Untargeted metabolomics can help researchers explore the biochemical changes behind disease biology, treatment response, microbial activity, environmental adaptation and other complex biological processes. In this article, Novogene Europe explains how LC-MS-based untargeted metabolomics works, how metabolite annotation should be interpreted, and why metabolomics is increasingly used alongside genomics, transcriptomics and other omics approaches.

What is untargeted metabolomics?

Untargeted metabolomics is a discovery-based approach used to profile small molecules, or metabolites, in biological samples. Because metabolites reflect biochemical activity close to the observed phenotype, metabolomics can help researchers understand how molecular changes are linked to functional biological outcomes.

Unlike targeted metabolomics, which focuses on a defined list of compounds, untargeted metabolomics aims to detect and annotate a broad range of metabolite features without limiting the analysis to a predefined panel. It is often used when researchers want to explore global metabolic changes, affected pathways, candidate biomarkers or biological differences between study groups.

However, untargeted metabolomics does not simply produce a confirmed list of every metabolite in a sample. LC-MS first detects molecular features, which are then processed, filtered and annotated using reference standards, spectral libraries and bioinformatics workflows.

Untargeted vs targeted metabolomics

Untargeted metabolomics is typically used for broad discovery. It can support hypothesis generation, pathway exploration and biomarker discovery when the researcher does not yet have a fixed compound list.

Targeted metabolomics is more focused. It is used when specific metabolites or compound panels need to be measured, often for validation, absolute quantification or detailed analysis of selected pathways.

The two approaches can be complementary. A study may begin with untargeted metabolomics to discover metabolic differences, then follow up with targeted assays to validate selected compounds in a larger cohort or a more specific experimental design.

How LC-MS-based untargeted metabolomics works

LC-MS-based untargeted metabolomics combines liquid chromatography with mass spectrometry. First, metabolites are extracted from the sample. Liquid chromatography separates compounds before they enter the mass spectrometer, which detects ionised compounds based on mass-to-charge ratio and signal intensity.

In LC-MS/MS workflows, selected ions can also be fragmented to generate MS/MS spectra. These spectra provide additional structural information and help support metabolite annotation.

A simplified workflow includes sample collection, metabolite extraction, LC-MS/MS detection, quality control monitoring, feature processing, metabolite annotation, statistical analysis and report generation. Each step can influence the final result, which is why sample quality, storage, extraction methods, QC design and annotation strategy all matter.

From features to biological interpretation

In untargeted metabolomics, detection and identification are not the same thing. The confidence of metabolite annotation depends on the quality of the available reference evidence, including accurate mass, retention time, MS/MS spectra, spectral libraries and authentic reference standards.

At Novogene Europe, untargeted metabolomics data analysis is supported by Novogene’s wider metabolomics annotation resources and bioinformatics workflows, including NovoMetDB-UM. These resources help researchers move from detected LC-MS features towards more biologically meaningful metabolite interpretation, while keeping annotation confidence clear.

How metabolomics supports multiomics research

Multiomics research combines different molecular layers to provide a more complete view of biological systems. Genomics can show genetic variation or biological potential. Transcriptomics can show which genes are being expressed. Epigenomics can help explain regulatory changes. Metagenomics can profile microbial composition and functional potential.

Metabolomics adds a downstream biochemical layer that is often closer to the observed phenotype. For example, RNA-seq may show that genes in a metabolic pathway are differentially expressed, while metabolomics can help assess whether related metabolites are also changing. In microbiome studies, sequencing can show which microbes are present or what functions may be encoded, while metabolomics can help explore metabolic changes associated with microbial activity, host response or environmental conditions.

By integrating metabolomics with other omics datasets, researchers can generate stronger biological hypotheses and better understand how molecular regulation is reflected in biochemical activity.

Planning an untargeted metabolomics study

Good study design is essential for reliable metabolomics results. Researchers should define the biological question, choose the right sample type, plan appropriate groups and biological replicates, and control sample collection, storage and handling as much as possible.

It is also important to declare buffers, culture media, salts, serum, preservatives or solvents, as these can affect LC-MS analysis. Suitable controls may include blank medium, blank extraction controls, pooled QC samples or other study-specific controls.

Novogene Europe can support researchers in reviewing sample type, study design and analysis goals before starting an untargeted metabolomics project.

FAQs

What sample types can be used for untargeted metabolomics?

Untargeted metabolomics can be applied to many biological sample types, including plasma, serum, urine, faeces, tissue, cells, microbial biomass, plant material and culture supernatant. Feasibility depends on sample type, sample amount, storage condition, buffer or medium composition, and the research goal.

Does untargeted metabolomics involve sequencing?

No. Untargeted metabolomics is not a sequencing-based service. It uses analytical chemistry platforms such as LC-MS/MS to detect small molecules. However, it can be combined with sequencing-based services, such as RNA-seq or metagenomics, in multiomics studies.

How many metabolites or features can untargeted metabolomics detect?

There is no fixed number, because results depend on sample type, biological complexity, sample quality, LC-MS method and annotation confidence. In suitable samples, untargeted metabolomics can detect thousands of molecular features, but not every feature can be assigned a confirmed metabolite name.

Can untargeted metabolomics identify every metabolite in a sample?

No. Untargeted metabolomics is designed for broad discovery, but no platform can identify every metabolite in a biological sample. Some compounds may be outside the detectable range of the method, present at very low abundance, unstable, or lacking sufficient reference information for confident annotation.

References

1. Schrimpe-Rutledge AC et al. Untargeted metabolomics strategies. J Am Soc Mass Spectrom. 2016.
2. Eicher T et al. Metabolomics and multi-omics integration. Metabolites. 2020.
3. González-Domínguez R et al. Pre-analytical processing for blood and urine metabolomics. Metabolites. 2020.

Learn more

Interested in applying untargeted metabolomics to your research? Explore Novogene Europe’s untargeted metabolomics service, download the service flyer, or contact our team to discuss your project.