Optimized sample preparation and data analysis for TMT proteomic analysis of cerebrospinal fluid applied to the identification of Alzheimer’s disease biomarkers

This article was originally published here

Clin Proteomics. 2022 May 14;19(1):13. doi: 10.1186/s12014-022-09354-0.


BACKGROUND: Cerebrospinal fluid (CSF) is an important biofluid for biomarkers of neurodegenerative diseases such as Alzheimer’s disease (AD). Using tandem mass tag (TMT) proteomics, thousands of proteins can be quantified simultaneously in large cohorts, making it a powerful tool for biomarker discovery. However, TMT proteomics in CSF is associated with analytical challenges regarding sample preparation and data processing. In this study, we address challenges ranging from data normalization to sample preparation to sample analysis.

METHOD: Using liquid chromatography-mass spectrometry (LC-MS), we analyzed multiplex TMT samples consisting of identical or individual CSF samples, assessed quantitation accuracy, and tested the performance of different approaches to data normalization. We reviewed the MS2 and MS3 acquisition strategies for quantitation accuracy and performed a comparative evaluation of filter-assisted sample preparation (FASP) and an in-solution protocol. Finally, four normalization approaches (median, quantile, total peptide amount, TAMPOR) were applied to the dataset previously published in the European Medical Information Framework for Multimodal Biomarker Discovery of Alzheimer’s Disease ( EMIF-AD MBD).

RESULTS: The correlation of TMT reporter ratios measured with amounts of enriched standard peptides was significantly lower for TMT multiplexes composed of individual CSF samples compared to those composed of aliquots of a single CSF pool, demonstrating that the heterogeneous composition of the CSF sample influences the quantification of TMT. Comparison of TMT reporter normalization methods showed that the correlation could be improved by applying median and quantile based normalization. The slope was improved by acquiring data in MS3 mode, but at the cost of a 29% decrease in the number of identified proteins. Preparation of FASP and solution samples from CSF showed a 73% overlap in identified proteins. Finally, using optimized data normalization, we present a list of 64 biomarker candidates (clinical AD vs controls, p

CONCLUSION: We assessed several analytical aspects of TMT proteomics in CSF. The results of our study provide practical guidelines to improve quantification accuracy and facilitate the design of sample preparation and analytical protocol. The list of AD biomarkers extracted from the EMIF-AD cohort may provide a valuable basis for future biomarker studies and help elucidate pathogenic mechanisms in AD.

PMID:35568819 | DOI:10.1186/s12014-022-09354-0

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