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

被引:0
|
作者
Smith, Bradley J. [1 ]
Guest, Paul C. [1 ,2 ,3 ]
Martins-de-Souza, Daniel [1 ,4 ,5 ,6 ,7 ]
机构
[1] Univ Estadual Campinas, Inst Biol, Dept Biochem & Tissue Biol, Lab Neuroprote, Sao Paulo, Brazil
[2] Otto von Guericke Univ, Dept Psychiat, Magdeburg, Germany
[3] Otto von Guericke Univ, Lab Translat Psychiat, Magdeburg, Germany
[4] Univ Estadual Campinas, Expt Med Res Cluster, Sao Paulo, Brazil
[5] Natl Council Sci & Technol Dev, Natl Inst Biomarkers Neuropsychiat, Sao Paulo, Brazil
[6] DOr Inst Res & Educ, Sao Paulo, Brazil
[7] INCT Modelling Human Complex Dis 3D Platforms Mo, Sao Paulo, Brazil
基金
巴西圣保罗研究基金会;
关键词
biomarkers; mass spectrometry; proteomics; metabolomics; clinical translatability; CHROMATOGRAPHY MASS-SPECTROMETRY; PROTEOME ANALYSIS; ARTIFICIAL-INTELLIGENCE; CEREBROSPINAL-FLUID; SCHIZOPHRENIA; BIOMARKERS; IDENTIFICATION; TECHNOLOGIES; IONIZATION; PROTEINS;
D O I
10.1146/annurev-anchem-061522-041154
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
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.
引用
收藏
页码:25 / 46
页数:22
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