Skip to main content
SpringerLink
Log in
Book cover

Advancements of Mass Spectrometry in Biomedical Research pp 613–633Cite as

Metabolomics and Biomarker Discovery

  • Chapter
  • First Online:

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 1140))

Abstract

Recently, metabolomics—the study of metabolite profiles within biological samples—has found a wide range of applications. This chapter describes the different techniques available for metabolomic analysis, the various samples that can be utilised for analysis and applications of both global and targeted metabolomic analysis to biomarker discovery in medicine.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Strimbu, K., & Tavel, J. A. (2010). What are biomarkers? Current Opinion in HIV and AIDS, 5(6), 463–466.

    Article  PubMed Central  PubMed  Google Scholar 

  2. Watanabe, M., Sheriff, S., Lewis, K. B., Cho, J., Tinch, S. L., Balasubramaniam, A., et al. (2012). Metabolic profiling comparison of human pancreatic ductal epithelial cells and three pancreatic cancer cell lines using NMR based Metabonomics. Journal of Molecular Biomarkers and Diagnosis, 3(2)., S3-002.

    Google Scholar 

  3. Yu, Z., Kastenmüller, G., He, Y., Belcredi, P., Möller, G., Prehn, C., et al. (2011). Differences between human plasma and serum metabolite profiles. PLoS One, 6(7), e21230.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  4. Bando, K., Kawahara, R., Kunimatsu, T., Sakai, J., Kimura, J., Funabashi, H., et al. (2010). Influences of biofluid sample collection and handling procedures on GC-MS based metabolomic studies. Journal of Bioscience and Bioengineering, 110(4), 491–499.

    Article  CAS  PubMed  Google Scholar 

  5. Lance, V. A., Patton, M. L., & Hagey, L. R. (2001). Identification of a series of C(21)O(2) pregnanes from fecal extracts of a pregnant black rhinoceros (Diceros bicornis minor). Steroids, 66(12), 875–881.

    Article  CAS  PubMed  Google Scholar 

  6. Dettmer, K., Nürnberger, N., Kaspar, H., Gruber, M. A., Almstetter, M. F., & Oefner, P. J. (2011). Metabolite extraction from adherently growing mammalian cells for metabolomics studies: Optimization of harvesting and extraction protocols. Analytical and Bioanalytical Chemistry, 399(3), 1127–1139.

    Article  CAS  PubMed  Google Scholar 

  7. Teng, Q., Huang, W., Collette, T. W., Ekman, D. R., & Tan, C. (2009). A direct cell quenching method for cell-culture based metabolomics. Metabolomics, 5, 199–208.

    Article  CAS  Google Scholar 

  8. Barnes, S., Benton, H. P., Casazza, K., Cooper, S. J., Cui, X., Du, X., et al. (2016). Training in metabolomics research. I. Designing the experiment, collecting and extracting samples and generating metabolomics data. Journal of Mass Spectrometry, 51(7), 461–475.

    Article  CAS  PubMed  Google Scholar 

  9. Gowda, G. A., Zhang, S., Gu, H., Asiago, V., Shanaiah, N., & Raftery, D. (2008). Metabolomics-based methods for early disease diagnostics. Expert Review of Molecular Diagnostics, 8(5), 617–633.

    Article  CAS  PubMed  Google Scholar 

  10. Brennan, L. (2014). NMR-based metabolomics: From sample preparation to applications in nutrition research. Progress in Nuclear Magnetic Resonance Spectroscopy, 83, 42–49.

    Article  CAS  PubMed  Google Scholar 

  11. Lewis, P. D., Lewis, K. E., Ghosal, R., Bayliss, S., Lloyd, A. J., Wills, J., et al. (2010). Evaluation of FTIR spectroscopy as a diagnostic tool for lung cancer using sputum. BMC Cancer, 10, 640.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  12. Ding, J., Xu, T., Tan, X., Jin, H., Shao, J., & Li, H. (2017). Raman spectrum: A potential biomarker for embryo assessment during in vitro fertilization. Experimental and Therapeutic Medicine, 13(5), 1789–1792.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  13. Zhang, A., Sun, H., Xu, H., Qiu, S., & Wang, X. (2013). Cell metabolomics. OMICS, 17(10), 495–501.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  14. Laponogov, I., Sadawi, N., Galea, D., Mirnezami, R., Veselkov, K. A., & Wren, J. (2018). ChemDistiller: An engine for metabolite annotation in mass spectrometry. Bioinformatics [Epub ahead of print].

    Google Scholar 

  15. Lee, S. Y., Park, N. H., Jeong, E. K., Wi, J. W., Kim, C. J., Kim, J. Y., et al. (2012). Comparison of GC/MS and LC/MS methods for the analysis of propofol and its metabolites in urine. Journal of Chromatography. B, Analytical Technologies in the Biomedical and Life Sciences, 900, 1–10.

    Article  CAS  PubMed  Google Scholar 

  16. Lei, Z., Huhman, D. V., & Sumner, L. W. (2011). Mass spectrometry strategies in metabolomics. The Journal of Biological Chemistry, 286(29), 25435–25442.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  17. Kapoore, R., Coyle, R., Staton, C. A., Brown, N. J., & Vaidyanathan, S. (2017). Influence of washing and quenching in profiling the metabolome of adherent mammalian cells: A case study with the metastatic breast cancer cell line MDA-MB-231. Analyst, 142(1), 2038–2049.

    Article  CAS  PubMed  Google Scholar 

  18. Liu, J., Liu, Y., Wang, Y., Abozeid, A., Zu, Y. G., & Tang, Z. H. (2017). The integration of GC-MS and LC-MS to assay the metabolomics profiling in Panax ginseng and Panax quinquefolius reveals a tissue- and species-specific connectivity of primary metabolites and ginsenosides accumulation. Journal of Pharmaceutical and Biomedical Analysis, 135, 176–185.

    Article  CAS  PubMed  Google Scholar 

  19. Manna, S. K., Patterson, A. D., Yang, Q., Krausz, K. W., Idle, J. R., Fornace Jr., A. J., et al. (2011). UPLC-MS-based urine metabolomics reveals indole-3-lactic acid and phenyllactic acid as conserved biomarkers for alcohol-induced liver disease in the Ppara-null mouse model. Journal of Proteome Research, 10(9), 4120–4133.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  20. Zhao, H., Liu, Y., Li, Z., Song, Y., Cai, X., Liu, Y., et al. (2018). Identification of essential hypertension biomarkers in human urine by non-targeted metabolomics based on UPLC-Q-TOF/MS. Clinica Chimica Acta, 486, 192–198.

    Article  CAS  Google Scholar 

  21. Wang, C., Liu, C., Wang, M., Ma, Q., Li, Y., Wang, T., et al. (2018). UPLC-HRMS-based plasma Metabolomic profiling of novel biomarkers by treatment with KDZI in cerebral ischemia reperfusion rats. Molecules, 23(6), 1315.

    Article  PubMed Central  CAS  Google Scholar 

  22. 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(17), 4015–4038.

    Article  CAS  PubMed  Google Scholar 

  23. Pietrowska, M., & Widłak, P. (2012). MALDI-MS-based profiling of serum proteome: Detection of changes related to progression of Cancer and response to anticancer treatment. International Journal of Proteomics, 2012, 926427.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  24. Siciliano, R. A., Mazzeo, M. F., Spada, V., Facchiano, A., d’Acierno, A., Stocchero, M., et al. (2014). Rapid peptidomic profiling of peritoneal fluid by MALDI-TOF mass spectrometry for the identification of biomarkers of endometriosis. Gynecological Endocrinology, 30(12), 872–876.

    Article  CAS  PubMed  Google Scholar 

  25. Wang, Y., Chen, J., Chen, L., Zheng, P., Xu, H. B., & Lu, J. (2014). Urinary peptidomics identifies potential biomarkers for major depressive disorder. Psychiatry Research, 217(1-2), 25–33.

    Article  CAS  PubMed  Google Scholar 

  26. Smith, A., Galli, M., Piga, I., Denti, V., Stella, M., Chinello, C., et al. (2019). Molecular signatures of medullary thyroid carcinoma by matrix-assisted laser desorption/ionisation mass spectrometry imaging. Proteomics, 191, 114–123.

    Article  CAS  PubMed  Google Scholar 

  27. Ser, Z., Liu, X., Tang, N. N., & Locasale, J. W. (2015). Extraction parameters for metabolomics from cultured cells. Analytical Biochemistry, 475, 22–28.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  28. Sellick, C. A., Knight, D., Croxford, A. S., Maqsood, A. F., Stephens, G. M., Goodacre, R., et al. (2010). Evaluation of extraction processes for intracellular metabolite profiling of mammalian cells: Matching extraction approaches to cell type and metabolite targets. Metabolomics, 6, 427–438.

    Article  CAS  Google Scholar 

  29. Lorenz, M. A., Burant, C. F., & Kennedy, R. T. (2011). Reducing time and increasing sensitivity in sample preparation for adherent mammalian cell metabolomics. Analytical Chemistry, 83(9), 3406–3414.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  30. Purwaha, P., Lorenzi, P. L., Silva, L. P., Hawke, D. H., & Weinstein, J. N. (2014). Targeted metabolomic analysis of amino acid response to L-asparaginase in adherent cells. Metabolomics, 10(5), 909–919.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  31. Sapcariu, S. C., Kanashova, T., Weindl, J. G., Dittmer, G., & Hiller, K. (2014). Simultaneous extraction of proteins and metabolites from cells in culture. MethodsX, 1, 74–80.

    Article  PubMed Central  PubMed  Google Scholar 

  32. Liu, L., Aa, J., Wang, G., Yan, B., Zhang, Y., Wang, X., et al. (2010). Differences in metabolite profile between blood plasma and serum. Analytical Biochemistry, 406(2), 105–112.

    Article  CAS  PubMed  Google Scholar 

  33. Al Awam, K., Haußleiter, I. S., Dudley, E., Donev, R., Brüne, M., Juckel, G., et al. (2017). Multiplatform metabolome and proteome profiling identifies serum metabolite and protein signatures as prospective biomarkers for schizophrenia. Schizophrenia Research, 185, 182–189.

    Article  Google Scholar 

  34. Liu, Y., Chen, T., Qiu, Y., Cheng, Y., Cao, Y., Zhao, A., et al. (2011). An ultrasonication-assisted extraction and derivatization protocol for GC/TOFMS-based metabolite profiling. Analytical and Bioanalytical Chemistry, 400(5), 1405–1417.

    Article  CAS  PubMed  Google Scholar 

  35. Thomas, C. E., Sexton, W., Benson, K., Sutphen, R., & Koomen, J. (2010). Urine collection and processing for protein biomarker discovery and quantification. Cancer Epidemiology, Biomarkers & Prevention, 19(4), 953–959.

    Article  CAS  Google Scholar 

  36. Novak, B. J., Blake, D. R., Meinardi, S., Rowland, F. S., Pontello, A., Cooper, D. M., et al. (2007). Exhaled methyl nitrate as a noninvasive marker of hyperglycemia in type 1 diabetes. Proceedings of the National Academy of Sciences of the United States of America, 104(40), 15613–15618.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  37. Davies, M. P., Barash, O., Jeries, R., Peled, N., Ilouze, M., Hyde, R., et al. (2014). Unique volatolomic signatures of TP53 and KRAS in lung cells. British Journal of Cancer, 111(6), 1213–1221.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  38. Thriumani, R., Zakaria, A., Hashim, Y. Z. H., Jeffree, A. I., Helmy, K. M., Kamarudin, L. M., et al. (2018). A study on volatile organic compounds emitted by in-vitro lung cancer cultured cells using gas sensor array and SPME-GCMS. BMC Cancer, 18(1), 362.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  39. Jia, Z., Zhang, H., Ong, C. N., Patra, A., Lu, Y., Lim, C. T., et al. (2018). Detection of lung Cancer: Concomitant volatile organic compounds and Metabolomic profiling of six Cancer cell lines of different histological origins. ACS Omega, 3(5), 5131–5140.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  40. Phillips, M., Bauer, T. L., Cataneo, R. N., Lebauer, C., Mundada, M., Pass, H. I., et al. (2015). Blinded validation of breath biomarkers of lung cancer, a potential ancillary to chest CT screening. PLoS One, 10(12), e0142484.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  41. Chin, S. T., Romano, A., Doran, S. L. F., & Hanna, G. B. (2018). Cross-platform mass spectrometry annotation in breathomics of oesophageal-gastric cancer. Scientific Reports, 8(1), 5139.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  42. Barash, O., Zhang, W., Halpern, J. M., Hua, Q. L., Pan, Y. Y., Kayal, H., et al. (2015). Differentiation between genetic mutations of breast cancer by breath volatolomics. Oncotarget, 6(42), 44864–44876.

    Article  PubMed Central  PubMed  Google Scholar 

  43. Meyer, N., Dallinga, J. W., Nuss, S. J., Moonen, E. J., van Berkel, J. J., Akdis, C., et al. (2014). Defining adult asthma endotypes by clinical features and patterns of volatile organic compounds in exhaled air. Respiratory Research, 15, 136.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  44. Jareño-Esteban, J. J., Muñoz-Lucas, M. Á., Gómez-Martín, Ó., Utrilla-Trigo, S., Gutiérrez-Ortega, C., Aguilar-Ros, A., et al. (2017). Study of 5 volatile organic compounds in exhaled breath in chronic obstructive pulmonary disease. Archivos de Bronconeumología, 53(5), 251–256.

    Article  PubMed  Google Scholar 

  45. Besa, V., Teschler, H., Kurth, I., Khan, A. M., Zarogoulidis, P., Baumbach, J. I., et al. (2015). Exhaled volatile organic compounds discriminate patients with chronic obstructive pulmonary disease from healthy subjects. International Journal of Chronic Obstructive Pulmonary Disease, 10, 399–406.

    CAS  PubMed Central  PubMed  Google Scholar 

  46. Rieder, F., Kurada, S., Grove, D., Cikach, F., Lopez, R., Patel, N., et al. (2016). A distinct colon-derived breath metabolome is associated with inflammatory bowel disease, but not its complications. Clinical and Translational Gastroenterology, 7(11), e201.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  47. Baranska, A., Mujagic, Z., Smolinska, A., Dallinga, J. W., Jonkers, D. M., Tigchelaar, E. F., et al. (2016). Volatile organic compounds in breath as markers for irritable bowel syndrome: A metabolomic approach. Alimentary Pharmacology & Therapeutics, 44(1), 45–56.

    Article  CAS  Google Scholar 

  48. Fernández Del Río, R., O’Hara, M. E., Holt, A., Pemberton, P., Shah, T., Whitehouse, T., et al. (2015). Volatile biomarkers in breath associated with liver cirrhosis – comparisons of pre- and post-liver transplant breath samples. eBioMedicine, 2(9), 1243–1250.

    Article  PubMed Central  PubMed  Google Scholar 

  49. Mansoor, J. K., Schelegle, E. S., Davis, C. E., Walby, W. F., Zhao, W., Aksenov, A. A., et al. (2014). Analysis of volatile compounds in exhaled breath condensate in patients with severe pulmonary arterial hypertension. PLoS One, 9(4), e95331.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  50. Sukul, P., Schubert, J. K., Oertel, P., Kamysek, S., Taunk, K., Trefz, P., et al. (2016). FEV manoeuvre induced changes in breath VOC compositions: An unconventional view on lung function tests. Scientific Reports, 6, 28029.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  51. Sukul, P., Trefz, P., Schubert, J. K., & Miekisch, W. (2014). Immediate effects of breath holding maneuvers onto composition of exhaled breath. Journal of Breath Research, 8(3), 037102.

    Article  CAS  PubMed  Google Scholar 

  52. Cancer research UK. (2017). Pancreatic cancer statistics. [www. Document] accessed at http://www.cancerresearchuk.org/health-professional/cancer-statistics/statistics-by-cancer-type/pancreatic-cancer.

  53. Freelove, R., & Walling, A. D. (2006). Pancreatic cancer: Diagnosis and management. American Family Physician, 73(3), 485–492.

    PubMed  Google Scholar 

  54. Holly, E. A., Chaliha, I., Bracci, P. M., & Gautam, M. (2004). Signs and symptoms of pancreatic cancer: A population-based case-control study in the San Francisco Bay area. Clinical Gastroenterology and Hepatology, 2(6), 510–517.

    Article  PubMed  Google Scholar 

  55. Ballehaninna, U. K., & Chamberlain, R. S. (2012). The clinical utility of serum CA 19-9 in the diagnosis, prognosis and management of pancreatic adenocarcinoma: An evidence based appraisal. Journal of Gastrointestinal Oncology, 3(2), 105–119.

    CAS  PubMed Central  PubMed  Google Scholar 

  56. Li, D., Xie, K., Wolff, R., & Abbruzzese, J. L. (2004). Pancreatic cancer. Lancet, 363(9414), 1049–1057.

    Article  CAS  PubMed  Google Scholar 

  57. Lindahl, A., Heuchel, R., Forshed, J., Lehtiö, J., Löhr, M., & Nordström, A. (2017). Discrimination of pancreatic cancer and pancreatitis by LC-MS metabolomics. Metabolomics, 13(5), 61.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  58. van Heerde, M. J., Buijs, J., Hansen, B. E., de Waart, M., van Eijck, C. H., Kazemier, G., et al. (2014). Serum level of Ca 19-9 increases ability of IgG4 test to distinguish patients with autoimmune pancreatitis from those with pancreatic carcinoma. Digestive Diseases and Sciences, 59(6), 1322–1329.

    PubMed  Google Scholar 

  59. Rückert, F., Pilarsky, C., & Grützmann, R. (2010). Serum tumor markers in pancreatic Cancer—Recent discoveries. Cancers (Basel), 2(2), 1107–1124.

    Article  CAS  Google Scholar 

  60. Kim, J. E., Lee, K. T., Lee, J. K., Paik, S. W., Rhee, J. C., & Choi, K. W. (2004). Clinical usefulness of carbohydrate antigen 19-9 as a screening test for pancreatic cancer in an asymptomatic population. Journal of Gastroenterology and Hepatology, 19(2), 182–186.

    Article  PubMed  Google Scholar 

  61. Velstra, B., Bonsing, B. A., Mertens, B. J., van der Burgt, Y. E., Huijbers, A., Vasen, H., et al. (2013). Detection of pancreatic cancer using serum protein profiling. HPB: The Official Journal of the International Hepato Pancreato Biliary Association, 15(8), 602–610.

    Article  Google Scholar 

  62. Kim, K., Ahn, S., Lim, J., Yoo, B. C., Hwang, J. H., & Jang, W. (2015). Detection of pancreatic cancer biomarkers using mass spectrometry. Cancer Informatics, 13(Suppl 7), 45–53.

    PubMed Central  PubMed  Google Scholar 

  63. Daemen, A., Peterson, D., Sahu, N., McCord, R., Du, X., Liu, B., et al. (2015). Metabolite profiling stratifies pancreatic ductal adenocarcinomas into subtypes with distinct sensitivities to metabolic inhibitors. Proceedings of the National Academy of Sciences of the United States of America, 112(32), E4410–E4417.

    CAS  PubMed Central  PubMed  Google Scholar 

  64. Xie, G., Lu, L., Qiu, Y., Ni, Q., Zhang, W., Gao, Y. T., et al. (2015). Plasma metabolite biomarkers for the detection of pancreatic cancer. Journal of Proteome Research, 14(2), 1195–1202.

    Article  CAS  PubMed  Google Scholar 

  65. Davis, V. W., Schiller, D. E., Eurich, D., Bathe, O. F., & Sawyer, M. B. (2013). Metabolomics and surgical oncology: Potential role for small molecule biomarkers. Annals of Surgical Oncology, 20(Suppl 3), S415–S423.

    Article  PubMed  Google Scholar 

  66. Cui, Y., Shu, X. O., Li, H. L., Yang, G., Wen, W., Gao, Y. T., et al. (2017). Prospective study of urinary prostaglandin E2 metabolite and pancreatic cancer risk. International Journal of Cancer, 141(12), 2423–2429.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  67. Wang, D., & DuBois, R. N. (2013). An inflammatory mediator, prostaglandin E2, in colorectal cancer. Cancer Journal, 19(6), 502–510.

    Article  CAS  Google Scholar 

  68. Luan, H., Liu, L. F., Tang, Z., Zhang, M., Chua, K. K., Song, J. X., et al. (2015). Comprehensive urinary metabolomic profiling and identification of potential noninvasive marker for idiopathic Parkinson’s disease. Scientific Reports, 5, 13888.

    Article  PubMed Central  PubMed  Google Scholar 

  69. Hatano, T., Saiki, S., Okuzumi, A., Mohney, R. P., & Hattori, N. (2016). Identification of novel biomarkers for Parkinson’s disease by metabolomic technologies. Journal of Neurology, Neurosurgery, and Psychiatry, 87(3), 295–301.

    Article  PubMed  Google Scholar 

  70. Han, W., Sapkota, S., Camicioli, R., Dixon, R. A., & Li, L. (2017). Profiling novel metabolic biomarkers for Parkinson’s disease using in-depth metabolomic analysis. Movement Disorders, 32(12), 1720–1728.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  71. Gevi, F., Zolla, L., Gabriele, S., & Persico, A. M. (2016). Urinary metabolomics of young Italian autistic children supports abnormal tryptophan and purine metabolism. Molecular Autism, 7, 47.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  72. Bent, S., Lawton, B., Warren, T., Widjaja, F., Dang, K., Fahey, J. W., et al. (2018). Identification of urinary metabolites that correlate with clinical improvements in children with autism treated with sulforaphane from broccoli. Molecular Autism, 9, 35.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  73. Liu, M. L., Zheng, P., Liu, Z., Xu, Y., Mu, J., Guo, J., et al. (2014). GC-MS based metabolomics identification of possible novel biomarkers for schizophrenia in peripheral blood mononuclear cells. Molecular BioSystems, 10(9), 2398–2406.

    Article  CAS  PubMed  Google Scholar 

  74. Tasic, L., Pontes, J. G. M., Carvalho, M. S., Cruz, G., Dal Mas, C., Sethi, S., et al. (2017). Metabolomics and lipidomics analyses by 1H nuclear magnetic resonance of schizophrenia patient serum reveal potential peripheral biomarkers for diagnosis. Schizophrenia Research, 185, 182–189.

    Article  PubMed  Google Scholar 

  75. Liu, M. L., Zhang, X. T., Du, X. Y., Fang, Z., Liu, Z., Xu, Y., et al. (2015). Severe disturbance of glucose metabolism in peripheral blood mononuclear cells of schizophrenia patients: A targeted metabolomic study. Journal of Translational Medicine, 13, 226.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  76. Cao, B., Jin, M., Brietzke, E., McIntyre, R. S., Wang, D., Rosenblat, J. D., et al. (2019). Serum metabolic profiling using small molecular water-soluble metabolites in individuals with schizophrenia: A longitudinal study using a pre-post-treatment design. Psychiatry and Clinical Neurosciences, 73, 100–108.

    Article  CAS  PubMed  Google Scholar 

  77. Weir, J. M., Wong, G., Barlow, C. K., Greeve, M. A., Kowalczyk, A., Almasy, L., et al. (2013). Plasma lipid profiling in a large population-based cohort. Journal of Lipid Research, 54, 2898–2908.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  78. Seyer, A., Boudah, S., Broudin, S., Junot, C., & Colsch, B. (2016). Annotation of the human cerebrospinal fluid lipidome using high resolution mass spectrometry and a dedicated data processing workflow. Metabolomics, 12, 91.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  79. Herzog, K., Pras-Raves, M. L., Vervaart, M. A., Luyf, A. C., van Kampen, A. H., Wanders, R. J., et al. (2016). Lipidomic analysis of fibroblasts from Zellweger spectrum disorder patients identifies disease-specific phospholipid ratios. Journal of Lipid Research, 57(8), 1447–1454.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  80. Al-Naamani, N., Sagliani, K. D., Dolnikowski, G. G., Warburton, R. R., Toksoz, D., Kayyali, U., et al. (2016). Plasma 12- and 15-hydroxyeicosanoids are predictors of survival in pulmonary arterial hypertension. Pulmonary Circulation, 6(2), 224–233.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  81. Lebkuchen, A., Carvalho, V. M., Venturini, G., Salgueiro, J. S., Freitas, L. S., Dellavance, A., et al. (2018). Metabolomic and lipidomic profile in men with obstructive sleep apnoea: Implications for diagnosis and biomarkers of cardiovascular risk. Scientific Reports, 8(1), 11270.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  82. Chan, P., Suridjan, I., Mohammad, D., Herrmann, N., Mazereeuw, G., Hillyer, L. M., et al. (2018). Novel phospholipid signature of depressive symptoms in patients with coronary artery disease. Journal of the American Heart Association, 7(10)., pii: e008278.

    Google Scholar 

  83. Purwaha, P., Gu, F., Piyarathna, D. W. B., Rajendiran, T., Ravindran, A., Omilian, A. R., et al. (2018). Unbiased Lipidomic profiling of triple-negative breast Cancer tissues reveals the Association of Sphingomyelin Levels with patient disease-free survival. Metabolites, 8(3)., pii: E41.

    Google Scholar 

  84. Kim, H. Y., Lee, H., Kim, S. H., Jin, H., Bae, J., & Choi, H. K. (2017). Discovery of potential biomarkers in human melanoma cells with different metastatic potential by metabolic and lipidomic profiling. Scientific Reports, 7(1), 8864.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  85. de Figueiredo Junior, A. G., Serafim, P. V. P., de Melo, A. A., Felipe, A. V., Lo Turco, E. G., da Silva, I. D. C. G., et al. (2018). Analysis of the lipid profile in patients with colorectal Cancer in advanced stages. Asian Pacific Journal of Cancer Prevention, 19(5), 1287–1293.

    PubMed  Google Scholar 

  86. Fernandes Messias, M. C., Mecatti, G. C., Figueiredo Angolini, C. F., Eberlin, M. N., Credidio, L., Real Martinez, C. A., et al. (2018). Plasma Lipidomic signature of rectal adenocarcinoma reveals potential biomarkers. Frontiers in Oncology, 7, 325.

    Article  PubMed Central  PubMed  Google Scholar 

  87. Braicu, E. I., Darb-Esfahani, S., Schmitt, W. D., Koistinen, K. M., Heiskanen, L., Pöhö, P., et al. (2017). High-grade ovarian serous carcinoma patients exhibit profound alterations in lipid metabolism. Oncotarget, 8(61), 102912–102922.

    Article  PubMed Central  PubMed  Google Scholar 

  88. Ren, C., Liu, J., Zhou, J., Liang, H., Wang, Y., Sun, Y., et al. (2018). Lipidomic analysis of serum samples from migraine patients. Lipids in Health and Disease, 17(1), 22.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  89. Blasco, H., Veyrat-Durebex, C., Bocca, C., Patin, F., Vourc’h, P., Kouassi Nzoughet, J., et al. (2017). Lipidomics reveals cerebrospinal-fluid signatures of ALS. Scientific Reports, 7(1), 17652.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  90. O’Gorman, A., Suvitaival, T., Ahonen, L., Cannon, M., Zammit, S., Lewis, G., et al. (2017). Identification of a plasma signature of psychotic disorder in children and adolescents from the Avon longitudinal study of parents and children (ALSPAC) cohort. Translational Psychiatry, 7(9), e1240.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  91. Aquino, A., Alexandrino, G. L., Guest, P. C., Augusto, F., Gomes, A. F., Murgu, M., et al. (2018). Blood-based Lipidomics approach to evaluate biomarkers associated with response to olanzapine, risperidone, and quetiapine treatment in schizophrenia patients. Frontiers in Psychiatry, 9, 209.

    Article  PubMed Central  PubMed  Google Scholar 

  92. Alexandre-Gouabau, M. C., Moyon, T., Cariou, V., Antignac, J. P., Qannari, E. M., Croyal, M., et al. (2018). Breast Milk Lipidome is associated with early growth trajectory in preterm infants. Nutrients, 10(2)., pii: E164.

    Google Scholar 

  93. Anand, S., Young, S., Esplin, M. S., Peaden, B., Tolley, H. D., Porter, T. F., et al. (2016). Detection and confirmation of serum lipid biomarkers for preeclampsia using direct infusion mass spectrometry. Journal of Lipid Research, 57(4), 687–696.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  94. Balog, J., Sasi-Szabó, L., Kinross, J., Lewis, M. R., Muirhead, L. J., Veselkov, K., et al. (2013). Intraoperative tissue identification using rapid evaporative ionization mass spectrometry. Science Translational Medicine, 5(194), 194ra93.

    Article  CAS  PubMed  Google Scholar 

  95. Alexander, J., Gildea, L., Balog, J., Speller, A., McKenzie, J., Muirhead, L., et al. (2017). A novel methodology for in vivo endoscopic phenotyping of colorectal cancer based on real-time analysis of the mucosal lipidome: A prospective observational study of the iKnife. Surgical Endoscopy, 31(3), 1361–1370.

    Article  PubMed  Google Scholar 

  96. St John, E. R., Balog, J., McKenzie, J. S., Rossi, M., Covington, A., Muirhead, L., et al. (2017). Rapid evaporative ionisation mass spectrometry of electrosurgical vapours for the identification of breast pathology: Towards an intelligent knife for breast cancer surgery. Breast Cancer Research, 19(1), 59.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Phelps, D. L., Balog, J., Gildea, L. F., Bodai, Z., Savage, A., El-Bahrawy, M. A., et al. (2018). The surgical intelligent knife distinguishes normal, borderline and malignant gynaecological tissues using rapid evaporative ionisation mass spectrometry (REIMS). British Journal of Cancer, 118(10), 1349–1358.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  98. Wang, X., Zhao, B. S., Roundtree, I. A., Lu, Z., Han, D., Ma, H., et al. (2015). N(6)-methyladenosine modulates messenger RNA translation efficiency. Cell, 161(6), 1388–1399.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  99. Durbin, A. F., Wang, C., Marcotrigiano, J., & Gehrke, L. (2016). RNAs containing modified nucleotides fail to trigger RIG-I conformational changes for innate immune signaling. MBio, 7(5)., pii: e00833-16.

    Google Scholar 

  100. Shigi, N., Suzuki, T., Tamakoshi, M., Oshima, T., & Watanabe, K. (2002). Conserved bases in the TΨC loop of tRNA are determinants for thermophile-specific 2-Thiouridylation at position 54. The Journal of Biological Chemistry, 277(42), 39128–391235.

    Article  CAS  PubMed  Google Scholar 

  101. Pathak, C., Jaiswal, Y. K., & Vinayak, M. (2008). Modulation in the activity of lactate dehydrogenase and level of c-Myc and c-Fos by modified base queuine in cancer. Cancer Biology & Therapy, 7(1), 85–91.

    Article  CAS  Google Scholar 

  102. Liu, X. C., & Scouten, W. H. (2000). Boronate affinity chromatography. Methods in Molecular Biology, 147, 119–128.

    Article  CAS  PubMed  Google Scholar 

  103. Dudley, E., El-Shakawi, S., Games, D. E., & Newton, R. P. (2000). Development of a purification procedure for the isolation of nucleosides from urine prior to mass spectrometric analysis. Nucleosides, Nucleotides & Nucleic Acids, 19(3), 545–558.

    Article  CAS  Google Scholar 

  104. Solomon, S. J., Fischbein, A., Sharma, O. K., & Borek, E. (1985). Modified nucleosides in asbestos workers at high risk of malignant disease: Results of a preliminary study applying discriminant analysis. British Journal of Industrial Medicine, 42(8), 560–562.

    CAS  PubMed Central  PubMed  Google Scholar 

  105. Waszczuk-Jankowska, M., Markuszewski, M. J., Markuszewski, M., & Kaliszan, R. (2012). Comparison of RP-HPLC columns used for determination of nucleoside metabolic patterns in urine of cancer patients. Bioanalysis, 4(10), 1185–1194.

    Article  CAS  PubMed  Google Scholar 

  106. Langridge, J. I., McClure, T. D., Ei-Shakawi, S., Fielding, A., Schram, K. H., & Newton, R. P. (1993). Gas chromatography/mass spectrometric analysis of urinary nucleosides in cancer patients; potential of modified nucleosides as tumour markers. Rapid Communications in Mass Spectrometry, 7(6), 427–434.

    Article  CAS  PubMed  Google Scholar 

  107. Edmonds, C. G., Vestal, M. L., & McCloskey, J. A. (1985). Thermospray liquid chromatography-mass spectrometry of nucleosides and of enzymatic hydrolysates of nucleic acids. Nucleic Acids Research, 13(22), 8197–8206.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  108. Dudley, E., El-Sharkawi, S., Games, D. E., & Newton, R. P. (2000). Analysis of urinary nucleosides. I. Optimisation of high performance liquid chromatography/electrospray mass spectrometry. Rapid Communications in Mass Spectrometry, 14(14), 1200–1207.

    Article  CAS  PubMed  Google Scholar 

  109. Dudley, E., Tuytten, R., Bond, A., Lemière, F., Brenton, A. G., Esmans, E. L., et al. (2005). Study of the mass spectrometric fragmentation of pseudouridine: Comparison of fragmentation data obtained by matrix-assisted laser desorption/ionisation post-source decay, electrospray ion trap multistage mass spectrometry, and by a method utilising electrospray quadrupole time-of-flight tandem mass spectrometry and in-source fragmentation. Rapid Communications in Mass Spectrometry, 19(21), 3075–3085.

    Article  CAS  PubMed  Google Scholar 

  110. Tuytten, R., Lemière, F., Esmans, E. L., Herrebout, W. A., van der Veken, B. J., Maes, B. U., et al. (2007). Role of nitrogen Lewis basicity in boronate affinity chromatography of nucleosides. Analytical Chemistry, 79(17), 6662–6669.

    Article  CAS  PubMed  Google Scholar 

  111. Tuytten, R., Lemière, F., Van Dongen, W., Witters, E., Esmans, E. L., Newton, R. P., et al. (2008). Development of an on-line SPE-LC–ESI-MS method for urinary nucleosides: Hyphenation of aprotic Boronic acid chromatography with hydrophilic interaction LC–ESI-MS. Analytical Chemistry, 80(4), 1263–1271.

    Article  CAS  PubMed  Google Scholar 

  112. Szymańska, E., Markuszewski, M. J., Capron, X., van Nederkassel, A. M., Heyden, Y. V., Markuszewski, M., et al. (2007). Increasing conclusiveness of metabonomic studies by cheminformatic preprocessing of capillary electrophoretic data on urinary nucleoside profiles. Journal of Pharmaceutical and Biomedical Analysis, 43(2), 413–420.

    Article  CAS  PubMed  Google Scholar 

  113. Mao, Y., Zhao, X., Wang, S., & Cheng, Y. (2007). Urinary nucleosides based potential biomarker selection by support vector machine for bladder cancer recognition. Analytica Chimica Acta, 598(1), 34–40.

    Article  CAS  PubMed  Google Scholar 

  114. Zheng, Y. F., Kong, H. W., Xiong, J. H., Lv, S., & Xu, G. W. (2005). Clinical significance and prognostic value of urinary nucleosides in breast cancer patients. Clinical Biochemistry, 38(1), 24–30.

    Article  CAS  PubMed  Google Scholar 

  115. Frickenschmidt, A., Frohlich, H., Bullinger, D., Zell, A., Laufer, S., Gleiter, C. H., et al. (2008). Metabonomics in cancer diagnosis: Mass spectrometry-based profiling of urinary nucleosides from breast cancer patients. Biomarkers, 13(4), 435–449.

    Article  CAS  PubMed  Google Scholar 

  116. Zheng, Y. F., Yang, J., Zhao, X. J., Feng, B., Kong, H. W., Chen, Y. J., et al. (2005). Urinary nucleosides as biological markers for patients with colorectal cancer. World Journal of Gastroenterology, 11(25), 3871–3876.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  117. Rozalski, R., Gackowski, D., Siomek-Gorecka, A., Starczak, M., Modrzejewska, M., Banaszkiewicz, Z., et al. (2015). Urinary 5-hydroxymethyluracil and 8-oxo-7,8-dihydroguanine as potential biomarkers in patients with colorectal cancer. Biomarkers, 31, 1–5.

    Google Scholar 

  118. Yano, T., Shoji, F., Baba, H., Koga, T., Shiraishi, T., Orita, H., et al. (2009). Significance of the urinary 8-OHdG level as an oxidative stress marker in lung cancer patients. Lung Cancer, 63(1), 111–114.

    Article  PubMed  Google Scholar 

  119. Zhang, Y. R., Shi, L., Wu, H., Tang, D. D., Wang, S. M., Liu, H. M., et al. (2014). Urinary modified nucleosides as novel biomarkers for diagnosis and prognostic monitoring of urothelial bladder cancer. Tumori, 100(6), 660–666.

    CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Swansea University Medical School, Swansea University, Swansea, Wales, UK

    Kathryn Sinclair & Ed Dudley

Authors
  1. Kathryn Sinclair
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ed Dudley
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ed Dudley .

Editor information

Editors and Affiliations

  1. Biochemistry and Proteomics Group, Department of Chemistry and Biomolecular Science, Clarkson University, Potsdam, NY, USA

    Alisa G. Woods

  2. Biochemistry and Proteomics Group, Department of Chemistry and Biomolecular Science, Clarkson University, Potsdam, NY, USA

    Costel C. Darie

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Sinclair, K., Dudley, E. (2019). Metabolomics and Biomarker Discovery. In: Woods, A., Darie, C. (eds) Advancements of Mass Spectrometry in Biomedical Research. Advances in Experimental Medicine and Biology, vol 1140. Springer, Cham. https://doi.org/10.1007/978-3-030-15950-4_37

Download citation

Publish with us

Policies and ethics

Search

Navigation