See how researchers are applying the ZenoTOF 8600 system across omics workflows

Access on-demand scientific presentations featuring real-world applications in proteomics, metabolomics, lipidomics, and multi-omics discovery.

Real data. Real workflows. Designed for confident evaluation.

Real research, beyond performance claims

These on-demand presentations feature researchers sharing how they apply the ZenoTOF 8600 system across diverse omics workflows.

Explore how advanced acquisition strategies and fragmentation approaches support:

• deeper identification across complex datasets
• more confident interpretation of MS/MS data
• scalable workflows from discovery to large-cohort studies
• structural insight across proteins, metabolites, and lipids

What you will learn

• Move from detection to confident interpretation
• Scale from individual experiments to population datasets
• Capture structure, modification, and function together
• Evaluate real workflows for omics research

Featured scientific presentations

Gary Patti (Washington University)

Solving chimeric MS/MS challenges in metabolomics and exposomics

• Understand why conventional MS/MS approaches, including DDA, challenge untargeted metabolomics, lipidomics, and chemical exposomics analyses
• Learn how scanning DIA significantly increases the number of features that can be resolved by deconvolution, leading to more confidence and more IDs
• Explore scalable approaches for large cohort chemical exposomics and why high-quality data matters for real-world disease research

Result: More confident identification across complex datasets

Lindsay Pino (Talus Bio)

Chemoproteomics with residue-level confidence

• Detect covalent drug binding events with higher confidence
• Improve site localization and quantitative hit calling
• Move from inference to direct measurement of drug-protein target engagement

Result: Faster, more confident decisions in drug interaction and drug discovery workflows

Nicola Zamboni (ETH Zurich)

Deeper metabolomics and lipidomics insights at scale

• Increase identification coverage of metabolites and lipids across complex datasets
• Improve MS/MS selectivity while maintaining acquisition speed
• Scale from LC workflows to high-throughput analysis

Result: Confident detection of low-abundance and biologically relevant features

Lingjun Li (University of Wisconsin)

Advancing multi-omics workflows with structural insight

• Enable LC-MS/MS workflows across multi-omics workflows, such as global proteomics, glycoproteomics, and lipidomics
• Use tunable electron-activated dissociation (EAD) fragmentation for enhanced structural characterization
• Gain insight from low-level, single-cell inputs to complex biological samples

Result: A more complete biological picture across workflows