Development of Data Independent Acquisition Methods to Systematically Analyze the Human Proteome

Abstract

Data independent acquisition (DIA) mass spectrometry is a powerful technique that is improving the reproducibility and throughput of mass spectrometry-based proteomics studies. Here we explore several new approaches to leverage this technology. First we present an overview of modern data independent acquisition techniques, and demonstrate their internal detection rate and quantification consistency relative to targeted parallel reaction monitoring (PRM) and data dependent acquisition (DDA) methods despite large variations in data sampling strategies. Second, we use DIA experiments to construct a prediction model that helps determine optimal peptide selection for targeted experiments. Third, we introduce a new experimental workflow that uses chromatogram libraries to enable sensitive peptide detection in quantitative experiments, while still maintaining the throughput necessary for large scale experiments. Finally, we discuss a new approach to statistically validate phosphopeptide positional isomers and explore their prevalence in studies of Human cells.