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J Proteome Res. 2017 Feb 3;16(2):665-676. doi: 10.1021/acs.jproteome.6b00727. Epub 2017 Jan 3.

Quantitative Proteomics Based on Optimized Data-Independent Acquisition in Plasma Analysis.

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Department of Medicine, University of Washington , Seattle, Washington 98195, United States.
Department of Medicine, University of Pittsburgh , Pittsburgh, Pennsylvania 15261, United States.
Department of Medicine, Mayo Clinic , Rochester, Minnesota 55905, United States.
Proteomics Resource, University of Washington , Seattle, Washington 98195, United States.
Biostatistics, University of Texas MD Anderson Cancer Center , Houston, Texas 77030, United States.


The advent of high-resolution and frequency mass spectrometry has ushered in an era of data-independent acquisition (DIA). This approach affords enormous multiplexing capacity and is particularly suitable for clinical biomarker studies. However, DIA-based quantification of clinical plasma samples is a daunting task due to the high complexity of clinical plasma samples, the diversity of peptides within the samples, and the high biologic dynamic range of plasma proteins. Here we applied DIA methodology, including a highly reproducible sample preparation and LC-MS/MS analysis, and assessed its utility for clinical plasma biomarker detection. A pancreatic cancer-relevant plasma spectral library was constructed consisting of over 14 000 confidently identified peptides derived from over 2300 plasma proteins. Using a nonhuman protein as the internal standard, various empirical parameters were explored to maximize the reliability and reproducibility of the DIA quantification. The DIA parameters were optimized based on the quantification cycle times and fragmentation profile complexity. Higher analytical and biological reproducibility was recorded for the tryptic peptides without labile residues and missed cleavages. Quantification reliability was developed for the peptides identified within a consistent retention time and signal intensity. Linear analytical dynamic range and the lower limit of quantification were assessed, suggesting the critical role of sample complexity in optimizing DIA settings. Technical validation of the assay using a cohort of clinical plasma indicated the robustness and unique advantage for targeted analysis of clinical plasma samples in the context of biomarker development.


data-independent acquisition (DIA); mass spectrometry; pancreatic cancer; plasma; proteomics

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