Assessment of a narrow-window dia-PASEF method for high-throughput proteomics

Significance of the Topic

High‐throughput proteomics is essential for drug discovery, biomarker research and systems biology. Rapid, accurate protein identification and quantitation enable large‐scale studies while maintaining data quality and reproducibility.

Objectives and Study Overview

This study aimed to develop and assess a narrow‐window dia‐PASEF method on the timsTOF HT platform, using 5 Da m/z isolation windows and a 15‐minute LC gradient, to achieve deep proteome coverage and robust quantitation in high‐throughput settings.

Methodology and Instrumentation

Sample preparation included tryptic digests of human cell lysate, yeast and Escherichia coli. Chromatographic separation employed a 25 cm × 75 µm IonOpticks Aurora Ultimate column with a 15‐minute gradient. The optimized dia‐PASEF scheme covered 350–900 m/z and 0.8–1.1 1/k0, using 110 windows of 5 Da width across 44 TIMS frames interleaved with 10 MS1 scans. TIMS accumulation and ramp times were set to 30 ms, yielding cycle times of 1.76 s for MS2 and 0.45 s for MS1. Data were processed in Spectronaut v19 using the directDIA+ workflow with MS1 quantitation enabled.

Main Results and Discussion

• Human digest: 8 850 protein groups and 134 728 peptides identified in triplicate 15‐minute runs; 88 % of proteins quantified with CV < 10 %.
• Yeast digest: 4 677 protein groups and 68 744 peptides; median CV values of 3 % at protein level and 8 % at peptide level.
• Hybrid proteome (HeLa:yeast:E. coli): 14 366 protein groups and 170 917 peptides identified across six injections; quantitation ratios (HeLa 1:1, yeast 3:2, E. coli 2:3) matched theoretical values (median log₂ ratios ≈ 0, 0.58, –0.58) with low variance.

Reducing window widths and constraining the m/z and mobility ranges lowered spectral complexity, enabling rapid cycle times and improved specificity.

Practical Implications

• Enables proteome‐wide analyses in under 20 minutes, supporting large sample cohorts.
• Delivers high reproducibility and quantitation accuracy, suitable for biomarker validation and quality control.
• Library‐free processing simplifies workflow and reduces method development time.

Future Trends and Applications

• Further narrowing of isolation windows and shorter gradients for ultra‐high throughput.
• Integration of machine learning for real‐time window optimization and spectral prediction.
• Application in clinical proteomics for rapid diagnostics and personalized medicine.
• Expansion to single‐cell proteomics and large‐scale cohort studies.

Conclusion

The optimized narrow‐window dia‐PASEF method with 5 Da isolation windows and a focused m/z/mobility range achieves rapid, deep proteome coverage and precise quantitation in 15‐minute gradients, meeting the demands of high‐throughput proteomics workflows.

References

No external references provided.