Techniques to identify proteins in samples are potentially reaching a limit where almost all proteins that are expressed are being measured, with many publications showing the identification of ~10,000 proteins. The ability to identify all peptides/proteins in a sample through stochastic data-dependent acquisition (DDA) approaches may never be achieved due to the extreme complexity of the proteome (proteoforms, modified peptides and abhorrently spliced variants).

Latest data-independent acquisition (DIA) methods are significantly improving the ability to assign parent mass with fragments, enabling the identification of a wider range of peptides/proteins in a single analysis, providing better sequence coverage and higher quality quantitation.

ZT Scan DIA, a Zeno trap-enabled scanning DIA workflow using better parent ion/fragment ion association, was implemented on a research-modified Zeno trap-enabled QTOF system. Both a mixture of 3 proteomes at variable ratios and mixtures of other species including multiple human cell line digests were used to evaluate this novel scan mode for identification of more peptides/proteins in a single analysis. Data was processed using library-free mode using a combined proteome FASTA. Custom Python scripts were used for analysis of the different outputs for further analysis/visualization of results.

Utilizing multiple data aspects to reference a peptide in a complex LC-MS profile provides more confident peptide identification. By investigating the number of confidently identified protein groups within replicate runs and by then determining the sequence coverage for the different proteins, we show that the use of an additional dimensionality in DIA data provides an improvement in both the coverage as well as the number of protein groups identified using microflow gradients. We show a significant improvement in the sequence coverage of identified proteins. The coverage of the proteins is more significantly increased at the lower intensity range.