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Insights from ASMS 2026 breakfast seminar presentations by Prof. Nicola Zamboni and Prof. Gary Patti

For years, metabolomics researchers have faced a frustrating reality: modern high-resolution mass spectrometers can detect tens of thousands of molecular features, yet only a fraction can be confidently identified. The challenge is no longer finding molecules. It is generating the high-quality MS/MS data needed to confidently assign structures and extract meaningful biological insights.

At ASMS 2026, Professors Nicola Zamboni (ETH Zurich) and Gary Patti (Washington University in St. Louis) presented compelling evidence that ZT Scan DIA on the ZenoTOF 8600 is helping overcome this challenge. Their message was clear: by rethinking how MS/MS data are acquired, researchers can identify more molecules, improve confidence in annotations, and scale discovery studies without sacrificing data quality.

Why are metabolomics researchers still struggling to identify most of the molecules they detect?

One of the biggest challenges in metabolomics is not feature detection. It’s identification. As Gary Patti explained:

“We see lots of features, but the fraction of those that we can confidently identify is disproportionately small. This is largely universally recognized as a rate-limiting problem in small molecule analysis.”

Researchers routinely detect thousands to tens of thousands of molecular features. Yet many remain unidentified because the MS/MS data needed for confident library matching are often incomplete, mixed, or difficult to interpret.

The question is no longer how to detect more signals. The question is how to generate cleaner, richer MS/MS data that allow researchers to confidently determine what those signals represent.

Reason 1: How does ZT Scan DIA generate more identifications than traditional DDA?

ZT Scan DIA is helping researchers get more value from every sample by acquiring MS/MS information more comprehensively than traditional DDA workflows.

“In general we see 30% or more MS/MS-confirmed identifications compared to the same instrument in all types of matrices.” – Nicola Zamboni

Using pesticide-spiked serum samples, Zamboni demonstrated that ZT Scan DIA delivered approximately 30–90% more MS/MS-confirmed identifications than DDA, with the greatest gains observed for low-abundance compounds where DDA often fails to trigger fragmentation events.

The advantages extend beyond metabolomics. In lipidomics experiments, ZT Scan DIA increased annotated lipids from 633 to 975, an approximately 50% increase over DDA.

“Having this DIA approach acquiring data for every single feature … allows us to identify more.”

For researchers, this means more detected features can be translated into confident identifications and meaningful biological insights.

Reason 2: How does ZT Scan DIA improve confidence through cleaner MS/MS spectra?

Many researchers assume that DDA generates pure spectra while DIA creates chimeric spectra. Gary Patti’s work suggests the reality is more complicated.

“In metabolomics, lipidomics and exposomics, DIA has been associated with a somewhat negative connotation, the idea being that you get messy data that you can’t interpret. What I’m going to present today is a case that using DIA actually gives you cleaner data.”

His team demonstrated that approximately 53% of MS/MS spectra acquired in a standard DDA metabolomics workflow were still chimeric, meaning fragments from multiple precursor ions were mixed together. This is important because spectral libraries are built from pure standards. When experimental spectra contain fragments from multiple compounds, confidence in library matching decreases and many features remain unidentified.

ZT Scan DIA addresses chimeric spectra using narrow scanning isolation windows and precursor-fragment association based on characteristic scanning patterns. As the isolation window moves across precursor masses, fragment ions generate distinctive signal profiles that allow software to associate fragments back to the correct precursor. Automated deconvolution algorithms can then reconstruct cleaner spectra from complex datasets. The result is substantially improved spectral purity and more usable MS/MS information.

“When you do ZT Scan DIA, you can solve almost all of the chimeric data.”

These observations align with recent SCIEX technical studies demonstrating that ZT Scan DIA 3.0 reduces chimeric spectral overlap and improves MS/MS purity, leading to stronger spectral matches, improved metabolic coverage, and a richer foundation for downstream analysis.

Reason 3: How can ZT Scan DIA scale discovery today while enabling quantitative workflows tomorrow?

The benefits of cleaner MS/MS data extend beyond identification. As precursor-fragment relationships become more specific and spectral purity improves, researchers gain access to higher-quality fragment ion data that may support quantitative measurements in addition to structural confirmation.

Historically, untargeted discovery and quantitation have often been treated as separate workflows. However, the same improvements in spectral quality highlighted by both Zamboni and Patti point toward a future where a single acquisition can support both objectives.

This concept is further supported by SCIEX technical studies exploring untargeted quantitative metabolomics and the role of high-purity MS/MS data in improving quantitative performance. In other words, the value of cleaner spectra may not stop at better identifications. They may also help lay the foundation for extracting quantitative insights directly from discovery datasets, helping narrow the traditional gap between discovery and quantitation.

More data is only valuable if it can be processed efficiently. ZT Scan DIA datasets can contain hundreds of thousands, or even millions, of MS/MS spectra. Historically, this volume of information would have created a significant analytical bottleneck.

Zamboni demonstrated how automated workflows can now handle feature detection, fragment association, deconvolution, and identification directly from raw data with minimal user intervention. Processing can be completed in minutes per file and scaled across large studies through parallelized workflows.

This scalability is particularly important for emerging applications such as exposomics and population-scale metabolomics. Patti highlighted efforts to investigate environmental exposures across more than 20,000 individuals, while Zamboni demonstrated workflows capable of delivering rich MS/MS information at extremely high throughput.

Researchers no longer need to choose between comprehensive MS/MS coverage and large-scale studies.

Is metabolomics approaching its shift from DDA to DIA?

The presentations from Nicola Zamboni and Gary Patti suggest that metabolomics may be approaching a transition similar to the one proteomics experienced years ago, moving from selective acquisition to more comprehensive DIA based workflows.

ZT Scan DIA is helping researchers:

• Generate more MS/MS confirmed identifications.
• Improve confidence through cleaner, deconvoluted MS/MS spectra.
• Increase metabolome coverage through more comprehensive MS/MS acquisition.
• Scale workflows from individual studies to population level investigations.
• Create a stronger foundation for future quantitative workflows.

Perhaps most importantly, these advances are being achieved within a single workflow that combines discovery, identification, and the potential for quantitative insight.

ZT Scan DIA is not simply generating more data. It is helping researchers generate more useful data, enabling more identifications, greater confidence, and new opportunities to combine discovery and quantitation within a single workflow.

Want to see for yourself?

Watch the full presentations from Prof. Nicola Zamboni and Prof. Gary Patti here:

The talks provide a deeper dive into identification gains, chimeric spectrum resolution, scalable processing workflows, and the emerging opportunity to combine comprehensive discovery with quantitative analysis in a single experiment.

Learn more about ZT Scan Dia by accessing the technical notes:

ZT Scan DIA 3.0 metabolomics analysis reduces chimeric spectral overlap to improve metabolic coverage and quantitative analysis

ZT Scan DIA with deconvolution improves the MS/MS purity, coverage, and identification rate in metabolomics analysis compared to DDA and SWATH

Download the full dataset here to evaluate the performance of the ZenoTOF 8600 system