Targeted and untargeted metabolomics have traditionally served different purposes. Targeted methods provide precise measurements of known metabolites, while untargeted analysis helps researchers detect unexpected compounds and biological patterns.
Hybrid metabolomics aims to combine these strengths in one workflow. In an episode of Concentrating on Chromatography, produced in collaboration with Separation Science, hosts David Oliva and Candice Gokey speak with Dr. Matthew Lewis, Vice President of Metabolomics and Lipidomics at Bruker Daltonics and Head of Markets at TOFWERK, about how this approach could generate more information from each sample.
Moving Beyond Targeted or Untargeted Analysis
Targeted metabolomics begins with a defined list of compounds. This knowledge allows laboratories to optimize detection, characterize interferences, construct calibration curves, and obtain absolute quantitative values.
Untargeted metabolomics makes fewer assumptions. High-resolution mass spectrometry captures broad molecular profiles that can reveal unknown metabolites or environmental exposures. However, the resulting peak areas do not provide absolute concentrations without further calibration.
Lewis explains that the boundary between these approaches has started to blur as targeted panels expand. Some now cover more than 1,000 metabolites, combining broad coverage with the controls associated with quantitative assays. Hybrid workflows extend this convergence by retaining targeted quantitation and broader discovery data.
Understanding the Limits of Untargeted Workflows
Lewis challenges the idea that untargeted analysis captures everything in a sample. Collection, storage, extraction, chromatography, ionization, and data processing all influence which molecules remain visible. These decisions introduce biases even when researchers have not defined specific analytical targets.
No single method covers the complete metabolome. Combining NMR, GC–MS, reversed-phase LC–MS, HILIC–MS, and other techniques can expand coverage, but each analysis consumes sample, instrument time, and resources.
Chemical derivatization offers another option. Modifying selected metabolite classes can make more compounds suitable for a limited number of robust analytical separations. Standardized workflows can control this process while incorporating reference standards, internal standards, and calibration materials.
Producing Two Results From One Acquisition
In the hybrid workflow discussed by Lewis, laboratories prepare each sample and acquire the data once. They then process the raw data through two routes.
One stream extracts calibrated quantitative values for known metabolites. The other produces broader molecular profiles for untargeted investigation. Researchers still process the data twice, but they no longer need separate sample preparations and acquisitions.
This distinction matters when scientists work with limited or irreplaceable material, including archived biofluids, longitudinal cohort samples, and specimens collected from remote environments. Extracting more information from each aliquot can preserve these samples for future studies.
Strengthening Standardization
Laboratories often develop their own extraction procedures, chromatographic methods, spectral libraries, and processing pipelines. These differences can make it difficult to compare results across instruments and studies.
Lewis identifies standardization as a priority as metabolomics moves toward clinical research, large cohort studies, exposome analysis, and systems biology. Researchers need reproducible measurements to compare individual samples with reference databases or combine datasets from multiple studies.
He also highlights the complementary strengths of NMR and mass spectrometry. NMR provides stable, intrinsically quantitative measurements, while mass spectrometry offers greater analytical depth. Using these orthogonal techniques can strengthen the validation of biological findings.
Creating a Digital Metabolome Archive
Faster acquisition enables laboratories to collect quantitative MS1 information and structural MS/MS data during the same run. Ion mobility adds another molecular descriptor.
Trapped ion mobility spectrometry separates ions according to properties including charge, size, and shape. Lewis compares the technique to gas-phase chromatography inside the mass spectrometer.
This additional separation can help resolve isomers, isobars, and interferences that LC or mass analysis alone may not distinguish. It also measures collision cross section (CCS), which reflects an ion’s three-dimensional structure.
Combining retention time, mass-to-charge ratio, signal intensity, fragmentation data, and CCS creates a detailed digital record of each sample. Researchers could revisit this archive as spectral libraries, annotation software, and artificial intelligence tools improve.
Expanding Access to Metabolomics
Hybrid workflows could make metabolomics more accessible to core facilities and laboratories without dedicated method developers. Standardized methods can reduce the need to build each assay from the ground up while improving comparability across laboratories.
This accessibility could support closer integration between metabolomics, lipidomics, genomics, transcriptomics, and proteomics. Challenges remain, particularly in combining targeted and untargeted processing and annotating derivatized metabolites. Even so, hybrid metabolomics offers a practical route toward conserving samples, improving standardization, and preserving discovery potential without sacrificing quantitative data.
Learn More:
- Explore the Concentrating On Chromatography podcast to dive into the frontiers of chromatography, mass spectrometry, and sample preparation with host David Oliva.
Connect with Dr. Matthew Lewis:
- LinkedIn: Matthew R. Lewis
