Micro-Data-Independent Acquisition for High-Throughput Proteomics and Sensitive Peptide Mass Spectrum Identification.

Abstract

State-of-the-art strategies for proteomics are not able to rapidly interrogate complex peptide mixtures in an untargeted manner with sensitive peptide and protein identification rates. We describe a data-independent acquisition (DIA) approach, microDIA (μDIA), that applies a novel tandem mass spectrometry (MS/MS) mass spectral deconvolution method to increase the specificity of tandem mass spectra acquired during proteomics experiments. Using the μDIA approach with a 10 min liquid chromatography gradient allowed detection of 3.1-fold more HeLa proteins than the results obtained from data-dependent acquisition (DDA) of the same samples. Additionally, we found the μDIA MS/MS deconvolution procedure is critical for resolving modified peptides with relatively small precursor mass shifts that cause the same peptide sequence in modified and unmodified forms to theoretically cofragment in the same raw MS/MS spectra. The μDIA workflow is implemented in the PROTALIZER software tool which fully automates tandem mass spectral deconvolution, queries every peptide with a library-free search algorithm against a user-defined protein database, and confidently identifies multiple peptides in a single tandem mass spectrum. We also benchmarked μDIA against DDA using a 90 min gradient analysis of HeLa and Escherichia coli peptides that were mixed in predefined quantitative ratios, and our results showed μDIA provided 24% more true positives at the same false positive rate.