timsplot: A Python Shiny App for Visualizing timsTOF Proteomics Results

Importance of the Topic

The timsTOF technology combines trapped ion mobility spectrometry with parallel accumulation–serial fragmentation (PASEF), delivering high sensitivity and speed in proteomics experiments.
The volume and complexity of timsTOF datasets require intuitive tools for rapid data inspection, quality control, and exploratory analysis.

Objectives and Overview

This summary presents timsplot, a Python Shiny application designed to visualize raw and processed timsTOF proteomics data.
It supports report outputs from Spectronaut, FragPipe, DIA-NN, and Bruker ProteoScape, as well as raw MOMA events for comprehensive data exploration.
Demonstrations include a dilution series experiment analyzed by DIA-PASEF and an immunopeptidomics study using DDA-PASEF.

Methodology and Instrumentation

Sample preparation consisted of a Promega K562 digest dilution series with injection loads from 15 pg to 16 ng, and class I and II immunopeptides.
Liquid chromatography was performed on an IonOpticks 25 cm C18 column using 30-minute (dilution series) or 60-minute (immunopeptides) gradients.
Mass spectrometry analysis employed a Bruker timsTOF Ultra or timsTOF Ultra 2 coupled to a NanoElute 2 LC system.
Post-acquisition processing used Spectronaut 19.4 for DIA-PASEF data and FragPipe for DDA-PASEF immunopeptide data.

Used Instrumentation

• Bruker timsTOF Ultra and Ultra 2 mass spectrometers
• NanoElute 2 LC system
• IonOpticks 25 cm C18 columns
• Spectronaut 19.4 software
• FragPipe software
• DIA-NN and Bruker ProteoScape support

Main Results and Discussion

In the dilution series experiment, timsplot identified up to 7 000 protein groups and 110 000 peptides, detecting over 2 000 proteins at the lowest load (15 pg).
Visualization modules provided ID counts, coefficient of variation plots, and IM vs. m/z heatmaps with DIA window overlays.
In the immunopeptidomics study, the app revealed up to 5 000 proteins and 30 000 precursors, peptide length distributions, charge state profiles, and sequence motif analyses.
These results demonstrate the app’s flexibility in handling diverse proteomic workflows and high-throughput data sets.

Benefits and Practical Applications

timsplot offers a user-friendly interface for both novices and experts, streamlining data exploration without custom scripting.
Modular design allows tailored visualization for quantitative proteomics, PTM analysis, immunopeptidomics, and more.
Downloadable metadata and exportable tables facilitate integration into QA/QC pipelines and presentation materials.

Future Trends and Opportunities

Expansion of timsplot modules to include advanced machine learning–driven analytics and multi-omics integration.
Cloud-based deployment could enable collaborative analysis and real-time monitoring of proteomic experiments.
Enhancements in interoperability with additional search engines and data formats will broaden its applicability.

Conclusion

timsplot represents a versatile and accessible solution for visualizing timsTOF proteomics data, supporting multiple search outputs and raw data formats.
Its modular architecture and comprehensive plotting functions address key challenges in data exploration and quality assessment.

Reference

1. Willems S. et al. Molecular & Cellular Proteomics. 2020;20:100149. DOI: 10.1016/j.mcpro.2021.100149.