Authors:

Nathaniel Merrick Miller

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Chemical labeling has become a crucial part of many disciplines of biochemistry. Proteomics, for example, uses isotopic labeling to run more than one sample at once in a mass spectrometer, decreasing variation, increasing proteomic depth, and allowing for direct comparison of the intensity of peptides between channels. While DDA (data-dependent acquisition) has been the main method used in LC-MS/MS-based proteomic analysis for the last 15 years, DIA (data-independent acquisition) has become increasingly competitive in terms of unbiased detection and better quantification. Recently, several studies have demonstrated advanced proteomics methods by combining chemical labeling and DIA. Moreover, more bioinformatic tools have been developed to improve DIA data analyses. Therefore, we set out to determine the most effective proteomics pipelines for multiplex isotope labeling-based DIA analysis. We also apply this same technique to the phosphoproteome, a subfield of proteomics that has shown biological and diagnostic relevance. In this study, several proteomics pipelines including different data acquisition methods and bioinformatic tools were performed and carefully evaluated. In both the proteomic and phosphoproteomic analyses, the directDIA analysis offered the best quantification results between multiplexed channels. Still, it was comparable to or lagged behind DDA and library DIA in identification results and reproducibility. Our preliminary results demonstrated that while different DIA methods outperform DDA in certain areas, overall, the current DIA algorithms still need to be improved to obtain better quantitative detection. Keywords: Proteomics; Isotopic Labeling; Mass Spectrometry; Extracellular Vesicles; Phosphoproteomics

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Purdue University / 2024

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Co-authors:

Nathaniel Merrick Miller