We introduced methods and concepts of ISOQuant in

  • Conference presentations
    • ASMS 2011
    • DGMS 2012 (Poznan)
    • GCB 2012 (Jena)
    • HUPO 2014 (Madrid) [DIN A0, DIN A4]

  • Related publications

    • Distler, U., Kuharev, J., Navarro, P., Levin, Y., Schild, H., & Tenzer, S. (2014). Drift time-specific collision energies enable deep-coverage data-independent acquisition proteomics. Nature Methods, 11(2), 167–170. http://doi.org/10.1038/nmeth.2767


      We present a data-independent acquisition mass spectrometry method, ultradefinition (UD) MSE. This approach utilizes ion mobility drift time-specific collision-energy profiles to enhance precursor fragmentation efficiency over current MSE and high-definition (HD) MSE data-independent acquisition techniques. UDMSE provided high reproducibility and substantially improved proteome coverage of the HeLa cell proteome compared to previous implementations of MSE, and it also outperformed a state-of-the-art data-dependent acquisition workflow. Additionally, we report a software tool, ISOQuant, for processing label-free quantitative UDMSE data.


    • Kuharev, J., Navarro, P., Distler, U., Jahn, O., & Tenzer, S. (2015). In-depth evaluation of software tools for data-independent acquisition based label-free quantification. PROTEOMICS. http://doi.org/10.1002/pmic.201400396


      Label-free quantification (LFQ) based on data-independent acquisition workflows currently experiences increasing popularity. Several software tools have been recently published or are commercially available. The present study focuses on the evaluation of three different software packages (Progenesis, synapter, and ISOQuant) supporting ion mobility enhanced data-independent acquisition data. In order to benchmark the LFQ performance of the different tools, we generated two hybrid proteome samples of defined quantitative composition containing tryptically digested proteomes of three different species (mouse, yeast, Escherichia coli). This model dataset simulates complex biological samples containing large numbers of both unregulated (background) proteins as well as up- and downregulated proteins with exactly known ratios between samples. We determined the number and dynamic range of quantifiable proteins and analyzed the influence of applied algorithms (retention time alignment, clustering, normalization, etc.) on quantification results. Analysis of technical reproducibility revealed median coefficients of variation of reported protein abundances below 5% for MSE data for Progenesis and ISOQuant. Regarding accuracy of LFQ, evaluation with synapter and ISOQuant yielded superior results compared to Progenesis. In addition, we discuss reporting formats and user friendliness of the software packages. The data generated in this study have been deposited to the ProteomeXchange Consortium with identifier PXD001240 (http://proteomecentral.proteomexchange.org/dataset/PXD001240).