Mass Spectrometry Applications in Biological Imaging

Mass spectrometry imaging (MSI) combines molecular specificity with spatial context, enabling the in situ visualisation of peptides, lipids, metabolites and pharmaceuticals within biological tissues. Techniques such as matrix‐assisted laser desorption/ionisation (MALDI), desorption electrospray ionisation (DESI) and secondary ion mass spectrometry (SIMS) differ in ionisation mechanisms, spatial resolution and sample preparation, yet share the capacity for label‐free, multiplexed detection. By mapping molecular distributions at resolutions ranging from tens of micrometres to the subcellular scale, MSI has transformed our understanding of metabolic heterogeneity in health and disease. Applications span the discovery of tissue‐specific biomarkers, the elucidation of drug distribution and metabolism, the characterisation of tumour microenvironments and the exploration of plant metabolism. Recent advances in instrumentation, computational analysis and multimodal integration with optical or chromatographic methods have further enhanced sensitivity, throughput and quantitative performance. The global significance of MSI is underscored by its translation to intraoperative diagnostics, where real‐time molecular feedback can guide surgical decisions, and by its adoption across academic and clinical laboratories for precision medicine and systems biology studies.

Research from Nature Portfolio

Innovations in nanostructured materials have yielded platforms for direct laser desorption/ionisation mass spectrometry capable of detecting drugs and metabolites in microlitre‐scale biological fluids without extensive sample preparation. Tunable silver nanoshells deposited on silica templates function as both an ionisation substrate and signal amplifier, enabling the rapid quantification of glucose in cerebrospinal fluid, monitoring of postoperative infections and real‐time evaluation of blood–brain‐barrier permeability for pharmacokinetic studies. This approach demonstrates the integration of material science with MSI to achieve high sensitivity and minimal sample volume requirements for precision diagnostics.

A complementary advance applies machine learning to serum metabolic patterns acquired via ferric particle‐assisted laser desorption/ionisation mass spectrometry. Using submicrolitre serum volumes and acquisition times under one second, a sparse regression model identifies a panel of metabolites within the 100–400 Da range that distinguishes early‐stage lung adenocarcinoma from controls with high sensitivity and specificity. This work exemplifies the synergy of rapid MSI workflows and computational analysis for non‐invasive cancer screening and paves the way for low‐cost, large‐scale clinical deployment of metabolic profiling tests.

Mass Spectrometry Applications in Biological Imaging publication trend

The graph below shows the total number of articles in mass spectrometry applications in biological imaging across all publications each year (not limited to Nature Index journals).

Technical terms

Imaging mass spectrometry (IMS): A suite of techniques that map the spatial distribution of molecules by recording mass spectra at discrete locations on a sample surface.

Matrix‐assisted laser desorption/ionisation (MALDI): An IMS ionisation method that uses a crystallised low‐molecular‐weight matrix to absorb laser energy and assist desorption and ionisation of analytes.

Desorption electrospray ionisation (DESI): A spray‐based ambient IMS technique that applies charged solvent droplets to the sample surface, desorbs molecules and transfers them into the mass spectrometer.

Secondary ion mass spectrometry (SIMS): A high‐resolution IMS method that bombards the sample with a focused ion beam, ejecting secondary ions that are analysed by mass spectrometry.

References

1. Mass spectrometry imaging with high resolution in mass and space. Histochemistry and Cell Biology (2013).
2. Mass spectrometry imaging for plant biology: a review. Phytochemistry Reviews (2015).
3. Plasmonic silver nanoshells for drug and metabolite detection. Nature Communications (2017).
4. Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma. Nature Communications (2020).
5. Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry. Mass Spectrometry Reviews (2019).
6. Advances in mass spectrometry based single-cell metabolomics. Analyst (2019).