Spatial metabolomics in medicinal plant research: current advances, technical challenges, and future perspectives
Clinical Snapshot
PICO Framework
| P — Population | Medicinal plants (as biological subjects of study); by extension, researchers and clinicians engaged in natural product drug discovery and phytochemical characterisation |
| I — Intervention | Spatial metabolomics techniques (in situ metabolite profiling, mass spectrometry imaging, spatial multi-omics integration, AI-assisted imaging analytics) |
| C — Comparator | Conventional/traditional metabolomics approaches relying on tissue homogenisation and bulk extraction |
| O — Outcomes | Characterisation of spatial heterogeneity and dynamic accumulation of secondary metabolites; elucidation of spatiotemporal biosynthetic regulatory networks; translational applications in drug discovery and industrial phytochemistry |
Bottom Line
This review from Zhejiang Sci-Tech University provides a timely synthesis of spatial metabolomics as an emerging platform for characterising secondary metabolites in medicinal plants. By enabling in situ, spatially resolved metabolite profiling — rather than relying on bulk tissue homogenisation — spatial metabolomics offers genuine methodological advantages for understanding where and when bioactive compounds accumulate within plant tissues. The integration with spatial multi-omics, single-cell analysis, and AI-assisted imaging represents a conceptually powerful framework for deciphering biosynthetic regulatory networks relevant to drug discovery. However, as a narrative review without a documented systematic search strategy or formal quality assessment of included studies, the conclusions carry inherent risk of selection and confirmation bias. No quantitative synthesis is presented, and the translational pathway from spatial metabolite mapping to clinically validated therapeutics remains largely aspirational. For Australian clinicians and researchers, the practical relevance lies primarily in pharmaceutical R&D and quality assurance of botanical medicines rather than immediate clinical practice change. Senior clinicians should regard this as a horizon-scanning review identifying a promising research methodology, rather than evidence warranting practice change. Independent replication and prospective validation studies are needed before clinical translation claims can be substantiated.
Key Findings
P Value: Not applicable — no original statistical analyses performed
Effect Size: Not applicable — review article; no primary effect size data reported
Primary Outcome: Narrative synthesis of spatial metabolomics advances enabling in situ profiling of secondary metabolite spatial heterogeneity and dynamic accumulation in medicinal plants, overcoming tissue homogenisation limitations of conventional metabolomics
Nnt Or Sensitivity: Not applicable — methodological review; no diagnostic, therapeutic, or prognostic metrics calculable from available data
Confidence Interval: Not reported — narrative review without quantitative synthesis
Clinical Application
Spatial metabolomics platforms (mass spectrometry imaging, DESI-MSI, MALDI-MSI, SIMS) require substantial capital investment and specialist expertise. Currently feasible only in well-resourced academic research centres and pharmaceutical R&D facilities. Not applicable to routine clinical or community pharmacy settings. Integration with AI-assisted imaging adds computational infrastructure requirements. Australia has growing research capacity in metabolomics through institutions such as Metabolomics Australia (Bio21 Institute, University of Melbourne) and the Australian National Phenome Centre (Murdoch University). The TGA regulates complementary medicines including herbal products under the Therapeutic Goods Act 1989; spatial metabolomics could strengthen quality control and standardisation of listed herbal medicines. The RACGP does not currently provide specific guidance on spatial metabolomics applications. PBS listing of plant-derived pharmaceuticals (e.g., paclitaxel, vincristine analogues) could benefit from improved biosynthetic pathway characterisation enabled by this technology. Australian native medicinal plants represent an underexplored resource where spatial metabolomics could accelerate bioactive compound discovery. Primarily relevant to researchers in natural product chemistry, pharmacognosy, and pharmaceutical sciences. Indirect clinical relevance to prescribers utilising plant-derived medicines, complementary medicine practitioners, and pharmaceutical developers engaged in botanical drug development pipelines.
Abstract
Secondary metabolites derived from medicinal plants represent a critical resource for drug discovery and play an indispensable role in the prevention and treatment of major human diseases. Spatial metabolomics has emerged as a transformative approach that overcomes the inherent limitations of traditional metabolomics, particularly tissue homogenisation, by enabling precise, in situ profiling of the spatial heterogeneity and dynamic accumulation of key bioactive compounds. When integrated with complementary technologies such as spatial multi-omics (SMO), single-cell analysis, and artificial intelligence (AI)-assisted imaging, this paradigm provides a powerful framework for deciphering the spatiotemporal regulatory networks that govern secondary metabolite biosynthesis. This review systematically compares conventional and spatial metabolomics, synthesises recent advances in medicinal plant research, critically evaluates current technical challenges and optimisation strategies, and outlines future directions enabled by multi-omics integration and intelligent computational analytics. Collectively, these insights aim to establish a theoretical foundation for the in-depth investigation of medicinal plants and to support their translational application and industrial advancement.
References
- 1.Gong, Y., Yang, D., & Hou, Z. (2026). Spatial metabolomics in medicinal plant research: current advances, technical challenges, and future perspectives. Analytical Methods: Advancing Methods and Applications. https://doi.org/10.1039/d6ay00572a
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