MaxDIA enables library-based and library-free data-independent acquisition proteomics

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.publishedVersio

dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts.

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

Plasma membrane proteome analysis identifies a role of barley membrane steroid binding protein in root architecture response to salinity:Barley plasma membrane proteins under salinity

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

diaPASEF: parallel accumulation–serial fragmentation combined with data-independent acquisition

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.ISSN:1548-7105ISSN:1548-709

MaxDIA enables library-based and library-free data-independent acquisition proteomics

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