dia-PASEF Visualization Tool: a shiny App for data visualization and exploration of dia-PASEF data

Importance of the Topic

Data independent acquisition with dia-PASEF combines trapped ion mobility spectrometry and parallel accumulation–serial fragmentation to deliver unprecedented sensitivity, throughput, and reproducibility in proteomics. Advances in TIMS hardware and software have made dia-PASEF a cornerstone method in biological and clinical research.

Objectives and Study Overview

This work presents the dia-PASEF Visualization Tool, a Shiny application that streamlines interactive exploration and visualization of dia-PASEF datasets. It supports native outputs from DIA-NN, timsDIANN, and Spectronaut. Demonstrations include a BSA dilution series for quantitative performance assessment and a large-scale phosphoproteomics dataset.

Methodology and Instrumentation

Samples: BSA peptides spiked into a Promega K562 human digest at concentrations from 380 amol to 12.5 fmol.
Instrumentation: Bruker timsTOF HT coupled to an EvoSep 30SPD system.
Acquisition: dia-PASEF covering 350–1200 m/z and 0.7–1.3 1/k0 with a 1.5 s cycle time.
Data Analysis: Spectronaut v17.6 for initial processing; Shiny app imports reports from Spectronaut, timsDIANN, and DIA-NN for visualization.

Key Results and Discussion

• Identification of ~9 000 proteins and ~140 000 peptide precursor ions per 40 min MS run.
• Data completeness > 90% across five orders of magnitude in protein abundance.
• Median coefficient of variation below 10%, indicating high reproducibility.
• Excellent linearity (r2 > 0.95) across the entire dilution series.
• QC module detected an LC leak via increasing FWHM over sequential runs.
• PTM module enabled rapid comparison of phosphoproteome coverage, identification overlaps, PCA clustering, and quantification correlations between short and long gradients.

Benefits and Practical Applications

• Streamlined visualization accelerates decision-making and instrument QC.
• Interactive filtering of target proteins or peptides supports focused analyses, including PTMs.
• Direct compatibility with common DIA software reduces data preprocessing time.
• Prevents manual sifting through raw data, saving time and resources.

Future Trends and Opportunities

• Integration of AI-driven anomaly detection and pattern recognition for enhanced QC.
• Extension to multi-omics workflows and cross-platform data integration.
• Cloud-based deployment for collaborative, large-scale proteomics studies.
• Modular expansion enabling custom analytical pipelines and novel visualization modules.

Conclusion

The dia-PASEF Visualization Tool delivers a user-friendly, efficient interface for exploring complex proteomic datasets. By leveraging native outputs from leading DIA software, it enhances reproducibility, expedites QC, and accelerates insight generation in research and clinical laboratories.

Reference

1. Meier F. et al. diaPASEF: parallel accumulation–serial fragmentation combined with data-independent acquisition. Nat Methods 17, 1229–1236 (2020).
2. Oliinyk D., Meier F. Ion mobility-resolved phosphoproteomics with dia-PASEF and short gradients. Proteomics 23, e2200032 (2023).