Mass spectrometry-based proteomic techniques to identify cerebrospinal fluid biomarkers for diagnosing suspected central nervous system infections. A systematic review

• Main Authors: Tehmina Bharucha, Bevin Gangadharan, Abhinav Kumar, Xavier de Lamballerie, Paul N. Newton, Markus Winterberg, Audrey Dubot-Pérès, Nicole Zitzmann
• Other Authors: Aix Marseille Université
• Format: Article
• Published: 2020
• Subjects: Medicine
• Online Access: https://repository.li.mahidol.ac.th/handle/123456789/51342
• Institution: Mahidol University

© 2019 The Author(s) Objectives: Central nervous system (CNS) infections account for considerable death and disability every year. An urgent research priority is scaling up diagnostic capacity, and introduction of point-of-care tests. We set out to assess current evidence for the application of mass spectrometry (MS) peptide sequencing in identification of diagnostic biomarkers for CNS infections. Methods: We performed a systematic review (PROSPERO–CRD42018104257) using PRISMA guidelines on use of MS to identify cerebrospinal fluid (CSF) biomarkers for diagnosing CNS infections. We searched PubMed, Embase, Web of Science, and Cochrane for articles published from 1 January 2000 to 1 February 2019, and contacted experts. Inclusion criteria involved primary research except case reports, on the diagnosis of infectious diseases except HIV, applying MS to human CSF samples, and English language. Results: 4,620 papers were identified, of which 11 were included, largely confined to pre-clinical biomarker discovery, and eight (73%) published in the last five years. 6 studies performed further work termed verification or validation. In 2 of these studies, it was possible to extract data on sensitivity and specificity of the biomarkers detected by ELISA, ranging from 89–94% and 58–92% respectively. Conclusions: The findings demonstrate feasibility and potential of the methods in a variety of infectious diseases, but emphasise the need for strong interdisciplinary collaborations to ensure appropriate study design and biomarker validation.