Proteomic profiling is used to discover biomarkers, having a great potential for prognostic and predictive biomarkers; however, it still misses the sensitivity to quantify all the markers of interests. Therefore, targeted quantitation experiments of the potential markers are analyzed in the validation phase. To address these challenges, we develop a novel intelligent data acquisition “Hybrid-DIA” MS strategy that enables comprehensive proteome profiling via high resolution MS1-based data-independent-acquisition (HRMS1-DIA)1 MS and on-the-fly intelligently switching the acquisition mode to parallel reaction monitoring (PRM) for sensitive and absolute quantification of the markers, substantially increasing throughput and reducing sample consumption.
The global profiling and quantitation performance of Hybrid-DIA MS are investigated and benchmarked against the standard DIA MS methods by analyzing a mixture of stable isotope labelled peptides with a concentration range of 4 order of magnitude spiked in a HELA digest on the same LC system coupled to the Orbitrap Exploris 480 MS. Similar number of proteins are identified with 1% FDR and quantified with CV<20% by both the Hybrid-DIA and DIA experiments; while, the LOD and LOQ of the targeted quantitation of spiked in peptides reach the 20-50 attomole range with the hybrid-DIA method. Depleted plasma sample spiked with a mix of isotope labelled plasma reference peptides representing relevant targets for biomarker screening are also analyzed by the standard DIA MS and Hybrid-DIA MS methods. Hybrid-DIA method can simultaneously quantify a promising number of endogenous biomarkers with high precision and reproducibility, while simultaneously comprehensive profiling plasma proteomes in one single experiment.
This novel Hybrid-DIA MS methodology presents a new capability to combine the data-driven and hypothesis-driven approaches in one go, enabling broad proteotype digitization via DIA scans and simultaneously sensitive quantitation of the markers of interests to support clinical decision-making.