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Mass Spectrometry-Based Shotgun Lipidomics for Cancer Research

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Cancer Metabolomics

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 1280))

Abstract

Shotgun lipidomics is an analytical approach for large-scale and systematic analysis of the composition, structure, and quantity of cellular lipids directly from lipid extracts of biological samples by mass spectrometry. This approach possesses advantages of high throughput and quantitative accuracy, especially in absolute quantification. As cancer research deepens at the level of quantitative biology and metabolomics, the demand for lipidomics approaches such as shotgun lipidomics is becoming greater. In this chapter, the principles, approaches, and some applications of shotgun lipidomics for cancer research are overviewed.

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Abbreviations

APCI:

atmospheric pressure chemical ionization

APPI:

atmospheric pressure photoionization

CID:

collision-induced dissociation

CL:

cardiolipin

CoA:

coenzyme A

DAG:

diacylglycerol

DESI:

desorption electrospray ionization

ESI:

electrospray ionization

FTICR:

Fourier-transform ion cyclotron resonance

IS:

internal standard

LIPID MAPS:

lipid metabolites and pathway strategy

MAG:

monoacylglycerol

MALDI:

matrix-assisted laser desorption/ionization

MDMS-SL:

multidimensional MS-based shotgun lipidomics

MS:

mass spectrometry

MS/MS:

tandem MS

NEFA:

non-esterified fatty acid

NLS:

neutral loss scan

PA:

phosphatidic acid

PC:

phosphatidylcholine

PE:

phosphatidylethanolamine

PG:

phosphatidylglycerol

PI:

phosphatidylinositol

PIS:

precursor ion scan

PS:

phosphatidylserine

QqQ:

triple quadrupole

Q-TOF:

quadrupole time-of-flight

S1P:

sphingosine-1-phosphate

SIM:

selected ion monitoring

SIMS:

secondary ion mass spectrometry

S/N:

signal-to-noise

SM:

sphingomyelin

SRM/MRM:

selected/multiple reaction monitoring

ST:

sulfatide

TAG:

triacylglycerol

References

  1. Fahy, E., Subramaniam, S., Murphy, R. C., Nishijima, M., Raetz, C. R., Shimizu, T., Spener, F., van Meer, G., Wakelam, M. J., & Dennis, E. A. (2009). Update of the LIPID MAPS comprehensive classification system for lipids. Journal of Lipid Research, 50(Suppl), S9–S14.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  2. Fahy, E., Subramaniam, S., Brown, H. A., Glass, C. K., Merrill, A. H., Jr., Murphy, R. C., Raetz, C. R., Russell, D. W., Seyama, Y., Shaw, W., Shimizu, T., Spener, F., van Meer, G., VanNieuwenhze, M. S., White, S. H., Witztum, J. L., & Dennis, E. A. (2005). A comprehensive classification system for lipids. Journal of Lipid Research, 46, 839–861.

    Article  CAS  PubMed  Google Scholar 

  3. Sud, M., Fahy, E., Cotter, D., Brown, A., Dennis, E. A., Glass, C. K., Merrill, A. H., Jr., Murphy, R. C., Raetz, C. R., Russell, D. W., & Subramaniam, S. (2007). LMSD: LIPID MAPS structure database. Nucleic Acids Research, 35, D527–D532.

    Article  CAS  PubMed  Google Scholar 

  4. van Meer, G., Voelker, D. R., & Feigenson, G. W. (2008). Membrane lipids: Where they are and how they behave. Nature Reviews. Molecular Cell Biology, 9, 112–124.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Han, X. (2007). Neurolipidomics: Challenges and developments. Frontiers in Bioscience, 12, 2601–2615.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Shevchenko, A., & Simons, K. (2010). Lipidomics: Coming to grips with lipid diversity. Nature Reviews. Molecular Cell Biology, 11, 593–598.

    Article  CAS  PubMed  Google Scholar 

  7. Gross, R. W., & Han, X. (2011). Lipidomics at the interface of structure and function in systems biology. Chemistry & Biology, 18, 284–291.

    Article  CAS  Google Scholar 

  8. Yang, K., & Han, X. (2016). Lipidomics: Techniques, applications, and outcomes related to biomedical sciences. Trends in Biochemical Sciences, 41, 954–969.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Loizides-Mangold, U., Perrin, L., Vandereycken, B., Betts, J. A., Walhin, J. P., Templeman, I., Chanon, S., Weger, B. D., Durand, C., Robert, M., Paz Montoya, J., Moniatte, M., Karagounis, L. G., Johnston, J. D., Gachon, F., Lefai, E., Riezman, H., & Dibner, C. (2017). Lipidomics reveals diurnal lipid oscillations in human skeletal muscle persisting in cellular myotubes cultured in vitro. Proceedings of the National Academy of Sciences of the United States of America, 114, E8565–E8574.

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Ji, J., Kline, A. E., Amoscato, A., Samhan-Arias, A. K., Sparvero, L. J., Tyurin, V. A., Tyurina, Y. Y., Fink, B., Manole, M. D., Puccio, A. M., Okonkwo, D. O., Cheng, J. P., Alexander, H., Clark, R. S., Kochanek, P. M., Wipf, P., Kagan, V. E., & Bayir, H. (2012). Lipidomics identifies cardiolipin oxidation as a mitochondrial target for redox therapy of brain injury. Nature Neuroscience, 15, 1407–1413.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Han, X., & Gross, R. W. (2003). Global analyses of cellular lipidomes directly from crude extracts of biological samples by ESI mass spectrometry: A bridge to lipidomics. Journal of Lipid Research, 44, 1071–1079.

    Article  CAS  PubMed  Google Scholar 

  12. Han, X., & Gross, R. W. (2005). Shotgun lipidomics: Multidimensional MS analysis of cellular lipidomes. Expert Review of Proteomics, 2, 253–264.

    Article  CAS  PubMed  Google Scholar 

  13. Yang, K., Cheng, H., Gross, R. W., & Han, X. (2009). Automated lipid identification and quantification by multidimensional mass spectrometry-based shotgun lipidomics. Analytical Chemistry, 81, 4356–4368.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Ferreri, C., & Chatgilialoglu, C. (2015). Membrane lipidomics for personalized health. Chichester\West Sussex\Hoboken: Wiley.

    Book  Google Scholar 

  15. Han, X. (2016). Lipidomics for studying metabolism. Nature Reviews. Endocrinology, 12, 668–679.

    Article  CAS  PubMed  Google Scholar 

  16. Griffiths, W. J., & Wang, Y. (2009). Mass spectrometry: From proteomics to metabolomics and lipidomics. Chemical Society Reviews, 38, 1882–1896.

    Article  CAS  PubMed  Google Scholar 

  17. Han, X., & Gross, R. W. (2005). Shotgun lipidomics: Electrospray ionization mass spectrometric analysis and quantitation of cellular lipidomes directly from crude extracts of biological samples. Mass Spectrometry Reviews, 24, 367–412.

    Article  CAS  PubMed  Google Scholar 

  18. Han, X., Yang, K., & Gross, R. W. (2012). Multi-dimensional mass spectrometry-based shotgun lipidomics and novel strategies for lipidomic analyses. Mass Spectrometry Reviews, 31, 134–178.

    Article  CAS  PubMed  Google Scholar 

  19. Wang, M., Wang, C., Han, R. H., & Han, X. (2016). Novel advances in shotgun lipidomics for biology and medicine. Progress in Lipid Research, 61, 83–108.

    Article  CAS  PubMed  Google Scholar 

  20. Rustam, Y. H., & Reid, G. E. (2018). Analytical challenges and recent advances in mass spectrometry based Lipidomics. Analytical Chemistry, 90, 374–397.

    Article  CAS  PubMed  Google Scholar 

  21. Cajka, T., & Fiehn, O. (2016). Toward merging untargeted and targeted methods in mass spectrometry-based metabolomics and Lipidomics. Analytical Chemistry, 88, 524–545.

    Article  CAS  PubMed  Google Scholar 

  22. Jiang, X., Cheng, H., Yang, K., Gross, R. W., & Han, X. (2007). Alkaline methanolysis of lipid extracts extends shotgun lipidomics analyses to the low-abundance regime of cellular sphingolipids. Analytical Biochemistry, 371, 135–145.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Han, X., Yang, K., Yang, J., Fikes, K. N., Cheng, H., & Gross, R. W. (2006). Factors influencing the electrospray intrasource separation and selective ionization of glycerophospholipids. Journal of the American Society for Mass Spectrometry, 17, 264–274.

    Article  CAS  PubMed  Google Scholar 

  24. Brugger, B., Erben, G., Sandhoff, R., Wieland, F. T., & Lehmann, W. D. (1997). Quantitative analysis of biological membrane lipids at the low picomole level by nano-electrospray ionization tandem mass spectrometry. Proceedings of the National Academy of Sciences of the United States of America, 94, 2339–2344.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Wang, M., Han, R. H., & Han, X. (2013). Fatty acidomics: Global analysis of lipid species containing a carboxyl group with a charge-remote fragmentation-assisted approach. Analytical Chemistry, 85, 9312–9320.

    Article  CAS  PubMed  Google Scholar 

  26. Wang, C., Wang, M., & Han, X. (2015). Comprehensive and quantitative analysis of lysophospholipid molecular species present in obese mouse liver by shotgun lipidomics. Analytical Chemistry, 87, 4879–4887.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Wang, C., Palavicini, J. P., Wang, M., Chen, L., Yang, K., Crawford, P. A., & Han, X. (2016). Comprehensive and quantitative analysis of Polyphosphoinositide species by shotgun Lipidomics revealed their alterations in db/db mouse brain. Analytical Chemistry, 88, 12137–12144.

    Article  CAS  PubMed  Google Scholar 

  28. Kind, T., Liu, K. H., Lee, D. Y., DeFelice, B., Meissen, J. K., & Fiehn, O. (2013). LipidBlast in silico tandem mass spectrometry database for lipid identification. Nature Methods, 10, 755–758.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Wang, M., Wang, C., & Han, X. (2017). Selection of internal standards for accurate quantification of complex lipid species in biological extracts by electrospray ionization mass spectrometry-what, how and why? Mass Spectrometry Reviews, 36, 693–714.

    Article  CAS  PubMed  Google Scholar 

  30. Ekroos, K., Chernushevich, I. V., Simons, K., & Shevchenko, A. (2002). Quantitative profiling of phospholipids by multiple precursor ion scanning on a hybrid quadrupole time-of-flight mass spectrometer. Analytical Chemistry, 74, 941–949.

    Article  CAS  PubMed  Google Scholar 

  31. Ejsing, C. S., 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. Analytical Chemistry, 78, 6202–6214.

    Article  CAS  PubMed  Google Scholar 

  32. Schwudke, D., Oegema, J., Burton, L., Entchev, E., Hannich, J. T., Ejsing, C. S., Kurzchalia, T., & Shevchenko, A. (2006). Lipid profiling by multiple precursor and neutral loss scanning driven by the data-dependent acquisition. Analytical Chemistry, 78, 585–595.

    Article  CAS  PubMed  Google Scholar 

  33. Cajka, T., & Fiehn, O. (2014). Comprehensive analysis of lipids in biological systems by liquid chromatography-mass spectrometry. Trends in Analytical Chemistry, 61, 192–206.

    Article  CAS  PubMed  Google Scholar 

  34. Schuhmann, K., Herzog, R., Schwudke, D., Metelmann-Strupat, W., Bornstein, S. R., & Shevchenko, A. (2011). Bottom-up shotgun lipidomics by higher energy collisional dissociation on LTQ Orbitrap mass spectrometers. Analytical Chemistry, 83, 5480–5487.

    Article  CAS  PubMed  Google Scholar 

  35. Schwudke, D., Hannich, J. T., Surendranath, V., Grimard, V., Moehring, T., Burton, L., Kurzchalia, T., & Shevchenko, A. (2007). Top-down lipidomic screens by multivariate analysis of high-resolution survey mass spectra. Analytical Chemistry, 79, 4083–4093.

    Article  CAS  PubMed  Google Scholar 

  36. Holewinski, R. J., Parker, S. J., Matlock, A. D., Venkatraman, V., & Van Eyk, J. E. (2016). Methods for SWATH: Data independent acquisition on TripleTOF mass spectrometers. Methods in Molecular Biology, 1410, 265–279.

    Article  CAS  PubMed  Google Scholar 

  37. 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, 195–213.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Bilbao, A., Varesio, E., Luban, J., Strambio-De-Castillia, C., Hopfgartner, G., Muller, M., & Lisacek, F. (2015). Processing strategies and software solutions for data-independent acquisition in mass spectrometry. Proteomics, 15, 964–980.

    Article  CAS  PubMed  Google Scholar 

  39. Chen, J., & Nichols, K. K. (2018). Comprehensive shotgun lipidomics of human meibomian gland secretions using MS/MS(all) with successive switching between acquisition polarity modes. Journal of Lipid Research, 59, 2223–2236.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Gao, F., McDaniel, J., Chen, E. Y., Rockwell, H., Lynes, M. D., Tseng, Y. H., Sarangarajan, R., Narain, N. R., & Kiebish, M. A. (2016). Monoacylglycerol analysis using MS/MSALL quadruple time of flight mass spectrometry. Metabolites, 6, 25.

    Google Scholar 

  41. Norris, J. L., & Caprioli, R. M. (2013). Analysis of tissue specimens by matrix-assisted laser desorption/ionization imaging mass spectrometry in biological and clinical research. Chemical Reviews, 113, 2309–2342.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Berry, K. A., Hankin, J. A., Barkley, R. M., Spraggins, J. M., Caprioli, R. M., & Murphy, R. C. (2011). MALDI imaging of lipid biochemistry in tissues by mass spectrometry. Chemical Reviews, 111, 6491–6512.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Ellis, S. R., Paine, M. R. L., Eijkel, G. B., Pauling, J. K., Husen, P., Jervelund, M. W., Hermansson, M., Ejsing, C. S., & Heeren, R. M. A. (2018). Automated, parallel mass spectrometry imaging and structural identification of lipids. Nature Methods, 15, 515–518.

    Article  CAS  PubMed  Google Scholar 

  44. Zavalin, A., Yang, J., Hayden, K., Vestal, M., & Caprioli, R. M. (2015). Tissue protein imaging at 1 mu m laser spot diameter for high spatial resolution and high imaging speed using transmission geometry MALDI TOF MS. Analytical and Bioanalytical Chemistry, 407, 2337–2342.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Baker, T. C., Han, J., & Borchers, C. H. (2017). Recent advancements in matrix-assisted laser desorption/ionization mass spectrometry imaging. Current Opinion in Biotechnology, 43, 62–69.

    Article  CAS  PubMed  Google Scholar 

  46. Buchberger, A. R., DeLaney, K., Johnson, J., & Li, L. J. (2018). Mass spectrometry imaging: A review of emerging advancements and future insights. Analytical Chemistry, 90, 240–265.

    Article  CAS  PubMed  Google Scholar 

  47. Flinders, B., Huizing, L. R. S., van Heerden, M., Cuyckens, F., Neumann, U. P., van der Laan, L. J. W., Damink, S. W. M. O., Heeren, R. M. A., Schaap, F. G., & Vreeken, R. J. (2018). Cross-species molecular imaging of bile salts and lipids in liver: Identification of molecular structural markers in health and disease. Analytical Chemistry, 90, 11835–11846.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Ibrahim, H., Jurcic, K., Wang, J. S. H., Whitehead, S. N., & Yeung, K. K. C. (2017). 1,6-Diphenyl-1,3,5-hexatriene (DPH) as a novel matrix for MALDI MS imaging of fatty acids, phospholipids, and sulfatides in brain tissues. Analytical Chemistry, 89, 12828–12836.

    Article  CAS  PubMed  Google Scholar 

  49. Wang, X. D., Han, J., Chou, A., Yang, J. C., Pan, J. X., & Borchers, C. H. (2013). Hydroxyflavones as a new family of matrices for MALDI tissue imaging. Analytical Chemistry, 85, 7566–7573.

    Article  CAS  PubMed  Google Scholar 

  50. Calvano, C. D., Monopoli, A., Cataldi, T. R. I., & Palmisano, F. (2018). MALDI matrices for low molecular weight compounds: An endless story? Analytical and Bioanalytical Chemistry, 410, 4015–4038.

    Article  CAS  PubMed  Google Scholar 

  51. Wang, J., Qiu, S., Chen, S., Xiong, C., Liu, H., Wang, J., Zhang, N., Hou, J., He, Q., & Nie, Z. (2015). MALDI-TOF MS imaging of metabolites with a N-(1-naphthyl) ethylenediamine dihydrochloride matrix and its application to colorectal cancer liver metastasis. Analytical Chemistry, 87, 422–430.

    Article  CAS  PubMed  Google Scholar 

  52. Wang, J., Wang, C., & Han, X. (2018). Enhanced coverage of lipid analysis and imaging by matrix-assisted laser desorption/ionization mass spectrometry via a strategy with an optimized mixture of matrices. Analytica Chimica Acta, 1000, 155–162.

    Article  CAS  PubMed  Google Scholar 

  53. Cheng, H., Sun, G., Yang, K., Gross, R. W., & Han, X. (2010). Selective desorption/ionization of sulfatides by MALDI-MS facilitated using 9-aminoacridine as matrix. Journal of Lipid Research, 51, 1599–1609.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Shanta, S. R., Zhou, L. H., Park, Y. S., Kim, Y. H., Kim, Y., & Kim, K. P. (2011). Binary matrix for MALDI imaging mass spectrometry of phospholipids in both ion modes. Analytical Chemistry, 83, 1252–1259.

    Article  CAS  PubMed  Google Scholar 

  55. Schroter, J., Fulop, A., Hopf, C., & Schiller, J. (2018). The combination of 2,5-dihydroxybenzoic acid and 2,5-dihydroxyacetophenone matrices for unequivocal assignment of phosphatidylethanolamine species in complex mixtures. Analytical and Bioanalytical Chemistry, 410, 2437–2447.

    Article  PubMed  CAS  Google Scholar 

  56. Lu, M., Yang, X., Yang, Y., Qin, P., Wu, X., & Cai, Z. (2017). Nanomaterials as assisted matrix of laser desorption/ionization time-of-flight mass spectrometry for the analysis of small molecules. Nanomaterials, 7, 87.

    Google Scholar 

  57. Yagnik, G. B., Hansen, R. L., Korte, A. R., Reichert, M. D., Vela, J., & Lee, Y. J. (2016). Large scale nanoparticle screening for small molecule analysis in laser desorption ionization mass spectrometry. Analytical Chemistry, 88, 8926–8930.

    Article  CAS  PubMed  Google Scholar 

  58. Shrivas, K., Agrawal, K., & Wu, H. F. (2011). Application of platinum nanoparticles as affinity probe and matrix for direct analysis of small biomolecules and microwave digested proteins using matrix-assisted laser desorption/ionization mass spectrometry. Analyst, 136, 2852–2857.

    Article  CAS  PubMed  Google Scholar 

  59. Spencer, M. T., Furutani, H., Oldenburg, S. J., Darlington, T. K., & Prather, K. A. (2008). Gold nanoparticles as a matrix for visible-wavelength single-particle matrix-assisted laser desorption/ionization mass spectrometry of small biomolecules. Journal of Physical Chemistry C, 112, 4083–4090.

    Article  CAS  Google Scholar 

  60. Niziol, J., & Ruman, T. (2013). Surface-transfer mass spectrometry imaging on a Monoisotopic silver nanoparticle enhanced target. Analytical Chemistry, 85, 12070–12076.

    Article  CAS  PubMed  Google Scholar 

  61. Dong, X. L., Cheng, J. S., Li, J. H., & Wang, Y. S. (2010). Graphene as a novel matrix for the analysis of small molecules by MALDI-TOF MS. Analytical Chemistry, 82, 6208–6214.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Lu, M. H., Lai, Y. Q., Chen, G. N., & Cai, Z. W. (2011). Matrix interference-free method for the analysis of small molecules by using negative ion laser desorption/ionization on graphene flakes. Analytical Chemistry, 83, 3161–3169.

    Article  CAS  PubMed  Google Scholar 

  63. Chen, S. M., Zheng, H. Z., Wang, J. N., Hou, J., He, Q., Liu, H. H., Xiong, C. Q., Kong, X. L., & Nie, Z. X. (2013). Carbon Nanodots as a matrix for the analysis of low-molecular-weight molecules in both positive- and negative-ion matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and quantification of glucose and uric acid in real samples. Analytical Chemistry, 85, 6646–6652.

    Article  CAS  PubMed  Google Scholar 

  64. Chen, Y. S., Ding, J., He, X. M., Xu, J., & Feng, Y. Q. (2018). Synthesis of tellurium nanosheet for use in matrix assisted laser desorption/ionization time-of-flight mass spectrometry of small molecules. Microchimica Acta, 185, 368.

    Article  PubMed  CAS  Google Scholar 

  65. Zhao, Y., Tang, M., Liao, Q., Li, Z., Li, H., Xi, K., Tan, L., Zhang, M., Xu, D., & Chen, H. Y. (2018). Disposable MoS2-arrayed MALDI MS Chip for high-throughput and rapid quantification of sulfonamides in multiple real samples. ACS Sensors, 3, 806–814.

    Article  CAS  PubMed  Google Scholar 

  66. Wang, J., Sun, J., Wang, J., Liu, H., Xue, J., & Nie, Z. (2017). Hexagonal boron nitride nanosheets as a multifunctional background-free matrix to detect small molecules and complicated samples by MALDI mass spectrometry. Chemical Communications, 53, 8114–8117.

    Article  CAS  PubMed  Google Scholar 

  67. Pirro, V., Guffey, S. C., Sepulveda, M. S., Mahapatra, C. T., Ferreira, C. R., Jarmusch, A. K., & Cooks, R. G. (2016). Lipid dynamics in zebrafish embryonic development observed by DESI-MS imaging and nanoelectrospray-MS. Molecular BioSystems, 12, 2069–2079.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Henderson, F., Jones, E., Denbeigh, J., Christie, L., Batey, M. A., Claude, E., Williams, K. J., & McMahon, A. (2018). Automated, high-throughput 3D desorption electrospray ionization (DESI) mass spectrometry imaging of a xenograft model of glioblastoma. Cancer Research, 78, 33–33.

    Article  Google Scholar 

  69. Pirro, V., Alfaro, C. M., Jarmusch, A. K., Hattab, E. M., Cohen-Gadol, A. A., & Cooks, R. G. (2017). Intraoperative assessment of tumor margins during glioma resection by desorption electrospray ionization-mass spectrometry. Proceedings of the National Academy of Sciences of the United States of America, 114, 6700–6705.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Calligaris, D., Caragacianu, D., Liu, X. H., Norton, I., Thompson, C. J., Richardson, A. L., Golshan, M., Easterling, M. L., Santagata, S., Dillon, D. A., Jolesz, F. A., & Agar, N. Y. R. (2014). Application of desorption electrospray ionization mass spectrometry imaging in breast cancer margin analysis. Proceedings of the National Academy of Sciences of the United States of America, 111, 15184–15189.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Jarmusch, A. K., Pirro, V., Baird, Z., Hattab, E. M., Cohen-Gadol, A. A., & Cooks, R. G. (2016). Lipid and metabolite profiles of human brain tumors by desorption electrospray ionization-MS. Proceedings of the National Academy of Sciences of the United States of America, 113, 1486–1491.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Zhou, Z. P., & Zare, R. N. (2017). Personal information from latent fingerprints using desorption electrospray ionization mass spectrometry and machine learning. Analytical Chemistry, 89, 1369–1372.

    Article  CAS  PubMed  Google Scholar 

  73. Bich, C., Touboul, D., & Brunelle, A. (2014). Cluster TOF-SIMS imaging as a tool for micrometric histology of lipids in tissue. Mass Spectrometry Reviews, 33, 442–451.

    Article  CAS  PubMed  Google Scholar 

  74. Milac, T. I., Randolph, T. W., & Wang, P. (2012). Analyzing LC-MS/MS data by spectral count and ion abundance: Two case studies. Statistics and Its Interface, 5, 75–87.

    Article  PubMed  PubMed Central  Google Scholar 

  75. Smith, C. A., Want, E. J., O’Maille, G., Abagyan, R., & Siuzdak, G. (2006). XCMS: Processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Analytical Chemistry, 78, 779–787.

    Article  CAS  PubMed  Google Scholar 

  76. Benton, H. P., Wong, D. M., Trauger, S. A., & Siuzdak, G. (2008). XCMS2: Processing tandem mass spectrometry data for metabolite identification and structural characterization. Analytical Chemistry, 80, 6382–6389.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. 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. Journal of Lipid Research, 42, 663–672.

    Article  PubMed  Google Scholar 

  78. Han, X., & Gross, R. W. (2001). Quantitative analysis and molecular species fingerprinting of triacylglyceride molecular species directly from lipid extracts of biological samples by electrospray ionization tandem mass spectrometry. Analytical Biochemistry, 295, 88–100.

    Article  CAS  PubMed  Google Scholar 

  79. Bowden, J. A., Shao, F., Albert, C. J., Lally, J. W., Brown, R. J., Procknow, J. D., Stephenson, A. H., & Ford, D. A. (2011). Electrospray ionization tandem mass spectrometry of sodiated adducts of cholesteryl esters. Lipids, 46, 1169–1179.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Checa, A., Bedia, C., & Jaumot, J. (2015). Lipidomic data analysis: Tutorial, practical guidelines and applications. Analytica Chimica Acta, 885, 1–16.

    Article  CAS  PubMed  Google Scholar 

  81. O’Connor, A., Brasher, C. J., Slatter, D. A., Meckelmann, S. W., Hawksworth, J. I., Allen, S. M., & O’Donnell, V. B. (2017). LipidFinder: A computational workflow for discovery of lipids identifies eicosanoid-phosphoinositides in platelets. JCI Insight, 2, e91634.

    Article  PubMed  PubMed Central  Google Scholar 

  82. Fahy, E., Alvarez-Jarreta, J., Brasher, C. J., Nguyen, A., Hawksworth, J. I., Rodrigues, P., Meckelmann, S., Allen, S. M., & O’Donnell, V. B. (2019). LipidFinder on LIPID MAPS: Peak filtering, MS searching and statistical analysis for lipidomics. Bioinformatics, 35, 685–687.

    Google Scholar 

  83. Husen, P., Tarasov, K., Katafiasz, M., Sokol, E., Vogt, J., Baumgart, J., Nitsch, R., Ekroos, K., & Ejsing, C. S. (2013). Analysis of lipid experiments (ALEX): A software framework for analysis of high-resolution shotgun Lipidomics data, Plos one, 8, e79736.

    Google Scholar 

  84. Guijas, C., Montenegro-Burke, J. R., Domingo-Almenara, X., Palermo, A., Warth, B., Hermann, G., Koellensperger, G., Huan, T., Uritboonthai, W., Aisporna, A. E., Wolan, D. W., Spilker, M. E., Benton, H. P., & Siuzdak, G. (2018). METLIN: A technology platform for identifying Knowns and unknowns. Analytical Chemistry, 90, 3156–3164.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Wishart, D. S., Tzur, D., Knox, C., Eisner, R., Guo, A. C., Young, N., Cheng, D., Jewell, K., Arndt, D., Sawhney, S., Fung, C., Nikolai, L., Lewis, M., Coutouly, M. A., Forsythe, I., Tang, P., Shrivastava, S., Jeroncic, K., Stothard, P., Amegbey, G., Block, D., Hau, D. D., Wagner, J., Miniaci, J., Clements, M., Gebremedhin, M., Guo, N., Zhang, Y., Duggan, G. E., Macinnis, G. D., Weljie, A. M., Dowlatabadi, R., Bamforth, F., Clive, D., Greiner, R., Li, L., Marrie, T., Sykes, B. D., Vogel, H. J., & Querengesser, L. (2007). HMDB: the Human Metabolome Database. Nucleic Acids Research, 35, D521–D526.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Ali, Z., & Bhaskar, S. B. (2016). Basic statistical tools in research and data analysis. Indian Journal of Anaesthesia, 60, 662–669.

    Article  PubMed  PubMed Central  Google Scholar 

  87. Xia, J., Sinelnikov, I. V., Han, B., & Wishart, D. S. (2015). MetaboAnalyst 3.0--making metabolomics more meaningful. Nucleic Acids Research, 43, W251–W257.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Kanehisa, M., & Goto, S. (2000). KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Research, 28, 27–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Junker, B. H., Klukas, C., & Schreiber, F. (2006). VANTED: A system for advanced data analysis and visualization in the context of biological networks. BMC Bioinformatics, 7, 109.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  90. Santos, C. R., & Schulze, A. (2012). Lipid metabolism in cancer. The FEBS Journal, 279, 2610–2623.

    Article  CAS  PubMed  Google Scholar 

  91. DeBerardinis, R. J., & Chandel, N. S. (2016). Fundamentals of cancer metabolism. Science Advances, 2, e1600200.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  92. Liberti, M. V., & Locasale, J. W. (2016). The Warburg effect: How does it benefit Cancer cells? Trends in Biochemical Sciences, 41, 211–218.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Fhaner, C. J., Liu, S., Ji, H., Simpson, R. J., & Reid, G. E. (2012). Comprehensive lipidome profiling of isogenic primary and metastatic colon adenocarcinoma cell lines. Analytical Chemistry, 84, 8917–8926.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Hu, Q., Wang, M., Cho, M. S., Wang, C., Nick, A. M., Thiagarajan, P., Aung, F. M., Han, X., Sood, A. K., & Afshar-Kharghan, V. (2016). Lipid profile of platelets and platelet-derived microparticles in ovarian cancer. BBA Clinical, 6, 76–81.

    Article  PubMed  PubMed Central  Google Scholar 

  95. 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, P. A., Caprioli, R. M., Waelkens, E., & Swinnen, J. V. (2015). Non-small cell lung cancer is characterized by dramatic changes in phospholipid profiles. International Journal of Cancer, 137, 1539–1548.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Kiebish, M. A., Han, X., Cheng, H., Chuang, J. H., & Seyfried, T. N. (2008). Cardiolipin and electron transport chain abnormalities in mouse brain tumor mitochondria: Lipidomic evidence supporting the Warburg theory of cancer. Journal of Lipid Research, 49, 2545–2556.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Zhang, J., Feider, C. L., Nagi, C., Yu, W., Carter, S. A., Suliburk, J., Cao, H. S. T., & Eberlin, L. S. (2017). Detection of metastatic breast and thyroid cancer in lymph nodes by desorption electrospray ionization mass spectrometry imaging. Journal of the American Society for Mass Spectrometry, 28, 1166–1174.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Eberlin, L. S., Tibshirani, R. J., Zhang, J., Longacre, T. A., Berry, G. J., Bingham, D. B., Norton, J. A., Zare, R. N., & Poultsides, G. A. (2014). Molecular assessment of surgical-resection margins of gastric cancer by mass-spectrometric imaging. Proceedings of the National Academy of Sciences of the United States of America, 111, 2436–2441.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Gharpure, K. M., Pradeep, S., Sans, M., Rupaimoole, R., Ivan, C., Wu, S. Y., Bayraktar, E., Nagaraja, A. S., Mangala, L. S., Zhang, X., Haemmerle, M., Hu, W., Rodriguez-Aguayo, C., McGuire, M., Mak, C. S. L., Chen, X., Tran, M. A., Villar-Prados, A., Pena, G. A., Kondetimmanahalli, R., Nini, R., Koppula, P., Ram, P., Liu, J., Lopez-Berestein, G., Baggerly, K., Eberlin, S. L., & Sood, A. K. (2018). FABP4 as a key determinant of metastatic potential of ovarian cancer. Nature Communications, 9, 2923.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  100. Marien, E., Meister, M., Muley, T., Gomez Del Pulgar, T., Derua, R., Spraggins, J. M., Van de Plas, R., Vanderhoydonc, F., Machiels, J., Binda, M. M., Dehairs, J., Willette-Brown, J., Hu, Y., Dienemann, H., Thomas, M., Schnabel, P. A., Caprioli, R. M., Lacal, J. C., Waelkens, E., & Swinnen, J. V. (2016). Phospholipid profiling identifies acyl chain elongation as a ubiquitous trait and potential target for the treatment of lung squamous cell carcinoma. Oncotarget, 7, 12582–12597.

    Article  PubMed  PubMed Central  Google Scholar 

  101. Perez, O., Margolis, M., Santander, A. M., Martinez, M., Bhattacharya, S., & Torroella-Kouri, M. (2014). Breast cancer and obesity impact the lipid composition of breast adipose tissue: A preliminary study using shotgun lipidomics. Cancer Research, 74. Abstract # 3496.

    Google Scholar 

  102. Min, H. K., Lim, S., Chung, B. C., & Moon, M. H. (2011). Shotgun lipidomics for candidate biomarkers of urinary phospholipids in prostate cancer. Analytical and Bioanalytical Chemistry, 399, 823–830.

    Article  CAS  PubMed  Google Scholar 

  103. Farrokhi Yekta, R., Rezaie Tavirani, M., Arefi Oskouie, A., Mohajeri-Tehrani, M. R., & Soroush, A. R. (2017). The metabolomics and lipidomics window into thyroid cancer research. Biomarkers, 22, 595–603.

    CAS  PubMed  Google Scholar 

  104. Mayers, J. R., Wu, C., Clish, C. B., Kraft, P., Torrence, M. E., Fiske, B. P., Yuan, C., Bao, Y., Townsend, M. K., Tworoger, S. S., Davidson, S. M., Papagiannakopoulos, T., Yang, A., Dayton, T. L., Ogino, S., Stampfer, M. J., Giovannucci, E. L., Qian, Z. R., Rubinson, D. A., Ma, J., Sesso, H. D., Gaziano, J. M., Cochrane, B. B., Liu, S., Wactawski-Wende, J., Manson, J. E., Pollak, M. N., Kimmelman, A. C., Souza, A., Pierce, K., Wang, T. J., Gerszten, R. E., Fuchs, C. S., Heiden, M. G. V., & Wolpin, B. M. (2014). Elevation of circulating branched-chain amino acids is an early event in human pancreatic adenocarcinoma development. Nature Medicine, 20, 1193–1198.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. 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. European Journal of Cancer, 70, 122–132.

    Article  CAS  PubMed  Google Scholar 

  106. Mazar, J., Zhao, W., Khalil, A. M., Lee, B., Shelley, J., Govindarajan, S. S., Yamamoto, F., Ratnam, M., Aftab, M. N., Collins, S., Finck, B. N., Han, X., Mattick, J. S., Dinger, M. E., & Perera, R. J. (2014). The functional characterization of long noncoding RNA SPRY4-IT1 in human melanoma cells. Oncotarget, 5, 8959–8969.

    Article  PubMed  PubMed Central  Google Scholar 

  107. Zhao, Y., Ling, Z., Hao, Y., Pang, X., Han, X., Califano, J. A., Shan, L., & Gu, X. (2017). MiR-124 acts as a tumor suppressor by inhibiting the expression of sphingosine kinase 1 and its downstream signaling in head and neck squamous cell carcinoma. Oncotarget, 8, 25005–25020.

    Article  PubMed  PubMed Central  Google Scholar 

  108. Sahoo, A., Lee, B., Boniface, K., Seneschal, J., Sahoo, S. K., Seki, T., Wang, C., Das, S., Han, X., Steppie, M., Seal, S., Taieb, A., & Perera, R. J. (2017). MicroRNA-211 regulates oxidative phosphorylation and energy metabolism in human vitiligo. The Journal of Investigative Dermatology, 137, 1965–1974.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Muth, A., Pandey, V., Kaur, N., Wason, M., Baker, C., Han, X., Johnson, T. R., Altomare, D. A., & Phanstiel, O. (2014). Synthesis and biological evaluation of antimetastatic agents predicated upon dihydromotuporamine C and its carbocyclic derivatives. Journal of Medicinal Chemistry, 57, 4023–4034.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgments

This work was partially supported by NIH/NIA (RF1 AG061872), intramural institutional research funds from the University of Texas Health Science Center at San Antonio (UT Health SA), the Mass Spectrometry Core Facility of UT Health SA, and the Methodist Hospital Foundation endowment.

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Authors and Affiliations

  1. Barshop Institute for Longevity and Aging Studies, San Antonio, TX, USA

    Jianing Wang, Chunyan Wang & Xianlin Han

  2. Department of Medicine – Diabetes, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA

    Xianlin Han

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  1. Jianing Wang
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Correspondence to Xianlin Han .

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  1. University of California, Los Angeles, Los Angeles, CA, USA

    Shen Hu

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Wang, J., Wang, C., Han, X. (2021). Mass Spectrometry-Based Shotgun Lipidomics for Cancer Research. In: Hu, S. (eds) Cancer Metabolomics. Advances in Experimental Medicine and Biology, vol 1280. Springer, Cham. https://doi.org/10.1007/978-3-030-51652-9_3

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