The data includes outputs from FragPipe, Maxquant, DIA-NN and Spectronaut

Viewing the scarce amount of protein material coming from the bacterial pathogen in infection models and despite the availability of contemporary, highly sensitive and fast scanning mass spectrometers, the power requirement still not suffices to study the host and pathogen proteomes simultaneously. In the present work we aimed to establish a DIA mass spectrometry workflow for improving the protein identification and quantification of LC-MS/MS, particularly in case of complex samples containing a fairly low amount of peptide material derived from Salmonella, therefore enabling simultaneous host and pathogen protein expression profiling reflecting actual infection conditions.

Ketosis is a common metabolic disease in dairy cows during early lactation. However, information about the metabolomic and proteomic profiles associated with the incidence and progression of ketosis is still limited. In this study, an integrated metabolomics and proteomics approach was performed on blood serum sampled from cows diagnosed with clinical ketosis (case, ≥ 2.60 mmol/L plasma β-hydroxybutyrate; BHBA) and healthy controls (control, < 1.0 mmol/L BHBA). Samples were taken 2 weeks before parturition and 2 weeks after parturition from 19 animals (nine cases, 10 controls). All serum samples (n = 38) were subjected to Liquid Chromatography-Mass Spectrometry (LC-MS) based metabolomic analysis, and 20 samples underwent Data-Independent Acquisition (DIA) LC-MS based proteomic analysis. A total of 97 metabolites and 540 proteins were successfully identified, and multivariate analysis revealed significant differences in both metabolomic and proteomic profiles between cases and controls. We investigated clinical ketosis-associated metabolomic and proteomic changes using statistical analyses. Correlation analysis of statistically significant metabolites and proteins showed 78 strong correlations (correlation coefficient, R ≥ 0.7) between 38 metabolites and 25 proteins, which were then mapped to pathways using IMPaLA. Results showed that ketosis altered a wide range of metabolic pathways, such as metabolism, metabolism of proteins, gene expression and post-translational protein modification, vitamin metabolism, signaling, and disease related pathways. Findings presented here are relevant for identifying molecular targets for ketosis and biomarkers for ketosis detection during the transition period.

Source code and example dataset for LipidMS v3.0.3: a commercially available pooled human serum sample was analyzed in positive and negative detection modes and using MS1, DIA and DDA approaches. The obtained datasets were processed using LipidMS v3.0, MS-DIAL v4.80 or a combination of data pre-processing in XCMS v3.16 and lipid annotation in LipidMS v3.0.

This repository contains:

- Raw data for positive and negative polarities using MS scan, DIA and DDA acquisition modes.

- R scripts for processing with LipidMS v3.0.3 and XCMS v3.16.1 and parameters used for processing with MS-DIAL v4.80.

- Source code for LipidMS v3.0.3.

- Results obtained for the 3 different softwares employed.

- Tutorials for LipidMS R package and online application.

Nontarget data acquisition for target analysis (nDATA) workflows using liquid chromatography-high-resolution accurate mass (LC-HRAM) spectrometry, spectral screening software, and a compound database have generated interest because of their potential for screening of pesticides in foods. However, these procedures and particularly the instrument processing software need to be thoroughly evaluated before implementation in routine analysis. In this work, 25 laboratories participated in a collaborative study to evaluate an nDATA workflow on high moisture produce (apple, banana, broccoli, carrot, grape, lettuce, orange, potato, strawberry, and tomato). Samples were extracted in each laboratory by quick, easy, cheap, effective, rugged, and safe (QuEChERS), and data were acquired by ultrahigh-performance liquid chromatography (UHPLC) coupled to a high-resolution quadrupole Orbitrap (QOrbitrap) or quadrupole time-of-flight (QTOF) mass spectrometer operating in full-scan mass spectrometry (MS) data-independent tandem mass spectrometry (LC-FS MS/DIA MS/MS) acquisition mode. The nDATA workflow was evaluated using a restricted compound database with 51 pesticides and vendor processing software. Pesticide identifications were determined by retention time (tR, ±0.5 min relative to the reference retention times used in the compound database) and mass errors (δM) of the precursor (RTP, δM ≤ ±5 ppm) and product ions (RTPI, δM ≤ ±10 ppm). The elution profiles of all 51 pesticides were within ±0.5 min among 24 of the participating laboratories. Successful screening was determined by false positive and false negative rates of

Ketosis is a common metabolic disease in dairy cows during early lactation. However, information about the metabolomic and proteomic profiles associated with the incidence and progression of ketosis is still limited. In this study, an integrated metabolomics and proteomics approach was performed on blood serum sampled from cows diagnosed with clinical ketosis (case, ≥ 2.60 mmol/L plasma β-hydroxybutyrate; BHBA) and healthy controls (control, < 1.0 mmol/L BHBA). Samples were taken 2 weeks before parturition and 2 weeks after parturition from 19 animals (nine cases, 10 controls). All serum samples (n = 38) were subjected to Liquid Chromatography-Mass Spectrometry (LC-MS) based metabolomic analysis, and 20 samples underwent Data-Independent Acquisition (DIA) LC-MS based proteomic analysis. A total of 97 metabolites and 540 proteins were successfully identified, and multivariate analysis revealed significant differences in both metabolomic and proteomic profiles between cases and controls. We investigated clinical ketosis-associated metabolomic and proteomic changes using statistical analyses. Correlation analysis of statistically significant metabolites and proteins showed 78 strong correlations (correlation coefficient, R ≥ 0.7) between 38 metabolites and 25 proteins, which were then mapped to pathways using IMPaLA. Results showed that ketosis altered a wide range of metabolic pathways, such as metabolism, metabolism of proteins, gene expression and post-translational protein modification, vitamin metabolism, signaling, and disease related pathways. Findings presented here are relevant for identifying molecular targets for ketosis and biomarkers for ketosis detection during the transition period.

Modern mass spectrometers routinely allow deep proteome coverage in a single experiment. These methods are typically operated at nano and micro flow regimes, but they often lack throughput and chromatographic robustness, which is critical for large-scale studies. In this context, we have developed, optimized and benchmarked LC-MS methods combining the robustness and throughput of analytical flow chromatography with the added sensitivity provided by the Zeno trap across a wide range of cynomolgus monkey and human matrices of interest for toxicological studies and clinical biomarker discovery. SWATH data independent acquisition (DIA) experiments with Zeno trap activated (Zeno SWATH DIA) provided a clear advantage over conventional SWATH DIA in all sample types tested with improved sensitivity, quantitative robustness and signal linearity as well as increased protein coverage by up to 9-fold. Using a 10-min gradient chromatography, up to 3,300 proteins were identified in tissues at 2 µg peptide load. Importantly, the performance gains with Zeno SWATH translated into better biological pathway representation and improved the ability to identify dysregulated proteins and pathways associated with two metabolic diseases in human plasma. Finally, we demonstrate that this method is highly stable over time with the acquisition of reliable data over the injection of 1,000+ samples (14.2 days of uninterrupted acquisition) without the need for human intervention or normalization. Altogether, Zeno SWATH DIA methodology allows fast, sensitive and robust proteomic workflows using analytical flow and is amenable to large-scale studies. This work provides detailed method performance assessment on a variety of relevant biological matrices and serves as a valuable resource for the proteomics community.

The DIA-DB is a web server for the prediction of diabetes drugs that uses two different and complementary approaches: (a) comparison by shape similarity against a curated database of approved antidiabetic drugs and experimental small molecules and (b) inverse virtual screening of the input molecules chosen by the users against a set of therapeutic protein targets identified as key elements in diabetes. As a proof of concept DIA-DB was successfully applied in an integral workflow for the identification of the antidiabetic chemical profile in a complex crude plant extract. To this end, we conducted the extraction and LC-MS based chemical profile analysis of Sclerocarya birrea and subsequently utilized this data as input for our server. The server is open to all users, registration is not necessary, and a detailed report with the results of the prediction is sent to the user by email once calculations are completed. This is a novel public domain database and web server specific for diabetes drugs and can be accessed online through http://bio-hpc.eu/software/dia-db/.

Breeding schemes for meat production in rabbits involved a three-way cross of specialized lines in which a paternal line inseminates maternal crossbred females. Paternal line or terminal sire are selected for growth traits, being the males used for the production of fertile dose at the insemination centres and farms. So high growth rate males must produce, in addition, semen in sufficient quantity and quality to meet the demand of insemination and, nevertheless, several studies have been showed that selection for growth have effects on reproduction performance in female and males. In rabbits, negative effects has been observed in ovulation induction, prenatal survival and genetic correlation to fertility. Many factors influence the production and quality of rabbit semen, management as collection frequency, environment (season or photoperiod, nutrition and genetic. Most of the previous studies have been focused in the effects of selection on the seminal and sperm parameters but, little attention has been paid to the protein seminal plasma or sperm composition and if these changes could be affect the fertility of seminal doses obtained from the paternal males. The aim of this study was to evaluate if selection program by daily gain in fattening period has changed seminal traits, plasma and sperm proteoma and, the fertility of semen when it is used in artificial insemination. To do this we uses two re-derived groups of paternal males obtained from vitrified embryos with a difference of 18 generations between both groups.

Supplementary analyzed data from Manuscript: TITLE: Maturation kinetics of a multiprotein complex revealed by metabolic labeling. JOURNAL: CELL. Article Type: Research Article Authors: Evgeny Onischenko*, Elad Noor*, Jonas S. Fischer*, Ludovic Gillet, Matthias Wojtynek, Pascal Vallotton, Karsten Weis *Equally Contributing Authors Corresponding Authors: Evgeny Onischenko and Karsten Weis

Related to Figure S2; Table S4

Single-lysine precursor ion labelling in KARMA assays for a subset of proteins (10 NUP baits). The target protein complex is isolated from cell lysates by affinity pulldowns at several time points following the onset of metabolic labeling, tryptically digested and analyzed with LC-MS on an Orbitrap mass-spectrometer. The MS2 fragmentation spectra are acquired in a DIA mode for all samples. Zero time point samples (containing only light lysine) are additionally analyzed in a DDA mode to produce assay spectral libraries. Peptide intensities are extracted from the DIA datasets with Spectronaut software (Biognosis) using complementary assay spectral libraries.

Individual plots: Protein labeling H/(H+L) in each post-labeling sample (red track) is determined based on the summed intensities of light (L) and heavy (H) constituent high quality precursor ions (green tracks). Low-quality precursor ions are excluded from quantification (gry tracks). y-axis: fractional labeling H/(H+L); x-axis: individual samples (10 handles, 3 replicates, 3 timepoints).

Mycoplasma gallisepticum (MG) is one of the smallest free-living and self-replicating organisms, it is characterized by lack of cell wall and reduced genome size. As a result of genome reduction, MG has a limited variety of DNA-binding proteins (DBP) and transcription factors. To investigate the dynamic changes of the proteomic profile of MG nucleoid, that may assist in revealing its mechanisms of functioning, regulation of chromosome organization and stress adaptation, a quantitative proteomic study was performed on MG nucleoids obtained from the cell culture in logarithmic and stationary phases of synchronous growth. MG cells were grown in a liquid medium with a 9h starvation period. Nucleoids were obtained from the cell culture at the 26th and the 50th hour (logarithmic and stationary phases respectively) by sucrose density gradient centrifugation. LC-MS analysis was carried out on an Ultimate 3000 RSLCnano HPLC system connected to a Fusion Lumos mass spectrometer, controlled by XCalibur software (ThermoFisher Scientific) via a nanoelectrospray source (Thermo Fisher Scientific). For comprehensive peptide library generation one sample from each biological replicate was run in DDA mode. Then, all the sample were run in a single LC-MS DIA run. Identification of DDA files and DIA quantitation was performed with MaxQuant and Skyline software.

Shotgun (bottom-up) approach has been widely applied in large-scale proteomics studies. The inherent shortages of shotgun approach lie in that the generated peptides often overwhelm the analytical capacity of current LC–MS/MS systems and that high-abundance proteins often hamper the identification of low-abundance proteins when analyzing complex samples. To reduce the sample complexity and relieve the problems caused by abundant proteins, herein we introduce a modified selective proteomics approach, termed ENCHANT, for enzyme and chemical assisted N-terminal blocked peptides analysis. Modified from our previous Nα-acetylome approach, ENCHANT aims to analyze three kinds of peptides, acetylated protein N-termini, N-terminal glutamine and N-terminal cysteine containing peptides. Application of ENCHANT to HeLa cells allowed to identify 3375 proteins, 19.6% more than that by conventional shotgun approach. More importantly, ENCHANT demonstrated an excellent complementarity to conventional shotgun approach with the overlap of 34.5%. In terms of quantification using data independent acquisition (DIA) technology, ENCHANT quantified 23.9% more proteins than conventional shotgun approach with the overlap of 27.6%. Therefore, our results strongly suggest that ENCHANT is a promising selective proteomics approach, which is complementary to conventional shotgun approach in both qualitative and quantitative proteomics studies. Data are available via ProteomeXchange with identifier PXD007863.

Direct infusion shotgun proteome analysis (DISPA) is a new paradigm for expedited mass spectrometry-based proteomics, but the original data analysis workflow was onerous. Here, we introduce CsoDIAq, a user-friendly software package for the identification and quantification of peptides and proteins from DISPA data. In addition to establishing a complete and automated analysis workflow with a graphical user interface, CsoDIAq introduces algorithmic concepts to spectrum-spectrum matching to improve peptide identification speed and sensitivity. These include spectra pooling to reduce search time complexity and a new spectrum–spectrum match score called match count and cosine, which improves target discrimination in a target-decoy analysis. Fragment mass tolerance correction also increased the number of peptide identifications. Finally, we adapt CsoDIAq to standard LC–MS DIA and show that it outperforms other spectrum–spectrum matching software.

Wheat amylase/trypsin inhibitors (ATIs) have gained significant relevance as inducers of intestinal and extra-intestinal inflammation. In this study, we present a novel hybrid data-independent acquisition (DIA) liquid chromatography–mass spectrometry (LC-MS) approach, combining QconCAT technology with short microflow LC gradients and DIA and apply the method toward the quantitative proteome analysis of ATI extracts. The presented method is fast, robust, and reproducible and provides precise QconCAT-based absolute quantification of major ATI proteins while simultaneously quantifying the proteome by label-free quantification (LFQ). We analyzed extracts of 60 varieties of common wheat grown in replication and evaluated the reproducibility and precision of the workflow for the quantification of ATIs. Applying the method to analyze different wheat species (i.e., common wheat, spelt, durum wheat, emmer, and einkorn) and comparing the results to published data, we validated inter-laboratory and cross-methodology reproducibility of ATI quantification, which is essential in the context of large-scale breeding projects. Additionally, we applied our workflow to assess environmental effects on ATI expression, analyzing ATI content and proteome of same varieties grown at different locations. Finally, we explored the potential of combining QconCAT-based absolute quantification with DIA-based LFQ proteome analysis for the generation of new hypotheses or assay development.

Label-free quantification (LFQ) and isobaric labeling quantification (ILQ) are among the most popular protein quantification workflows in discovery proteomics. Here, we compared the TMT SPS/MS3 10-plex workflow to a label free single shot data-independent acquisition (DIA) workflow on a controlled sample set. The sample set consisted of ten samples derived from 10 biological replicates of mouse cerebelli spiked with the UPS2 protein standard in five different concentrations. For a fair comparison, we matched the instrument time for the two workflows. The LC–MS data were acquired at two facilities to assess interlaboratory reproducibility. Both methods resulted in a high proteome coverage (>5000 proteins) with low missing values on protein level (

Proteomics has become an increasingly important tool in medical and medicinal applications. It is necessary to improve the analytical throughput for these applications, particularly in large-scale drug screening to enable measurement of a large number of samples. In this study, we aimed to establish an ultrafast proteomic method based on 5-min gradient LC and quadrupole-Orbitrap mass spectrometer (Q-Orbitrap MS). We precisely optimized data-independent acquisition (DIA) parameters for 5-min gradient LC and reached a depth of >5000 and 4200 proteins from 1000 and 31.25 ng of HEK293T cell digest in a single-shot run, respectively. The throughput of our method enabled the measurement of approximately 80 samples/day, including sample loading, column equilibration, and wash running time. We demonstrated that our method is applicable for the screening of chemical responsivity via a cell stimulation assay. These data show that our method enables the capture of biological alterations in proteomic profiles with high sensitivity, suggesting the possibility of large-scale screening of chemical responsivity.

Stereospecific recognition of metabolites plays a significant role in the detection of potential disease biomarkers thereby providing new insights in diagnosis and prognosis. D-Hdroxy/amino acids are recognized as potential biomarkers in several metabolic disorders. Despite continuous advances in metabolomics technologies, the simultaneous measurement of different classes of enantiomeric metabolites in a single analytical run remains challenging. Here, we develop a novel strategy for untargeted chiral metabolomics of hydroxy/amine groups (−OH/–NH2) containing metabolites, including all hydroxy acids (HAs) and amino acids (AAs), by chiral derivatization coupled with liquid chromatography-high resolution tandem mass spectrometry (LC-HR-MS/MS). Diacetyl-tartaric anhydride (DATAN) was used for the simultaneous derivatization of–OH/–NH2 containing metabolites as well as the resulting diastereomers, and all the derivatized metabolites were resolved in a single analytical run. Data independent MS/MS acquisition (DIA) was applied to positively identify DATAN-labeled metabolites based on reagent specific diagnostic fragment ions. We discriminated chiral from achiral metabolites based on the reversal of elution order of D and L isomers derivatized with the enantiomeric pair (±) of DATAN in an untargeted manner. Using the developed strategy, a library of 301 standards that consisted of 214 chiral and 87 achiral metabolites were separated and detected in a single analytical run. This approach was then applied to investigate the enantioselective metabolic profile of the bone marrow (BM) and peripheral blood (PB) plasma samples from patients with acute myeloid leukemia (AML) at diagnosis and following completion of the induction phase of chemotherapeutic treatment. The sensitivity and selectivity of the developed method enabled the detection of trace levels of the D-enantiomer of HAs and AAs in primary plasma patient samples. Several of these metabolites were significantly altered in response to chemotherapy. The developed LC-HR-MS method entails a valuable step forward in chiral metabolomics.

Untargeted lipidomics using liquid chromatography (LC) coupled with tandem mass spectrometry (MS) is essential for large cohort studies. Using a fast LC gradient of less than 10 min for the rapid screening of lipids decreases the annotation rate, because of the lower coverage of the MS/MS spectra caused by the narrow peak width. A systematic procedure is proposed in this study to achieve a high annotation rate in fast LC-based untargeted lipidomics by integrating data-dependent acquisition (DDA) and sequential window acquisition of all-theoretical mass spectrometry data-independent acquisition (SWATH-DIA) techniques using the updated MS-DIAL program. This strategy uses variable SWATH-DIA methods for quality control (QC) samples, which are a mixture of biological samples that were analyzed multiple times to correct the MS signal drift. In contrast, biological samples are analyzed using DDA to facilitate the structural elucidation of lipids using the pure spectrum to the maximum extent. The workflow is demonstrated using an 8.6 min LC gradient, where the QC samples are analyzed using five different SWATH-DIA methods. The use of both DDA and SWATH-DIA achieves a 1.7-fold annotation coverage from publicly available benchmark data obtained using a fast LC-DDA-MS technique and offers 95.3% lipid coverage, as compared to the benchmark data set from a 25 min LC gradient. This study demonstrates that harmonized improvements in analytical conditions and informatics tools provide a comprehensive lipidome in fast LC-based untargeted lipidomics, not only for large-scale studies but also for small-scale experiments, contributing to both clinical applications and basic biology.

Liquid chromatography tandem mass spectrometry (LC–MS/MS) analysis of secreted proteins has contributed to our understanding of human disease and physiology but is limited by its need for accurate protein database annotation. Common assumptions used in proteomics of perfect protease specificity are inaccurate for secreted proteins, which are cleaved by numerous endogenous proteases. Here, we describe the generation of an optimized protein database that divides proteins into their individual biological chains and peptides to allow fast identification of semi-tryptic peptides from secreted proteins using fully tryptic searches. We applied this biologically annotated database to previously published human plasma proteome data sets containing either DIA or DDA data, using Spectronaut, DIA-NN, MaxDIA, and MaxQuant. Using our annotated database, we greatly reduced search times while achieving similar protein and peptide identifications compared to that obtained from standard approaches using semi-tryptic searches. Furthermore, our database enables the identification of biologically relevant semi-tryptic peptides using data analysis packages that are not capable of semi-tryptic searches. Together, these findings demonstrate that our annotated database is more capable than currently available databases for secreted protein analysis and is particularly useful for large-scale plasma proteome analysis.

Cronobacter sakazakii is foodborne pathogen that causes serious illnesses such as necrotizing enterocolitis, meningitis and septicemia in infants. However, the virulence determinants and mechanisms of pathogenicity of these species remain unclear. In this study, multilocus sequence typing (MLST) was performed on 34 C. sakazakii strains and two strains with the same sequence type (ST) but distinct adhesion/invasion capabilities were selected for identification of differentially expressed proteins using data-independent acquisition (DIA) proteomic analysis. A total of 2,203 proteins were identified and quantified. Among these proteins, 210 exhibited differential expression patterns with abundance ratios ≥3 or ≤0.33 and P values ≤0.05. Among these 210 proteins, 67 were expressed higher, and 143 were expressed lower in C. sakazakii SAKA80220 (strongly adhesive/invasive strain) compared with C. sakazakii SAKA80221 (weakly adhesive/invasive strain). Based on a detailed analysis of the differentially expressed proteins, the highly expressed genes involved in flagellar assembly, lipopolysaccharide synthesis, LuxS/AI-2, energy metabolic pathways and iron-sulfur cluster may be associated with the adhesion/invasion capability of C. sakazakii. To verify the accuracy of the proteomic results, real-time qPCR was used to analyze the expression patterns of some genes at the transcriptional level, and consistent results were observed. This study, for the first time, used DIA proteomic to investigate potential adhesion/invasion related factors as a useful reference for further studies on the pathogenic mechanism of C. sakazakii.