SWATH-MS analysis of cerebrospinal fluid to generate a robust battery of biomarkers for Alzheimer’s disease

Sun Ah Park, Jin Myung Jung, Jun Sung Park, Jeong Ho Lee, Bumhee Park, Hyung Jun Kim, Jeong Ho Park, Won Seok Chae, Jee Hyang Jeong, Seong Hye Choi, Je Hyun Baek

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18 Scopus citations


Cerebrospinal fluid (CSF) Aβ42 and tau protein levels are established diagnostic biomarkers of Alzheimer’s disease (AD). However, their inadequacy to represent clinical efficacy in drug trials indicates the need for new biomarkers. Sequential window acquisition of all theoretical fragment ion spectra (SWATH)-based mass spectrometry (MS) is an advanced proteomic tool for large-scale, high-quality quantification. In this study, SWATH-MS showed that VGF, chromogranin-A, secretogranin-1, and opioid-binding protein/cell adhesion molecule were significantly decreased in 42 AD patients compared to 39 controls, whereas 14-3-3ζ was increased (FDR < 0.05). In addition, 16 other proteins showed substantial changes (FDR < 0.2). The expressions of the top 21 analytes were closely interconnected, but were poorly correlated with CSF Aβ42, tTau, and pTau181 levels. Logistic regression analysis and data mining were used to establish the best algorithm for AD, which created novel biomarker panels with high diagnostic value (AUC = 0.889 and 0.924) and a strong correlation with clinical severity (all p < 0.001). Targeted proteomics was used to validate their usefulness in a different cohort (n = 36) that included patients with other brain disorders (all p < 0.05). This study provides a list of proteins (and combinations thereof) that could serve as new AD biomarkers.

Original languageEnglish
Article number7423
JournalScientific Reports
Issue number1
StatePublished - 1 Dec 2020

Bibliographical note

Funding Information:
This work was supported by Basic Science Research Program (NRF-2018R1A2B6009439 and 2019R1A5A2026045) and the Original Technology Research Program for Brain Science (NRF-2018M3C7A1056293) of the National Research Foundation of Korea (NRF) funded by the Korean government, MSIT, and a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (HI14C1942). We thank Prof. So Young Moon and Song Mi Han for their support during the revision.

Publisher Copyright:
© 2020, The Author(s).


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