Back

Enhancing tandem MS sensitivity and peptide identification via ion pre-accumulation in an Orbitrap mass spectrometer

Harking, F.; Guzman, U. H.; Kraegenbring, J.; STEWART, H.; Ayzikov, K.; Koch, H.; Fort, K. L.; Harder, A.; Olsen, J. V.

2025-02-25 biochemistry
10.1101/2025.02.20.639277 bioRxiv
Show abstract

High-throughput mass spectrometry-based proteomics has gained increasing interest for both academic and industrial applications. As implementation of faster gradients has facilitated higher sample throughput, mass spectrometers must adapt to shorter analysis times by enhancing scanning speed and sensitivity. For Orbitrap mass spectrometers, faster scan rates are constrained by the need for sufficient ion accumulation time, particularly given limitations on duty cycle at high repetition rate, and transient length, which determines analyzer sensitivity and resolving power. In this context, implementing alternative ion scheduling and better ion signal-processing strategies are needed to unleash the speed of these instruments. Here, we introduce a new scanning strategy termed pre-accumulation, which enables the storage of ions in the bent flatapole in parallel to the operation of the C-trap/IRM, leading to a significant improvement in ion beam utilization and enabling for the first time scanning speeds of >70 Hz on hybrid Orbitrap instruments. The combination of pre-accumulation and increased scan speeds notably enhances peptide and protein group identifications for short LC gradients and improves sensitivity for high-throughput applications. These benefits were further amplified when coupled with the full mass range phase-constrained spectrum deconvolution method ({Phi}SDM), especially for fast, lower-resolving Orbitrap measurements used with short LC gradients. Overall, we demonstrate that pre-accumulation of ions in the bent flatapole offers distinct advantages, particularly for conditions with reduced signal input. Since no hardware changes are required, this approach is highly attractive for Orbitrap mass spectrometers operated with fast MS/MS acquisition methods.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.