RSD values across the lots at 14.6% for PSC, 9.3% for DBS, and 8.6% for wet plasma. Strong correlations of lipid peak intensities between wet plasma and PSCs were observed, but less for DBS. Lipid recovery and stability were comparable between the PSC and DBS samples, with roughly 60% of annotated lipids stable at room temperature after 28 days. Overall, PSCs provide a better alternative for quantitative blood lipidomic analyses compared to dried blood spots. However, problems with lipid stability for samples handled and shipped at room temperature are currently unavoidable outside of a clinical setting. Data transferability and comparability to standard plasma is lipid and lipid class dependent.

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References

  1. Stephenson DJ, Hoeferlin LA, Chalfant CE. Lipidomics in translational research and the clinical significance of lipid-based biomarkers. Transl Res. 2017;189:13–29. https://doi.org/10.1016/j.trsl.2017.06.006.

    Article  Google Scholar 

  2. Naudí A, Cabré R, Jové M, Ayala V, Gonzalo H, Portero-Otín M, et al. Lipidomics of human brain aging and Alzheimer’s disease pathology. Int Rev Neurobiol. 2015;122:133–89. https://doi.org/10.1016/bs.irn.2015.05.008.

    Article  Google Scholar 

  3. Meikle PJ, Wong G, Barlow CK, Kingwell BA. Lipidomics: potential role in risk prediction and therapeutic monitoring for diabetes and cardiovascular disease. Pharmacol Ther. 2014;143:12–23. https://doi.org/10.1016/j.pharmthera.2014.02.001.

    Article  Google Scholar 

  4. Lv J, Zhang L, Yan F, Wang X. Clinical lipidomics: a new way to diagnose human diseases. Clin Transl Med. 2018;7:12. https://doi.org/10.1186/s40169-018-0190-9.

    Article  Google Scholar 

  5. Poetto AS, Posocco B, Gagno S, Orleni M, Zanchetta M, Iacuzzi V, et al. A new dried blood spot LC-MS/MS method for therapeutic drug monitoring of palbociclib, ribociclib, and letrozole in patients with cancer. J Chromatogr B. 2021;1185:122985. https://doi.org/10.1016/j.jchromb.2021.122985.

    Article  Google Scholar 

  6. Yu M, Dolios G, Yong-Gonzalez V, Bjorkqvist O, Colicino E, Halfvarson J, et al. Untargeted metabolomics profiling and hemoglobin normalization for archived newborn dried blood spots from a refrigerated biorepository. J Pharm Biomed Anal. 2020;191:113574. https://doi.org/10.1016/j.jpba.2020.113574.

    Article  Google Scholar 

  7. Koulman A, Prentice P, Yong MCY, Matthews L, Bond NJ, Eiden M, et. al. The development and validation of a fast and robust dried blood spot based lipid profiling method to study infant metabolism. Metabolomics 2014;10:1018-1025. https://doi.org/10.1007/s11306-014-0628-z

  8. Lam SM, Tian H, Shui G. Lipidomics, en route to accurate quantitation. Biochim Biophys Acta Mol Cell Biol Lipids. 2017;1862:752–61. https://doi.org/10.1016/j.bbalip.2017.02.008.

    Article  Google Scholar 

  9. Velghe S, Delahaye L, Stove CP. Is the hematocrit still an issue in quantitative dried blood spot analysis? J Pharm Biomed Anal. 2019;163:188–96. https://doi.org/10.1016/j.jpba.2018.10.010.

    Article  Google Scholar 

  10. Meikle TG, Huynh K, Giles C, Meikle PJ. Clinical lipidomics: realizing the potential of lipid profiling. J Lipid Res. 2021;62:100127. https://doi.org/10.1016/j.jlr.2021.100127.

    Article  Google Scholar 

  11. Kim J, Woenker T, Adamec J, Regnier FE. Simple, miniaturized blood plasma extraction method. Anal Chem. 2013;85:11501–8. https://doi.org/10.1021/ac402735y.

    Article  Google Scholar 

  12. Li Y, Jiang Y, Cao H, Lin H, Ren W, Huang J, et al. Therapeutic drug monitoring of valproic acid using a dried plasma spot sampling device. J Mass Spectrom. 2021;56:4603. https://doi.org/10.1002/jms.4603.

    Article  Google Scholar 

  13. Long NP, Park S, Anh NH, Kim SJ, Kim HM, Yoon SJ, et al. Advances in liquid chromatography-mass spectrometry-based lipidomics: a look ahead. J Anal Test. 2020;4:183–97. https://doi.org/10.1007/s41664-020-00135-y.

    Article  Google Scholar 

  14. Cajka T, Fiehn O. Increasing lipidomic coverage by selecting optimal mobile-phase modifiers in LC–MS of blood plasma. Metabolomics. 2016;12:34. https://doi.org/10.1007/s11306-015-0929-x.

    Article  Google Scholar 

  15. Koelmel JP, Kroeger NM, Gill EL, Ulmer CZ, Bowden JA, Patterson RE, et al. Expanding lipidome coverage using LC–MS/MS data-dependent acquisition with automated exclusion list generation. J Am Soc Mass Spectrom. 2017;28:908–17. https://doi.org/10.1007/s13361-017-1608-0.

    Article  Google Scholar 

  16. Tsugawa H, Cajka T, Kind T, Ma Y, Higgins B, Ikeda K, et al. MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nat Methods. 2015;12:523–6. https://doi.org/10.1038/nmeth.3393.

    Article  Google Scholar 

  17. Kind T, Liu K, Lee DY, DeFelice B, Meissen JK, Fiehn O. LipidBlast in silico tandem mass spectrometry database for lipid identification. Nat Methods. 2013;10:755–8. https://doi.org/10.1038/nmeth.2551.

    Article  Google Scholar 

  18. DeFelice BC, Mehta SS, Samra S, Cajka T, Wancewicz B, Fahrmann JF, et al. Mass spectral feature list optimizer (MS-FLO): a tool to minimize false positive peak reports in untargeted liquid chromatography-mass spectroscopy (LC–MS) data processing. Anal Chem. 2017;89:3250–5. https://doi.org/10.1021/acs.analchem.6b04372.

    Article  Google Scholar 

  19. Fan S, Kind T, Cajka T, Hazen SL, Tang WHW, Kaddurah-Daouk R, et al. Systematic error removal using random forest for normalizing large-scale untargeted lipidomics data. Anal Chem. 2019;91:3590–6. https://doi.org/10.1021/acs.analchem.8b05592.

    Article  Google Scholar 

  20. Liebisch G, Fahy E, Aoki J, Dennis EA, Durand T, Ejsing CS, et al. Update on LIPID MAPS classification, nomenclature, and shorthand notation for MS-derived lipid structures. J Lipid Res. 2020;61:1539–55. https://doi.org/10.1194/jlr.S120001025.

    Article  Google Scholar 

  21. Gonzalez-Covarrubias V, Dane A, Hankemeier T, Vreeken RJ. The influence of citrate, EDTA, and heparin anticoagulants to human plasma LC–MS lipidomic profiling. Metabolomics. 2013;9:337–48. https://doi.org/10.1007/s11306-012-0450-4.

    Article  Google Scholar 

  22. Paglia G, Del Greco FM, Sigurdsson BB, Rainer J, Volani C, Hicka AA, et al. Influence of collection tubes during quantitative targeted metabolomics studies in human blood samples. Clin Chim Acta. 2018;486:320–8. https://doi.org/10.1016/j.cca.2018.08.014.

    Article  Google Scholar 

  23. Luginbuhl M, Gaugler S. Addressing new possibilities and new challenges: automated nondestructive hematocrit normalization for dried blood spots. Ther Drug Monit. 2021;43:346–50. https://doi.org/10.1097/FTD.0000000000000887.

    Article  Google Scholar 

  24. Luginbuhl M, Stoth F, Weinmann W, Gaugler S. Fully automated correction for the hematocrit bias of non-volumetric dried blood spot phosphatidylethanol analysis. Alcohol. 2021;94:17–23. https://doi.org/10.1016/j.alcohol.2021.04.002.

    Article  Google Scholar 

  25. Chepyala D, Kuo H, Su K, Liao H, Wang S, Chepyala SR, et al. Improved dried blood spot-based metabolomics analysis by a postcolumn infused-internal standard assisted liquid chromatography-electrospray ionization mass spectrometry method. Anal Chem. 2019;91:10702–12. https://doi.org/10.1021/acs.analchem.9b02050.

    Article  Google Scholar 

  26. Di Marino C, De Marco A, Pisanti A, Romanucci V. Effects of dried blood spot storage on lipidomic analysis. Molecules. 2018;28:403. https://doi.org/10.3390/molecules23020403.

    Article  Google Scholar 

  27. Prentice PM, Turner C, Wong MCY, Dalton RN. Stability of metabolites in dried blood spots stored at different temperatures over a 2-year period. Bioanalysis. 2013;5:1507–14. https://doi.org/10.4155/bio.13.121.

    Article  Google Scholar 

  28. Faller A, Richter B, Kluge M, Koenig P, Seitz HK, Skopp G. Stability of phosphatidylethanol species in spiked and authentic whole blood and matching dried blood spots. Int J Legal Med. 2013;127:603–10. https://doi.org/10.1007/s00414-012-0799-y.

    Article  Google Scholar 

  29. Ulmer CZ, Koelmel JP, Jones CM, Garrett TJ, Aristizabal-Henao JJ, Vesper HW, et al. A review of efforts to improve lipid stability during sample preparation and standardization efforts to ensure accuracy in the reporting of lipid measurements. Lipids. 2021;56:3–16. https://doi.org/10.1002/lipd.12263.

    Article  Google Scholar 

  30. Koelmel JP, Jones CM, Ulmer CZ, Garrett TJ, Yost RA, Schock TB, et al. Examining heat treatment for stabilization of the lipidome. Bioanalysis. 2018;10:291–305. https://doi.org/10.4155/bio-2017-0209.

    Article  Google Scholar 

  31. Wang X, Gu X, Song H, Song Q, Gao X, Lu Y, et al. Phenylmethanesulfonyl fluoride pretreatment stabilizes plasma lipidome in lipidomic and metabolomic analysis. Anal Chim Acta. 2015;893:77–83. https://doi.org/10.1016/j.aca.2015.08.049.

    Article  Google Scholar 

  32. Liu G, Muehlhauser S, Gibson RA. A method for long term stabilization of long chain polyunsaturated fatty acids in dried blood spots and its clinical application. Prostaglandins Leukot Essent Fatty Acids. 2014;91:251–60. https://doi.org/10.1016/j.plefa.2014.09.009.

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank Emily Olson and Michele Mietus-Snyder of Children’s National Hospital for their contributions to the initial validation experiments.

Funding

This study was supported by the National Institutes of Health grants U19 AG023122 and R01HL157535.

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Correspondence to Oliver Fiehn.

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Bishop, L.M., Fiehn, O. Comprehensive lipidomic profiling by plasma separation cards. Anal Bioanal Chem 415, 193–201 (2023). https://doi.org/10.1007/s00216-022-04399-4

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