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Beck M.,European Molecular Biology Laboratory | Schmidt A.,University of Basel | Malmstroem J.,BMC Inc | Malmstroem J.,Biognosys | And 9 more authors.
Molecular Systems Biology | Year: 2011

The generation of mathematical models of biological processes, the simulation of these processes under different conditions, and the comparison and integration of multiple data sets are explicit goals of systems biology that require the knowledge of the absolute quantity of the system's components. To date, systematic estimates of cellular protein concentrations have been exceptionally scarce. Here, we provide a quantitative description of the proteome of a commonly used human cell line in two functional states, interphase and mitosis. We show that these human cultured cells express at least ĝ̂1/410 000 proteins and that the quantified proteins span a concentration range of seven orders of magnitude up to 20 000 000 copies per cell. We discuss how protein abundance is linked to function and evolution. © 2011 EMBO and Macmillan Publishers Limited All rights reserved.

Agency: European Commission | Branch: H2020 | Program: RIA | Phase: BIOTEC-2-2015 | Award Amount: 10.71M | Year: 2016

Recent developments in omics technologies demand implementation of systems biology approaches to facilitate analysis and interpretation of the generated complex datasets.This is essential for biotechnological as well as preclinical and clinical applications. In comparison to previous approaches, most cancer relevant studies are confined to pattern recognition or at best modelling of single pathways, rather than the complex pathways and cross-talk determining cancer progression and drug response. Systematic tools that evaluate and validate personalised medicine approaches on a preclinical level are missing; an important prerequisite for translation into clinical practice. The overall objective of CanPathPro is to build and validate a new biotechnological application: a combined experimental and systems biology platform, which will be utilized in testing cancer signaling hypotheses in biomedical research and life sciences. Thus, the proposed project will focus on developing and refining bioinformatic and experimental tools for the evaluation of systems biology modelling predictions. Components comprise a highly controlled mouse experimental system, NGS, a quantitative proteomics based read-out of changes in pathway signalling and an integrative systems biology model for data integration. Testable hypotheses about biological systems will be generated and experimentally validated. The developed system tools will be made available to researchers, SMEs and industry for practical applications. Following this project, a commercial platform for interpretation and analysis of complex omics data and for deriving and testing new hypotheses will be set up by the participating companies and academic partners. CanPathPro will enhance the competitive potential of the SMEs involved expanding in the field of biotechnology, personalised medicine and drug development and also provide new opportunities for other SMEs working in the field of bioinformatics and biomedical applications.

Escher C.,Biognosys | Reiter L.,Biognosys | Maclean B.,University of Washington | Ossola R.,Biognosys | And 4 more authors.
Proteomics | Year: 2012

Multiple reaction monitoring (MRM) has recently become the method of choice for targeted quantitative measurement of proteins using mass spectrometry. The method, however, is limited in the number of peptides that can be measured in one run. This number can be markedly increased by scheduling the acquisition if the accurate retention time (RT) of each peptide is known. Here we present iRT, an empirically derived dimensionless peptide-specific value that allows for highly accurate RT prediction. The iRT of a peptide is a fixed number relative to a standard set of reference iRT-peptides that can be transferred across laboratories and chromatographic systems. We show that iRT facilitates the setup of multiplexed experiments with acquisition windows more than four times smaller compared to in silico RT predictions resulting in improved quantification accuracy. iRTs can be determined by any laboratory and shared transparently. The iRT concept has been implemented in Skyline, the most widely used software for MRM experiments. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Reiter L.,Biognosys | Reiter L.,ETH Zurich | Reiter L.,University of Zürich | Rinner O.,Biognosys | And 11 more authors.
Nature Methods | Year: 2011

Selected reaction monitoring (SRM) is a targeted mass spectrometric method that is increasingly used in proteomics for the detection and quantification of sets of preselected proteins at high sensitivity, reproducibility and accuracy. Currently, data from SRM measurements are mostly evaluated subjectively by manual inspection on the basis of ad hoc criteria, precluding the consistent analysis of different data sets and an objective assessment of their error rates. Here we present mProphet, a fully automated system that computes accurate error rates for the identification of targeted peptides in SRM data sets and maximizes specificity and sensitivity by combining relevant features in the data into a statistical model. © 2011 Nature America, Inc. All rights reserved.

Picotti P.,ETH Zurich | Rinner O.,ETH Zurich | Rinner O.,Biognosys | Stallmach R.,ETH Zurich | And 8 more authors.
Nature Methods | Year: 2010

Selected reaction monitoring (SRM) uses sensitive and specific mass spectrometric assays to measure target analytes across multiple samples, but it has not been broadly applied in proteomics owing to the tedious assay development process for each protein. We describe a method based on crude synthetic peptide libraries for the high-throughput development of SRM assays. We illustrate the power of the approach by generating and applying validated SRM assays for all Saccharomyces cerevisiae kinases and phosphatases. © 2010 Nature America, Inc. All rights reserved.

Gillet L.C.,ETH Zurich | Navarro P.,ETH Zurich | Tate S.,ABSciex | Rost H.,ETH Zurich | And 6 more authors.
Molecular and Cellular Proteomics | Year: 2012

Most proteomic studies use liquid chromatography coupled to tandem mass spectrometry to identify and quantify the peptides generated by the proteolysis of a biological sample. However, with the current methods it remains challenging to rapidly, consistently, reproducibly, accurately, and sensitively detect and quantify large fractions of proteomes across multiple samples. Here we present a new strategy that systematically queries sample sets for the presence and quantity of essentially any protein of interest. It consists of using the information available in fragment ion spectral libraries to mine the complete fragment ion maps generated using a data-independent acquisition method. For this study, the data were acquired on a fast, high resolution quadrupole-quadrupole time-of-flight (TOF) instrument by repeatedly cycling through 32 consecutive 25-Da precursor isolation windows (swaths). This SWATH MS acquisition setup generates, in a single sample injection, time-resolved fragment ion spectra for all the analytes detectable within the 400-1200 m/z precursor range and the user-defined retention time window. We show that suitable combinations of fragment ions extracted from these data sets are sufficiently specific to confidently identify query peptides over a dynamic range of 4 orders of magnitude, even if the precursors of the queried peptides are not detectable in the survey scans. We also show that queried peptides are quantified with a consistency and accuracy comparable with that of selected reaction monitoring, the gold standard proteomic quantification method. Moreover, targeted data extraction enables ad libitum quantification refinement and dynamic extension of protein probing by iterative re-mining of the once-and-forever acquired data sets. This combination of unbiased, broad range precursor ion fragmentation and targeted data extraction alleviates most constraints of present proteomic methods and should be equally applicable to the comprehensive analysis of other classes of analytes, beyond proteomics. © 2012 by The American Society for Biochemistry and Molecular Biology, Inc.

Karlsson C.,Lund University | Malmstrom L.,ETH Zurich | Aebersold R.,ETH Zurich | Aebersold R.,University of Zürich | And 2 more authors.
Nature Communications | Year: 2012

Selected reaction monitoring mass spectrometry (SRM-MS) is a targeted proteomics technology used to identify and quantify proteins with high sensitivity, specificity and high reproducibility. Execution of SRM-MS relies on protein-specific SRM assays, a set of experimental parameters that requires considerable effort to develop. Here we present a proteome-wide SRM assay repository for the gram-positive human pathogen group A Streptococcus. Using a multi-layered approach we generated SRM assays for 10,412 distinct group A Streptococcus peptides followed by extensive testing of the selected reaction monitoring assays in >200 different group A Streptococcus protein pools. Based on the number of SRM assay observations we created a rule-based selected reaction monitoring assay-scoring model to select the most suitable assays per protein for a given cellular compartment and bacterial state. The resource described here represents an important tool for deciphering the group A Streptococcus proteome using selected reaction monitoring and we anticipate that concepts described here can be extended to other pathogens. © 2012 Macmillan Publishers Limited. All rights reserved.

Ori A.,Structural and Computational Biology Unit | Banterle N.,Structural and Computational Biology Unit | Iskar M.,Structural and Computational Biology Unit | Andres-Pons A.,Structural and Computational Biology Unit | And 8 more authors.
Molecular Systems Biology | Year: 2013

To understand the structure and function of large molecular machines, accurate knowledge of their stoichiometry is essential. In this study, we developed an integrated targeted proteomics and super-resolution microscopy approach to determine the absolute stoichiometry of the human nuclear pore complex (NPC), possibly the largest eukaryotic protein complex. We show that the human NPC has a previously unanticipated stoichiometry that varies across cancer cell types, tissues and in disease. Using large-scale proteomics, we provide evidence that more than one third of the known, well-defined nuclear protein complexes display a similar cell type-specific variation of their subunit stoichiometry. Our data point to compositional rearrangement as a widespread mechanism for adapting the functions of molecular machines toward cell type-specific constraints and context-dependent needs, and highlight the need of deeper investigation of such structural variants. Copyright © 2013 EMBO and Macmillan Publishers Limited.

The invention relates to the analysis of compounds with mass spectrometry and more particularly to instruments, substances, and methods for polypeptide analysis, in particular in targeted proteomics applications and based on indexed retention time as peptide specific property. The method of chemical analysis comprises the steps of: a) providing a first complex sample comprising a set of at least two reference peptides associated to an indexed retention time scale (iRT), as well as at least one further peptide; b) performing LC-MS on said complex sample and determining the empirical retention time values (RTe) of the reference peptides and of the at least one further peptide; c) translating the empirical retention time values (RTe) of the reference peptides and of the at least one further peptide into the indexed retention time scale and associating to each reference peptide a reference indexed retention time value (iRTr) and to the at least one further peptide an associated indexed retention time value (iRTa); d) providing a second complex sample comprising at least one polypeptide as well as said set of the at least two reference peptides; e) performing LC-MS on said second complex sample and determining the empirical retention time values (RTe) of the reference peptides; f) translating the empirical retention time values (RTe) of the reference peptides into the indexed retention time scale by numerically adapting the transformation function for the conversion of the retention time values (RTe) into indexed retention time values such that the calculated indexed retention time values (iRTe) calculated based on the measured retention time values (RTe) of the reference peptides match the assigned indexed retention time values (iRTr) of the reference peptides; g) determining the predicted empirical retention time value (RTp) of the at least one further peptide by using the numerically adapted transformation function determined in step f).

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