8 research outputs found

    Proteomics Quality Control: Quality Control Software for MaxQuant Results

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    Mass spectrometry-based proteomics coupled to liquid chromatography has matured into an automatized, high-throughput technology, producing data on the scale of multiple gigabytes per instrument per day. Consequently, an automated quality control (QC) and quality analysis (QA) capable of detecting measurement bias, verifying consistency, and avoiding propagation of error is paramount for instrument operators and scientists in charge of downstream analysis. We have developed an R-based QC pipeline called Proteomics Quality Control (PTXQC) for bottom-up LC–MS data generated by the MaxQuant software pipeline. PTXQC creates a QC report containing a comprehensive and powerful set of QC metrics, augmented with automated scoring functions. The automated scores are collated to create an overview heatmap at the beginning of the report, giving valuable guidance also to nonspecialists. Our software supports a wide range of experimental designs, including stable isotope labeling by amino acids in cell culture (SILAC), tandem mass tags (TMT), and label-free data. Furthermore, we introduce new metrics to score MaxQuant’s Match-between-runs (MBR) functionality by which peptide identifications can be transferred across Raw files based on accurate retention time and <i>m</i>/<i>z</i>. Last but not least, PTXQC is easy to install and use and represents the first QC software capable of processing MaxQuant result tables. PTXQC is freely available at https://github.com/cbielow/PTXQC

    On Mass Ambiguities in High-Resolution Shotgun Lipidomics

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    Mass-spectrometry-based lipidomics aims to identify as many lipid species as possible from complex biological samples. Due to the large combinatorial search space, unambiguous identification of lipid species is far from trivial. Mass ambiguities are common in direct-injection shotgun experiments, where an orthogonal separation (e.g., liquid chromatography) is missing. Using the rich information within available lipid databases, we generated a comprehensive rule set describing mass ambiguities, while taking into consideration the resolving power (and its decay) of different mass analyzers. Importantly, common adduct species and isotopic peaks are accounted for and are shown to play a major role, both for perfect mass overlaps due to identical sum formulas and resolvable mass overlaps. We identified known and hitherto unknown mass ambiguities in high- and ultrahigh resolution data, while also ranking lipid classes by their propensity to cause ambiguities. On the basis of this new set of ambiguity rules, guidelines and recommendations for experimentalists and software developers of what constitutes a solid lipid identification in both MS and MS/MS were suggested. For researchers new to the field, our results are a compact source of ambiguities which should be accounted for. These new findings also have implications for the selection of internal standards, peaks used for internal mass calibration, optimal choice of instrument resolution, and sample preparation, for example, in regard to adduct ion formation

    Western-blot of crude antennal extracts of male and female <i>An. gambiae</i>, using polyclonal antisera against OBPs 9, 4 and 5.

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    <p>Left panels: SDS-PAGE of crude extracts (Ex) and sample of purified OBPs as indicated by their numbers. Right panels: Western-blot analysis of crude extracts (Ex) performed with the three antisera. A sample of OBPs 9, 4 and 5 (0.5 µg of each protein) utilised for raising the antibodies was also loaded on the same gel. OBP4 and 5 are not detectable in our experimental conditions, while OBP9 is present in both sexes, in agreement with the shotgun experiment results. Molecular weight markers are, from the top: Bovine serum albumin (66 kDa), Ovalbumin (45 kDa), Carbonic anhydrase (29 kDa), Trypsin inhibitor (20 kDa), α-Lactalbumin (14 kDa).</p

    Two-dimensional gel electrophoretic separation of extracts from 100 fourth instar larvae and 100 pupae of <i>An. gambiae</i>.

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    <p>The gel was stained with colloidal Coomassie Brilliant Blue and all the spots migrating with apparent molecular weight lower than 24(coverage by aminoacid sequence up to 61.87%), found in several spots (red circles). In larvae we could also detect OBP21 (Entry code in Uniprot Q8I8S3; coverage by aminoacid sequence 9.16%) and SAP3 (coverage by aminoacid sequence up to 18.25%), present in spots where also OBP9 was identified. Molecular weight markers are, from the top: Phosphorylase b, from rabbit muscle (97 kDa), Bovine serum albumin (66 kDa), Ovalbumin (45 kDa), Carbonic anhydrase (29 kDa), Trypsin inhibitor (20 kDa), α-Lactalbumin (14 kDa).</p

    Abundance of OBPs, CSPs and other proteins in the antennae of <i>An. gambiae</i> males and females, as reported in Table 1.

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    <p>The evaluation of relative abundance (in arbitrary units) is based on the values produced by MaxQuant (see text). The values are the averages of three sets of analyses. Error bars represent standard error of the mean. By far the most abundant proteins in male antennae are OBP9, SAP1 and SAP3, in agreement with the results of the 2D-gel (Figure 1).</p

    Two-dimensional gel electrophoretic separation of an extract from 1,100 antennae of <i>An. gambiae</i>.

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    <p>The gel was stained with colloidal Coomassie Brilliant Blue and all the spots migrating with apparent molecular weight lower than 40: Phosphorylase b, from rabbit muscle (97 kDa), Bovine serum albumin (66 kDa), Ovalbumin (45 kDa), Carbonic anhydrase (29 kDa), Trypsin inhibitor (20 kDa), α-Lactalbumin (14 kDa).</p
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