General statistical framework for quantitative proteomics by stable isotope labeling

The combination of stable isotope labeling (SIL) with mass spectrometry (MS) allows comparison of the abundance of thousands of proteins in complex mixtures. However, interpretation of the large data sets generated by these techniques remains a challenge because appropriate statistical standards are lacking. Here, we present a generally applicable model that accurately explains the behavior of data obtained using current SIL approaches, including 18O, iTRAQ, and SILAC labeling, and different MS instruments. The model decomposes the total technical variance into the spectral, peptide, and protein variance components, and its general validity was demonstrated by confronting 48 experimental distributions against 18 different null hypotheses. In addition to its general applicability, the performance of the algorithm was at least similar than that of other existing methods. The model also provides a general framework to integrate quantitative and error information fully, allowing a comparative analysis of the results obtained from different SIL experiments. The model was applied to the global analysis of protein alterations induced by low H2O2 concentrations in yeast, demonstrating the increased statistical power that may be achieved by rigorous data integration. Our results highlight the importance of establishing an adequate and validated statistical framework for the analysis of high-throughput data

In vitro Transcription/Translation System: A Versatile Tool in the Search for Missing Proteins

Approximately 18% of all human genes purported to encode proteins have not been directly evidenced at the protein level, according to the validation criteria established by neXtProt, and are considered as “missing” proteins. One of the goals of the Chromosome-Centric Human Proteome Project (C-HPP) is to identify as many of these “missing” proteins as possible in human samples using mass spectrometry-based methods. To further this goal, a consortium of C–HPP teams (chromosomes 5, 10, 16 and 19) has joined forces to devise new strategies to identify “missing” proteins by use of a cell-free in vitro transcription/translation system (IVTT). The proposed strategy employs LC-MS/MS data-dependent acquisition (DDA) and targeted selective reaction monitoring (SRM) methods to scrutinize low complexity samples derived from IVTT translation. The optimized assays are then applied to identify “missing” proteins in human cells and tissues. We describe the approach and show proof-of-concept results for development of LC-SRM assays for identification of eighteen “missing” proteins. We believe that the IVTT system, when coupled with downstream mass spectrometric identification, can be applied to identify proteins that have eluded more traditional methods of detection

A multicentric study to evaluate the use of relative retention times in targeted proteomics

Despite the maturity reached by targeted proteomic strategies, reliable and standardized protocols are urgently needed to enhance reproducibility among different laboratories and analytical platforms, facilitating a more widespread use in biomedical research. To achieve this goal, the use of dimensionless relative retention times (iRT), defined on the basis of peptide standard retention times (RT), has lately emerged as a powerful tool. The robustness, reproducibility and utility of this strategy were examined for the first time in a multicentric setting, involving 28 laboratories that included 24 of the Spanish network of proteomics laboratories (ProteoRed-ISCIII). According to the results obtained in this study, dimensionless retention time values (iRTs) demonstrated to be a useful tool for transferring and sharing peptide retention times across different chromatographic set-ups both intra- and inter-laboratories. iRT values also showed very low variability over long time periods. Furthermore, parallel quantitative analyses showed a high reproducibility despite the variety of experimental strategies used, either MRM (multiple reaction monitoring) or pseudoMRM, and the diversity of analytical platforms employed. BIOLOGICAL SIGNIFICANCE: From the very beginning of proteomics as an analytical science there has been a growing interest in developing standardized methods and experimental procedures in order to ensure the highest quality and reproducibility of the results. In this regard, the recent (2012) introduction of the dimensionless retention time concept has been a significant advance. In our multicentric (28 laboratories) study we explore the usefulness of this concept in the context of a targeted proteomics experiment, demonstrating that dimensionless retention time values is a useful tool for transferring and sharing peptide retention times across different chromatographic set-ups.All laboratories from Spain are members of ProteoRed (Plataforma de Recursos Biomoleculares y Bioinformáticos) and are supported by grant PT13/0001 funded by Instituto de Salud Carlos III (ISCIII) and FEDER.S

A multicentric study to evaluate the use of relative retention times in targeted proteomics

Despite the maturity reached by targeted proteomic strategies, reliable and standardized protocols are urgently needed to enhance reproducibility among different laboratories and analytical platforms, facilitating a more widespread use in biomedical research. To achieve this goal, the use of dimensionless relative retention times (iRT), defined on the basis of peptide standard retention times (RT), has lately emerged as a powerful tool. The robustness, reproducibility and utility of this strategy were examined for the first time in a multicentric setting, involving 28 laboratories that included 24 of the Spanish network of proteomics laboratories (ProteoRed-ISCIII). According to the results obtained in this study, dimensionless retention time values (iRTs) demonstrated to be a useful tool for transferring and sharing peptide retention times across different chromatographic set-ups both intra- and inter-laboratories. iRT values also showed very low variability over long time periods. Furthermore, parallel quantitative analyses showed a high reproducibility despite the variety of experimental strategies used, either MRM (multiple reaction monitoring) or pseudoMRM, and the diversity of analytical platforms employed. [Biological significance]: From the very beginning of proteomics as an analytical science there has been a growing interest in developing standardized methods and experimental procedures in order to ensure the highest quality and reproducibility of the results. In this regard, the recent (2012) introduction of the dimensionless retention time concept has been a significant advance. In our multicentric (28 laboratories) study we explore the usefulness of this concept in the context of a targeted proteomics experiment, demonstrating that dimensionless retention time values is a useful tool for transferring and sharing peptide retention times across different chromatographic set-ups.All laboratories from Spain are members of ProteoRed (Plataforma de Recursos Biomoleculares y Bioinformáticos) and are supported bygrant PT13/0001 funded by Instituto de Salud Carlos III (ISCIII) andFEDERPeer Reviewe

Methodologies to generate, extract, purify and fractionate yeast ECM for analytical use in proteomics and glycomics

In a multicellular organism, the extracellular matrix (ECM) provides a cell-supporting scaffold and helps maintaining the biophysical integrity of tissues and organs. At the same time it plays crucial roles in cellular communication and signalling, with implications in spatial organisation, motility and differentiation. Similarly, the presence of an ECM-like extracellular polymeric substance is known to support and protect bacterial and fungal multicellular aggregates, such as biofilms or colonies. However, the roles and composition of this microbial ECM are still poorly understood.Authors would like to acknowledge Joana Tulha for assistance on yeasts overlay photographs, and to Rui Armada for C. albicans ECM SDS-PAGE experiment. The proteomic analysis was carried out at the proteomics facility UCM-PCM, a member of the ProteoRed network. The polysaccharide analysis was performed at the Laboratory of Glycoconjugates Biochemistry and Cellular Biology, UFRJ, Brazil. Fabio Faria-Oliveira was supported by a PhD scholarship SFRH/BD/45368/2008 from FCT (Fundacao para a Ciencia e a Tecnologia). This work was funded by Marie Curie Initial Training Network GLYCOPHARM (PITN-GA-2012-317297), and by FCT/MEC through Portuguese funds (PIDDAC) - PEst-OE/BIA/UI4050/2014. The authors would also like to acknowledge Hugh S. Johnson for critical reading of the manuscript regarding English usage