Metabolomics Data Quality Improvements Advance Systems Biology
High‑quality data and multiomics connectivity are redefining metabolomics workflows.
As metabolomics and lipidomics mature into essential pillars of systems biology, laboratories face mounting pressure to improve data quality, expand scalability, and unify increasingly complex, multi‑layered datasets. These demands have only grown as research moves toward larger patient cohorts, deeper mechanistic insights, and more rigorous reproducibility requirements. At analytica 2026, Dr. Matthew Lewis, vice president of metabolomics and lipidomics at Bruker Daltonics, emphasized that meeting these challenges requires more than faster instruments—it depends on the ability to generate, monitor, and interconnect high‑quality multidimensional data.
In a conversation with Technology Networks, Lewis explored why data quality remains the defining challenge in metabolomics, how Bruker is supporting AI‑ready data workflows, and why multiomics data interconnectivity is the next frontier.
Data quality as the cornerstone of modern metabolomics
What key technological advancements are currently driving the most meaningful improvements in data quality, workflow efficiency, and biological insight across metabolomics?
When asked which technological advances are driving the most meaningful improvements in workflow efficiency, Lewis was unequivocal: data quality improvements are a cornerstone for advancing the field.
Bruker’s instruments have long been engineered with precision in mind, but Lewis noted an increasing emphasis on helping users see and validate that quality in real time. Its QSee™ solution suite enables researchers to automatically track instrument performance both longitudinally and in real-time, assessing data integrity across multiple dimensions.
“Data reproducibility is a huge topic in the metabolomics field… enabling researchers to easily visualize the quality of data with depth is key for us.” — Dr. Matthew Lewis
Metabolomics is inherently comparative—researchers often evaluate hundreds of patient samples, meaning even subtle variation can bias results. Traditional workflows to assess data quality during acquisition required analysts to overlay LC-MS chromatograms visually, a process Lewis described as “very superficial,” offering few assurances that deeper statistical and data quality anomalies weren’t lurking beneath.
Bruker’s QSee solution changes that dynamic by pulling the same metrics typically examined post hoc—coefficients of variation, signal stability, retention drift—forward into real‑time monitoring. This prevents painful surprises after an entire batch has been processed. When paired with QSee’s pre-experiment performance testing capabilities and long-term data logging in TwinScape™, researchers have a comprehensive view and assurance of their instrument performance and data quality, both across and within experimental batches.
Why data‑centric quality control (QC) tools matter:
- Longitudinal performance tracking reduces hidden variability
- Real‑time statistical monitoring elevates confidence in metabolomics comparability
- QC insights support consistent results across large‑scale, multi‑week studies
Designing AI‑ready data
How is Bruker Daltonics integrating AI into its metabolomics platforms, and what new capabilities or efficiencies do you see this unlocking for researchers?
While AI has been a dominant theme across analytical sciences, Lewis emphasized that Bruker’s approach is intentionally pragmatic. Rather than building proprietary AI models that may conflict with users’ diverse analytical strategies or may not comply with global customer’s IT policies, the company focuses on making data AI‑ready through robust APIs and accessible formats. “We hand the data off in a way that people can work it right into their models” said Lewis.
This strategy acknowledges a key reality in omics research: AI workflows vary tremendously between institutions. Some rely on in‑house machine learning, others use bespoke bioinformatics pipelines, and many continue to iterate and experiment. By ensuring that Bruker’s data products easily integrate into these environments, researchers retain maximum flexibility.
Sustainability through upgradeability
How is Bruker Daltonics approaching sustainability in its mass spectrometry portfolio, and what do you see as the most impactful opportunities for greener lab workflows?
Sustainability discussions in analytical instrumentation often focus on energy consumption and consumables. Lewis highlighted a less‑discussed—and uniquely impactful—form of sustainability: platform upgradeability.
“You don’t just replace your old box with a new box. We have a lineage for each platform,” explained Lewis. Rather than requiring labs to purchase entirely new systems every few years, many Bruker platforms—such as the timsTOF Pro, Pro 2, and HT series, or systems within the SCP and Ultra line—can be upgraded along their technology path. This preserves previous investments, reduces waste, and lengthens the operational life of high‑value systems.
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With timsMetabo™ representing the beginning of a new platform generation, Lewis expects upgradeability to continue as a core differentiator that keeps researchers operating on the cutting edge.
Benefits of upgradeable platforms:
- Reduced environmental and financial waste compared with full system replacement
- Ability to access new capabilities without disruptive instrument turnover
- Long‑term continuity across research programs and collaborations
Usability and scalability: Precision at population scale
How is Bruker Daltonics supporting scalability and usability to help labs generate comprehensive metabolomic datasets more efficiently?
Metabolomics datasets continue to grow in both size and complexity, and Lewis stressed that usability is inseparable from scalability. Precision is paramount and Bruker’s QSee solution plays a central role in ensuring results remain trustworthy across large cohorts.
Researchers no longer need to rely on subjective data inspection. Instead, automated statistical checks surface issues early, preventing the scenario Lewis described as “the thing we never want to see”—when data appears high‑quality until after downstream analysis contradicts it.
Multiomics integration: A growing but complex frontier
Looking ahead, what emerging trends or unmet needs in research do you believe will shape Bruker Daltonics’ innovation strategy in the coming years?
Lewis pointed to multiomics data integration as one of the most important—and most challenging—emerging needs. Researchers increasingly want to combine proteomics, metabolomics, lipidomics, and spatial data to build unified biological narratives.
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“Systems biology is an important goal on the horizon, specifically the interconnection of the data across different omics and levels, for example, connecting data at a population level, to the tissue level, all the way down to a single cell.” — Dr. Matthew Lewis
This range in analysis capabilities allows researchers to “drill down or drill up” depending on whether they are tracing the origin of a circulating biomarker or assessing the systemic relevance of a local discovery.
This interconnectivity is vital in areas such as microbiome sciences and exposomics, where environmental exposure data must integrate with biological measurements—expanding both the complexity and importance of workflow‑ready interconnectivity.
Key drivers of next‑generation multiomics:
- Unified datasets spanning tissues, cell types, and molecular layers
- Tools for tracing biomarker origins across biological scales
- Growing interest in microbiome and exposome interactions with human health
As metabolomics and lipidomics push into increasingly ambitious territory, the future of the field will be shaped not only by instrumentation but by data quality, workflow interconnectivity, and scalable multiomics analysis.
Key takeaways:
- High‑quality, deeply monitored data remains the foundation of reliable metabolomics.
- AI‑ready data structures, flexible APIs, and upgradeable platforms ensure labs can grow and modernize without sacrificing autonomy or sustainability.
- Multiomics interconnectivity—from single cells to population‑scale datasets—is the next major frontier, driving innovation across microbiome research, exposomics, and systems biology.
This content includes text that has been created with the assistance of generative AI and has undergone editorial review before publishing. Technology Networks' AI policy can be found here.
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