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Enhanced analyte detection using in-source fragmentation of field asymmetric waveform ion mobility spectrometry-selected ions in combination with time-of-flight mass spectrometry
journal contributionposted on 18.09.2014 by Lauren J. Brown, Robert W. Smith, Danielle E. Toutoungi, Jim Reynolds, Anthony W.T. Bristow, Andrew Ray, Ashley Sage, Ian D. Wilson, Daniel J. Weston, Billy Boyle, Colin Creaser
Any type of content formally published in an academic journal, usually following a peer-review process.
Miniaturized ultra high field asymmetric waveform ion mobility spectrometry (FAIMS) is used for the selective transmission of differential mobility-selected ions prior to in-source collision-induced dissociation (CID) and time-of-flight mass spectrometry (TOFMS) analysis. The FAIMS-in-source collision induced dissociation-TOFMS (FISCID-MS) method requires only minor modification of the ion source region of the mass spectrometer and is shown to significantly enhance analyte detection in complex mixtures. Improved mass measurement accuracy and simplified product ion mass spectra were observed following FAIMS preselection and subsequent in-source CID of ions derived from pharmaceutical excipients, sufficiently close in m/z (17.7 ppm mass difference) that they could not be resolved by TOFMS alone. The FISCID-MS approach is also demonstrated for the qualitative and quantitative analysis of mixtures of peptides with FAIMS used to filter out unrelated precursor ions thereby simplifying the resulting product ion mass spectra. Liquid chromatography combined with FISCID-MS was applied to the analysis of coeluting model peptides and tryptic peptides derived from human plasma proteins, allowing precursor ion selection and CID to yield product ion data suitable for peptide identification via database searching. The potential of FISCID-MS for the quantitative determination of a model peptide spiked into human plasma in the range of 0.45−9.0 μg/mL is demonstrated, showing good reproducibility (%RSD < 14.6%) and linearity (R2 > 0.99).
The authors thank Loughborough University, Agilent Technologies, and Owlstone Limited for financial support.