dia-PASEF® applied on different gradient lengths

Importance of the topic

Mass spectrometry–based proteomics is central to large-scale protein identification and quantification in biological research. Traditional data-dependent acquisition (DDA) methods suffer from stochastic precursor selection, leading to missing values and reduced reproducibility across sample cohorts. Data-independent acquisition (DIA) overcomes this limitation by fragmenting all ions within defined m/z windows, but at the cost of longer cycle times or lower specificity when windows are widened. The dia-PASEF approach combines trapped ion mobility spectrometry (TIMS) with parallel accumulation–serial fragmentation (PASEF) and DIA, maximizing ion utilization and sequencing speed. Evaluating dia-PASEF with shortened chromatographic gradients addresses the need for higher throughput without compromising depth of coverage or quantitation precision.

Objectives and study overview

The study aimed to assess the performance of dia-PASEF on two standard proteome samples—HeLa cell lysate and yeast digest—using three gradient lengths (90, 60, and 30 minutes). Key goals included:

• Quantifying the number of reliably identified protein groups and peptides across different gradient durations.
• Evaluating reproducibility of identifications and quantitative precision in replicate injections.
• Determining library coverage and practical throughput limits for single-shot analyses.

Methodology and instrumentation

Sample preparation:

• Yeast digest (Promega) and whole HeLa cell lysate (CIL Biotech) were reduced, alkylated, and digested with trypsin (1:100 enzyme:protein).

Chromatography and mass spectrometry:

• nanoElute nano-LC system coupled to a timsTOF Pro mass spectrometer.
• Reversed-phase C18 column (25 cm × 75 μm, 1.6 μm beads) at 50 °C, flow rate 400 nL/min.
• Linear gradients from 2% to 37% acetonitrile in 0.1% formic acid over 30, 60, or 90 minutes.

Dia-PASEF acquisition:

• Instrument firmware adapted for DIA windows synchronized to TIMS elution.
• Two isolation windows per 100 ms TIMS cycle; sixteen cycles spanning the m/z–ion mobility plane for doubly/triply charged precursors.
• Total cycle time: 1.7 s (1 MS1 + 16 × 100 ms MS/MS scans), balancing data-points per chromatographic peak with specificity.

Data analysis:

• Project-specific spectral libraries generated from 48 high-pH reversed-phase fractions (90 min gradients) using DDA PASEF.
• Targeted extraction and quantitation in Spectronaut (1% FDR at PSM, peptide, and protein levels), with in-run calibration and interference correction.

Main results and discussion

Identification performance:

• In 90 min runs, HeLa: average 97 363 peptide precursors and 7 678 protein groups; yeast: 52 370 precursors and 4 410 protein groups.
• Reducing to 60 min yielded ~86 821 peptides/7 513 proteins (HeLa) and ~43 018 peptides/4 344 proteins (yeast).
• With 30 min gradients: ~58 679 peptides/6 395 proteins (HeLa) and ~30 404 peptides/4 032 proteins (yeast).

Reproducibility and quantitation:

• Triplicate injections showed minimal cumulative gains (31 proteins for HeLa, 74 for yeast), indicating high reproducibility.
• Median coefficient of variation on protein level was 8.6%; 89% of HeLa proteins had CV < 20%, and 80% had CV < 10% (90 min gradient).

Library coverage:

• 90 min single shots covered 66% of HeLa and 86% of yeast protein groups in the library.
• Even with 30 min gradients, coverage remained high at 55% (HeLa) and 79% (yeast).

Benefits and practical applications of the method

dia-PASEF delivers:

• Deep proteome coverage without prefractionation.
• High throughput enabled by short gradients.
• Robust and precise quantitation across large sample cohorts.
• Efficient ion usage (up to 100% duty cycle) for enhanced sensitivity.

Applications include biomarker discovery, pharmaceutical QC/QA, clinical proteomics, and systems biology where both throughput and depth are critical.

Future trends and potential applications

Emerging directions:

• Integration with ultra-high-throughput chromatographic platforms (e.g., Evosep One) for sub-30 min gradients and hundreds of samples per day.
• Development of real-time library matching and machine-learning–driven acquisition schemes to further optimize window placement.
• Expansion to post-translational modification–focused workflows (phosphoproteomics, glycoproteomics) leveraging ion mobility separation.
• Cloud-based data processing pipelines and AI-assisted interpretation for large-scale clinical studies.

Conclusion

dia-PASEF on the timsTOF Pro platform combines the sensitivity and speed of PASEF with the reproducibility of DIA, enabling deep and precise proteome analysis even with gradient lengths as short as 30 minutes. This approach addresses the missing value problem of DDA, supports high-throughput studies, and opens avenues for advanced proteomic applications across research and industry.

Reference

• Meier F. et al. A novel parallel accumulation–serial fragmentation (PASEF) method for proteomic analyses. Mol Cell Proteomics. 2018; doi:10.1074/mcp.TIR118.000900.
• Meier F. et al. dia-PASEF: combining ion mobility and data-independent acquisition for deep proteome coverage. bioRxiv. 2019; doi:10.1101/656207.
• Wang G. et al. Efficient cell lysis and protein digestion protocols in proteomics. J Proteome Res. 2015;14(6):2397–2403.