Hybrid Metabolomics: Can Researchers Finally Combine Quantitation and Discovery?

Insights from a recent episode of Concentrating on Chromatography featuring Dr. Matthew Lewis of Bruker

For years, metabolomics researchers have faced a familiar tradeoff: choose a targeted workflow to obtain validated, quantitative measurements of known metabolites, or choose an untargeted workflow to cast a wider net for discovery. Increasingly, hybrid metabolomics is challenging the idea that laboratories must make that choice.

In a recent episode of Concentrating on Chromatography, Organomation General Manager David Oliva and co-host Candice Gokey spoke with Dr. Matthew Lewis of Bruker about the technologies, workflow decisions, and data strategies shaping this transition. Their conversation covered targeted and untargeted metabolomics, chemical derivatization, sample conservation, standardization, ion mobility, collision cross section (CCS), and the case for creating data that remains useful long after an initial study ends.

The central idea is straightforward: hybrid metabolomics aims to combine the quantitative confidence of targeted analysis with the broader discovery potential of untargeted profiling. In practice, that goal depends on much more than the mass spectrometer. Sample collection, extraction, derivatization, evaporation, chromatographic separation, calibration, acquisition strategy, and data processing all influence what a laboratory can confidently report. For laboratories designing these workflows, metabolomics sample preparation in toxicology and clinical testing provides a useful starting point for considering how preparation choices shape downstream analytical outcomes.

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What is Hybrid Metabolomics?

Hybrid metabolomics refers to integrated mass spectrometry workflows that connect targeted quantitative analysis with broader, discovery-oriented metabolite profiling. Rather than treating targeted and untargeted methods as completely separate experiments, a hybrid approach seeks to extract complementary information from the same sample preparation, acquisition, or tightly coordinated workflow.

Targeted and untargeted metabolomics are often presented as opposites, but each addresses a different analytical need.

The scientific literature has described hybrid mass spectrometry approaches as a way to bridge the strengths of targeted and untargeted metabolomics, particularly when studies need both quantitative rigor and wider chemical coverage. See Bridging Targeted and Untargeted Mass Spectrometry-Based Metabolomics via Hybrid Approaches.

For high-resolution workflows that must balance chemical coverage, reproducibility, and sample conservation, Organomation’s guide to sample preparation for LC-QTOF-MS analysis outlines the practical extraction, concentration, dry-down, reconstitution, and clarification steps that often precede analysis.

As Dr. Lewis explained during the interview, targeted workflows start with prior knowledge: researchers know which chemical species they want to measure, can configure the method around those compounds, and can use calibration curves and reference standards to support absolute quantitation. Untargeted workflows, by comparison, aim to collect broader chemical information without requiring the analyst to specify every compound in advance.

“There’s no such thing as an untargeted platform. There’s just an uninformed platform.” — Dr. Matthew Lewis

The statement is intentionally provocative, but it captures an important analytical reality. Every metabolomics workflow carries selectivity and bias. The choice of sample matrix, collection protocol, extraction solvent, derivatization chemistry, chromatographic mode, ionization source, mass analyzer, acquisition method, and processing pipeline all influence which metabolites are ultimately observed.

In other words, a workflow may be discovery-oriented without being chemically neutral.

Why Hybrid Workflows Matter

The pressure to combine quantitative and discovery capabilities is growing as metabolomics moves beyond small exploratory studies. Researchers increasingly want to compare large cohorts, monitor individuals over time, study interactions between biology and environmental exposure, and connect metabolomics with genomics, transcriptomics, proteomics, and other layers of systems biology.

A purely targeted assay may answer a specific clinical or biological question exceptionally well. For example, if a researcher needs to monitor a known drug metabolite, a disease marker, or a defined pathway, a targeted quantitative method may be the best fit.

But biology is rarely limited to the molecules a study team expected to find.

Unexpected dietary compounds, contaminants, endogenous metabolites, microbial products, or exposure-related chemicals may influence a biological system in ways that a narrow target panel cannot capture. That is why researchers often hesitate to give up the discovery potential of untargeted analysis—even when they also need the calibration and comparability of a targeted workflow.

Hybrid metabolomics offers a practical middle ground:

- Quantify known metabolites with internal standards and calibration curves.
- Capture broader profiling information that may support later discovery.
- Reduce the need to prepare and acquire the same sample multiple times.
- Preserve more analytical information for later reprocessing, annotation, and meta-analysis.
- Create a more standardized foundation for large cohorts and cross-study comparisons.

Dr. Lewis described this as an effort to “remove the choice” between following known biology and discovering what researchers did not know to look for in advance.

Targeted vs. Untargeted Metabolomics: The Practical Difference

The terms “targeted” and “untargeted” can become confusing because they are sometimes used interchangeably with instrument types. In reality, the workflow is defined by more than whether a laboratory uses a triple quadrupole, time-of-flight instrument, Orbitrap, or another high-resolution mass spectrometer.

Targeted Metabolomics

In targeted metabolomics, researchers define the compounds of interest before acquisition. The assay is typically optimized around those analytes, including their expected retention times, transitions or accurate masses, internal standards, calibration range, matrix effects, and likely interferences.

A robust targeted method can provide:

- Absolute or highly defensible quantitative results.
- Strong assay validation.
- Known calibration performance.
- Defined limits of detection and quantitation.
- More direct comparability across batches and studies when method conditions are controlled.

Targeted workflows are especially valuable when a study requires concentration values rather than relative changes in signal intensity.

Untargeted Metabolomics

Untargeted metabolomics seeks broad chemical coverage. Instead of limiting acquisition to a predefined list of analytes, the laboratory collects data on as many detectable molecular features as the method can reasonably capture.

This broader view makes untargeted analysis useful for:

- Hypothesis generation.
- Biomarker discovery.
- Environmental and exposure studies.
- Systems biology research.
- Metabolic phenotyping.
- Investigating samples where the relevant chemistry is incompletely understood.

However, a detected feature is not automatically an identified metabolite. Feature annotation, structural confirmation, batch correction, and distinguishing biological changes from sample-preparation or instrumental artifacts can require substantial downstream work.

Whether a laboratory is performing targeted quantitation, broad screening, or a combined workflow, preparing samples for LC-MS/MS analysis is an important part of limiting matrix effects and creating extracts compatible with robust chromatography and ionization.

As Dr. Lewis noted, a larger peak in an untargeted dataset does not necessarily mean more of a compound is present. In electrospray ionization, compounds have different response factors. A peak may be large because the analyte is abundant, because it ionizes efficiently, or because matrix conditions favor its response. Calibration standards and validated methods are essential for converting signal into reliable concentration data.

Sample Preparation Is the Foundation

Hybrid metabolomics does not remove the need for careful sample preparation. If anything, it makes sample preparation more important because a single preparation may now support both targeted quantitative measurements and broader discovery-oriented profiling.

For Organomation’s chromatography and sample-preparation audience, this is the key operational message: the quality of the data cannot exceed the consistency and suitability of the sample preparation workflow.

Before any LC-MS or GC-MS acquisition begins, laboratories must make decisions that influence chemical recovery and reproducibility. Effective liquid chromatography sample preparation begins with matching cleanup, concentration, and reconstitution strategies to the sample matrix, analyte chemistry, and downstream separation method.

- How should the sample be collected, stored, and thawed?

- Which extraction solvent best supports the target chemical classes?

- Are proteins, salts, phospholipids, or other matrix components adequately controlled?

- Does the workflow require derivatization?

- Is solvent evaporation necessary before reconstitution?

- Will the concentration step affect analyte stability or recovery?

- Does the final reconstitution solvent match the chromatographic starting conditions?

- Can the protocol be reproduced across plates, batches, operators, and study sites?

These questions are not merely procedural. They determine whether a metabolomics dataset can be interpreted, compared, and reused.

The Metabolomics Standards Initiative minimum reporting standards for chemical analysis emphasize reporting sample preparation, experimental analysis, quality control, metabolite identification, and data preprocessing. The standards are intended to help maximize the value and reproducibility of metabolomics data—not to prescribe one universal protocol.

For serum and other complex biofluids, protein precipitation, solvent removal, and final reconstitution are common points of variability. Organomation’s resource on serum sample preparation for LC-MS and GC-MS discusses these foundational steps and their role in producing concentrated, analysis-ready extracts.

For laboratories working with limited material, the workflow design becomes even more consequential. Clinical specimens, archived biobank samples, longitudinal cohorts, field-collected environmental material, and difficult-to-recollect biological samples all carry a finite analytical budget. Every transfer, dry-down, reconstitution, injection, or repeat preparation can consume material and introduce variability.

During the interview, Gokey highlighted this issue through the example of hard-won field samples that may not be collected again easily. Lewis extended the concept to biobanked specimens: even common biofluids become precious when tied to historical cohorts or limited-access studies.

Chemical Derivatization: A Tool for Broader Coverage

One of the central workflow elements discussed in the episode was chemical derivatization. In metabolomics, derivatization is a deliberate chemical transformation used to make certain analytes more suitable for a given analytical workflow.

Depending on the method, derivatization may help improve:

- Chromatographic retention.
- Detection sensitivity.
- Ionization efficiency.
- Selectivity.
- Separation of chemically challenging compounds.
- Compatibility of multiple metabolite classes with a limited set of analyses.

In the Biocrates workflow discussed by Dr. Lewis, derivatization is used to help bring a broad portion of metabolite space into a standardized workflow that requires fewer analyses than a fully multimodal approach.

This matters because maximizing metabolome coverage often means combining multiple analytical techniques: for example, NMR, GC-MS, reversed-phase LC-MS, HILIC-MS, and additional specialized separations. That strategy can be scientifically powerful, but it can also become expensive, time-consuming, and sample-intensive.

Derivatization is also a familiar consideration in GC-MS sample preparation for metabolomics, where nonvolatile or thermally labile metabolites commonly require chemical modification before analysis. The precise chemistry differs by platform and analyte class, but the broader lesson is the same: derivatization must be integrated into a controlled and validated workflow.

Each additional method may provide new information, but the incremental benefit must be weighed against additional sample use, personnel time, instrument time, data-analysis burden, and opportunities for batch-to-batch variation.

Derivatization is not automatically a shortcut, however. It introduces chemistry that must be controlled.

A well-designed derivatization workflow should account for:

- Reaction completeness.
- Reagent consistency.
- Incubation time and temperature.
- Solvent compatibility.
- Potential side reactions.
- Analyte stability.
- Internal-standard behavior.
- Derivative mass shifts.
- Effects on MS/MS fragmentation and library matching.

For that reason, validated kit-based workflows can offer practical value to laboratories that lack the resources to build, optimize, and validate every component from the ground up. Dr. Lewis noted that standardized kits can include preloaded standards, calibration curves, internal standards, and derivatization chemistry in a format designed to reduce the method-development burden for users.

Why Standardization Is Central to Metabolomics

Metabolomics has enormous potential in clinical research, epidemiology, environmental science, nutrition, and systems biology. But realizing that potential requires data that can be compared across batches, laboratories, instruments, and time.

That is where standardization becomes essential.

Standardization does not mean every laboratory must use the same instrument or abandon innovation. It means laboratories should understand and document how their results were generated, use appropriate reference materials and quality controls, and apply workflows that support meaningful comparison.

At a minimum, standardized metabolomics practice should include attention to:

- Sample collection and storage conditions.
- Extraction and cleanup procedures.
- Internal standards and calibration strategy.
- Derivatization conditions, where applicable.
- Chromatographic method and retention-time control.
- MS acquisition parameters.
- Quality-control samples and batch design.
- Data-processing workflow and software versioning.
- Confidence levels for metabolite identification.
- Metadata storage and data stewardship.

The Metabolomics Standards Initiative’s chemical-analysis reporting recommendations highlight the need to document instrumentation, acquisition mode, sample introduction, calibration, data preprocessing, and quality-control information.

For core facilities, clinical research groups, and interdisciplinary teams, standardized kits and published methods may lower the barrier to entry. In the interview, Lewis described how the growing coverage of modern kits changes the calculation for laboratories deciding between building a custom method and adopting a validated platform. Rather than developing an assay around a limited number of compounds, researchers may be able to access larger standardized panels while focusing their internal expertise on study design, quality control, biological interpretation, and data analysis.

From Data Files to a Digital Metabolome Archive

One of the most compelling ideas in the interview was the concept of a digital metabolome archive.

Traditionally, a metabolomics study might quantify samples first and perform additional characterization later. A team could identify interesting signals during data analysis, then return weeks or months afterward to perform targeted MS/MS experiments on selected samples.

That approach can work, but it has limitations. Chromatography can change. Instrument performance can shift. Samples may be depleted. The relationship between quantitative results and later structural characterization may become harder to reconstruct.

Dr. Lewis’s proposed alternative is to preserve a more complete analytical record for every sample. In this model, the archive may include:

• Quantitative targeted measurements.
• MS1 feature data.
• MS/MS fragmentation information.
• Retention time.
• Calibration data.
• Internal-standard information.
• Sample-preparation metadata.
• Data-processing parameters.
• Additional ion-mobility measurements, where available.

The goal is not simply to create larger data files. It is to create more interpretable and reusable ones.

A well-documented dataset can be revisited when reference libraries improve, when an unknown feature becomes biologically relevant, when a new biomarker hypothesis emerges, or when a research group performs a broader meta-analysis. That is particularly valuable for biobanks and long-term cohort studies, where the original samples may be impossible to replace.

NMR, Mass Spectrometry, and Orthogonal Validation

The conversation also explored the relationship between NMR-based metabolomics and mass spectrometry-based metabolomics.

As Lewis explained, the two methods are fundamentally orthogonal because their underlying physics differ. That distinction matters for validation. If two different analytical technologies support the same biological conclusion, confidence in the result can increase because the agreement is less likely to stem from a shared instrumental artifact.

NMR offers several notable advantages in metabolomics:

• It is intrinsically quantitative under appropriate conditions.
• It is highly reproducible.
• The sample does not directly contact the measurement hardware in the same way it does in many chromatographic systems.
• It can be especially effective for standardized, routine measurement of selected analytes in complex biofluids.

Mass spectrometry, in contrast, typically offers much greater sensitivity and metabolome depth. It can detect many more low-abundance analytes, but it also brings complications such as ion suppression, variable ionization efficiencies, and matrix effects.

This is why a multimodal strategy can be so valuable. NMR may provide stable quantitative measurements for some compound classes, while LC-MS, GC-MS, and ion mobility mass spectrometry expand chemical coverage and structural information.

The challenge is to balance scientific richness with cost, throughput, sample consumption, and analytical complexity.

What TIMS and CCS Add to Metabolomics

Ion mobility mass spectrometry adds another separation dimension to metabolomics. While liquid chromatography separates compounds in the liquid phase and mass spectrometry separates ions by mass-to-charge ratio, ion mobility separates ions in the gas phase based on how they move through a gas under an electric field.

Trapped ion mobility spectrometry, or TIMS, is one form of ion mobility used in modern mass spectrometry workflows.

For chromatography professionals, Lewis offered a useful analogy: TIMS can be thought of as a gas-phase separation that operates within the mass spectrometer. It adds orthogonality to the LC separation and mass measurement, potentially helping resolve compounds that remain difficult to distinguish based on retention time and alone.

Ion mobility can help differentiate:

• Isomers.
• Isobars.
• Co-eluting interferences.
• Structurally related metabolites.
• Features that may have similar mass but different gas-phase behavior.

A key output of ion mobility is the collision cross section, or CCS. CCS is a molecular descriptor related to an ion’s effective size and shape in the gas phase. It can act as another piece of evidence for metabolite annotation alongside accurate mass, retention time, and MS/MS fragmentation.

Research on ion mobility CCS libraries has demonstrated that CCS can support multidimensional metabolite annotation, including for known and unknown metabolites. The AllCCS ion mobility collision cross-section atlas reports an integrated multidimensional matching strategy for annotating known and unknown metabolites using experimental and predicted CCS values.

However, CCS should be viewed as complementary evidence—not a complete substitute for authentic reference standards, validated retention-time matching, suitable MS/MS evidence, or orthogonal confirmation where appropriate.

This distinction is especially important in derivatization-based workflows. Chemical derivatization changes the structure and mass of the analyte being measured. That can complicate traditional library matching, but it also creates an opportunity for software-assisted approaches that model derivatized structures and predict properties such as CCS. Dr. Lewis described this as an important component of making hybrid high-resolution workflows practical for broader annotation.

What Hybrid Metabolomics Means for Sample Preparation

For metabolomics laboratories, the practical lesson is clear: a hybrid workflow can only succeed if preparation is designed for the complete analytical objective.

When one sample preparation may yield both quantitative and discovery-oriented data, laboratories should prioritize the following.

Preserve scarce samples

Use sample volumes and preparation steps deliberately. Consider whether a single well-designed extraction, concentration, and acquisition strategy can answer multiple questions before dividing precious material across redundant workflows.

Validate concentration and dry-down conditions

Solvent evaporation can be a critical step for concentrating extracts, exchanging solvents, and preparing samples for reconstitution. The use of nitrogen evaporation for metabolic extraction illustrates why solvent removal should be designed around the chemical characteristics of the metabolites, the extraction solvent, and the desired final injection solvent.

For sensitive or volatile metabolites, “dry to completeness” may not always be the right endpoint. Time, temperature, gas flow, pressure, container format, and endpoint control should be validated around recovery, reproducibility, and compatibility with the downstream assay. Practical considerations for optimizing this step are discussed in Organomation’s overview of the power of nitrogen blowdown evaporation.

Match reconstitution to chromatography

The reconstitution solvent should support both analyte solubility and chromatographic performance. A mismatch between reconstitution conditions and the initial mobile phase can compromise peak shape, retention, and quantitative reliability.

Use quality control throughout the plate

For 96-well plate-based methods, consistency matters across the full workflow. Standardize plate maps, extraction timing, reagent addition, derivatization, evaporation conditions, reconstitution, injection order, pooled quality-control samples, and blank placement.

Plate-based standardization is especially relevant to kit-driven metabolomics. The workflow described in Organomation’s article on high-throughput metabolomics with the Biocrates MxP Quant 500 kit and MICROVAP illustrates how controlled drying, automated preparation, and LC-MS/MS analysis can be integrated into a higher-throughput metabolomics workflow.

Document every critical step

A hybrid workflow is only as reusable as its metadata. Preserve records of extraction chemistry, standards, derivatization conditions, dry-down parameters, reconstitution solvent, instrument settings, acquisition mode, and data-processing methods.

The Bottom Line

Hybrid metabolomics is not a single instrument feature or a universal replacement for targeted or untargeted analysis. It is a workflow philosophy: design experiments so that a limited sample can deliver reliable quantitative information today while preserving broader chemical evidence for discovery tomorrow.

The discussion with Dr. Matthew Lewis underscores several important themes for modern chromatography and mass spectrometry laboratories:

• Targeted and untargeted metabolomics address different analytical needs, but the boundary between them is becoming more flexible.
• Sample preparation, derivatization, chromatography, ionization, and data processing all influence what a workflow can detect and quantify.
• Standardization is essential for reproducibility, large cohorts, multi-site studies, and long-term data reuse.
• Chemical derivatization can broaden the utility of a limited number of analyses when it is incorporated into a validated workflow.
• Ion mobility and CCS can add valuable orthogonal information for separating and annotating metabolites.
• For limited or irreplaceable samples, generating more reliable information from each aliquot is both a scientific and operational advantage.

As metabolomics becomes more integrated with systems biology, clinical research, exposure science, and longitudinal population studies, the question is shifting. Instead of asking whether a laboratory should pursue targeted or untargeted analysis, researchers may increasingly ask:

How can we create a standardized workflow that preserves quantitative confidence, discovery potential, and long-term data value from every sample?

For sample-preparation teams, that conversation begins well before the mass spectrometer. It begins with a validated, reproducible process for extracting, concentrating, drying, reconstituting, and documenting the chemistry that enters the analytical workflow.

As hybrid workflows evolve, reproducible extraction and concentration remain essential to turning a complex biological sample into defensible chromatographic data. For additional context on the expanding role of sample preparation in advanced workflows, see Organomation’s discussion of LC-MS metabolomics and emerging ionization technologies and tandem chromatography-mass spectrometry in modern workflows.

Frequently Asked Questions

What is hybrid metabolomics?

Hybrid metabolomics combines targeted and untargeted mass spectrometry strategies. The objective is to quantify known metabolites using validated standards and calibration while also collecting broader data that can support discovery of unexpected compounds or previously uncharacterized features. See Bridging Targeted and Untargeted Mass Spectrometry-Based Metabolomics via Hybrid Approaches.

What is the difference between targeted and untargeted metabolomics?

Targeted metabolomics measures predefined analytes and commonly uses internal standards and calibration curves to support quantitative results. Untargeted metabolomics surveys a broad set of detectable features to support discovery, but identifying each feature and assigning absolute concentrations can require additional analysis.

Why is sample preparation important in metabolomics?

Sample preparation affects which metabolites are extracted, how stable they remain, the degree of matrix interference, derivatization performance, chromatographic behavior, and the reproducibility of the final dataset. The Metabolomics Standards Initiative chemical-analysis reporting standards therefore emphasize clear documentation of preparation, quality control, instrumentation, and data processing.

What does CCS mean in mass spectrometry?

CCS stands for collision cross section. In ion mobility mass spectrometry, CCS is a gas-phase molecular descriptor related to an ion’s effective size and shape. It can complement accurate mass, retention time, and MS/MS fragmentation data during metabolite annotation. See the AllCCS ion mobility collision cross-section atlas.

Can one LC-MS run support both targeted and untargeted analysis?

In some hybrid workflows, yes. The feasibility depends on the sample preparation, standards, instrument capabilities, acquisition approach, and data-processing pipeline. Dr. Lewis described a strategy in which one sample preparation and acquisition can yield both targeted quantitative data and a separate broader profiling data stream.