Lipidomics: Techniques, Applications, and Outcomes Related to Biomedical Sciences

Trends in Biochemical Sciences

Volumn 41, Issue 11, 2016, Pages 954-969

  Yang, Kui ^a   Han, Xianlin ^b,c  

^a WASHINGTON UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b BURNHAM INSTITUTE   (United States)

^c ZHEJIANG CHINESE MEDICAL UNIVERSITY   (China)

Author keywords

biomedical science; lipid metabolism; lipidome; lipidomics; mass spectrometry; shotgun lipidomics

Indexed keywords

LIPID; SOLVENT;

CHEMISTRY; EXTRACTION; EYE DISEASE; HUMAN; LIPID ANALYSIS; LIPIDOMICS; LIQUID CHROMATOGRAPHY; MASS SPECTROMETRY; METABOLIC SYNDROME X; NEOPLASM; NEUROLOGIC DISEASE; NONHUMAN; NUTRITION; OUTCOME ASSESSMENT; PERSONALIZED MEDICINE; PRIORITY JOURNAL; REVIEW; TANDEM MASS SPECTROMETRY; CARDIOVASCULAR DISEASES; DEVICES; DIABETES MELLITUS; LIPID METABOLISM; LIQUID LIQUID EXTRACTION; MEDICAL RESEARCH; METABOLISM; NEOPLASMS; NEURODEGENERATIVE DISEASES; OBESITY; PROCEDURES;

BIOMEDICAL RESEARCH; CARDIOVASCULAR DISEASES; CHROMATOGRAPHY, LIQUID; DIABETES MELLITUS; HUMANS; LIPID METABOLISM; LIPIDS; LIQUID-LIQUID EXTRACTION; MASS SPECTROMETRY; METABOLIC SYNDROME X; NEOPLASMS; NEURODEGENERATIVE DISEASES; OBESITY; SOLVENTS;

EID: 84994715828     PISSN: 09680004     EISSN: 13624326     Source Type: Journal    
DOI: 10.1016/j.tibs.2016.08.010     Document Type: Review

References (143)

1. 1 Yetukuri, L., et al. Bioinformatics strategies for lipidomics analysis: characterization of obesity related hepatic steatosis. BMC Syst. Biol., 1, 2007, 12.
2. 2 Han, X., Jiang, X., A review of lipidomic technologies applicable to sphingolipidomics and their relevant applications. Eur. J. Lipid Sci. Technol. 111 (2009), 39–52.
3. 3 Yang, K., et al. Automated lipid identification and quantification by multi-dimensional mass spectrometry-based shotgun lipidomics. Anal. Chem. 81 (2009), 4356–4368.
4. 4 Han, X., et al. Multi-dimensional mass spectrometry-based shotgun lipidomics and novel strategies for lipidomic analyses. Mass Spectrom. Rev. 31 (2012), 134–178.
5. 5 Blanksby, S.J., Mitchell, T.W., Advances in mass spectrometry for lipidomics. Annu. Rev. Anal. Chem. 3 (2010), 433–465.
6. 6 Witting, M., Schmitt-Kopplin, P., The Caenorhabditis elegans lipidome: a primer for lipid analysis in Caenorhabditis elegans. Arch. Biochem. Biophys. 589 (2016), 27–37.
7. 7 Yang, L., et al. Recent advances in lipidomics for disease research. J. Sep. Sci. 39 (2016), 38–50.
8. 8 Crick, P.J., Guan, X.L., Lipid metabolism in mycobacteria - insights using mass spectrometry-based lipidomics. Biochim. Biophys. Acta 1861 (2016), 60–67.
9. 9 Sethi, S., et al. Analytical approaches for lipidomics and its potential applications in neuropsychiatric disorders. World J. Biol. Psychiatry, 2016, 10.3109/15622975.2015.1117656 Published online January 26, 2016.
10. 10 Wang, M., et al. Novel advances in shotgun lipidomics for biology and medicine. Prog. Lipid Res. 61 (2016), 83–108.
11. 11 Wang, M., et al. Selection of internal standards for accurate quantification of complex lipid species in biological extracts by electrospray ionization mass spectrometry - What, how and why?. Mass Spectrom. Rev., 2016, 10.1002/mas.21492 Published online January 15, 2016.
12. 12 Keereetaweep, J., Chapman, K.D., Lipidomic analysis of endocannabinoid signaling: targeted metabolite identification and quantification. Neural Plast., 2016, 2016, 2426398.
13. 13 Hyotylainen, T., Oresic, M., Bioanalytical techniques in nontargeted clinical lipidomics. Bioanalysis 8 (2016), 351–364.
14. 14 Kunisawa, J., Kiyono, H., Sphingolipids and epoxidized lipid metabolites in the control of gut immunosurveillance and allergy. Front. Nutr., 3, 2016, 3.
15. 15 Laaksonen, R., Identifying new risk markers and potential targets for coronary artery disease: the value of the lipidome and metabolome. Cardiovasc. Drugs Ther. 30 (2016), 19–32.
16. 16 Michaelson, L.V., et al. Plant sphingolipids: their importance in cellular organization and adaption. Biochim. Biophys. Acta 1861 (2016), 1329–1335.
17. 17 Rolim, A.E., et al. Lipidomics in the study of lipid metabolism: current perspectives in the omic sciences. Gene 554 (2015), 131–139.
18. 18 Brugger, B., Lipidomics: analysis of the lipid composition of cells and subcellular organelles by electrospray ionization mass spectrometry. Annu. Rev. Biochem. 83 (2014), 79–98.
19. 19 Dowhan, W., Lipids and extracellular materials. Annu. Rev. Biochem. 83 (2014), 45–49.
20. 20 Vaz, F.M., et al. Principles and practice of lipidomics. J. Inherit. Metab. Dis. 38 (2015), 41–52.
21. 21 Li, M., et al. Analytical methods in lipidomics and their applications. Anal. Chem. 86 (2014), 161–175.
22. 22 Ghaste, M., et al. Applications of Fourier transform ion cyclotron resonance (FT-ICR) and orbitrap based high resolution mass spectrometry in metabolomics and lipidomics. Int. J. Mol. Sci., 17, 2016, 816.
23. 23 Hyotylainen, T., Oresic, M., Optimizing the lipidomics workflow for clinical studies – practical considerations. Anal. Bioanal. Chem. 407 (2015), 4973–4993.
24. 24 Seppanen-Laakso, T., Oresic, M., How to study lipidomes. J. Mol. Endocrinol. 42 (2009), 185–190.
25. 25 Furse, S., et al. Isolation of lipids from biological samples. Mol. Membr. Biol. 32 (2015), 55–64.
26. 26 Murphy, R.C., et al. Imaging of lipid species by MALDI mass spectrometry. J. Lipid Res. 50:Suppl (2009), S317–S322.
27. 27 Han, X., et al. Metabolomics in early Alzheimer's disease: identification of altered plasma sphingolipidome using shotgun lipidomics. PLoS One, 6, 2011, e21643.
28. 28 Merrill, A.H. Jr., et al. Sphingolipidomics: high-throughput, structure-specific, and quantitative analysis of sphingolipids by liquid chromatography tandem mass spectrometry. Methods 36 (2005), 207–224.
29. 29 Jiang, X., et al. Alkaline methanolysis of lipid extracts extends shotgun lipidomics analyses to the low abundance regime of cellular sphingolipids. Anal. Biochem. 371 (2007), 135–145.
30. 30 Stahlman, M., et al. High throughput oriented shotgun lipidomics by quadrupole time-of-flight mass spectrometry. J. Chromatogr. B 877 (2009), 2664–2672.
31. 31 Dennis, E.A., et al. A mouse macrophage lipidome. J. Biol. Chem. 285 (2010), 39976–39985.
32. 32 Fauland, A., et al. A comprehensive method for lipid profiling by liquid chromatography-ion cyclotron resonance mass spectrometry. J. Lipid Res. 52 (2011), 2314–2322.
33. 33 Cajka, T., Fiehn, O., Comprehensive analysis of lipids in biological systems by liquid chromatography-mass spectrometry. Trends Analyt. Chem. 61 (2014), 192–206.
34. 34 Paglia, G., et al. Applications of ion-mobility mass spectrometry for lipid analysis. Anal. Bioanal. Chem. 407 (2015), 4995–5007.
35. 35 Junot, C., et al. High resolution mass spectrometry based techniques at the crossroads of metabolic pathways. Mass Spectrom. Rev. 33 (2014), 471–500.
36. 36 Wang, M., et al. Accurate mass searching of individual lipid species candidates from high-resolution mass spectra for shotgun lipidomics. Rapid Commun. Mass Spectrom. 28 (2014), 2201–2210.
37. 37 Checa, A., et al. Lipidomic data analysis: tutorial, practical guidelines and applications. Anal. Chim. Acta 885 (2015), 1–16.
38. 38 Zhao, Y.Y., et al. Ultra-performance liquid chromatography-mass spectrometry as a sensitive and powerful technology in lipidomic applications. Chem. Biol. Interact. 220 (2014), 181–192.
39. 39 Yang, K., Han, X., Accurate quantification of lipid species by electrospray ionization mass spectrometry – meets a key challenge in lipidomics. Metabolites 1 (2011), 21–40.
40. 40 Hyotylainen, T., Oresic, M., Systems biology strategies to study lipidomes in health and disease. Prog. Lipid Res. 55 (2014), 43–60.
41. 41 Meikle, P.J., et al. Lipidomics: potential role in risk prediction and therapeutic monitoring for diabetes and cardiovascular disease. Pharmacol. Ther. 143 (2014), 12–23.
42. 42 Darabi, M., et al. Therapeutic applications of reconstituted HDL: when structure meets function. Pharmacol. Ther. 157 (2016), 28–42.
43. 43 Kontush, A., et al. Unraveling the complexities of the HDL lipidome. J. Lipid Res. 54 (2013), 2950–2963.
44. 44 Darabi, M., Kontush, A., Can phosphatidylserine enhance atheroprotective activities of high-density lipoprotein?. Biochimie 120 (2016), 81–86.
45. 45 Rasmiena, A.A., et al. Metabolomics and ischaemic heart disease. Clin. Sci. (Lond.) 124 (2013), 289–306.
46. 46 Han, X., Lipid alterations in the earliest clinically recognizable stage of Alzheimer's disease: implication of the role of lipids in the pathogenesis of Alzheimer's disease. Curr. Alzheimer Res. 2 (2005), 65–77.
47. 47 Santos, C.R., Schulze, A., Lipid metabolism in cancer. FEBS J. 279 (2012), 2610–2623.
48. 48 Murph, M., et al. Liquid chromatography mass spectrometry for quantifying plasma lysophospholipids: potential biomarkers for cancer diagnosis. Methods Enzymol. 433 (2007), 1–25.
49. 49 Fresno Vara, J.A., et al. PI3K/Akt signalling pathway and cancer. Cancer Treat. Rev. 30 (2004), 193–204.
50. 50 Zhou, L., Beuerman, R.W., Tear analysis in ocular surface diseases. Prog. Retin. Eye Res. 31 (2012), 527–550.
51. 51 Pieragostino, D., et al. Unraveling the molecular repertoire of tears as a source of biomarkers: beyond ocular diseases. Proteomics Clin. Appl. 9 (2015), 169–186.
52. 52 Lim, A., et al. Lipid-based therapy for ocular surface inflammation and disease. Trends Mol. Med. 21 (2015), 736–748.
53. 53 Smilowitz, J.T., et al. Nutritional lipidomics: molecular metabolism, analytics, and diagnostics. Mol. Nutr. Food Res. 57 (2013), 1319–1335.
54. 54 Corella, D., Ordovas, J.M., Biomarkers: background, classification and guidelines for applications in nutritional epidemiology. Nutr. Hosp. 31:Suppl 3 (2015), 177–188.
55. 55 Jungnickel, H., Luch, A., A personalized life: biomarker monitoring from cradle to grave. EXS 101 (2012), 471–498.
56. 56 Maskrey, B.H., et al. Emerging importance of ω-3 fatty acids in the innate immune response: molecular mechanisms and lipidomic strategies for their analysis. Mol. Nutr. Food Res. 57 (2013), 1390–1400.
57. 57 Visioli, F., Lipidomics to assess ω-3 bioactivity. J. Clin. Med. 4 (2015), 1753–1760.
58. 58 Lamaziere, A., et al. How lipidomics provides new insight into drug discovery. Expert Opin. Drug Discov. 9 (2014), 819–836.
59. 59 Vihervaara, T., et al. Lipidomics in drug discovery. Drug Discov. Today 19 (2014), 164–170.
60. 60 Puri, R., et al. The emerging role of plasma lipidomics in cardiovascular drug discovery. Expert Opin. Drug Discov. 7 (2012), 63–72.
61. 61 Marinkovic, T., Oresic, M., Modeling strategies to study metabolic pathways in progression to type 1 diabetes – challenges and opportunities. Arch. Biochem. Biophys. 589 (2016), 131–137.
62. 62 Yan, Q., Translational bioinformatics approaches for systems and dynamical medicine. Methods Mol. Biol. 1175 (2014), 19–34.
63. 63 Zhao, Y.Y., et al. Lipidomics applications for discovering biomarkers of diseases in clinical chemistry. Int. Rev. Cell Mol. Biol. 313 (2014), 1–26.
64. 64 Heinonen, M.T., et al. New insights and biomarkers for type 1 diabetes. Scand. J. Immunol. 82 (2015), 244–253.
65. 65 Di Girolamo, F., et al. The role of mass spectrometry in the ‘omics’ era. Curr. Org. Chem. 17 (2013), 2891–2905.
66. 66 Merched, A.J., Chan, L., Nutrigenetics and nutrigenomics of atherosclerosis. Curr. Atheroscler. Rep., 15, 2013, 328.
67. 67 Almeida, R., et al. Comprehensive lipidome analysis by shotgun lipidomics on a hybrid quadrupole-orbitrap-linear ion trap mass spectrometer. J. Am. Soc. Mass Spectrom. 26 (2015), 133–148.
68. 68 Quehenberger, O., et al. Lipidomics reveals a remarkable diversity of lipids in human plasma. J. Lipid Res. 51 (2010), 3299–3305.
69. 69 Han, X., Lipidomics for studying metabolism. Nat. Rev. Endocrinol., 2016, 10.1038/nrendo.2016.98 Published online July 29, 2016.
70. 70 Kiebish, M.A., et al. Dynamic simulation of cardiolipin remodeling: greasing the wheels for an interpretative approach to lipidomics. J. Lipid Res. 51 (2010), 2153–2170.
71. 71 Han, R.H., et al. Simulation of triacylglycerol ion profiles: bioinformatics for interpretation of triacylglycerol biosynthesis. J. Lipid Res. 54 (2013), 1023–1032.
72. 72 Casanovas, A., et al. Quantitative analysis of proteome and lipidome dynamics reveals functional regulation of global lipid metabolism. Chem. Biol. 22 (2015), 412–425.
73. 73 Loizides-Mangold, U., On the future of mass-spectrometry-based lipidomics. FEBS J. 280 (2013), 2817–2829.
74. 74 Postle, A.D., Hunt, A.N., Dynamic lipidomics with stable isotope labelling. J. Chromatogr. B 877 (2009), 2716–2721.
75. 75 Ecker, J., Liebisch, G., Application of stable isotopes to investigate the metabolism of fatty acids, glycerophospholipid and sphingolipid species. Prog. Lipid Res. 54 (2014), 14–31.
76. 76 Rubakhin, S.S., et al. Profiling metabolites and peptides in single cells. Nat. Methods 8 (2011), S20–S29.
77. 77 Jung, H.R., et al. High throughput quantitative molecular lipidomics. Biochim. Biophys. Acta 1811 (2011), 925–934.
78. 78 Sokol, E., et al. Profiling of lipid species by normal-phase liquid chromatography, nanoelectrospray ionization, and ion trap-orbitrap mass spectrometry. Anal. Biochem. 443 (2013), 88–96.
79. 79 Myers, D.S., et al. Quantitative analysis of glycerophospholipids by LC-MS: acquisition, data handling, and interpretation. Biochim. Biophys. Acta 1811 (2011), 748–757.
80. 80 Brouwers, J.F., Liquid chromatographic-mass spectrometric analysis of phospholipids, Chromatography. ionization and quantification. Biochim. Biophys. Acta 1811 (2011), 763–775.
81. 81 Smith, R., et al. LC-MS alignment in theory and practice: a comprehensive algorithmic review. Brief. Bioinform. 16 (2015), 104–117.
82. 82 Wang, C., et al. Applications of mass spectrometry for cellular lipid analysis. Mol. Biosyst. 11 (2015), 698–713.
83. 83 Papan, C., et al. Systematic screening for novel lipids by shotgun lipidomics. Anal. Chem. 86 (2014), 2703–2710.
84. 84 Wang, M., Han, X., Multidimensional mass spectrometry-based shotgun lipidomics. Methods Mol. Biol. 1198 (2014), 203–220.
85. 85 Lintonen, T.P., et al. Differential mobility spectrometry-driven shotgun lipidomics. Anal. Chem. 86 (2014), 9662–9669.
86. 86 Meikle, P.J., et al. Plasma lipidomic analysis of stable and unstable coronary artery disease. Arterioscler. Thromb. Vasc. Biol. 31 (2011), 2723–2732.
87. 87 Stegemann, C., et al. Comparative lipidomics profiling of human atherosclerotic plaques. Circ. Cardiovasc. Genet. 4 (2011), 232–242.
88. 88 Chen, X., et al. Plasma metabolomics reveals biomarkers of the atherosclerosis. J. Sep. Sci. 33 (2010), 2776–2783.
89. 89 Hinterwirth, H., et al. Lipidomics: quest for molecular lipid biomarkers in cardiovascular disease. Circ. Cardiovasc. Genet. 7 (2014), 941–954.
90. 90 Kolovou, G., et al. Lipidomics in vascular health: current perspectives. Vasc. Health Risk Manag. 11 (2015), 333–342.
91. 91 Ban, R.H., et al. Lipidomic profiling at the interface of metabolic surgery and cardiovascular disease. Curr. Atheroscler. Rep., 16, 2014, 455.
92. 92 Hoefer, I.E., et al. Novel methodologies for biomarker discovery in atherosclerosis. Eur. Heart J. 36 (2015), 2635–2642.
93. 93 Spickett, C.M., Pitt, A.R., Oxidative lipidomics coming of age: advances in analysis of oxidized phospholipids in physiology and pathology. Antioxid. Redox Signal. 22 (2015), 1646–1666.
94. 94 Isobe, Y., Arita, M., Identification of novel ω-3 fatty acid-derived bioactive metabolites based on a targeted lipidomics approach. J. Clin. Biochem. Nutr. 55 (2014), 79–84.
95. 95 Kasuga, K., et al. Bioanalytical insights into mediator lipidomics. J. Pharm. Biomed. Anal. 113 (2015), 151–162.
96. 96 Li, M., et al. A not-stop-flow online normal-/reversed-phase two-dimensional liquid chromatography-quadrupole time-of-flight mass spectrometry method for comprehensive lipid profiling of human plasma from atherosclerosis patients. J. Chromatogr. A 1372 (2014), 110–119.
97. 97 Dennis, E.A., Norris, P.C., Eicosanoid storm in infection and inflammation. Nat. Rev. Immunol. 15 (2015), 511–523.
98. 98 Astarita, G., et al. Targeted lipidomic strategies for oxygenated metabolites of polyunsaturated fatty acids. Biochim. Biophys. Acta 1851 (2015), 456–468.
99. 99 Wong, G., et al. Inclusion of plasma lipid species improves classification of individuals at risk of type 2 diabetes. PLoS One, 8, 2013, e76577.
100. 100 Weir, J.M., et al. Plasma lipid profiling in a large population-based cohort. J. Lipid Res. 54 (2013), 2898–2908.
101. 101 Meikle, P.J., et al. Plasma lipid profiling shows similar associations with prediabetes and type 2 diabetes. PLoS One, 8, 2013, e74341.
102. 102 Shui, G., et al. Polar lipid derangements in type 2 diabetes mellitus: potential pathological relevance of fatty acyl heterogeneity in sphingolipids. Metabolomics 9 (2013), 786–799.
103. 103 Reinehr, T., et al. Changes in the serum metabolite profile in obese children with weight loss. Eur. J. Nutr. 54 (2015), 173–181.
104. 104 Floegel, A., et al. Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach. Diabetes 62 (2013), 639–648.
105. 105 Han, X., et al. Alterations in myocardial cardiolipin content and composition occur at the very earliest stages of diabetes: a shotgun lipidomics study. Biochemistry 46 (2007), 6417–6428.
106. 106 Forouhi, N.G., et al. Differences in the prospective association between individual plasma phospholipid saturated fatty acids and incident type 2 diabetes: the EPIC-InterAct case-cohort study. Lancet Diabetes Endocrinol. 2 (2014), 810–818.
107. 107 Stahlman, M., et al. Clinical dyslipidaemia is associated with changes in the lipid composition and inflammatory properties of apolipoprotein-B-containing lipoproteins from women with type 2 diabetes. Diabetologia 55 (2012), 1156–1166.
108. 108 Gooding, J.R., et al. Metabolomics applied to the pancreatic islet. Arch. Biochem. Biophys. 589 (2016), 120–130.
109. 109 Frohnert, B.I., Rewers, M.J., Metabolomics in childhood diabetes. Pediatr. Diabetes 17 (2016), 3–14.
110. 110 Wood, P.L., et al. Targeted lipidomics of frontal cortex and plasma diacylglycerols (DAG) in mild cognitive impairment and Alzheimer's disease: validation of DAG accumulation early in the pathophysiology of Alzheimer's disease. J. Alzheimers Dis. 48 (2015), 537–546.
111. 111 Wood, P.L., et al. Non-targeted lipidomics of CSF and frontal cortex grey and white matter in control, mild cognitive impairment, and Alzheimer's disease subjects. Acta Neuropsychiatr. 27 (2015), 270–278.
112. 112 Proitsi, P., et al. Plasma lipidomics analysis finds long chain cholesteryl esters to be associated with Alzheimer's disease. Transl. Psychiatry, 5, 2015, e494.
113. 113 Vodicka, P., et al. Mass spectrometry analysis of wild-type and knock-in Q140/Q140 Huntington's disease mouse brains reveals changes in glycerophospholipids including alterations in phosphatidic acid and lyso-phosphatidic acid. J. Huntingtons Dis. 4 (2015), 187–201.
114. 114 Pruss, H., et al. Proresolution lipid mediators in multiple sclerosis - differential, disease severity-dependent synthesis - a clinical pilot trial. PLoS One, 8, 2013, e55859.
115. 115 Mazereeuw, G., et al. Platelet activating factors in depression and coronary artery disease: a potential biomarker related to inflammatory mechanisms and neurodegeneration. Neurosci. Biobehav. Rev. 37 (2013), 1611–1621.
116. 116 Wood, P.L., Mass spectrometry strategies for clinical metabolomics and lipidomics in psychiatry, neurology, and neuro-oncology. Neuropsychopharmacology 39 (2014), 24–33.
117. 117 Enriquez-Algeciras, M., Bhattacharya, S.K., Lipidomic mass spectrometry and its application in neuroscience. World J. Biol. Chem. 4 (2013), 102–110.
118. 118 Chen, X., et al. Plasma lipidomics profiling identified lipid biomarkers in distinguishing early-stage breast cancer from benign lesions. Oncotarget, 2016, 10.18632/oncotarget.9124 Published online May 2, 2016.
119. 119 Yang, L., et al. Comprehensive lipid profiling of plasma in patients with benign breast tumor and breast cancer reveals novel biomarkers. Anal. Bioanal. Chem. 407 (2015), 5065–5077.
120. 120 Li, J., et al. Integration of lipidomics and transcriptomics unravels aberrant lipid metabolism and defines cholesteryl oleate as potential biomarker of prostate cancer. Sci. Rep., 6, 2016, 20984.
121. 121 Marien, E., et al. Phospholipid profiling identifies acyl chain elongation as a ubiquitous trait and potential target for the treatment of lung squamous cell carcinoma. Oncotarget 7 (2016), 12582–12597.
122. 122 Zhang, Y., et al. High resolution mass spectrometry coupled with multivariate data analysis revealing plasma lipidomic alteration in ovarian cancer in Asian women. Talanta 150 (2016), 88–96.
123. 123 Abbassi-Ghadi, N., et al. A comparison of DESI-MS and LC-MS for the lipidomic profiling of human cancer tissue. J. Am. Soc. Mass Spectrom. 27 (2016), 255–264.
124. 124 Cifkova, E., et al. Lipidomic differentiation between human kidney tumors and surrounding normal tissues using HILIC-HPLC/ESI-MS and multivariate data analysis. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 1000 (2015), 14–21.
125. 125 Jiao, J., et al. Targeted deletion and lipidomic analysis identify epithelial cell COX-2 as a major driver of chemically induced skin cancer. Mol. Cancer Res. 12 (2014), 1677–1688.
126. 126 Li, S., et al. Lipidomic analysis of epidermal lipids: a tool to predict progression of inflammatory skin disease in humans. Expert. Rev. Proteomics 13 (2016), 451–456.
127. 127 Zhang, Q., Wakelam, M.J., Lipidomics in the analysis of malignancy. Adv. Biol. Regul. 54 (2014), 93–98.
128. 128 Bhattacharya, S.K., Recent advances in shotgun lipidomics and their implication for vision research and ophthalmology. Curr. Eye Res. 38 (2013), 417–427.
129. 129 Lam, S.M., et al. Lipidomic analysis of human tear fluid reveals structure-specific lipid alterations in dry eye syndrome. J. Lipid Res. 55 (2014), 299–306.
130. 130 Butovich, I.A., Tear film lipids. Exp. Eye Res. 117 (2013), 4–27.
131. 131 Gordon, W.C., Bazan, N.G., Mediator lipidomics in ophthalmology: targets for modulation in inflammation, neuroprotection and nerve regeneration. Curr. Eye Res. 38 (2013), 995–1005.
132. 132 Heilbronn, L.K., et al. The effect of short-term overfeeding on serum lipids in healthy humans. Obesity (Silver Spring) 21 (2013), E649–E659.
133. 133 Nestel, P.J., et al. Specific plasma lipid classes and phospholipid fatty acids indicative of dairy food consumption associate with insulin sensitivity. Am. J. Clin. Nutr. 99 (2014), 46–53.
134. 134 Hyotylainen, T., et al. Lipidomics in nutrition and food research. Mol. Nutr. Food Res. 57 (2013), 1306–1318.
135. 135 Lamaziere, A., et al. Comparison of docosahexaenoic acid uptake in murine cardiomyocyte culture and tissue: significance to physiologically relevant studies. Prostaglandins Leukot. Essent. Fatty Acids 94 (2015), 49–54.
136. 136 Lamaziere, A., et al. Lipidomics of hepatic lipogenesis inhibition by ω-3 fatty acids. Prostaglandins Leukot. Essent. Fatty Acids 88 (2013), 149–154.
137. 137 Li, W., et al. Comparative metabolomics analysis on hematopoietic functions of herb pair Gui-Xiong by ultra-high-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry and pattern recognition approach. J. Chromatogr. A 1346 (2014), 49–56.
138. 138 Dehairs, J., et al. Lipidomics in drug development. Drug Discov. Today Technol. 13 (2015), 33–38.
139. 139 Janis, M.T., et al. Beyond LDL-C lowering: distinct molecular sphingolipids are good indicators of proprotein convertase subtilisin/kexin type 9 (PCSK9) deficiency. Atherosclerosis 228 (2013), 380–385.
140. 140 Yan, S.K., et al. ‘Omics’ in pharmaceutical research: overview, applications, challenges, and future perspectives. Chin. J. Nat. Med. 13 (2015), 3–21.
141. 141 Wagener, A.H., et al. Toward composite molecular signatures in the phenotyping of asthma. Ann. Am. Thorac. Soc. 10 (2013), S197–S205.
142. 142 Antunes-Martins, A., et al. Systems biology approaches to finding novel pain mediators. Wiley Interdiscip. Rev. Syst. Biol. Med. 5 (2013), 11–35.
143. 143 Ferguson, J.F., et al. Nutrigenomics, the microbiome, and gene-environment interactions: new directions in cardiovascular disease research, prevention, and treatment: A scientific statement from the American Heart Association. Circ. Cardiovasc. Genet. 9 (2016), 291–313.