Simple, efficient and thorough shotgun proteomic analysis with PatternLab V.

Shotgun proteomics aims to identify and quantify the thousands of proteins in complex mixtures such as cell and tissue lysates and biological fluids. This approach uses liquid chromatography coupled with tandem mass spectrometry and typically generates hundreds of thousands of mass spectra that require specialized computational environments for data analysis. PatternLab for proteomics is a unified computational environment for analyzing shotgun proteomic data. PatternLab V (PLV) is the most comprehensive and crucial update so far, the result of intensive interaction with the proteomics community over several years. All PLV modules have been optimized and its graphical user interface has been completely updated for improved user experience. Major improvements were made to all aspects of the software, ranging from boosting the number of protein identifications to faster extraction of ion chromatograms. PLV provides modules for preparing sequence databases, protein identification, statistical filtering and in-depth result browsing for both labeled and label-free quantitation. The PepExplorer module can even pinpoint de novo sequenced peptides not already present in the database. PLV is of broad applicability and therefore suitable for challenging experimental setups, such as time-course experiments and data handling from unsequenced organisms. PLV interfaces with widely adopted software and community initiatives, e.g., Comet, Skyline, PEAKS and PRIDE. It is freely available at http://www.patternlabforproteomics.org .

Mixed-Data Acquisition: Next-Generation Quantitative Proteomics Data Acquisition.

We present the Mixed-Data Acquisition (MDA) strategy for mass spectrometry data acquisition. MDA combines Data-Dependent Acquisition (DDA) and Data-Independent Acquisition (DIA) in the same run, thus doing away with the requirements for separate DDA spectral libraries. MDA is a natural result from advances in mass spectrometry, such as high scan rates and multiple analyzers, and is tailored toward exploiting these features. We demonstrate MDA's effectiveness on a yeast proteome analysis by overcoming a common bottleneck for XIC-based label-free quantitation; namely, the coelution of precursors when m/z values cannot be distinguished. We anticipate that MDA will become the next mainstream data generation approach for proteomics. MDA can also serve as an orthogonal validation approach for DDA experiments. Specialized software for MDA data analysis is made available on the project's website.

Quantitative Proteomic Map of the Trypanosomatid Strigomonas culicis: The Biological Contribution of its Endosymbiotic Bacterium.

Strigomonas culicis is a kinetoplastid parasite of insects that maintains a mutualistic association with an intracellular symbiotic bacterium, which is highly integrated into the protist metabolism: it furnishes essential compounds and divides in synchrony with the eukaryotic nucleus. The protist, conversely, can be cured of the endosymbiont, producing an aposymbiotic cell line, which presents a diminished ability to colonize the insect host. This obligatory association can represent an intermediate step of the evolution towards the formation of an organelle, therefore representing an interesting model to understand the symbiogenesis theory. Here, we used shotgun proteomics to compare the S. culicis endosymbiont-containing and aposymbiotic strains, revealing a total of 11,305 peptides, and up to 2,213 proteins (2,029 and 1,452 for wild type and aposymbiotic, respectively). Gene ontology associated to comparative analysis between both strains revealed that the biological processes most affected by the elimination of the symbiont were the amino acid synthesis, as well as protein synthesis and folding. This large-scale comparison of the protein expression in S. culicis marks a step forward in the comprehension of the role of endosymbiotic bacteria in monoxenous trypanosomatid biology, particularly because trypanosomatids expression is mostly post-transcriptionally regulated.