Benchmarking diagonal-PASEF data acquisition for high-throughput proteomics applications

Importance of the Topic

The continuous improvement of data‐independent acquisition strategies is critical for proteomics laboratories aiming to increase throughput, sensitivity and quantitative accuracy. Diagonal‐PASEF extends conventional dia‐PASEF by dynamically tracking the ion mobility–m/z distribution of peptide precursors. This approach promises to maximize ion sampling efficiency, reduce chemical noise and deliver deeper proteome coverage across a wide range of sample loads.

Objectives and Study Overview

This study benchmarks various diagonal‐PASEF acquisition schemes against static and variable window dia‐PASEF controls. The primary goals are:

• To evaluate peptide and protein identifications over a dilution series of HeLa digests (5–1000 ng).
• To compare quantitative precision across methods.
• To optimize slice numbers for high‐throughput applications.

Methodology and Instrumentation

HeLa cell line digests were separated by nano‐HPLC (300 nL/min, 25 cm IonOpticks column) using a 17-minute nonlinear gradient. Data were acquired on a timsTOF HT instrument employing:

• Static dia‐PASEF (55 Da constant windows).
• Variable dia‐PASEF (9.5–382.5 Da windows).
• Diagonal‐PASEF with 1, 2, 4, 8 and 12 mobility slices (continuous diagonal scanning).

Raw data processing was performed in Spectronaut 19.8 using the directDIA pipeline and Legacy mode for diagonal acquisitions, with IM and RT sampling reductions tailored to slice count.

Main Results and Discussion

Across all sample loads, diagonal‐PASEF schemes with fewer than four slices outperformed static and variable dia‐PASEF by achieving up to 36% more peptide identifications and 16% more protein groups at 5 ng input. At high loads (100–1000 ng), methods with increased slice numbers offered incremental gains, with the 12‐slice diagonal approach yielding up to 18% more protein groups and 12% more peptides than the 1‐slice variant. Quantitative precision remained excellent for all diagonal settings, though a slight decline in CV‐based reproducibility occurred with higher slice counts (e.g., 77.3% peptides <20% CV at 100 ng for 1‐slice vs. 56.7% for 12‐slice).

Benefits and Practical Applications

Diagonal‐PASEF provides:

• Enhanced proteome coverage at low sample inputs, beneficial for limited or precious samples.
• Superior identification rates compared to standard dia‐PASEF workflows.
• High quantitative precision suitable for biomarker discovery and routine QC.

Future Trends and Potential Applications

As instrument speed and software support continue to advance, diagonal‐PASEF may be combined with real‐time acquisition steering and deeper library‐free analysis. Potential developments include:

• Integration with machine‐learning‐driven window optimization.
• Expansion to multi‐omic workflows (e.g., metabolomics, lipidomics).
• Implementation in clinical proteomics for rapid, high‐sensitivity assays.

Conclusion

This benchmarking study demonstrates that diagonal‐PASEF acquisition on timsTOF HT enhances peptide and protein identifications while maintaining strong quantitative reproducibility. Tailoring slice numbers allows researchers to balance depth and precision across a wide dynamic range of inputs, making diagonal‐PASEF a versatile tool for high‐throughput proteomic applications.

References

• Below C., Bernhardt O.M., Kaspar‐Schoenefeld S., et al. Benchmarking diagonal‐PASEF data acquisition for high‐throughput proteomics applications. ASMS 2025, TP192.