diaPASEF: label-free quantification of highly complex proteomes

Significance of the Topic

Mass spectrometry–based quantitative proteomics faces challenges in reproducible protein identification and quantification across complex samples. Data-independent acquisition (DIA) addresses the “missing value” problem encountered in data-dependent methods, but further improvements in ion utilization and selectivity are needed for deep proteome analysis. The diaPASEF approach on the timsTOF Pro platform integrates trapped ion mobility spectrometry with DIA to enhance duty cycle, sensitivity, and quantitative accuracy for highly complex proteome samples.

Objectives and Study Overview

This study evaluates the performance of diaPASEF for label-free quantification of a three-proteome mixture (HeLa, yeast, Escherichia coli) in defined ratios. Key goals include:

• Assessing the depth of protein identification in 100-min LC runs.
• Determining quantitative reproducibility across technical replicates.
• Validating measured protein ratios against theoretical mixing ratios.

Methodology and Instrumentation

Sample Preparation and Chromatography:

• Tryptic digests of HeLa, yeast, and E. coli combined into two mixtures (HYE-A: 65% human/30% yeast/5% E. coli; HYE-B: 65% human/15% yeast/20% E. coli).
• Reverse-phase C18 separation (25 cm × 75 µm, 1.6 µm beads) with a 100 min gradient (2–37% acetonitrile, 0.1% formic acid) at 400 nL/min and 50 °C column temperature.

diaPASEF Acquisition Mode:

• Dual TIMS analyzer traps ions and separates them by collisional cross section (CCS) in a 100 ms cycle.
• Two DIA isolation windows per TIMS separation; 16 scans covering doubly and triply charged ions along the m/z–ion mobility plane with 25 m/z windows.
• Total cycle time of 1.7 s (one MS1 survey scan + 16 diaPASEF MS/MS scans), yielding an MS/MS acquisition rate of ~109 Hz.

Used Instrumentation

• nanoElute nanoflow UHPLC system (Bruker Daltonics).
• timsTOF Pro quadrupole time-of-flight mass spectrometer with dual TIMS analyzer (Bruker Daltonics).
• Reversed-phase C18 column (IonOpticks).

Results and Discussion

Protein Identification and Coverage:

• Average of 8 080 (HYE-A) and 8 029 (HYE-B) protein groups identified at 1% FDR with ≥ 2 peptides per protein.
• Dynamic range spanning ~5 orders of magnitude in protein abundance.

Quantitative Reproducibility:

• Median coefficient of variation (CV) across technical replicates below 10%: 6.7–7.2% at protein level, 8.1–8.6% at peptide level.
• Data completeness of ~95.9% at protein group level, indicating minimal missing values.

Accuracy of Measured Ratios:

• Human proteins centered at theoretical ratio (1:1) with log2 ratio ≈ 0.
• Yeast median ratio of 1.9 vs. expected 2.0; E. coli median ratio of 0.3 vs. expected 0.25.

Benefits and Practical Applications

• Deep proteome coverage (> 8 000 proteins) in single 100 min runs from 200 ng sample input.
• High quantitative precision and reproducibility suited for large cohort studies.
• Enhanced ion usage efficiency via coupling of TIMS ion mobility and DIA windows.
• Robust label-free quantification without the need for isotopic labels.

Future Trends and Applications

• Integration of library-free and machine learning methods for spectral library generation.
• Expansion to clinical proteomics and biomarker discovery in large patient cohorts.
• Automation of diaPASEF workflows for high-throughput screening.
• Advances in TIMS technology to further increase ion sampling efficiency and throughput.

Conclusion

diaPASEF on the timsTOF Pro platform combines ion mobility separation and data-independent acquisition to achieve deep, accurate, and reproducible label-free quantification of complex proteomes. The method delivers over 8 000 protein identifications per run, across a wide dynamic range and with excellent quantitative precision, making it a powerful tool for large-scale quantitative proteomics studies.

References

• Meier F et al (2019) bioRxiv; doi:10.1101/656207
• Cox J et al (2014) Molecular & Cellular Proteomics; doi:10.1074/mcp.M113.031591
• Meier F et al (2018) Molecular & Cellular Proteomics; doi:10.1074/mcp.TIR118.000900
• Navarro P et al (2016) Nature Biotechnology; doi:10.1038/nbt.3685
• Meier F et al (2015) Journal of Proteome Research; doi:10.1021/acs.jproteome.5b00932