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

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.