Increasing Proteome Depth While Maintaining Quantitative Precision in Short-Gradient Data-Independent Acquisition Proteomics

The combination of short liquid chromatography (LC) gradients and data-independent acquisition (DIA) by mass spectrometry (MS) has proven its huge potential for high-throughput proteomics. However, the optimization of isolation window schemes resulting in a certain number of data points per peak (DPPP) is understudied, although it is one of the most important parameters for the outcome of this methodology. In this study, we show that substantially reducing the number of DPPP for short-gradient DIA massively increases protein identifications while maintaining quantitative precision. This is due to a large increase in the number of precursors identified, which keeps the number of data points per protein almost constant even at long cycle times. When proteins are inferred from its precursors, quantitative precision is maintained at low DPPP while greatly increasing proteomic depth. This strategy enabled us to quantify 6018 HeLa proteins (>80 000 precursor identifications) with coefficients of variation below 20% in 30 min using a Q Exactive HF, which corresponds to a throughput of 29 samples per day. This indicates that the potential of high-throughput DIA-MS has not been fully exploited yet. Data are available via ProteomeXchange with identifier PXD036451.

Deep Learning Neural Network Prediction Method Improves Proteome Profiling of Vascular Sap of Grapevines during Pierce's Disease Development

Plant secretome studies highlight the importance of vascular plant defense proteins against pathogens. Studies on Pierce’s disease of grapevines caused by the xylem-limited bacterium Xylella fastidiosa (Xf) have detected proteins and pathways associated with its pathobiology. Despite the biological importance of the secreted proteins in the extracellular space to plant survival and development, proteome studies are scarce due to methodological challenges. Prosit, a deep learning neural network prediction method is a powerful tool for improving proteome profiling by data-independent acquisition (DIA). We explored the potential of Prosit’s in silico spectral library predictions to improve DIA proteomic analysis of vascular leaf sap from grapevines with Pierce’s disease. The combination of DIA and Prosit-predicted libraries increased the total number of identified grapevine proteins from 145 to 360 and Xf proteins from 18 to 90 compared to gas-phase fractionation (GPF) libraries. The new proteins increased the range of molecular weights, assisted in the identification of more exclusive peptides per protein, and increased identification of low-abundance proteins. These improvements allowed identification of new functional pathways associated with cellular responses to oxidative stress, to be investigated further.