Optimization of wide window acquisition methods for improved proteome coverage

Importance of the Topic

Bottom-up proteomics relies on accurate interpretation of tandem mass spectra, but over 40% of data-dependent acquisition (DDA) spectra contain chimeric signals from multiple peptides. This complexity limits protein and peptide identifications, particularly in high-throughput or short-gradient workflows. The CHIMERYS intelligent search algorithm uses machine learning to predict fragment intensities and deconvolute mixed spectra, enabling broader proteome coverage and enhanced throughput when combined with wide window acquisition strategies.

Objectives and Study Overview

This study evaluates the synergistic effect of CHIMERYS data processing and varying MS2 isolation window widths on proteome coverage. Proteomic analyses of HeLa and yeast digests were conducted across gradient lengths from 8 to 90 minutes, sample loads of 200–500 ng, and isolation widths ranging from 0.4 Th to 8 Th. Both trap-elute and direct injection setups were assessed to determine optimal acquisition parameters for maximizing identifications and instrument utilization.

Methodology

• Sample preparation included Thermo Scientific HeLa and Promega yeast digest standards loaded at defined ng/μL concentrations.
• Liquid chromatography used Thermo Scientific Easy-Spray PepMap Neo columns in trap-elute (75 μm × 150 mm) and direct injection (75 μm × 500 mm) modes with flow rates adapted for gradients of 5.5 to 90 minutes.
• Mass spectrometry employed an Orbitrap Exploris 480 with scan ranges of m/z 350–1200, MS1 resolutions up to 120,000, and MS2 resolutions of 7,500–15,000 depending on samples per day (SPD).
• Data processing was performed in Proteome Discoverer 3.0 using three workflows: Sequest HT, Sequest HT with INFERYS rescoring, and CHIMERYS intelligent search with Percolator.

Used Instrumentation

• Thermo Scientific Orbitrap Exploris 480 mass spectrometer
• Thermo Scientific FAIMS Pro Duo interface (optional)
• Thermo Scientific Easy-Spray PepMap Neo 2 μm C18 columns (75 μm × 150 mm trap and 75 μm × 500 mm direct injection)
• Thermo Scientific PepMap Neo 5 μm C18 300 μm × 5 mm trap cartridges
• Proteome Discoverer 3.0 software

Main Results and Discussion

Comparative analysis revealed that CHIMERYS processing with wide isolation windows (2–4 Th) delivered the greatest gains across all conditions:

• For an 8-minute gradient, CHIMERYS doubled protein identifications compared to Sequest HT.
• Longer gradients (14.4–90 min) saw 20–60% more proteins and unique peptides versus traditional narrow windows.
• Unique peptides increased by up to 120% and protein groups by up to 68% at optimal widths.
• HeLa direct injection runs (60 and 90 min) demonstrated 16–37% more peptides and 13–27% more proteins over INFERYS rescoring.
• Wider windows beyond 4 Th reduced performance, highlighting an optimal range of 2–4 Th for synergistic benefits.
• Increased PSMs per MS2 spectrum improved instrument utilization without compromising identification quality.

Benefits and Practical Applications

The combined use of wide window acquisition and CHIMERYS processing enables:

• Substantially higher proteome coverage in both short and long gradients.
• Reduced run times or deeper coverage at constant throughput, benefiting core labs and high-throughput facilities.
• Robust performance across trap-elute and direct injection modes, suitable for diverse sample types and analytical goals.

Future Trends and Opportunities for Use

Emerging directions include real-time deconvolution of chimeric spectra during acquisition, integration of AI-driven algorithms with ion mobility separations (e.g., FAIMS, TIMS), and expansion to post-translational modification analysis and clinical proteomics. Further optimization of isolation window widths in conjunction with evolving hardware advances promises continued gains in depth and throughput.

Conclusion

The CHIMERYS intelligent search algorithm paired with optimized wide window MS2 acquisition (2–4 Th) consistently improves unique peptide and protein identifications across multiple gradient lengths, sample loads, and acquisition modes. This approach enhances both throughput and coverage, offering a powerful strategy for modern proteomics workflows.

References

1. Dorfer V., Maltsev S., Winkler S., Metchler K. CharmeRT: Boosting Peptide Identifications by Chimeric Spectra Identification and Retention Time Prediction. Journal of Proteome Research. 2018;17(8):2581–2589.