High Sensitivity Limited Material Proteomics Empowered by Data-Independent Acquisition on Linear Ion Traps

Teeradon Phlairaharn, Samuel Grégoire, Lukas R. Woltereck, Valdemaras Petrosius, Benjamin Furtwängler, Brian C. Searle*, Erwin M. Schoof*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review


In recent years, the concept of cell heterogeneity in biology has gained increasing attention, concomitant with a push toward technologies capable of resolving such biological complexity at the molecular level. For single-cell proteomics using Mass Spectrometry (scMS) and low-input proteomics experiments, the sensitivity of an orbitrap mass analyzer can sometimes be limiting. Therefore, low-input proteomics and scMS could benefit from linear ion traps, which provide faster scanning speeds and higher sensitivity than an orbitrap mass analyzer, however at the cost of resolution. We optimized an acquisition method that combines the orbitrap and linear ion trap, as implemented on a tribrid instrument, while taking advantage of the high-field asymmetric waveform ion mobility spectrometry (FAIMS) pro interface, with a prime focus on low-input applications. First, we compared the performance of orbitrap- versus linear ion trap mass analyzers. Subsequently, we optimized critical method parameters for low-input measurement by data-independent acquisition on the linear ion trap mass analyzer. We conclude that linear ion traps mass analyzers combined with FAIMS and Whisper flow chromatography are well-tailored for low-input proteomics experiments, and can simultaneously increase the throughput and sensitivity of large-scale proteomics experiments where limited material is available, such as clinical samples and cellular subpopulations.
Original languageEnglish
JournalJournal of Proteome Research
Issue number11
Pages (from-to)2815-2826
Number of pages12
Publication statusPublished - 2022


  • Peptide identification optimization
  • Mass spectrometry
  • Ultrasensitive proteomics
  • Data aquisition
  • Low-input applications


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