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Annual Review of Analytical Chemistry

Volume 17, 2024

Maximizing Analytical Performance in Biomolecular Discovery with LC-MS: Focus on Psychiatric Disorders

  • Bradley J. Smith1, Paul C. Guest1,2,3 and Daniel Martins-de-Souza1,4,5,6,7
  • 1Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas, São Paulo, Brazil; email: [email protected] 2Department of Psychiatry, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany 3Laboratory of Translational Psychiatry, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany 4Experimental Medicine Research Cluster, University of Campinas, São Paulo, Brazil 5National Institute of Biomarkers in Neuropsychiatry, National Council for Scientific and Technological Development, São Paulo, Brazil 6D'Or Institute for Research and Education, São Paulo, Brazil 7INCT in Modelling Human Complex Diseases with 3D Platforms (Model3D), São Paulo, Brazil
  • Vol. 17:25-46 (Volume publication date July 2024)
  • First published as a Review in Advance on February 29, 2024
  • Copyright © 2024 by the author(s).
    This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See credit lines of images or other third-party material in this article for license information.

Abstract

In this review, we discuss the cutting-edge developments in mass spectrometry proteomics and metabolomics that have brought improvements for the identification of new disease-based biomarkers. A special focus is placed on psychiatric disorders, for example, schizophrenia, because they are considered to be not a single disease entity but rather a spectrum of disorders with many overlapping symptoms. This review includes descriptions of various types of commonly used mass spectrometry platforms for biomarker research, as well as complementary techniques to maximize data coverage, reduce sample heterogeneity, and work around potentially confounding factors. Finally, we summarize the different statistical methods that can be used for improving data quality to aid in reliability and interpretation of proteomics findings, as well as to enhance their translatability into clinical use and generalizability to new data sets.

Keyword(s): biomarkers, clinical translatability, mass spectrometry, metabolomics, proteomics
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2024-07-17
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/content/journals/10.1146/annurev-anchem-061522-041154
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Maximizing Analytical Performance in Biomolecular Discovery with LC-MS: Focus on Psychiatric Disorders
Annual Review of Analytical Chemistry 17, 25 (2024); https://doi.org/10.1146/annurev-anchem-061522-041154
/content/journals/10.1146/annurev-anchem-061522-041154
/content/journals/10.1146/annurev-anchem-061522-041154
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