LC-MS/MS plays an important role in untargeted metabolomics because it can analyse complex biological samples and detect many small-molecule features in a single experiment. However, the output is not simply a confirmed list of every metabolite present in a sample. LC-MS-based workflows first generate molecular feature data, which then need to be processed, filtered and annotated before biological interpretation.

In this article, Novogene Europe explains how LC-MS/MS supports untargeted metabolomics, why high-resolution accurate-mass detection matters, and how instrument data can be translated into metabolite-level insight.

Why LC-MS/MS matters in untargeted metabolomics

Untargeted metabolomics is designed for broad discovery. Instead of measuring only a predefined list of compounds, it aims to detect and annotate a broad range of relevant molecular features within the selected analytical method.

This requires a workflow that can handle biological complexity. Samples such as plasma, serum, urine, tissue, faeces, cell extracts and culture supernatants may contain many compounds with different chemical properties and abundance levels. LC-MS/MS helps manage this complexity by combining chromatographic separation with mass spectrometry-based detection.

What liquid chromatography contributes

Liquid chromatography separates compounds before they enter the mass spectrometer. This is important because biological samples contain many molecules that may overlap in mass, retention behaviour or signal intensity.

By separating compounds over time, chromatography helps reduce signal overlap and improves the quality of downstream feature detection. Retention time also provides useful supporting information for metabolite annotation, especially when combined with accurate mass and MS/MS spectral evidence.

What mass spectrometry detects

After chromatographic separation, compounds enter the mass spectrometer and are ionised. The instrument detects ions based on their mass-to-charge ratio and signal intensity. In untargeted metabolomics, this produces molecular feature data rather than immediate confirmed metabolite identities.

A detected feature may represent a metabolite, isotope, adduct, fragment or another analytical signal. This is why data processing is essential. Peak detection, alignment, filtering, normalisation and QC review help convert raw instrument output into a more interpretable feature table.

Why MS/MS spectra support annotation

In LC-MS/MS workflows, selected ions can be fragmented to generate MS/MS spectra. These fragmentation patterns provide structural information that can be compared with spectral libraries, public databases, predicted spectra or authentic reference standards.

Higher-confidence metabolite annotation usually depends on several types of evidence agreeing with each other, such as accurate mass, retention time and MS/MS spectra. This is why detection and identification are not the same thing. A strong untargeted metabolomics workflow should make annotation confidence clear, rather than presenting every detected signal as a confirmed metabolite.

Why high-resolution accurate-mass detection matters

High-resolution accurate-mass mass spectrometry can support more precise feature detection and annotation workflows. It helps distinguish molecular features more clearly and provides accurate mass information that can be used alongside retention time and MS/MS spectra.

At Novogene Europe, untargeted metabolomics is supported by LC-MS/MS workflows using Thermo Scientific Vanquish UHPLC and Orbitrap Exploris 120 high-resolution mass spectrometry. This platform combination supports chromatographic separation, high-resolution accurate-mass detection and MS/MS-based spectral information for downstream metabolite annotation.

Why QC and data processing matter

The quality of untargeted metabolomics results depends not only on the instrument, but also on study design, sample handling, extraction, QC and data analysis. Pooled QC samples, blanks and appropriate controls can help monitor technical variation, background signals and batch-related effects.

For researchers, this means the best results usually come from clear experimental design and consistent sample handling. Sample type, storage condition, buffer or medium composition, treatment solvent, collection timing and biological replicates should all be considered before starting a study.

From LC-MS features to biological interpretation

The final goal of untargeted metabolomics is not simply to detect as many features as possible. The goal is to generate reliable data that can support biological interpretation, such as pathway changes, treatment effects, phenotype-associated metabolic shifts or candidate biomarkers.

At Novogene Europe, untargeted metabolomics 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 meaningful metabolite interpretation, while keeping annotation confidence clear.

FAQs

Does LC-MS/MS identify every metabolite in a sample?

No. LC-MS/MS-based 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 enough reference evidence for confident annotation.

What is the difference between LC-MS and LC-MS/MS?

LC-MS combines liquid chromatography with mass spectrometry. LC-MS/MS adds tandem mass spectrometry, where selected ions are fragmented to generate MS/MS spectra. These spectra provide additional structural information that can support metabolite annotation.

Why is high-resolution mass spectrometry useful?

High-resolution accurate-mass detection can help distinguish molecular features more clearly and provide precise mass information for annotation workflows. However, confident annotation still depends on supporting evidence such as retention time, MS/MS spectra, reference standards and spectral libraries.

Why are QC samples important?

QC samples help monitor technical variation, instrument stability, background signals and batch effects. They support more reliable feature detection and improve confidence in downstream statistical analysis and interpretation.

References

1. Cao M et al. Metabolite annotation using multi-layer metabolic networking. Nat Commun. 2022.
2. Thermo Fisher Scientific. Orbitrap Exploris 120 Mass Spectrometer.
3. Thermo Fisher Scientific. Vanquish HPLC and UHPLC Systems.

Learn more

Interested in applying LC-MS/MS-based untargeted metabolomics to your research? Explore Novogene Europe’s untargeted metabolomics service, read our introduction to untargeted metabolomics, download the service flyer, or contact our team to discuss your project.