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Medical Bioanalytics: Separation Techniques in Medical Diagnostics of Neurological Diseases and Disorders on Selected Examples

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Handbook of Bioanalytics

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Abstract

The chapter reviews selected separation techniques that are used to search for markers and link the symptoms of the diseases with the presence of specific metabolites in the body fluids of people suffering from Alzheimer’s, Parkinson’s disease, and autism spectrum disorders. These diseases are a growing, worldwide social and economic problem, while their early diagnosis and treatment are one of the priorities of modern medicine. A summary of the current status of the biological sample preparation and analytical methods used in establishing biomarkers, which can lead to a better understanding of the etiology of the diseases and precise prognosis or monitoring of disease progression, was presented. Methods widely used in the diagnosis of neurological disorders and diseases after suitable sample preparation include gas chromatography-mass spectrometry (GC-MS), gas chromatography with tandem mass spectrometry (GC-MS/MS), liquid chromatography-mass spectrometry (LC-MS), liquid chromatography with tandem mass spectrometry (LC-MS/MS), high-performance liquid chromatography (HPLC), ultra-performance liquid chromatography-mass spectrometry (UPLC-MS), and capillary electrophoresis-mass spectrometry (CE-MS). Diagnostic information, largely based on separation techniques, improves the accuracy of clinical activities and safety in the management of patients with neurological diseases.

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References

  1. Lottspeich, F. W., & Engels, J. W. (2018). Bioanalytics: Analytical methods and concepts in biochemistry and molecular biology. Wiley-VCH.

    Google Scholar 

  2. Alpert, A. J. (1990). Hydrophilic-interaction chromatography for the separation of peptides, nucleic acids and other polar compounds. Journal of Chromatography. A, 499, 177–196.

    Article  CAS  Google Scholar 

  3. Pitt, J. J. (2009). Principles and applications of liquid chromatography-mass spectrometry in clinical biochemistry. Clinical Biochemist Reviews, 30, 19–34.

    Google Scholar 

  4. Herrmann, A. M., Ritz, K., Nunan, N., et al. (2007). Nano-scale secondary ion mass spectrometry – a new analytical tool in biogeochemistry and soil ecology: A review article. Soil Biology and Biochemistry, 39, 1835–1850.

    Article  CAS  Google Scholar 

  5. Kitajewska, W., Szeląd, W., Kopański, Z., et al. (2014). Choroby cywilizacyjne i ich prewencja. Journal of Clinical Healthcare, 1, 3–7.

    Google Scholar 

  6. Burhenne, J. (2012). Bioanalytical method validation. Journal of Analytical and Bioanalytical Techniques, 3, e111.

    Article  Google Scholar 

  7. Owczarek, H., Nahaczewska, W., & Paliszkiewicz, A. (2009). Badania diagnostyczne w medycynie laboratoryjnej opartej na dowodach naukowych. Journal of Laboratory Diagnostics, 45, 247–251.

    Google Scholar 

  8. Gao, L., Li, J., Kasserra, C., et al. (2011). Precision and accuracy in the quantitative analysis of biological samples by accelerator mass spectrometry: Application in microdose absolute bioavailability studies. Analytical Chemistry, 83, 5607–5616.

    Article  CAS  PubMed  Google Scholar 

  9. Gaweł, M., & Potulska-Chromik, A. (2015). Choroby neurodegeneracyjne: choroba Alzheimera i Parkinsona. Postępy Nauk Medycznych, 7, 468–476.

    Google Scholar 

  10. Mousavi, M., Jonsson, P., Antti, H., et al. (2014). Serum metabolomic biomarkers of dementia. Dementia and Geriatric Cognitive Disorders Extra, 4, 252–262.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Han, W., Sapkota, S., Camicioli, R., et al. (2017). Profiling novel metabolic biomarkers for Parkinson’s disease using in-depth metabolomic analysis. Movement Disorders, 32, 1720–1728.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Schlesinger, I., & Schlesinger, N. (2008). Uric acid in Parkinson’s disease. Movement Disorders, 23, 1653–1657.

    Article  PubMed  Google Scholar 

  13. Ahmed, S., Santosh, W., Kumar, S., et al. (2009). Metabolic profiling of Parkinson’s disease: Evidence of biomarker from gene expression analysis and rapid neural network detection. Journal of Biomedical, 16, 63.

    Google Scholar 

  14. Skawina, B. (2016). Autyzm i zespół Aspergera. Objawy, przyczyny, diagnoza i współczesne metody terapeutyczne. Medzinárodná interdisciplinárna vedecká konferencia, Prešov, 7, 234–245.

    Google Scholar 

  15. Gątarek, P., Pawełczyk, M., Jastrzębski, K., et al. (2019). Analytical methods used in the study of Parkinson’s disease. Trends in Analytical Chemistry, 118, 292.

    Article  Google Scholar 

  16. Ząbek, A., & Młynarz, P. (2017). Metabolomika jako potencjalna metoda diagnostyczna w medycynie. LAB Laboratoria, Aparatura, Badania, 2, 20–26.

    Google Scholar 

  17. Van der Greef, J., & Smilde, A. K. (2005). Symbiosis of chemometrics and metabolomics: Past, present, and future. Journal of Chemometrics, 19, 376–386.

    Article  Google Scholar 

  18. Kabir, A., Locatelli, M., & Ulusoy, H. I. (2017). Recent trends in microextraction techniques employed in analytical and bioanalytical sample preparation. Separations, 4, 36.

    Article  Google Scholar 

  19. Chetwynd, A. J., Dunn, W. B., & Rodriguez-Blanco, G. (2017). Collection and preparation of clinical samples for metabolomics. In A. Sussulini (Ed.), Metabolomics: From fundamentals to clinical applications, advances in experimental medicine and biology (pp. 19–45). Springer.

    Chapter  Google Scholar 

  20. Li, W., Jian, W., & Fu, Y. (2019). Sample preparation in LC-MS bioanalysis. In W. Li, W. Jian, & Y. Fu (Eds.), Basic sample preparation techniques in LC-MS bioanalysis, protein precipitation, liquid–liquid extraction, and solid-phase extraction (1st ed., pp. 1–30). Wiley.

    Google Scholar 

  21. Niu, Y., Liu, J., Yang, R., et al. (2020). Atmospheric pressure chemical ionization source as an advantageous technique for gas chromatography-tandem mass spectrometry. TrAC, 132, 116053.

    CAS  Google Scholar 

  22. Syage, J. A., Hanold, K. A., Lynn, T. C., et al. (2004). Atmospheric pressure photoionization. II. Dual source ionization. Journal of Chromatography A, 1050, 137–149.

    CAS  PubMed  Google Scholar 

  23. Lu, W., Bennett, B. D., & Rabinowitz, J. D. (2008). Analytical strategies for LC-MS-based targeted metabolomics. Journal of Chromatography B, 871, 236–242.

    Article  CAS  Google Scholar 

  24. Unger, S., Li, W., Flarakos, J., et al. (2013). Role of LC-MS bioanalysis in drug discovery, development and therapeutic drug monitoring. In W. Li, J. Zhang, & F. L. S. Tse (Eds.), Handbook of LC-MS bioanalysis, best practices, experimental protocols, and regulations (pp. 3–14). Wiley.

    Google Scholar 

  25. Han, B., Lob, S., & Sablier, M. (2018). Benefit of the use of GCxGC/MS profiles for 1D GC/MS data treatment illustrated by the analysis of pyrolysis products from east Asian handmade papers. Journal of The American Society for Mass Spectrometry, 29, 1582–1593.

    Article  CAS  PubMed  Google Scholar 

  26. Keppler, E. A. H., Jenkins, C. L., Davis, T. J., et al. (2018). Advances in the application of comprehensive two-dimensional gas chromatography in metabolomics. Trends in Analytical Chemistry, 109, 275–286.

    Article  PubMed  Google Scholar 

  27. Nunes, V. O., Silva, R. V. S., Romeiro, G. A., et al. (2020). The speciation of the organic compounds of slow pyrolysis bio-oils from Brazilian tropical seed cake fruits using high-resolution techniques: GC×GC-TOFMS and ESI(±)-Orbitrap HRMS. Microchemical Journal, 153, 104514.

    Article  CAS  Google Scholar 

  28. Ahmed, F. E. (2009). Sample preparation and fractionation for proteome analysis and cancer biomarker discovery by mass spectrometry. Journal of Separation Science, 32, 771–798.

    CAS  PubMed  Google Scholar 

  29. Zheng, X., Wojcik, R., Zhang, X., et al. (2017). Coupling front-end separations, ion mobility spectrometry, and mass spectrometry for enhanced multidimensional biological and environmental analyses. Annual Review of Analytical Chemistry, 10, 71–92.

    Article  CAS  PubMed  Google Scholar 

  30. Nováková, L., Svoboda, P., & Pavlík, J. (2017). Ultra-high performance liquid chromatography. Liquid chromatography fundamentals and instrumentation. In S. Fanali, P. R. Haddad, C. F. Poole, & M. L. Riekkola (Eds.), Liquid chromatography (2nd ed., pp. 719–769). Elsevier.

    Chapter  Google Scholar 

  31. Allen, D. R., & McWhinney, B. C. (2019). Quadrupole time-of-flight mass spectrometry: A paradigm shift in toxicology screening applications. Clinical Biochemist Reviews, 40, 135–146.

    Article  Google Scholar 

  32. Phipps, W. S., Jones, P. M., & Patel, K. (2019). Amino and organic acid analysis: Essential tools in the diagnosis of inborn errors of metabolism. Advances in Clinical Chemistry, 92, 59–103.

    Article  CAS  PubMed  Google Scholar 

  33. Soga, T., Ohashi, Y., Ueno, Y., et al. (2003). Quantitative metabolome analysis using capillary electrophoresis mass spectrometry. Journal of Proteome Research, 2, 488–494.

    Article  CAS  PubMed  Google Scholar 

  34. González-Domínguez, R., García-Barrera, T., & García-Barrera, J. L. (2015). Metabolite profiling for the identification of altered metabolic pathways in Alzheimer’s disease. Journal of Pharmaceutical and Biomedical Analysis, 107, 75–81.

    Article  PubMed  Google Scholar 

  35. Inoue, K., Tsuchiya, H., Takayama, T., et al. (2015). Blood-based diagnosis of Alzheimer’s disease using fingerprinting metabolomics based on hydrophilic interaction liquid chromatography with mass spectrometry and multivariate statistical analysis. Journal of Chromatography. B, Analytical Technologies in the Biomedical and Life Sciences, 974, 24–34.

    Article  CAS  PubMed  Google Scholar 

  36. Paglia, G., Stocchero, M., Cacciatore, S., et al. (2016). Unbiased metabolomic investigation of Alzheimer’s disease brain points to dysregulation of mitochondrial aspartate metabolism. Journal of Proteome Research, 15, 608–618.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Graham, S. F., Chevallier, O. P., Elliott, C. T., et al. (2015). Untargeted metabolomic analysis of human plasma indicates differentially affected polyamine and L-arginine metabolism in mild cognitive impairment subjects converting to Alzheimer’s disease. PLoS One, 10, e0119452.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Guiraud, S. P., Montoliu, I., Da Silva, L., et al. (2017). High-throughput and simultaneous quantitative analysis of homocysteine–methionine cycle metabolites and co-factors in blood plasma and cerebrospinal fluid by isotope dilution LC-MS/MS. Analytical and Bioanalytical Chemistry, 409, 295–305.

    Article  CAS  PubMed  Google Scholar 

  39. Kim, S. H., Yang, J. S., Lee, J. C., et al. (2018). Lipidomic alterations in lipoproteins of patients with mild cognitive impairment and Alzheimer’s disease by asymmetrical flow field-flow fractionation and nanoflow ultrahigh performance liquid chromatography-tandem mass spectrometry. Journal of Chromatography. A, 1568, 91–100.

    Article  CAS  PubMed  Google Scholar 

  40. Muguruma, Y., Tsutsui, H., Noda, T., et al. (2018). Widely targeted metabolomics of Alzheimer’s disease postmortem cerebrospinal fluid based on 9-fluorenylmethyl chloroformate derivatized ultra-high performance liquid chromatography tandem mass spectrometry. Journal of Chromatography. B, Analytical Technologies in the Biomedical and Life Sciences, 1091, 53–66.

    Article  CAS  PubMed  Google Scholar 

  41. González-Domínguez, R., García, A., García-Barrera, T., et al. (2014). Metabolomic profiling of serum in the progression of Alzheimer’s disease by capillary electrophoresis-mass spectrometry. Electrophoresis, 35, 3321–3330.

    Article  PubMed  Google Scholar 

  42. Suominen, T., Haapala, M., Takala, A., et al. (2013). Neurosteroid analysis by gas chromatography-atmospheric pressure photoionization-tandem mass spectrometry. Analytica Chimica Acta, 794, 76–81.

    Article  CAS  PubMed  Google Scholar 

  43. Trupp, M., Jonsson, P., Ohrfelt, A., et al. (2014). Metabolite and peptide levels in plasma and CSF differentiating healthy controls from patients with newly diagnosed Parkinson’s disease. Journal of Parkinson's Disease, 4, 549–560.

    Article  CAS  PubMed  Google Scholar 

  44. Michell, A. W., Mosedale, D., Grainger, D. J., et al. (2008). Metabolomic analysis of urine and serum in Parkinson’s disease. Metabolomics, 4, 191.

    Article  CAS  Google Scholar 

  45. Roede, J. R., Uppal, K., Park, Y., et al. (2013). Serum metabolomics of slow vs. rapid motor progression Parkinson’s disease: A pilot study. PLoS One, 8, e77629.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Hur, Y., Tae, S., Koh, Y. J., et al. (2014). Simultaneous determination of five porphyrins in human urine and plasma using high performance liquid chromatography-tandem mass spectrometry. Mass Spectrometry Letters, 5, 42–48.

    Article  CAS  Google Scholar 

  47. Luan, H., Liu, L. F., Meng, N., et al. (2015). LC-MS-based urinary metabolite signatures in idiopathic Parkinson’s disease. Journal of Proteome Research, 14, 467–478.

    Article  CAS  PubMed  Google Scholar 

  48. Havelund, J. F., Andersen, A. D., Binzer, M., et al. (2017). Changes in kynurenine pathway metabolism in Parkinson patients with L-DOPA-induced dyskinesia. Journal of Neurochemistry, 142, 756–766.

    Article  CAS  PubMed  Google Scholar 

  49. Zhao, H., Wang, C., Zhao, N., et al. (2018). Potential biomarkers of Parkinson’s disease revealed by plasma metabolic profiling. Journal of Chromatography. B, Analytical Technologies in the Biomedical and Life Sciences, 1081–1082, 101–108.

    Article  PubMed  Google Scholar 

  50. Bobrowska-Korczak, B., Gątarek, P., Rosiak, A., et al. (2019). Reduced levels of modified nucleosides in the urine of autistic children. Preliminary studies. Analytical Biochemistry, 571, 62–67.

    Article  CAS  PubMed  Google Scholar 

  51. Kałużna-Czaplińska, J., Jóźwik-Pruska, J., & Axt, A. (2017). Chromatographic determination of harmalans in the urine of autistic children. Biomedical Chromatography, 31, 1–6.

    Article  Google Scholar 

  52. Grayaa, S., Zerbinati, C., Messedi, M., et al. (2018). Plasma oxysterol profiling in children reveals 24-hydroxycholesterol as a potential marker for Autism Spectrum Disorders. Biochimie, 153, 80–85.

    Article  CAS  PubMed  Google Scholar 

  53. Kałużna-Czaplińska, J., Michalska, M., & Rynkowski, J. (2010). Determination of tryptophan in urine of autistic and healthy children by gas chromatography/mass spectrometry. Medical Science Monitor, 16, CR488–CR492.

    PubMed  Google Scholar 

  54. Kałużna-Czaplińska, J., Socha, E., & Rynkowski, J. (2010). Determination of homovanillic acid and vanillylmandelic acid in urine of autistic children by gas chromatography/mass spectrometry. Medical Science Monitor, 16, CR445–CR450.

    PubMed  Google Scholar 

  55. Kuwabara, H., Yamasue, H., Koike, S., et al. (2013). Altered metabolites in the plasma of autism spectrum disorder: A capillary electrophoresis time-of-flight mass spectroscopy study. PLoS One, 8, e73814.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Noto, A., Fanos, V., Barberini, L., et al. (2014). The urinary metabolomics profile of an Italian autistic children population and their unaffected siblings. The Journal of Maternal-Fetal & Neonatal Medicine, 2, 46–52.

    Article  Google Scholar 

  57. Cozzolino, R., De Magistris, L., Saggese, P., et al. (2014). Use of solid-phase microextraction coupled to gas chromatography-mass spectrometry for determination of urinary volatile organic compounds in autistic children compared with healthy controls. Analytical and Bioanalytical Chemistry, 406, 4649–4662.

    Article  CAS  PubMed  Google Scholar 

  58. Kałużna-Czaplińska, J., Żurawicz, E., Struck, W., et al. (2014). Identification of organic acids as potential biomarkers in the urine of autistic children using gas chromatography/mass spectrometry. Journal of Chromatography. B, Analytical Technologies in the Biomedical and Life Sciences, 966, 70–76.

    Article  PubMed  Google Scholar 

  59. Gondalia, S., Mahon, P. J., & Palombo, E. (2013). Evaluation of biogenic amines in the faeces of children with and without autism by LC-MS/MS. International Journal of Biotechnology and Biochemistry, 9, 245–255.

    Google Scholar 

  60. Kałużna-Czaplińska, J., Michalska, M., & Rynkowski, J. (2011). Vitamin supplementation reduces the level of homocysteine in the urine of autistic children. Nutrition Research, 31, 318–321.

    Article  PubMed  Google Scholar 

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Authors and Affiliations

  1. Lodz University of Technology, Faculty of Chemistry, Institute of General and Ecological Chemistry, Lodz, Poland

    Joanna Kałużna-Czaplińska, Angelina Rosiak & Paulina Gątarek

Authors
  1. Joanna Kałużna-Czaplińska
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  2. Angelina Rosiak
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  3. Paulina Gątarek
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Corresponding author

Correspondence to Joanna Kałużna-Czaplińska .

Editor information

Editors and Affiliations

  1. Environmental Chemistry and Bioanalytics, Nicolaus Copernicus University, Torun, Poland

    Bogusław Buszewski

  2. Faculty of Chemistry, Silesian University of Technology, Gliwice, Poland

    Irena Baranowska

Section Editor information

  1. Centre National de la Recherche Scientifique (CNRS), Institute of Analytical Sciences and Physico-Chemistry for Environment (IPREM, UMR 5254 CNRS-UPPA), Pau, France

    Joanna Szpunar

  2. Faculty of Chemistry, Warsaw University of Technology, Warsaw, Poland

    Ryszard Łobiński

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Kałużna-Czaplińska, J., Rosiak, A., Gątarek, P. (2022). Medical Bioanalytics: Separation Techniques in Medical Diagnostics of Neurological Diseases and Disorders on Selected Examples. In: Buszewski, B., Baranowska, I. (eds) Handbook of Bioanalytics. Springer, Cham. https://doi.org/10.1007/978-3-030-63957-0_3-1

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  • DOI: https://doi.org/10.1007/978-3-030-63957-0_3-1

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  • Print ISBN: 978-3-030-63957-0

  • Online ISBN: 978-3-030-63957-0

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