Optimizing data-independent acquisition (DIA) spectral library workflows for plasma proteomics studies.

Traditional data-independent acquisition (DIA) workflows employ off-column fractionation with data-dependent acquisition (DDA) to generate spectral libraries for data extraction. Recent advances have led to the establishment of library-independent approaches for DIA analyses. The selection of a DIA workflow may affect the outcome of plasma proteomics studies. Here, we establish a gas-phase fractionation (GPF) workflow to create DIA libraries for DIA with parallel accumulation and serial fragmentation (diaPASEF). This workflow along with three other workflows, fractionated DDA libraries, fractionated DIA libraries, and predicted spectra libraries, were evaluated on 20 plasma samples from nonsmall cell lung cancer patients with low or high levels of IL-6. We sought to optimize protein identification and total experiment time. The novel GPF workflow for diaPASEF outperformed the traditional ddaPASEF workflow in the number of identified and quantified proteins. A library-independent workflow based on predicted spectra identified and quantified the most proteins in our experiment at the cost of computational power. Overall, the choice of DIA library workflow seemed to have a limited effect on the overall outcome of a plasma proteomics experiment, but it can affect the number of proteins identified and the total experiment time.