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. Source
Agency: Cordis | 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.
Malmstrom L.,ETH Zurich |
Malmstrom J.,Lund University |
Malmstrom J.,Biognosys |
Aebersold R.,ETH Zurich |
Aebersold R.,University of Zurich
Proteomics | Year: 2011
The rapidly increasing ability to sequence complete genomes of different microbial species and strains provides us with information regarding their genetic variability. Genetic variability is a mechanism for human pathogens to adapt to and avoid the immune system and to also develop resistance to antibiotics. However, the assessment of the contributions of individual genetic differences to resistance or other phenotypes is not a priori apparent from the genomic variability. Quantitative proteomics can provide accurate molecular phenotypes of microbes that are difficult to determine using alternative technologies. Over the recent few years we and others have developed a range of proteomic technologies for the quantitative analysis of microbial proteomes. Here, we describe the most commonly used techniques and discuss their strengths and weaknesses and illustrate their respective performance for the identification of virulence factors in Streptococcus pyogenes. © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. Source
Huttenhain R.,ETH Zurich |
Huttenhain R.,Competence Center for Systems Physiology and Metabolic Diseases |
Soste M.,ETH Zurich |
Selevsek N.,ETH Zurich |
And 12 more authors.
Science Translational Medicine | Year: 2012
The rigorous testing of hypotheses on suitable sample cohorts is a major limitation in translational research. This is particularly the case for the validation of protein biomarkers; the lack of accurate, reproducible, and sensitive assays for most proteins has precluded the systematic assessment of hundreds of potential marker proteins described in the literature. Here, we describe a high-throughput method for the development and refinement of selected reaction monitoring (SRM) assays for human proteins. The method was applied to generate such assays for more than 1000 cancer-associated proteins, which are functionally related to candidate cancer driver mutations. We used the assays to determine the detectability of the target proteins in two clinically relevant samples: plasma and urine. One hundred eighty-two proteins were detected in depleted plasma, spanning five orders of magnitude in abundance and reaching below a concentration of 10 ng/ml. The narrower concentration range of proteins in urine allowed the detection of 408 proteins. Moreover, we demonstrate that these SRM assays allow reproducible quantification by monitoring 34 biomarker candidates across 83 patient plasma samples. Through public access to the entire assay library, researchers will be able to target their cancer-associated proteins of interest in any sample type using the detectability information in plasma and urine as a guide. The generated expandable reference map of SRM assays for cancer-associated proteins will be a valuable resource for accelerating and planning biomarker verification studies. Source
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. Source