Real-world omics applications with the ZenoTOF 8600 system

Access on-demand scientific presentations with real-world applications in proteomics, metabolomics, lipidomics, and multi-omics discovery in this webinar: real data and real workflows, designed for a confident evaluation.

Real research, beyond performance claims

These on-demand presentations feature academics discussing how they use the ZenoTOF 8600 system in various omics workflows. Investigate how advanced acquisition methods and fragmentation approaches help:

• Improve identification for complex datasets
• Improve interpretation of MS/MS data
• Scale procedures for discovery and large cohort research
• Provide structural insight into proteins, metabolites, and lipids

Learn how to:

• Transition from detection to confident interpretation
• Extend from individual experiments to population datasets
• Capture structure, alteration, and function together
• Evaluate actual workflows for omics research

Featured scientific presentations

Gary Patti (Washington University): Solving chimeric MS/MS challenges in metabolomics and exposomics

• Understand why traditional MS/MS methods, such as DDA, pose challenges for untargeted metabolomics, lipidomics, and chemical exposure analyses
• Explore how scanning DIA enhances the number of features that can be resolved by deconvolution, resulting in higher confidence and more IDs
• Discover scalable methodologies for large cohort chemical exposomics and the importance of high-quality data in real-world illness research

The result? More confident identification across complex datasets

Lindsay Pino (Talus Bio): Chemoproteomics with residue-level confidence

• Identify covalent drug binding processes more confidently
• Enhance site localization and quantitative hit calling
• Switch from inference to precise assessment of drug-protein target interaction

The result? Decisions in drug-interaction and drug-discovery processes are made faster and with greater confidence

Nicola Zamboni (ETH Zurich): Deeper metabolomics and lipidomics insights at scale

• Improve identification of metabolites and lipids in complex datasets
• Improve MS/MS selectivity without sacrificing acquisition speed
• Transition from LC procedures to high-throughput analysis

The result? Reliable detection of low-abundance and physiologically relevant features

Lingjun Li (University of Wisconsin): Advancing multi-omics workflows with structural insight

• Enable LC-MS/MS workflows for many omics, including global proteomics, glycoproteomics, and lipidomics
• Use EAD fragmentation to improve structural characterization
• Understand complex biological samples by analyzing single-cell inputs

The result? A more comprehensive biological picture across the workflow