Cutting-edge omics research continues to push mass spectrometry toward greater sensitivity, throughput, and analytical depth. However, methods developed to answer specialized research questions do not always translate into workflows that other laboratories can implement with the same results.
During an interview at ASMS 2026, Thomas Moehring, Senior Director, OMICS Applications and Managing Director at Thermo Fisher Scientific, discussed the need to standardize omics workflows across the complete analytical process. He identifies this transition from research innovation to routine implementation as a key opportunity for the field.
Moving Research Methods into Routine Use
According to Moehring, research laboratories often configure methods and workflows around a specific scientific question. This flexibility allows scientists to test new approaches, optimize individual steps, and extend the limits of analytical performance.
The next challenge involves making those methods available beyond the laboratory that developed them. Moehring explains that laboratories must convert successful research workflows into standardized processes that other sites can adopt and reproduce.
This transition requires more than transferring an instrument method. Laboratories must account for differences in equipment, operator experience, sample handling, software, and data analysis. A workflow that depends on extensive manual adjustment or specialist knowledge may perform well in its original setting but prove difficult to reproduce elsewhere.
Connecting the Complete Workflow
Moehring emphasizes that standardization must cover the full analytical chain rather than focus on mass spectrometry alone. The process begins with sample preparation and continues through chromatographic separation, mass detection, data processing, and interpretation.
Variation at any stage can affect the final result. Inconsistent sample preparation may alter recovery or introduce bias. Changes in separation performance can affect retention times, peak shape, and ionization. Differences in acquisition or processing settings can then change which compounds or biomolecules the laboratory detects.
Moehring’s workflow-level perspective highlights the need to evaluate how each stage interacts with the next. Increasing mass spectrometer speed or sensitivity offers limited value when sample preparation restricts throughput or data processing creates a bottleneck.
Standardized connections between these stages could help laboratories maintain performance across longer analytical sequences, multiple operators, and different sites.
Using AI to Accelerate Data Processing
Data analysis represents another important part of this transition. Modern omics platforms generate large, complex datasets that can require extensive processing before researchers can reach a biological conclusion.
Moehring points to artificial intelligence and other computational tools as part of the wider workflow. These technologies can help laboratories process data, recognize patterns, and accelerate interpretation.
However, dependable computational analysis still requires consistent input data. Standardized preparation, separation, acquisition, and quality-control procedures provide a stronger foundation for AI-supported processing. Without that consistency, analytical variation may limit the reliability of the resulting models and conclusions.
Closing the Implementation Gap
Moehring sees an opportunity to close the gap between what researchers can demonstrate in specialist environments and what laboratories can apply as a routine method.
Achieving that goal will require coordinated development across sample preparation, separation science, mass spectrometry, software, and data interpretation. It will also require workflows that reduce dependence on individual expertise without limiting scientific capability.
By standardizing the complete analytical process, laboratories can make advanced omics methods more reproducible, transferable, and accessible across the research community.
