Data-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. We extend an established targeted data extraction workflow by inclusion of the ion mobility dimension for both signal extraction and scoring and thereby increase the specificity for precursor identification. Data acquired from whole proteome digests and mixed organism samples demonstrate deep proteome coverage and a high degree of reproducibility as well as quantitative accuracy, even from 10 ng sample amounts.
MaxDIA 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. This is the foundation of discovery DIA-hypothesis-free analysis of DIA samples without library and with reliable FDR control. MaxDIA performs three- or four-dimensional feature detection of fragment data, and scoring of matches is augmented by machine learning on the features of an identification. MaxDIA's bootstrap DIA workflow performs multiple rounds of matching with increasing quality of recalibration and stringency of matching to the library. Combining MaxDIA with two new technologies-BoxCar acquisition and trapped ion mobility spectrometry-both lead to deep and accurate proteome quantification.
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GEOZS, IJS, IMTLJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK, ZAGLJ
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. It quantifies over 5300 proteins in single injections recorded at 200 samples per day throughput using Evosep One chromatography system on a timsTOF Pro mass spectrometer and almost 9000 proteins in single injections recorded with a 93-min nanoflow gradient on timsTOF Pro 2, from 200 ng of HeLa peptides. A user-friendly implementation is provided through the incorporation of the algorithms in the DIA-NN software and by the FragPipe workflow for spectral library generation.
Although 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. In total, 588 plasma membrane proteins were identified by mass spectrometry, of which 182 were either cultivar or salinity stress responsive. Three candidate proteins with increased abundance in the tolerant cv. Morex were involved either in sterol binding (a GTPase‐activating protein for the adenosine diphosphate ribosylation factor ZIGA2, and a membrane steroid binding protein MSBP) or in phospholipid synthesis (phosphoethanolamine methyltransferase PEAMT). Overexpression of barley MSBP conferred salinity tolerance to yeast cells, whereas the knock‐out of the heterologous AtMSBP1 increased salt sensitivity in Arabidopsis. Atmsbp1 plants showed a reduced number of lateral roots under salinity, and root‐tip‐specific expression of barley MSBP in Atmsbp1 complemented this phenotype. In barley, an increased abundance of MSBP correlates with reduced root length and lateral root formation as well as increased levels of auxin under salinity being stronger in the tolerant cv. Morex. Hence, we concluded the involvement of MSBP in phytohormone‐directed adaptation of root architecture in response to salinity.
Salt tolerance is an important agronomic trait, and there is a high interest in unravelling mechanisms leading to improved plant performance on saline soils. In a subcellular proteomics approach, we targeted the root plasma membranes from two barley cultivars with contrasting response to the salt stress treatment. Three candidate proteins with increased abundance in the salt‐tolerant cultivar were identified and further analysed. Among those, a membrane steroid binding protein, MSBP, conferred salinity tolerance to yeast cells, whereas knock‐out of the heterologous AtMSBP1 increased salt sensitivity in Arabidopsis. Concomitant phytohormone profiling indicated an involvement of MSBP in phytohormone‐directed adaptation of root architecture in response to salinity.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
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). Multivariate statistics revealed two distinct developmental patterns during the analysed grain developmental phases: proteins showed either highest abundance in the middle phase of development – in the transition phase – or at later developmental stages – within the storage phase. Verification of developmental patterns observed by proteomic analysis was done by applying hypothesis-driven approaches, namely Western Blot analysis and enzyme assays. High general metabolic activity of the grain with regard to protein synthesis, cell cycle regulation, defence against oxidative stress, and energy production via photosynthesis was observed in the transition phase. Proteins upregulated in the storage phase are related towards storage protein accumulation, and interestingly to the defence of storage reserves against pathogens. A mixed regulatory pattern for most enzymes detected in our study points to regulatory mechanisms at the level of protein isoforms.
In-depth understanding of early grain developmental processes of cereal caryopses is of high importance as they influence final grain weight and quality. Our knowledge about these processes is still limited, especially on proteome level. To identify key mechanisms in early barley grain development, a label-free data-independent proteomics acquisition approach has been applied. Our data clearly show, that proteins either exhibit highest expression during cellularization and the switch to the storage phase (transition phase, 5–7 DAF), or during storage product accumulation (10–16 DAF). The results highlight versatile cellular metabolic activity in the transition phase and strong convergence towards storage product accumulation in the storage phase. Notably, both phases are characterized by particular protective mechanism, such as scavenging of oxidative stress and defence against pathogens, during the transition and the storage phase, respectively.
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•Label-free proteomics revealed distinct developmental patterns in barley grains.•Transition phase shows high metabolic activity towards growth and differentiation.•Later accumulating proteins are mainly involved in storage-related processes.•Diverse defence mechanisms characterize transition and storage phase.•Mixed patterns for key metabolic enzymes highlight metabolic control at protein level.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Label-free proteome profiling reveals developmental-dependent patterns in young barley grains Kaspar-Schoenefeld, Stephanie; Kathleen MerxauthorLeibniz Institute of Plant Genetics and Crop Plant Research (IPK), Stadt Seeland OT Gatersleben, Germany; Anna Maria JozefowiczauthorLeibniz Institute of Plant Genetics and Crop Plant Research (IPK), Stadt Seeland OT Gatersleben, Germany ...
2015
Journal Article
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