Journal of Proteome ResearchBiochemistryPeer-reviewed journal article
Hybrid Quadrupole Mass Filter Radial Ejection Linear Ion Trap and Intelligent Data Acquisition Enable Highly Multiplex Targeted Proteomics
Philip M. RemesiD, Cristina C. JacobiD, Lilian R. HeiliD, Nicholas ShulmaniD, Brendan MacLeaniD, Michael J. MacCossiD
Abstract
Targeted mass spectrometry (MS) methods are powerful tools for selective and sensitive analysis of peptides identified by global discovery experiments. Selected reaction monitoring (SRM) is currently the most widely accepted MS method in the clinic, due to its reliability and analytical performance. However, due to limited throughput and the difficulty in setting up and analyzing large scale assays, SRM and parallel reaction monitoring (PRM) are typically used only for very refined assays of on the order of 100 targets or less. Here we introduce a new MS platform with a quadrupole mass filter, collision cell, linear ion trap architecture that has increased acquisition rates compared to the analogous hardware found in the Orbitrap Tribrid series instruments. The platform can target more analytes than existing SRM and PRM instruments - in the range of 5000 to 8000 peptides per hour. This capability for high multiplexing is enabled by acquisition rates of 70-100 Hz for peptide applications, and the incorporation of real-time chromatogram alignment that adjusts for retention time drift and enables narrow time scheduled acquisition windows. Finally, we describe a Skyline external software tool that implements the building of targeted methods based on data independent acquisition chromatogram libraries or unscheduled analysis of heavy labeled standards. We show that the platform delivers ~10x lower LOQs than traditional SRM analysis for a highly multiplex assay and also demonstrate how analytical figures of merit change while varying method duration with a constant number of analytes, or by keeping a constant time duration while varying the number of analytes.
- assay: proteomics
- research focus: drug response / drug discovery
- approach: deep learning
- approach: statistical model
- Citations
- 20
- FWCI
- 2.44
- Field percentile
- top 8%
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