Abstract
In shotgun proteomics, data-dependent precursor acquisition (DDA) is widely used to profile protein components in complex samples. Although very popular, there are some inherent limitations to the DDA approach, such as irreproducible precursor ion selection, under-sampling and long instrument cycle times. Unbiased ‘data-independent acquisition’ (DIA) strategies try to overcome those limitations. In MSE, which is supported by Waters Q-TOF instrument platforms, such as the Synapt G2-S, a wide band pass filter is used for precursor selection. During acquisition, alternating MS scans are collected at low and high collision energy (CE), providing precursor and fragment ion information, respectively. Introduction of ion mobility separation (IMS), which provides an additional dimension of separation, leads to an increase of identified peptides and proteins in MSE workflows. For label-free quantification of ion mobility based MSE data, we developed a bioinformatics pipeline, ISOQuant, allowing retention time alignment, clustering, normalization, isoform/homology filtering, absolute quantification and report generation. Thus, we are able to reproducibly quantify up to 2,500 proteins in a single LC-MS run. The workflow can be adapted to different kinds of proteomic samples providing a robust platform for DIA label-free proteomics.
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Acknowledgements
We thank all ISOQuant beta testers for their continuing critical evaluation of the software. We thank Ruben Spohrer for excellent sample preparation and H. Vissers and K. Richardson for discussions on data evaluation. This work was supported by Deutsche Forschungsgemeinschaft (INST 371/23-1 FUGG) to S.T., H.S., BMBF (e:Bio Express2Present, 0316179C) to S.T., the Forschungszentrum Immunologie (FZI), the Naturwissenschaftlich-Medizinische Forschungszentrum (NMFZ) and the Forschungszentrum Translationale Neurowissenschaften (FTN) of the Johannes Gutenberg University Mainz.
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Distler, U., Kuharev, J., Schild, H., Tenzer, S. (2013). Data-independent acquisition strategies for quantitative proteomics. In: de Almeida, A., et al. Farm animal proteomics 2013. Wageningen Academic Publishers, Wageningen. https://doi.org/10.3920/978-90-8686-776-9_16
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DOI: https://doi.org/10.3920/978-90-8686-776-9_16
Publisher Name: Wageningen Academic Publishers, Wageningen
Online ISBN: 978-90-8686-776-9
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