Advertisement

Lipidomic Analysis of Cancer Cell and Tumor Tissues

  • Islam Sk Ramiz 
  • Soumen Kanti MannaEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1928)

Abstract

Due to their role in cellular structure, energetics, and signaling, characterization of changes in cellular and extracellular lipid composition is of key importance to understand cancer biology. In addition, several mass spectrometry-based profiling as well as imaging studies have indicated that lipid molecules may be useful to augment existing biochemical and histopathological methods for diagnosis, staging, and prognosis of cancer. Therefore, analysis of lipidomic changes associated with cancer cells and tumor tissues can be useful for both fundamental and translational studies. Here, we provide a high-throughput single-extraction-based method that can be used for simultaneous lipidomic and metabolomic analysis of cancer cells or healthy or tumor tissue samples. In this chapter, a modified Bligh-Dyer method is described for extraction of lipids followed by analysis of fatty acid composition by gas chromatography-mass spectrometry (GC-MS) or untargeted lipidomics using electrospray ionization mass spectrometry (ESIMS) coupled with reverse-phase (RP) ultraperformance liquid chromatography (UPLC) followed by multivariate data analysis to identify features of interest.

Key words

Cancer Lipidomics RP-UPLC-ESIMS Fatty acid methyl ester GC-MS 

Notes

Acknowledgment

Authors would like to sincerely acknowledge the contribution of Mr. Kristopher W. Krausz in developing these methods and Dr. Frank J. Gonzalez (Laboratory of Metabolism, Center for Cancer Research, National Cancer Institute, Bethesda, USA) for his encouragement and support. This work was supported by Saha Institute of Nuclear Physics, Kolkata, India.

References

  1. 1.
    Fahy E, Cotter D, Sud M, Subramaniam S (2011) Lipid classification, structures and tools. Biochim Biophys Acta 1811(11):637–647.  https://doi.org/10.1016/j.bbalip.2011.06.009CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Nomura DK, Cravatt BF (2013) Lipid metabolism in cancer. Biochim Biophys Acta 1831(10):1497–1498.  https://doi.org/10.1016/j.bbalip.2013.08.001CrossRefPubMedGoogle Scholar
  3. 3.
    Beloribi-Djefaflia S, Vasseur S, Guillaumond F (2016) Lipid metabolic reprogramming in cancer cells. Oncogene 5:e189.  https://doi.org/10.1038/oncsis.2015.49CrossRefGoogle Scholar
  4. 4.
    Carracedo A, Cantley LC, Pandolfi PP (2013) Cancer metabolism: fatty acid oxidation in the limelight. Nat Rev Cancer 13(4):227–232.  https://doi.org/10.1038/nrc3483CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Liu Y (2006) Fatty acid oxidation is a dominant bioenergetic pathway in prostate cancer. Prostate Cancer Prostatic Dis 9(3):230–234.  https://doi.org/10.1038/sj.pcan.4500879CrossRefPubMedGoogle Scholar
  6. 6.
    Mashima T, Seimiya H, Tsuruo T (2009) De novo fatty-acid synthesis and related pathways as molecular targets for cancer therapy. Br J Cancer 100(9):1369–1372.  https://doi.org/10.1038/sj.bjc.6605007CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Kuhajda FP (2006) Fatty acid synthase and cancer: new application of an old pathway. Cancer Res 66(12):5977–5980.  https://doi.org/10.1158/0008-5472.CAN-05-4673CrossRefPubMedGoogle Scholar
  8. 8.
    Baenke F, Peck B, Miess H, Schulze A (2013) Hooked on fat: the role of lipid synthesis in cancer metabolism and tumour development. Dis Model Mech 6(6):1353–1363.  https://doi.org/10.1242/dmm.011338CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Lue HW, Podolak J, Kolahi K, Cheng L, Rao S, Garg D, Xue CH, Rantala JK, Tyner JW, Thornburg KL, Martinez-Acevedo A, Liu JJ, Amling CL, Truillet C, Louie SM, Anderson KE, Evans MJ, O’Donnell VB, Nomura DK, Drake JM, Ritz A, Thomas GV (2017) Metabolic reprogramming ensures cancer cell survival despite oncogenic signaling blockade. Genes Dev 31(20):2067–2084.  https://doi.org/10.1101/gad.305292.117CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Pascual G, Avgustinova A, Mejetta S, Martin M, Castellanos A, Attolini CS, Berenguer A, Prats N, Toll A, Hueto JA, Bescos C, Di Croce L, Benitah SA (2017) Targeting metastasis-initiating cells through the fatty acid receptor CD36. Nature 541(7635):41–45.  https://doi.org/10.1038/nature20791CrossRefPubMedGoogle Scholar
  11. 11.
    Sun P, Xia S, Lal B, Shi X, Yang KS, Watkins PA, Laterra J (2014) Lipid metabolism enzyme ACSVL3 supports glioblastoma stem cell maintenance and tumorigenicity. BMC Cancer 14:401.  https://doi.org/10.1186/1471-2407-14-401CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Yasumoto Y, Miyazaki H, Vaidyan LK, Kagawa Y, Ebrahimi M, Yamamoto Y, Ogata M, Katsuyama Y, Sadahiro H, Suzuki M, Owada Y (2016) Inhibition of fatty acid synthase decreases expression of stemness markers in glioma stem cells. PLoS One 11(1):e0147717.  https://doi.org/10.1371/journal.pone.0147717CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Darekar SD, Mushtaq M, Gurrapu S, Kovalevska L, Drummond C, Petruchek M, Tirinato L, Di Fabrizio E, Carbone E, Kashuba E (2015) Mitochondrial ribosomal protein S18-2 evokes chromosomal instability and transforms primary rat skin fibroblasts. Oncotarget 6(25):21016–21028.  https://doi.org/10.18632/oncotarget.4123CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Mitra R, Chao O, Urasaki Y, Goodman OB, Le TT (2012) Detection of lipid-rich prostate circulating tumour cells with coherent anti-Stokes Raman scattering microscopy. BMC Cancer 12:540.  https://doi.org/10.1186/1471-2407-12-540CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Dill AL, Ifa DR, Manicke NE, Costa AB, Ramos-Vara JA, Knapp DW, Cooks RG (2009) Lipid profiles of canine invasive transitional cell carcinoma of the urinary bladder and adjacent normal tissue by desorption electrospray ionization imaging mass spectrometry. Anal Chem 81(21):8758–8764.  https://doi.org/10.1021/ac901028bCrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Eberlin LS, Norton I, Dill AL, Golby AJ, Ligon KL, Santagata S, Cooks RG, Agar NY (2012) Classifying human brain tumors by lipid imaging with mass spectrometry. Cancer Res 72(3):645–654.  https://doi.org/10.1158/0008-5472.CAN-11-2465CrossRefPubMedGoogle Scholar
  17. 17.
    Hilvo M, Denkert C, Lehtinen L, Muller B, Brockmoller S, Seppanen-Laakso T, Budczies J, Bucher E, Yetukuri L, Castillo S, Berg E, Nygren H, Sysi-Aho M, Griffin JL, Fiehn O, Loibl S, Richter-Ehrenstein C, Radke C, Hyotylainen T, Kallioniemi O, Iljin K, Oresic M (2011) Novel theranostic opportunities offered by characterization of altered membrane lipid metabolism in breast cancer progression. Cancer Res 71(9):3236–3245.  https://doi.org/10.1158/0008-5472.CAN-10-3894CrossRefPubMedGoogle Scholar
  18. 18.
    Patterson AD, Maurhofer O, Beyoglu D, Lanz C, Krausz KW, Pabst T, Gonzalez FJ, Dufour JF, Idle JR (2011) Aberrant lipid metabolism in hepatocellular carcinoma revealed by plasma metabolomics and lipid profiling. Cancer Res 71(21):6590–6600.  https://doi.org/10.1158/0008-5472.CAN-11-0885CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Marien E, Meister M, Muley T, Fieuws S, Bordel S, Derua R, Spraggins J, Van de Plas R, Dehairs J, Wouters J, Bagadi M, Dienemann H, Thomas M, Schnabel PA, Caprioli RM, Waelkens E, Swinnen JV (2015) Non-small cell lung cancer is characterized by dramatic changes in phospholipid profiles. Int J Cancer 137(7):1539–1548.  https://doi.org/10.1002/ijc.29517CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Cifkova E, Holcapek M, Lisa M, Vrana D, Melichar B, Student V (2015) 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 1000:14–21.  https://doi.org/10.1016/j.jchromb.2015.07.011CrossRefPubMedGoogle Scholar
  21. 21.
    Liu Y, Chen Y, Momin A, Shaner R, Wang E, Bowen NJ, Matyunina LV, Walker LD, McDonald JF, Sullards MC, Merrill AH Jr (2010) Elevation of sulfatides in ovarian cancer: an integrated transcriptomic and lipidomic analysis including tissue-imaging mass spectrometry. Mol Cancer 9:186.  https://doi.org/10.1186/1476-4598-9-186CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Li J, Ren S, Piao HL, Wang F, Yin P, Xu C, Lu X, Ye G, Shao Y, Yan M, Zhao X, Sun Y, Xu G (2016) Integration of lipidomics and transcriptomics unravels aberrant lipid metabolism and defines cholesteryl oleate as potential biomarker of prostate cancer. Sci Rep 6:20984.  https://doi.org/10.1038/srep20984CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Patterson NH, Alabdulkarim B, Lazaris A, Thomas A, Marcinkiewicz MM, Gao ZH, Vermeulen PB, Chaurand P, Metrakos P (2016) Assessment of pathological response to therapy using lipid mass spectrometry imaging. Sci Rep 6:36814.  https://doi.org/10.1038/srep36814CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Del Boccio P, Perrotti F, Rossi C, Cicalini I, Di Santo S, Zucchelli M, Sacchetta P, Genovesi D, Pieragostino D (2017) Serum lipidomic study reveals potential early biomarkers for predicting response to chemoradiation therapy in advanced rectal cancer: a pilot study. Adv Radiat Oncol 2(2):118–124.  https://doi.org/10.1016/j.adro.2016.12.005CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Ma S, Jiang B, Deng W, Gu ZK, Wu FZ, Li T, Xia Y, Yang H, Ye D, Xiong Y, Guan KL (2015) D-2-hydroxyglutarate is essential for maintaining oncogenic property of mutant IDH-containing cancer cells but dispensable for cell growth. Oncotarget 6(11):8606–8620.  https://doi.org/10.18632/oncotarget.3330CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Skotland T, Ekroos K, Kauhanen D, Simolin H, Seierstad T, Berge V, Sandvig K, Llorente A (2017) Molecular lipid species in urinary exosomes as potential prostate cancer biomarkers. Eur J Cancer 70:122–132.  https://doi.org/10.1016/j.ejca.2016.10.011CrossRefPubMedGoogle Scholar
  27. 27.
    Armitage EG, Southam AD (2016) Monitoring cancer prognosis, diagnosis and treatment efficacy using metabolomics and lipidomics. Metabolomics 12:146.  https://doi.org/10.1007/s11306-016-1093-7CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Chen X, Chen H, Dai M, Ai J, Li Y, Mahon B, Dai S, Deng Y (2016) Plasma lipidomics profiling identified lipid biomarkers in distinguishing early-stage breast cancer from benign lesions. Oncotarget 7(24):36622–36631.  https://doi.org/10.18632/oncotarget.9124CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Zhou X, Mao J, Ai J, Deng Y, Roth MR, Pound C, Henegar J, Welti R, Bigler SA (2012) Identification of plasma lipid biomarkers for prostate cancer by lipidomics and bioinformatics. PLoS One 7(11):e48889.  https://doi.org/10.1371/journal.pone.0048889CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Cajka T, Fiehn O (2014) Comprehensive analysis of lipids in biological systems by liquid chromatography-mass spectrometry. Trends Anal Chem 61:192–206.  https://doi.org/10.1016/j.trac.2014.04.017CrossRefGoogle Scholar
  31. 31.
    Chen Y, Singh S, Matsumoto A, Manna SK, Abdelmegeed MA, Golla S, Murphy RC, Dong H, Song BJ, Gonzalez FJ, Thompson DC, Vasiliou V (2016) Chronic glutathione depletion confers protection against alcohol-induced steatosis: implication for redox activation of amp-activated protein kinase pathway. Sci Rep 6:29743.  https://doi.org/10.1038/srep29743CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Fahy E, Subramaniam S, Brown HA, Glass CK, Merrill AH Jr, Murphy RC, Raetz CR, Russell DW, Seyama Y, Shaw W, Shimizu T, Spener F, van Meer G, VanNieuwenhze MS, White SH, Witztum JL, Dennis EA (2005) A comprehensive classification system for lipids. J Lipid Res 46(5):839–861.  https://doi.org/10.1194/jlr.E400004-JLR200CrossRefPubMedGoogle Scholar
  33. 33.
    Fahy E, Subramaniam S, Murphy RC, Nishijima M, Raetz CR, Shimizu T, Spener F, van Meer G, Wakelam MJ, Dennis EA (2009) Update of the LIPID MAPS comprehensive classification system for lipids. J Lipid Res 50(Suppl):S9–S14.  https://doi.org/10.1194/jlr.R800095-JLR200CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Kind T, Liu KH, Lee DY, DeFelice B, Meissen JK, Fiehn O (2013) LipidBlast in silico tandem mass spectrometry database for lipid identification. Nat Methods 10(8):755–758.  https://doi.org/10.1038/nmeth.2551CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Tautenhahn R, Cho K, Uritboonthai W, Zhu Z, Patti GJ, Siuzdak G (2012) An accelerated workflow for untargeted metabolomics using the METLIN database. Nat Biotechnol 30(9):826–828.  https://doi.org/10.1038/nbt.2348CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Wishart DS, Feunang YD, Marcu A, Guo AC, Liang K, Vazquez-Fresno R, Sajed T, Johnson D, Li C, Karu N, Sayeeda Z, Lo E, Assempour N, Berjanskii M, Singhal S, Arndt D, Liang Y, Badran H, Grant J, Serra-Cayuela A, Liu Y, Mandal R, Neveu V, Pon A, Knox C, Wilson M, Manach C, Scalbert A (2018) HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res 46(D1):D608–D617.  https://doi.org/10.1093/nar/gkx1089CrossRefPubMedGoogle Scholar
  37. 37.
    Xia J, Wishart DS (2016) Using MetaboAnalyst 3.0 for comprehensive metabolomics data analysis. Curr Protoc Bioinformatics 55:14.10.11–14.10.91.  https://doi.org/10.1002/cpbi.11CrossRefGoogle Scholar
  38. 38.
    Bligh EG, Dyer WJ (1959) A rapid method of total lipid extraction and purification. Can J Biochem Physiol 37(8):911–917.  https://doi.org/10.1139/o59-099CrossRefPubMedGoogle Scholar
  39. 39.
    Schwudke D, Schuhmann K, Herzog R, Bornstein SR, Shevchenko A (2011) Shotgun lipidomics on high resolution mass spectrometers. Cold Spring Harb Perspect Biol 3(9):a004614.  https://doi.org/10.1101/cshperspect.a004614CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Hsu FF, Turk J (2009) Electrospray ionization with low-energy collisionally activated dissociation tandem mass spectrometry of glycerophospholipids: mechanisms of fragmentation and structural characterization. J Chromatogr B Analyt Technol Biomed Life Sci 877(26):2673–2695.  https://doi.org/10.1016/j.jchromb.2009.02.033CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Ferreira CR, Saraiva SA, Catharino RR, Garcia JS, Gozzo FC, Sanvido GB, Santos LF, Lo Turco EG, Pontes JH, Basso AC, Bertolla RP, Sartori R, Guardieiro MM, Perecin F, Meirelles FV, Sangalli JR, Eberlin MN (2010) Single embryo and oocyte lipid fingerprinting by mass spectrometry. J Lipid Res 51(5):1218–1227.  https://doi.org/10.1194/jlr.D001768CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Bowden JA, Shao F, Albert CJ, Lally JW, Brown RJ, Procknow JD, Stephenson AH, Ford DA (2011) Electrospray ionization tandem mass spectrometry of sodiated adducts of cholesteryl esters. Lipids 46(12):1169–1179.  https://doi.org/10.1007/s11745-011-3609-2CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    Houjou T, Yamatani K, Nakanishi H, Imagawa M, Shimizu T, Taguchi R (2004) Rapid and selective identification of molecular species in phosphatidylcholine and sphingomyelin by conditional neutral loss scanning and MS3. Rapid Commun Mass Spectrom 18(24):3123–3130.  https://doi.org/10.1002/rcm.1737CrossRefPubMedGoogle Scholar
  44. 44.
    Sugiura Y, Konishi Y, Zaima N, Kajihara S, Nakanishi H, Taguchi R, Setou M (2009) Visualization of the cell-selective distribution of PUFA-containing phosphatidylcholines in mouse brain by imaging mass spectrometry. J Lipid Res 50(9):1776–1788.  https://doi.org/10.1194/jlr.M900047-JLR200CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Lemaire R, Wisztorski M, Desmons A, Tabet JC, Day R, Salzet M, Fournier I (2006) MALDI-MS direct tissue analysis of proteins: Improving signal sensitivity using organic treatments. Anal Chem 78(20):7145–7153.  https://doi.org/10.1021/ac060565zCrossRefPubMedGoogle Scholar
  46. 46.
    Schwartz SA, Reyzer ML, Caprioli RM (2003) Direct tissue analysis using matrix-assisted laser desorption/ionization mass spectrometry: practical aspects of sample preparation. J Mass Spectrom 38(7):699–708.  https://doi.org/10.1002/jms.505CrossRefPubMedGoogle Scholar
  47. 47.
    Dettmer K, Nurnberger N, Kaspar H, Gruber MA, Almstetter MF, Oefner PJ (2011) Metabolite extraction from adherently growing mammalian cells for metabolomics studies: optimization of harvesting and extraction protocols. Anal Bioanal Chem 399(3):1127–1139.  https://doi.org/10.1007/s00216-010-4425-xCrossRefPubMedGoogle Scholar
  48. 48.
    Thomas MC, Mitchell TW, Harman DG, Deeley JM, Murphy RC, Blanksby SJ (2007) Elucidation of double bond position in unsaturated lipids by ozone electrospray ionization mass spectrometry. Anal Chem 79(13):5013–5022.  https://doi.org/10.1021/ac0702185CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Groessl M, Graf S, Knochenmuss R (2015) High resolution ion mobility-mass spectrometry for separation and identification of isomeric lipids. Analyst 140(20):6904–6911.  https://doi.org/10.1039/c5an00838gCrossRefPubMedGoogle Scholar
  50. 50.
    Kyle JE, Zhang X, Weitz KK, Monroe ME, Ibrahim YM, Moore RJ, Cha J, Sun X, Lovelace ES, Wagoner J, Polyak SJ, Metz TO, Dey SK, Smith RD, Burnum-Johnson KE, Baker ES (2016) Uncovering biologically significant lipid isomers with liquid chromatography, ion mobility spectrometry and mass spectrometry. Analyst 141(5):1649–1659.  https://doi.org/10.1039/c5an02062jCrossRefPubMedPubMedCentralGoogle Scholar
  51. 51.
    Williams PE, Klein DR, Greer SM, Brodbelt JS (2017) Pinpointing double bond and sn-positions in glycerophospholipids via hybrid 193 nm ultraviolet photodissociation (UVPD) mass spectrometry. J Am Chem Soc 139(44):15681–15690.  https://doi.org/10.1021/jacs.7b06416CrossRefPubMedPubMedCentralGoogle Scholar
  52. 52.
    Castro-Perez J, Roddy TP, Nibbering NM, Shah V, McLaren DG, Previs S, Attygalle AB, Herath K, Chen Z, Wang SP, Mitnaul L, Hubbard BK, Vreeken RJ, Johns DG, Hankemeier T (2011) Localization of fatty acyl and double bond positions in phosphatidylcholines using a dual stage CID fragmentation coupled with ion mobility mass spectrometry. J Am Soc Mass Spectrom 22(9):1552–1567.  https://doi.org/10.1007/s13361-011-0172-2CrossRefPubMedPubMedCentralGoogle Scholar
  53. 53.
    Hsu FF, Turk J (2010) Electrospray ionization multiple-stage linear ion-trap mass spectrometry for structural elucidation of triacylglycerols: assignment of fatty acyl groups on the glycerol backbone and location of double bonds. J Am Soc Mass Spectrom 21(4):657–669.  https://doi.org/10.1016/j.jasms.2010.01.007CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Biophysics and Structural Genomics DivisionSaha Institute of Nuclear Physics (HBNI)KolkataIndia

Personalised recommendations