The dia-PASEF technology uses ion mobility separation to reduce signal interferences and increase sensitivity in proteomic experiments. Here we present a two-dimensional peak-picking algorithm and generation of optimized spectral libraries, as well as take advantage of neural network-based processing of dia-PASEF data. Our computational platform boosts proteomic depth by up to 83% compared to previous work, and is specifically beneficial for fast proteomic experiments and those with low sample amounts.
View Article and Find Full Text PDFMaxDIA is a software platform for analyzing data-independent acquisition (DIA) proteomics data within the MaxQuant software environment. Using spectral libraries, MaxDIA achieves deep proteome coverage with substantially better coefficients of variation in protein quantification than other software. MaxDIA is equipped with accurate false discovery rate (FDR) estimates on both library-to-DIA match and protein levels, including when using whole-proteome predicted spectral libraries.
View Article and Find Full Text PDFData-independent acquisition modes isolate and concurrently fragment populations of different precursors by cycling through segments of a predefined precursor m/z range. Although these selection windows collectively cover the entire m/z range, overall, only a few per cent of all incoming ions are isolated for mass analysis. Here, we make use of the correlation of molecular weight and ion mobility in a trapped ion mobility device (timsTOF Pro) to devise a scan mode that samples up to 100% of the peptide precursor ion current in m/z and mobility windows.
View Article and Find Full Text PDFAlthough the physiological consequences of plant growth under saline conditions have been well described, understanding the core mechanisms conferring plant salt adaptation has only started. We target the root plasma membrane proteomes of two barley varieties, cvs. Steptoe and Morex, with contrasting salinity tolerance.
View Article and Find Full Text PDFUnlabelled: Due to its importance as a cereal crop worldwide, high interest in the determination of factors influencing barley grain quality exists. This study focusses on the elucidation of protein networks affecting early grain developmental processes. NanoLC-based separation coupled to label-free MS detection was applied to gain insights into biochemical processes during five different grain developmental phases (pre-storage until storage phase, 3days to 16days after flowering).
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