ABSTRACT: Used in 'Fast Multi-blind Modification Search through Tandem Mass Spectrometry' and 'CIFTER: automated charge state determination for peptide tandem mass spectra',
Project description:These datasets, Human Plasma (LTQ), HEK293 (LTQ), and Lens (LCQ Classic), were used in this work, 'Fast multi-blind modification search through tandem mass spectrometry' (MCP 2012).
Project description:In proteomics, fast, efficient and highly reproducible sample preparation are of utmost importance, particularly in view of fast scanning mass spectrometers enabling analyses of large sample series. To address this need, we have developed the web application MassSpecPreppy that operates on the open science OT-2 liquid handling robot from Opentrons. This platform can prepare up to 96 samples at once, performing tasks like BCA protein concentration determination, sample digestion with normalization, reduction/alkylation, enzymatic digestion, and peptide elution or loading specified peptide amounts onto Evotips in an automated and flexible manner.
Project description:Single analyses of a Zebrafish whole plasma digest were analyzed by both CE-MS/MS and LC-MS/MS. The identified peptides and their corresponding retention or migration times were used to train a 'blind' retention/migration time model using linear regression. This trained model provided us with information on the migration/retention coefficients resulting from the individual amino acids. All acquired tandem mass spectrometry data was processed in the Trans-Proteomic Pipeline (TPP) embedded in the Taverna scientific workflow manager. The raw data was converted to mzXML using compassXport 3.0 (Bruker) and searched with X!Tandem. The X!Tandem scores were then converted to pepXML, modeled and translated to probabilities for each peptide-spectral match by PeptideProphet. The X!Tandem search was here performed allowing a random error ±0.5 Da, +1 or +2 Da isotopic error, carbamidomethylation as fixed and oxidation as variable modification and the k-score plug-in.
Project description:RNA-protein interactions are central to biological regulation. Cross-linking immunoprecipitation (CLIP)-seq is a powerful tool for genome-wide interrogation of RNA-protein interactomes, but current CLIP methods are limited by challenging biochemical steps and fail to detect many classes of noncoding and non-human RNAs. Here we present FAST-iCLIP, an integrated pipeline with improved CLIP biochemistry and an automated informatic pipeline for comprehensive analysis across protein coding, noncoding, repetitive, retroviral, and non-human transcriptomes. FAST-iCLIP of Poly-C binding protein 2 (PCBP2) showed that PCBP2 bound CU-rich motifs in different topologies to recognize mRNAs and noncoding RNAs with distinct biological functions. FAST-iCLIP of PCBP2 in hepatitis C virus-infected cells enabled a joint analysis of the PCBP2 interactome with host and viral RNAs and their interplay. These results show that FAST-iCLIP can be used to rapidly discover and decipher mechanisms of RNA-protein recognition across the diversity of human and pathogen RNAs. Characterization of non-coding and pathogen RNA-protein interactions using an automated computational pipeline and improved iCLIP biochemistry
Project description:We developed an optimized multi-shot proteomics workflow based on high-resolution offline high pH reversed-phase peptide separation of high peptide loads collecting many fractions that were in turn analyzed by short online chromatographic separations and fast peptide sequencing using orbitrap tandem mass spectrometry.
Project description:We developed an optimized multi-shot proteomics workflow based on high-resolution offline high pH reversed-phase peptide separation of high peptide loads collecting many fractions that were in turn analyzed by short online chromatographic separations and fast peptide sequencing using orbitrap tandem mass spectrometry.
Project description:<p>Liquid chromatography coupled to mass spectrometry (LC-MS) has become a standard technology in metabolomics. In particular, label-free quantification based on LC-MS is easily amenable to large-scale studies and thus well suited to clinical metabolomics. Large-scale studies, however, require automated processing of the large and complex LC-MS datasets. We present a novel algorithm for the detection of mass traces and their aggregation into features (i.e. all signals caused by the same analyte species) that is computationally efficient and sensitive and that leads to reproducible quantification results. The algorithm is based on a sensitive detection of mass traces, which are then assembled into features based on mass-to-charge spacing, co-elution information, and a support vector machine–based classifier able to identify potential metabolite isotope patterns. The algorithm is not limited to metabolites but is applicable to a wide range of small molecules (e.g. lipidomics, peptidomics), as well as to other separation technologies. We assessed the algorithm's robustness with regard to varying noise levels on synthetic data and then validated the approach on experimental data investigating human plasma samples. We obtained excellent results in a fully automated data-processing pipeline with respect to both accuracy and reproducibility. Relative to state-of-the art algorithms, ours demonstrated increased precision and recall of the method. The algorithm is available as part of the open-source software package OpenMS and runs on all major operating systems. </p><p><br></p><p><strong>Plasma data</strong> is reported in the current study <a href='https://www.ebi.ac.uk/metabolights/MTBLS234' rel='noopener noreferrer' target='_blank'><strong>MTBLS234</strong></a>.</p><p><strong>Simulated data</strong> is reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS235' rel='noopener noreferrer' target='_blank'><strong>MTBLS235</strong></a>.</p>
Project description:The intention of this clinical study was to investigate the effect of GIP administration for 240 min on gene expression in human subcutaneous adipose tissue. Three conditions have been tersted: 1. Sole infusion of GIP or NaCl as control; 2. GIP or NaCl administration under euglycemic-hyperinsulinemic clamp conditions; 3. GIP or NaCl administration under hyperglycemic-hyperinsulinemic clamp conditions to mimic the postprandial state. In each participant a complete physical examination and evaluation of medical history was performed, including an oral glucose tolerance test (oGTT) with 75 g glucose after overnight fast to ensure the metabolic state. Standard fasting laboratory and clinical chemistry evaluations were done. Synthetic human GIP (1-42) was dissolved in saline (0.9% NaCl-solution) under sterile conditions. All studies were done in the morning in the overnight fasted state (>10h since last meal). The effect of GIP administration on gene expression in subcutaneous adipose tissue was studied under 3 different conditions in a single blind design. Either the participants received only a GIP- or a saline- infusion (0.9% NaCl-isotonic solution, Fresenius, Germany) for 240 min. At different investigation days participants underwent euglycemic (EU)- and hyperglycemic (HC), hyperinsulinemic clamps combined with GIP- or placebo-infusions for 240 min at different examination days in a randomized, single-blind, crossover design. The capillary glucose concentration was 80mg/dl during EU-clamp and 140mg/dl during HC-clamp. The following numbers of treatments were performed: EU with GIP-infusion (N=9); EU with NaCl-infusion (N=9); HC with GIP-infusion (N=8), HC with NaCl-infusion (N=8); sole GIP-infusion (N=11) and sole placebo-infusion (N=11). Between examination days an intermission time of at least 7 days was maintained.
Project description:We introduce a microfluidic platform that enables single-cell mass and growth rate measurements upstream of single-cell RNA-sequencing (scRNA-seq) to generate paired single-cell biophysical and transcriptional data sets. Biophysical measurements are collected with a serial suspended microchannel resonator platform (sSMR) that utilizes automated fluidic state switching to load individual cells at fixed intervals, achieving a throughput of 120 cells per hour. Each single-cell is subsequently captured downstream for linked molecular analysis using an automated collection system. From linked measurements of a murine leukemia (L1210) and pro-B cell line (FL5.12), we identify gene expression signatures that correlate significantly with cell mass and growth rate. In particular, we find that both cell lines display a cell-cycle signature that correlates with cell mass, with early and late cell-cycle signatures significantly enriched amongst genes with negative and positive correlations with mass, respectively. FL5.12 cells also show a significant correlation between single-cell growth efficiency and a G1-S transition signature, providing additional transcriptional evidence for a phenomenon previously observed through biophysical measurements alone. Importantly, the throughput and speed of our platform allows for the characterization of phenotypes in dynamic cellular systems. As a proof-of principle, we apply our system to characterize activated murine CD8+ T cells and uncover two unique features of CD8+ T cells as they become proliferative in response to activation: i) the level of coordination between cell cycle gene expression and cell mass increases, and ii) translation-related gene expression increases and shows a correlation with single-cell growth efficiency. Overall, our approach provides a new means of characterizing the transcriptional mechanisms of normal and dysfunctional cellular mass and growth rate regulation across a range of biological contexts.
Project description:Given the facilities for whole genome sequencing with next-generation sequencers, structural and functional gene annotation is now only based on automated prediction. However, errors in terms of gene structure are still frequently reported especially for the correct determination of initiation start codons. Here, we propose a strategy to enrich and detect protein N-termini by mass spectrometry in order to refine genome annotation. After selective protein N-termini derivatization using (N-Succinimidyloxycarbonylmethyl)tris(2,4,6-trimethoxyphenyl)phosphonium bromide (TMPPAc-OSu) as labeling reagent, protein digestion was performed with three proteases in parallel. TMPP-labeled N-terminal-most peptides were further resolved from internal peptides by the COmbined FRActional DIagonal Chromatography (COFRADIC) sorting methodology before analysis with tandem mass spectrometry. We refined the annotation of the genome of a model marine bacterium, Roseobacter denitrificans.