Skip to main content
SpringerLink
Log in

Optimizing the lipidomics workflow for clinical studies—practical considerations

  • Review
  • Published:
Analytical and Bioanalytical Chemistry Aims and scope Submit manuscript

Abstract

Lipidomics is increasingly being used in clinical research, offering new opportunities for disease prediction and detection. One of the key challenges of clinical applications of lipidomics is the high sensitivity of measured lipid levels to many analytical, physiological, and environmental factors, which therefore must be taken into account when designing the studies. Here we critically discuss the complete clinical lipidomics workflow, including selection of the subjects, the sample type, the sample preprocessing conditions, and the analytical method and methods for data processing. We also review the lipidomics applications which investigate the confounding factors such as age, gender, fasting time, and handling procedures for measuring blood lipid metabolites.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Hyotylainen T, Oresic M (2014) Systems biology strategies to study lipidomes in health and disease. Prog Lipid Res 55:43–60. doi:10.1016/j.plipres.2014.06.001

    CAS  Google Scholar 

  2. Astarita G, Kendall AC, Dennis EA, Nicolaou A (2014) Targeted lipidomic strategies for oxygenated metabolites of polyunsaturated fatty acids. Biochim Biophys Acta. doi:10.1016/j.bbalip.2014.11.012

    Google Scholar 

  3. Breier M, Wahl S, Prehn C, Fugmann M, Ferrari U, Weise M, Banning F, Seissler J, Grallert H, Adamski J, Lechner A (2014) Targeted metabolomics identifies reliable and stable metabolites in human serum and plasma samples. PLoS One 9(2):e89728. doi:10.1371/journal.pone.0089728

    Google Scholar 

  4. Ishikawa M, Maekawa K, Saito K, Senoo Y, Urata M, Murayama M, Tajima Y, Kumagai Y, Saito Y (2014) Plasma and serum lipidomics of healthy white adults shows characteristic profiles by subjects’ gender and age. PLoS One 9(3):e91806. doi:10.1371/journal.pone.0091806

    Google Scholar 

  5. Jørgenrud B, Jäntti S, Mattila I, Pöhö P, Rønningen KS, Yki-Järvinen H, Orešič M, Hyötyläinen T (2015) The influence of sample collection methodology and sample preprocessing on the blood metabolic profile. Bioanalysis (accepted)

  6. Gonzalez-Covarrubias V, Dane A, Hankemeier T, Vreeken RJ (2013) The influence of citrate, EDTA, and heparin anticoagulants to human plasma LC–MS lipidomic profiling. Metabolomics 9(2):337–348

    CAS  Google Scholar 

  7. Hammad SM, Pierce JS, Soodavar F, Smith KJ, Al Gadban MM, Rembiesa B, Klein RL, Hannun YA, Bielawski J, Bielawska A (2010) Blood sphingolipidomics in healthy humans: impact of sample collection methodology. J Lipid Res 51(10):3074–3087. doi:10.1194/jlr.D008532

    CAS  Google Scholar 

  8. Yang W, Chen Y, Xi C, Zhang R, Song Y, Zhan Q, Bi X, Abliz Z (2013) Liquid chromatography-tandem mass spectrometry-based plasma metabonomics delineate the effect of metabolites’ stability on reliability of potential biomarkers. Anal Chem 85(5):2606–2610. doi:10.1021/ac303576b

    CAS  Google Scholar 

  9. Taylor LA, Arends J, Hodina AK, Unger C, Massing U (2007) Plasma lyso-phosphatidylcholine concentration is decreased in cancer patients with weight loss and activated inflammatory status. Lipids Health Dis 6:17. doi:10.1186/1476-511X-6-17

    Google Scholar 

  10. Zivkovic AM, Wiest MM, Nguyen UT, Davis R, Watkins SM, German JB (2009) Effects of sample handling and storage on quantitative lipid analysis in human serum. Metabolomics 5(4):507–516. doi:10.1007/s11306-009-0174-2

    CAS  Google Scholar 

  11. Kudo I, Murakami M (2002) Phospholipase A2 enzymes. Prostaglandins Other Lipid Mediat 68–69:3–58

    Google Scholar 

  12. Lessig J, Fuchs B (2009) Plasmalogens in biological systems: their role in oxidative processes in biological membranes, their contribution to pathological processes and aging and plasmalogen analysis. Curr Med Chem 16(16):2021–2041

    CAS  Google Scholar 

  13. Onorato JM, Shipkova P, Minnich A, Aubry AF, Easter J, Tymiak A (2014) Challenges in accurate quantitation of lysophosphatidic acids in human biofluids. J Lipid Res 55(8):1784–1796. doi:10.1194/jlr.D050070

    CAS  Google Scholar 

  14. Folch J, Lees M, Sloane Stanley GH (1957) A simple method for the isolation and purification of total lipides from animal tissues. J Biol Chem 226(1):497–509

    CAS  Google Scholar 

  15. Matyash V, Liebisch G, Kurzchalia TV, Shevchenko A, Schwudke D (2008) Lipid extraction by methyl-tert-butyl ether for high-throughput lipidomics. J Lipid Res 49(5):1137–1146. doi:10.1194/jlr. D700041-JLR200

    CAS  Google Scholar 

  16. Sarafian MH, Gaudin M, Lewis MR, Martin FP, Holmes E, Nicholson JK, Dumas ME (2014) Objective set of criteria for optimization of sample preparation procedures for ultra-high throughput untargeted blood plasma lipid profiling by ultra performance liquid chromatography-mass spectrometry. Anal Chem 86(12):5766–5774. doi:10.1021/ac500317c

    CAS  Google Scholar 

  17. Wolf C, Quinn PJ (2008) Lipidomics: practical aspects and applications. Prog Lipid Res 47(1):15–36. doi:10.1016/j.plipres.2007.09.001

    CAS  Google Scholar 

  18. Cajka T, Fiehn O (2014) Comprehensive analysis of lipids in biological systems by liquid chromatography-mass spectrometry. Trends Anal Chem 61:192–206. doi:10.1016/j.trac.2014.04.017

    CAS  Google Scholar 

  19. Bollinger JG, Ii H, Sadilek M, Gelb MH (2010) Improved method for the quantification of lysophospholipids including enol ether species by liquid chromatography-tandem mass spectrometry. J Lipid Res 51(2):440–447. doi:10.1194/jlr.D000885

    CAS  Google Scholar 

  20. Zhao Z, Xu Y (2010) An extremely simple method for extraction of lysophospholipids and phospholipids from blood samples. J Lipid Res 51(3):652–659. doi:10.1194/jlr.D001503

    CAS  Google Scholar 

  21. Han X, Yang K, Gross RW (2012) Multi-dimensional mass spectrometry-based shotgun lipidomics and novel strategies for lipidomic analyses. Mass Spectrom Rev 31(1):134–178. doi:10.1002/mas.20342

    CAS  Google Scholar 

  22. Ellis SR, Brown SH, In Het Panhuis M, Blanksby SJ, Mitchell TW (2013) Surface analysis of lipids by mass spectrometry: more than just imaging. Prog Lipid Res 52(4):329–353. doi:10.1016/j.plipres.2013.04.005

    CAS  Google Scholar 

  23. Sandra K, Sandra P (2013) Lipidomics from an analytical perspective. Curr Opin Chem Biol 17(5):847–853. doi:10.1016/j.cbpa.2013.06.010

    CAS  Google Scholar 

  24. Harkewicz R, Dennis EA (2011) Applications of mass spectrometry to lipids and membranes. Annu Rev Biochem 80:301–325. doi:10.1146/annurev-biochem-060409-092612

    CAS  Google Scholar 

  25. Guan XL, Wenk MR (2012) Targeted and non-targeted analysis of membrane lipids using mass spectrometry. Methods Cell Biol 108:149–172. doi:10.1016/B978-0-12-386487-1.00008-0

    CAS  Google Scholar 

  26. Gorrochategui E, Casas J, Perez-Albaladejo E, Jauregui O, Porte C, Lacorte S (2014) Characterization of complex lipid mixtures in contaminant exposed JEG-3 cells using liquid chromatography and high-resolution mass spectrometry. Environ Sci Pollut Res Int 21(20):11907–11916. doi:10.1007/s11356-014-3172-5

    CAS  Google Scholar 

  27. Zhong Y, Hyung SJ, Ruotolo BT (2011) Characterizing the resolution and accuracy of a second-generation traveling-wave ion mobility separator for biomolecular ions. Analyst 136(17):3534–3541. doi:10.1039/c0an00987c

    CAS  Google Scholar 

  28. Schwudke D, Liebisch G, Herzog R, Schmitz G, Shevchenko A (2007) Shotgun lipidomics by tandem mass spectrometry under data-dependent acquisition control. Methods Enzymol 433:175–191

    CAS  Google Scholar 

  29. Papan C, Penkov S, Herzog R, Thiele C, Kurzchalia T, Shevchenko A (2014) Systematic screening for novel lipids by shotgun lipidomics. Anal Chem 86(5):2703–2710. doi:10.1021/ac404083u

    CAS  Google Scholar 

  30. Lintonen TP, Baker PR, Suoniemi M, Ubhi BK, Koistinen KM, Duchoslav E, Campbell JL, Ekroos K (2014) Differential mobility spectrometry-driven shotgun lipidomics. Anal Chem 86(19):9662–9669. doi:10.1021/ac5021744

    CAS  Google Scholar 

  31. Wang M, Huang Y, Han X (2014) Accurate mass searching of individual lipid species candidates from high-resolution mass spectra for shotgun lipidomics. Rapid Commun Mass Spectrom 28(20):2201–2210. doi:10.1002/rcm.7015

    CAS  Google Scholar 

  32. Stegemann C, Pechlaner R, Willeit P, Langley SR, Mangino M, Mayr U, Menni C, Moayyeri A, Santer P, Rungger G, Spector TD, Willeit J, Kiechl S, Mayr M (2014) Lipidomics profiling and risk of cardiovascular disease in the prospective population-based Bruneck study. Circulation 129(18):1821–1831. doi:10.1161/CIRCULATIONAHA.113.002500

    CAS  Google Scholar 

  33. Simons B, Kauhanen D, Sylvanne T, Tarasov K, Duchoslav E, Ekroos K (2012) Shotgun lipidomics by sequential precursor ion fragmentation on a hybrid quadrupole time-of-flight mass spectrometer. Metabolites 2(1):195–213. doi:10.3390/metabo2010195

    CAS  Google Scholar 

  34. Heiskanen LA, Suoniemi M, Ta HX, Tarasov K, Ekroos K (2013) Long-term performance and stability of molecular shotgun lipidomic analysis of human plasma samples. Anal Chem 85(18):8757–8763. doi:10.1021/ac401857a

    CAS  Google Scholar 

  35. Yang K, Cheng H, Gross RW, Han X (2009) Automated lipid identification and quantification by multidimensional mass spectrometry-based shotgun lipidomics. Anal Chem 81(11):4356–4368. doi:10.1021/ac900241u

    CAS  Google Scholar 

  36. Wang M, Fang H, Han X (2012) Shotgun lipidomics analysis of 4-hydroxyalkenal species directly from lipid extracts after one-step in situ derivatization. Anal Chem 84(10):4580–4586. doi:10.1021/ac300695p

    CAS  Google Scholar 

  37. Wang M, Han RH, Han X (2013) Fatty acidomics: global analysis of lipid species containing a carboxyl group with a charge-remote fragmentation-assisted approach. Anal Chem 85(19):9312–9320. doi:10.1021/ac402078p

    CAS  Google Scholar 

  38. Wang M, Hayakawa J, Yang K, Han X (2014) Characterization and quantification of diacylglycerol species in biological extracts after one-step derivatization: a shotgun lipidomics approach. Anal Chem 86(4):2146–2155. doi:10.1021/ac403798q

    CAS  Google Scholar 

  39. Bird SS, Marur VR, Sniatynski MJ, Greenberg HK, Kristal BS (2011) Serum lipidomics profiling using LC-MS and high-energy collisional dissociation fragmentation: focus on triglyceride detection and characterization. Anal Chem 83(17):6648–6657. doi:10.1021/ac201195d

    CAS  Google Scholar 

  40. Han X, Gross RW (1994) Electrospray ionization mass spectroscopic analysis of human erythrocyte plasma membrane phospholipids. Proc Natl Acad Sci U S A 91(22):10635–10639

    CAS  Google Scholar 

  41. Brugger B, Erben G, Sandhoff R, Wieland FT, Lehmann WD (1997) Quantitative analysis of biological membrane lipids at the low picomole level by nano-electrospray ionization tandem mass spectrometry. Proc Natl Acad Sci U S A 94(6):2339–2344

    CAS  Google Scholar 

  42. Koivusalo M, Haimi P, Heikinheimo L, Kostiainen R, Somerharju P (2001) Quantitative determination of phospholipid compositions by ESI-MS: effects of acyl chain length, unsaturation, and lipid concentration on instrument response. J Lipid Res 42(4):663–672

    Google Scholar 

  43. Yang K, Han X (2011) Accurate quantification of lipid species by electrospray ionization mass spectrometry - meet a key challenge in lipidomics. Metabolites 1(1):21–40. doi:10.3390/metabo1010021

    CAS  Google Scholar 

  44. Nygren H, Seppanen-Laakso T, Castillo S, Hyotylainen T, Oresic M (2011) Liquid chromatography-mass spectrometry (LC-MS)-based lipidomics for studies of body fluids and tissues. Methods Mol Biol 708:247–257. doi:10.1007/978-1-61737-985-7_15

    CAS  Google Scholar 

  45. Bird SS, Marur VR, Sniatynski MJ, Greenberg HK, Kristal BS (2011) Lipidomics profiling by high-resolution LC-MS and high-energy collisional dissociation fragmentation: focus on characterization of mitochondrial cardiolipins and monolysocardiolipins. Anal Chem 83(3):940–949. doi:10.1021/ac102598u

    CAS  Google Scholar 

  46. Castro-Perez JM, Kamphorst J, DeGroot J, Lafeber F, Goshawk J, Yu K, Shockcor JP, Vreeken RJ, Hankemeier T (2010) Comprehensive LC-MS E lipidomic analysis using a shotgun approach and its application to biomarker detection and identification in osteoarthritis patients. J Proteome Res 9(5):2377–2389. doi:10.1021/pr901094j

    CAS  Google Scholar 

  47. Seki H, Fukunaga K, Arita M, Arai H, Nakanishi H, Taguchi R, Miyasho T, Takamiya R, Asano K, Ishizaka A, Takeda J, Levy BD (2010) The anti-inflammatory and proresolving mediator resolvin E1 protects mice from bacterial pneumonia and acute lung injury. J Immunol 184(2):836–843. doi:10.4049/jimmunol.0901809

    CAS  Google Scholar 

  48. Myers DS, Ivanova PT, Milne SB, Brown HA (2011) Quantitative analysis of glycerophospholipids by LC-MS: acquisition, data handling, and interpretation. Biochim Biophys Acta 1811(11):748–757. doi:10.1016/j.bbalip.2011.05.015

    CAS  Google Scholar 

  49. Shui G, Bendt AK, Pethe K, Dick T, Wenk MR (2007) Sensitive profiling of chemically diverse bioactive lipids. J Lipid Res 48(9):1976–1984

    CAS  Google Scholar 

  50. Witting M, Maier TV, Garvis S, Schmitt-Kopplin P (2014) Optimizing a ultrahigh pressure liquid chromatography-time of flight-mass spectrometry approach using a novel sub-2mum core-shell particle for in depth lipidomic profiling of Caenorhabditis elegans. J Chromatogr A 1359:91–99. doi:10.1016/j.chroma.2014.07.021

    CAS  Google Scholar 

  51. Moon MH (2014) Phospholipid analysis by nanoflow liquid chromatography-tandem mass spectrometry. Mass Spectrom Lett 5:1–11

    Google Scholar 

  52. Zelena E, Dunn WB, Broadhurst D, Francis-McIntyre S, Carroll KM, Begley P, O’Hagan S, Knowles JD, Halsall A, Consortium H, Wilson ID, Kell DB (2009) Development of a robust and repeatable UPLC-MS method for the long-term metabolomic study of human serum. Anal Chem 81(4):1357–1364. doi:10.1021/ac8019366

    CAS  Google Scholar 

  53. Cai SS, Syage JA (2006) Comparison of atmospheric pressure photoionization, atmospheric pressure chemical ionization, and electrospray ionization mass spectrometry for analysis of lipids. Anal Chem 78(4):1191–1199. doi:10.1021/ac0515834

    CAS  Google Scholar 

  54. Cai SS, Short LC, Syage JA, Potvin M, Curtis JM (2007) Liquid chromatography-atmospheric pressure photoionization-mass spectrometry analysis of triacylglycerol lipids—effects of mobile phases on sensitivity. J Chromatogr A 1173(1–2):88–97. doi:10.1016/j.chroma.2007.10.008

    CAS  Google Scholar 

  55. Kliman M, May JC, McLean JA (2011) Lipid analysis and lipidomics by structurally selective ion mobility-mass spectrometry. Biochim Biophys Acta 1811(11):935–945. doi:10.1016/j.bbalip.2011.05.016

    CAS  Google Scholar 

  56. Shah V, Castro-Perez JM, McLaren DG, Herath KB, Previs SF, Roddy TP (2013) Enhanced data-independent analysis of lipids using ion mobility-TOFMSE to unravel quantitative and qualitative information in human plasma. Rapid Commun Mass Spectrom 27(19):2195–2200. doi:10.1002/rcm.6675

    CAS  Google Scholar 

  57. Kim HI, Kim H, Pang ES, Ryu EK, Beegle LW, Loo JA, Goddard WA, Kanik I (2009) Structural characterization of unsaturated phosphatidylcholines using traveling wave ion mobility spectrometry. Anal Chem 81(20):8289–8297. doi:10.1021/ac900672a

    CAS  Google Scholar 

  58. Paglia G, Angel P, Williams JP, Richardson K, Olivos HJ, Thompson JW, Menikarachchi L, Lai S, Walsh C, Moseley A, Plumb RS, Grant DF, Palsson BO, Langridge J, Geromanos S, Astarita G (2015) Ion mobility-derived collision cross section as an additional measure for lipid fingerprinting and identification. Anal Chem 87(2):1137–1144. doi:10.1021/ac503715v

    CAS  Google Scholar 

  59. Xiao JF, Zhou B, Ressom HW (2012) Metabolite identification and quantitation in LC-MS/MS-based metabolomics. Trends Anal Chem 32:1–14. doi:10.1016/j.trac.2011.08.009

    Google Scholar 

  60. Viswanathan CT, Bansal S, Booth B, DeStefano AJ, Rose MJ, Sailstad J, Shah VP, Skelly JP, Swann PG, Weiner R (2007) Quantitative bioanalytical methods validation and implementation: best practices for chromatographic and ligand binding assays. Pharm Res 24(10):1962–1973. doi:10.1007/s11095-007-9291-7

    CAS  Google Scholar 

  61. Kuligowski J, Perez-Guaita D, Lliso I, Escobar J, Leon Z, Gombau L, Solberg R, Saugstad OD, Vento M, Quintas G (2014) Detection of batch effects in liquid chromatography-mass spectrometry metabolomic data using guided principal component analysis. Talanta 130:442–448. doi:10.1016/j.talanta.2014.07.031

    CAS  Google Scholar 

  62. Scholz M, Gatzek S, Sterling A, Fiehn O, Selbig J (2004) Metabolite fingerprinting: detecting biological features by independent component analysis. Bioinformatics 20(15):2447–2454. doi:10.1093/bioinformatics/bth270

    CAS  Google Scholar 

  63. Wang W, Zhou H, Lin H, Roy S, Shaler TA, Hill LR, Norton S, Kumar P, Anderle M, Becker CH (2003) Quantification of proteins and metabolites by mass spectrometry without isotopic labeling or spiked standards. Anal Chem 75(18):4818–4826

    CAS  Google Scholar 

  64. Lee J, Park J, Lim MS, Seong SJ, Seo JJ, Park SM, Lee HW, Yoon YR (2012) Quantile normalization approach for liquid chromatography-mass spectrometry-based metabolomic data from healthy human volunteers. Anal Sci 28(8):801–805

    CAS  Google Scholar 

  65. Bijlsma S, Bobeldijk I, Verheij ER, Ramaker R, Kochhar S, Macdonald IA, van Ommen B, Smilde AK (2006) Large-scale human metabolomics studies: a strategy for data (pre-) processing and validation. Anal Chem 78(2):567–574. doi:10.1021/ac051495j

    CAS  Google Scholar 

  66. Wang SY, Kuo CH, Tseng YJ (2013) Batch Normalizer: a fast total abundance regression calibration method to simultaneously adjust batch and injection order effects in liquid chromatography/time-of-flight mass spectrometry-based metabolomics data and comparison with current calibration methods. Anal Chem 85(2):1037–1046. doi:10.1021/ac302877x

    CAS  Google Scholar 

  67. Katajamaa M, Oresic M (2007) Data processing for mass spectrometry-based metabolomics. J Chromatogr A 1158(1–2):318–328. doi:10.1016/j.chroma.2007.04.021

    CAS  Google Scholar 

  68. Castillo S, Gopalacharyulu P, Yetukuri L, Orešič M (2011) Algorithms and tools for the preprocessing of LC-MS metabolomics data. Chemom Intell Lab Syst 108:23–32

    CAS  Google Scholar 

  69. Husen P, Tarasov K, Katafiasz M, Sokol E, Vogt J, Baumgart J, Nitsch R, Ekroos K, Ejsing CS (2013) Analysis of lipid experiments (ALEX): a software framework for analysis of high-resolution shotgun lipidomics data. PLoS One 8(11):e79736. doi:10.1371/journal.pone.0079736

    CAS  Google Scholar 

  70. Herzog R, Schwudke D, Schuhmann K, Sampaio JL, Bornstein SR, Schroeder M, Shevchenko A (2011) A novel informatics concept for high-throughput shotgun lipidomics based on the molecular fragmentation query language. Genome Biol 12(1):R8. doi:10.1186/gb-2011-12-1-r8

    CAS  Google Scholar 

  71. Coble JB, Fraga CG (2014) Comparative evaluation of preprocessing freeware on chromatography/mass spectrometry data for signature discovery. J Chromatogr A 1358:155–164. doi:10.1016/j.chroma.2014.06.100

    CAS  Google Scholar 

  72. Lommen A (2009) MetAlign: interface-driven, versatile metabolomics tool for hyphenated full-scan mass spectrometry data preprocessing. Anal Chem 81(8):3079–3086. doi:10.1021/ac900036d

    CAS  Google Scholar 

  73. Katajamaa M, Oresic M (2005) Processing methods for differential analysis of LC/MS profile data. BMC Bioinform 6:179. doi:10.1186/1471-2105-6-179

    Google Scholar 

  74. Smith CA, Want EJ, O’Maille G, Abagyan R, Siuzdak G (2006) XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem 78(3):779–787. doi:10.1021/ac051437y

    CAS  Google Scholar 

  75. Styczynski MP, Moxley JF, Tong LV, Walther JL, Jensen KL, Stephanopoulos GN (2007) Systematic identification of conserved metabolites in GC/MS data for metabolomics and biomarker discovery. Anal Chem 79(3):966–973. doi:10.1021/ac0614846

    CAS  Google Scholar 

  76. Song H, Hsu FF, Ladenson J, Turk J (2007) Algorithm for processing raw mass spectrometric data to identify and quantitate complex lipid molecular species in mixtures by data-dependent scanning and fragment ion database searching. J Am Soc Mass Spectrom 18(10):1848–1858. doi:10.1016/j.jasms.2007.07.023

    CAS  Google Scholar 

  77. Haimi P, Uphoff A, Hermansson M, Somerharju P (2006) Software tools for analysis of mass spectrometric lipidome data. Anal Chem 78(24):8324–8331

    CAS  Google Scholar 

  78. Leavell MD, Leary JA (2006) Fatty acid analysis tool (FAAT): an FT-ICR MS lipid analysis algorithm. Anal Chem 78(15):5497–5503. doi:10.1021/ac0604179

    CAS  Google Scholar 

  79. Hubner G, Crone C, Lindner B (2009) lipID–a software tool for automated assignment of lipids in mass spectra. J Mass Spectrom 44(12):1676–1683. doi:10.1002/jms.1673

    Google Scholar 

  80. Houjou T, Yamatani K, Imagawa M, Shimizu T, Taguchi R (2005) A shotgun tandem mass spectrometric analysis of phospholipids with normal-phase and/or reverse-phase liquid chromatography/electrospray ionization mass spectrometry. Rapid Commum Mass Spectrom 19(5):654–666

    CAS  Google Scholar 

  81. Ejsing CS, Duchoslav E, Sampaio J, Simons K, Bonner R, Thiele C, Ekroos K, Shevchenko A (2006) Automated identification and quantification of glycerophospholipid molecular species by multiple precursor ion scanning. Anal Chem 78(17):6202–6214. doi:10.1021/ac060545x

    CAS  Google Scholar 

  82. Schwudke D, Oegema J, Burton L, Entchev E, Hannich JT, Ejsing CS, Kurzchalia T, Shevchenko A (2006) Lipid profiling by multiple precursor and neutral loss scanning driven by the data-dependent acquisition. Anal Chem 78(2):585–595

    CAS  Google Scholar 

  83. Kind T, Liu KH, Lee do Y, Defelice B, Meissen JK, Fiehn O (2013) LipidBlast in silico tandem mass spectrometry database for lipid identification. Nat Methods 10(8):755–758. doi:10.1038/nmeth.2551

    CAS  Google Scholar 

  84. Hackstadt AJ, Hess AM (2009) Filtering for increased power for microarray data analysis. BMC Bioinform 10:11. doi:10.1186/1471-2105-10-11

    Google Scholar 

  85. Bourgon R, Gentleman R, Huber W (2010) Independent filtering increases detection power for high-throughput experiments. Proc Natl Acad Sci U S A 107(21):9546–9551. doi:10.1073/pnas.0914005107

    CAS  Google Scholar 

  86. Foster JM, Moreno P, Fabregat A, Hermjakob H, Steinbeck C, Apweiler R, Wakelam MJ, Vizcaino JA (2013) LipidHome: a database of theoretical lipids optimized for high throughput mass spectrometry lipidomics. PLoS One 8(5):e61951. doi:10.1371/journal.pone.0061951

    CAS  Google Scholar 

  87. Pham HT, Trevitt AJ, Mitchell TW, Blanksby SJ (2013) Rapid differentiation of isomeric lipids by photodissociation mass spectrometry of fatty acid derivatives. Rapid Commun Mass Spectrom 27(7):805–815. doi:10.1002/rcm.6503

    CAS  Google Scholar 

  88. Pham HT, Ly T, Trevitt AJ, Mitchell TW, Blanksby SJ (2012) Differentiation of complex lipid isomers by radical-directed dissociation mass spectrometry. Anal Chem 84(17):7525–7532. doi:10.1021/ac301652a

    CAS  Google Scholar 

  89. Quehenberger O, Armando AM, Brown AH, Milne SB, Myers DS, Merrill AH, Bandyopadhyay S, Jones KN, Kelly S, Shaner RL, Sullards CM, Wang E, Murphy RC, Barkley RM, Leiker TJ, Raetz CR, Guan Z, Laird GM, Six DA, Russell DW, McDonald JG, Subramaniam S, Fahy E, Dennis EA (2010) Lipidomics reveals a remarkable diversity of lipids in human plasma. J Lipid Res 51(11):3299–3305. doi:10.1194/jlr.M009449

    CAS  Google Scholar 

  90. Kotronen A, Velagapudi VR, Yetukuri L, Westerbacka J, Bergholm R, Ekroos K, Makkonen J, Taskinen M-R, Oresic M, Yki-Järvinen H (2009) Saturated fatty acids containing triacylglycerols are better markers of insulin resistance than total serum triacylglycerol concentrations. Diabetologia 52(4):684–690

    CAS  Google Scholar 

  91. Vieu C, Terce F, Chevy F, Rolland C, Barbaras R, Chap H, Wolf C, Perret B, Collet X (2002) Coupled assay of sphingomyelin and ceramide molecular species by gas liquid chromatography. J Lipid Res 43(3):510–522

    CAS  Google Scholar 

  92. Nikkila J, Sysi-Aho M, Ermolov A, Seppanen-Laakso T, Simell O, Kaski S, Oresic M (2008) Gender-dependent progression of systemic metabolic states in early childhood. Mol Syst Biol 4:197. doi:10.1038/msb.2008.34

    Google Scholar 

  93. Mittelstrass K, Ried JS, Yu Z, Krumsiek J, Gieger C, Prehn C, Roemisch-Margl W, Polonikov A, Peters A, Theis FJ, Meitinger T, Kronenberg F, Weidinger S, Wichmann HE, Suhre K, Wang-Sattler R, Adamski J, Illig T (2011) Discovery of sexual dimorphisms in metabolic and genetic biomarkers. PLoS Genet 7(8):e1002215. doi:10.1371/journal.pgen.1002215

    CAS  Google Scholar 

  94. Weir JM, Wong G, Barlow CK, Greeve MA, Kowalczyk A, Almasy L, Comuzzie AG, Mahaney MC, Jowett JB, Shaw J, Curran JE, Blangero J, Meikle PJ (2013) Plasma lipid profiling in a large population-based cohort. J Lipid Res 54(10):2898–2908. doi:10.1194/jlr.P035808

    CAS  Google Scholar 

  95. Cai X, Perttula K, Pajouh SK, Hubbard A, Nomura DK, Rappaport SM (2014) Untargeted lipidomic profiling of human plasma reveals differences due to race, gender and smoking status. Metabolomics 4:131. doi:10.4172/2153-0769.1000131

    Google Scholar 

  96. Gooley JJ, Chua EC (2014) Diurnal regulation of lipid metabolism and applications of circadian lipidomics. J Genet Genomics 41(5):231–250. doi:10.1016/j.jgg.2014.04.001

    CAS  Google Scholar 

  97. Chua EC, Shui G, Lee IT, Lau P, Tan LC, Yeo SC, Lam BD, Bulchand S, Summers SA, Puvanendran K, Rozen SG, Wenk MR, Gooley JJ (2013) Extensive diversity in circadian regulation of plasma lipids and evidence for different circadian metabolic phenotypes in humans. Proc Natl Acad Sci U S A 110(35):14468–14473. doi:10.1073/pnas.1222647110

    CAS  Google Scholar 

  98. Dallmann R, Viola AU, Tarokh L, Cajochen C, Brown SA (2012) The human circadian metabolome. Proc Natl Acad Sci U S A 109(7):2625–2629. doi:10.1073/pnas.1114410109

    CAS  Google Scholar 

  99. Kasukawa T, Sugimoto M, Hida A, Minami Y, Mori M, Honma S, Honma K, Mishima K, Soga T, Ueda HR (2012) Human blood metabolite timetable indicates internal body time. Proc Natl Acad Sci U S A 109(37):15036–15041. doi:10.1073/pnas.1207768109

    CAS  Google Scholar 

  100. Ang JE, Revell V, Mann A, Mantele S, Otway DT, Johnston JD, Thumser AE, Skene DJ, Raynaud F (2012) Identification of human plasma metabolites exhibiting time-of-day variation using an untargeted liquid chromatography-mass spectrometry metabolomic approach. Chronobiol Int 29(7):868–881. doi:10.3109/07420528.2012.699122

    CAS  Google Scholar 

  101. Kim K, Mall C, Taylor SL, Hitchcock S, Zhang C, Wettersten HI, Jones AD, Chapman A, Weiss RH (2014) Mealtime, temporal, and daily variability of the human urinary and plasma metabolomes in a tightly controlled environment. PLoS One 9(1):e86223. doi:10.1371/journal.pone.0086223

    Google Scholar 

  102. Bonham MP, Linderborg KM, Dordevic A, Larsen AE, Nguo K, Weir JM, Gran P, Luotonen MK, Meikle PJ, Cameron-Smith D, Kallio HP, Sinclair AJ (2013) Lipidomic profiling of chylomicron triacylglycerols in response to high fat meals. Lipids 48(1):39–50. doi:10.1007/s11745-012-3735-5

    CAS  Google Scholar 

  103. Kim M, Suk J, Kim H, Jung H, Kim T, Park J (2010) Post-prandial lipid levels for assessing target goal achievement in type 2 diabetic patients taking statin. J Korean Med Sci 25(3):387–392. doi:10.3346/jkms.2010.25.3.387

    CAS  Google Scholar 

  104. Langsted A, Freiberg JJ, Nordestgaard BG (2008) Fasting and nonfasting lipid levels: influence of normal food intake on lipids, lipoproteins, apolipoproteins, and cardiovascular risk prediction. Circulation 118(20):2047–2056. doi:10.1161/CIRCULATIONAHA.108.804146

    CAS  Google Scholar 

  105. Perez-Caballero AI, Alcala-Diaz JF, Perez-Martinez P, Garcia-Rios A, Delgado-Casado N, Marin C, Yubero-Serrano E, Camargo A, Caballero J, Malagon MM, Tinahones FJ, Perez-Jimenez F, Lopez-Miranda J, Delgado-Lista J (2013) Lipid metabolism after an oral fat test meal is affected by age-associated features of metabolic syndrome, but not by age. Atherosclerosis 226(1):258–262. doi:10.1016/j.atherosclerosis.2012.10.052

    CAS  Google Scholar 

  106. Krug S, Kastenmuller G, Stuckler F, Rist MJ, Skurk T, Sailer M, Raffler J, Romisch-Margl W, Adamski J, Prehn C, Frank T, Engel KH, Hofmann T, Luy B, Zimmermann R, Moritz F, Schmitt-Kopplin P, Krumsiek J, Kremer W, Huber F, Oeh U, Theis FJ, Szymczak W, Hauner H, Suhre K, Daniel H (2012) The dynamic range of the human metabolome revealed by challenges. FASEB J 26(6):2607–2619. doi:10.1096/fj.11-198093

    CAS  Google Scholar 

  107. Zivkovic AM, Wiest MM, Nguyen U, Nording ML, Watkins SM, German JB (2009) Assessing individual metabolic responsiveness to a lipid challenge using a targeted metabolomic approach. Metabolomics 5(2):209–218. doi:10.1007/s11306-008-0136-0

    CAS  Google Scholar 

  108. Fusconi E, Pala V, Riboli E, Vineis P, Sacerdote C, Del Pezzo M, Santucci de Magistris M, Palli D, Masala G, Sieri S, Foggetti CE, Giurdanella MC, Tumino R, Krogh V (2003) Relationship between plasma fatty acid composition and diet over previous years in the Italian centers of the European Prospective Investigation into Cancer and Nutrition (EPIC). Tumori 89(6):624–635

    CAS  Google Scholar 

  109. Lankinen M, Schwab U, Erkkila A, Seppanen-Laakso T, Hannila ML, Mussalo H, Lehto S, Uusitupa M, Gylling H, Oresic M (2009) Fatty fish intake decreases lipids related to inflammation and insulin signaling–a lipidomics approach. PLoS ONE 4(4):e5258. doi:10.1371/journal.pone.0005258

    Google Scholar 

  110. Lankinen M, Schwab U, Kolehmainen M, Paananen J, Poutanen K, Mykkanen H, Seppanen-Laakso T, Gylling H, Uusitupa M, Oresic M (2011) Whole grain products, fish and bilberries alter glucose and lipid metabolism in a randomized, controlled trial: the Sysdimet study. PLoS One 6(8):e22646. doi:10.1371/journal.pone.0022646

    CAS  Google Scholar 

  111. Krajcovicova-Kudlackova M, Simoncic R, Bederova A, Grancicova E, Magalova T (1997) Influence of vegetarian and mixed nutrition on selected haematological and biochemical parameters in children. Nahrung 41(5):311–314

    CAS  Google Scholar 

  112. Krajcovicova-Kudlackova M, Simoncic R, Klvanova J, Bederova A, Babinska K, Grancicova E (1997) Plasma profile of fatty acids in vegetarians. Bratisl Lek Listy 98(1):23–27

    CAS  Google Scholar 

  113. Urquiaga I, Guasch V, Marshall G, San Martin A, Castillo O, Rozowski J, Leighton F (2004) Effect of Mediterranean and Occidental diets, and red wine, on plasma fatty acids in humans. An intervention study. Biol Res 37(2):253–261

    Google Scholar 

  114. Scaglioni S, Veduci E, Agostoni C, Vergani B, Stival G, Riva E, Giovannini M (2004) Dietary habits and plasma fatty acids levels in a population of Italian children: is there any relationship? Prostaglandins Leukot Essent Fat Acids 71(2):91–95. doi:10.1016/j.plefa.2004.01.002

    CAS  Google Scholar 

  115. Bondia-Pons I, Poho P, Bozzetto L, Vetrani C, Patti L, Aura AM, Annuzzi G, Hyotylainen T, Rivellese AA, Oresic M (2014) Isoenergetic diets differing in their n-3 fatty acid and polyphenol content reflect different plasma and HDL-fraction lipidomic profiles in subjects at high cardiovascular risk. Mol Nutr Food Res 58(9):1873–1882. doi:10.1002/mnfr.201400155

    CAS  Google Scholar 

  116. Pietilainen KH, Sysi-Aho M, Rissanen A, Seppanen-Laakso T, Yki-Jarvinen H, Kaprio J, Oresic M (2007) Acquired obesity is associated with changes in the serum lipidomic profile independent of genetic effects—a monozygotic twin study. PLoS One 2(2):e218. doi:10.1371/journal.pone.0000218

    Google Scholar 

Download references

Acknowledgments

This work was supported by the Academy of Finland (Centre of Excellence in Molecular Systems Immunology and Physiology Research 2012-2017, Decision No. 250114) and the EU FP7 project DEXLIFE (grant agreement no. 279228).

Author information

Authors and Affiliations

  1. Steno Diabetes Center, Niels Steensens Vej 2, 2820, Gentofte, Denmark

    Tuulia Hyötyläinen & Matej Orešič

  2. Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, 20520, Turku, Finland

    Tuulia Hyötyläinen & Matej Orešič

Authors
  1. Tuulia Hyötyläinen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Matej Orešič
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tuulia Hyötyläinen.

Additional information

Published in the topical collection Lipidomics with guest editor Michal Holčapek.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hyötyläinen, T., Orešič, M. Optimizing the lipidomics workflow for clinical studies—practical considerations. Anal Bioanal Chem 407, 4973–4993 (2015). https://doi.org/10.1007/s00216-015-8633-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00216-015-8633-2

Keywords

Search

Navigation