Recipient organisationUniversity of ExeterSource-published name: University of Exeter
Funding£722K
PeriodDec 2025 — Dec 2028
In plain English
AI plain-English summary
A new microscope will capture chemical fingerprints inside living plant cells without using dyes or labels, revealing exactly where specific molecules are located at the subcellular scale. Current techniques like mass spectrometry can identify which molecules are present in a plant sample, but they cannot show where those molecules sit within individual cells. Standard light microscopes offer high-resolution images but cannot distinguish one biochemical from another. This project bridges that gap by combining nonlinear optical techniques—four-wave mixing and broadband coherent anti-Stokes Raman scattering—with machine learning. The team will train computer models on metabolic mutants of *Arabidopsis thaliana* to decode the complex optical signals into maps of biomolecular composition. If successful, the technology will let plant biologists watch how photosynthesis, disease responses, and stress reactions unfold at the molecular level inside living cells. This could accelerate the development of crops that are more resilient to drought, pests, or changing climates. In the longer term, the technique could be adapted for use in other organisms, from microbes to animals. The project is primarily fundamental science, building a new tool for discovery rather than delivering an immediate commercial product.
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Context Current technologies for studying plant biochemistry, including metabolomics based on mass spectrometry (MS) or nuclear magnetic resonance (NMR), provide valuable molecular insights but lack the spatial resolution needed to understand how biomolecules are distributed within cells. MS imaging can provide spatial information across tissues and cells but not with the resolution of organelles within cells. This project proposes a novel imaging approach that bridges the gap between traditional microscopy and so called omic technologies. By combining high-resolution imaging with advanced biochemical analysis and machine learning, our technology will create new opportunities for functional analysis of plants at the subcellular scale. The Challenge the Project Addresses Light microscopy non-destructively interrogates subcellular structures, but the challenge is to decode the complex biochemistry hidden within optical signals, without the use of labels or dyes, to yield the level of molecular specificity provided by MS and NMR. Our project will address this challenge through a combination of cutting-edge optical physics, to enhance the molecular (i.e Raman) signatures imparted on light when it interacts with matter, and artificial intelligence to extract meaningful biochemical information from these complex signatures. Aims and Objectives Our aim is to create a transformative plant microscopy technology based on cutting-edge nonlinear optical techniques, four-wave-mixing and broadband Coherent anti-Stokes Raman Scattering, to acquire chemical fingerprints in plants at the subcellular scale. We will integrate this with machine-learning (a field of artificial intelligence) to develop computer models, trained using data from metabolic mutants of Arabidopsis thaliana, to map the fingerprints against biomolecular composition. This new chemical fingerprint imaging technology will shed new light on the cellular level biochemistry governing plant life, plant response to disease and environmental stresses. Our ultimate objective is the widespread adoption of the technology by the plant biology community. This will initially take place through specialised user access facilities and in the longer term, via translation into commercial microscopy products. Potential Applications and Benefits This innovative technology holds tremendous potential across plant biology, offering a unique capability to study biomolecular composition on a spatial scale unavailable with current analytical methods. The novel chemical imaging capability will provide unprecedented insights into key cellular processes, such as photosynthesis, as well as plant responses to biotic and abiotic stresses. It will also provide a new medium-throughput chemical phenotyping approach which will be trialled by outreach to potential users in the later stages of the project. In the longer term the technique will be transferable to organisms other than plants. Relevance to the BBSRC long-term research and innovation priorities The project sits within the research area Tools and Technology Underpinning Biological Research. The technology developed would support BBSRC’s research and innovation objective priorities by advancing frontiers of bioscience discovery in both Understanding the Rules of Life and Transformative Technologies. Moreover, it would provide scientists with a transformative capability to tackle the strategic challenge in Sustainable Agriculture and Food.
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