Understanding how cancer invades, metastasises and resists treatment requires seeing tumours as they truly exist: complex, three-dimensional cellular ecosystems with heterogeneous spatial gradients in cell density, oxygenation and immune infiltration.
Traditional microscopy techniques can struggle with this challenge. Confocal systems are too slow for large cleared samples and cause photobleaching. Commercial lightsheet platforms exist but remain expensive and closed to modification. Sectioning destroys the morphological context that reveals invasion corridors and micro-metastases.
Lightsheet microscopy for cancer research
Lightsheet microscopy offers a transformative solution, illuminating only the imaging plane to acquire large volumes quickly with minimal photodamage. When combined with tissue clearing methods and multicolour fluorescence excitation, lightsheet systems can image intact tumours and whole brains at subcellular resolution whilst preserving biological context across centimetre-scale specimens.
This White Paper examines how University College London’s Cancer Institute has deployed an open-source mesoSPIM lightsheet microscope powered by Oxxius MixxWave laser combiners to advance brain cancer research, demonstrating why compact, stable multicolour illumination has become infrastructure for cancer discovery.
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Why this matters for translational research
Brain tumours present distinctive imaging challenges: infiltrative growth that follows white matter tracts, perivascular spread creating distant micro-metastases, and delicate neural structures disrupted by mechanical sectioning. Small 2D sections cannot capture these spatial relationships or reveal the heterogeneous gradients that define tumour biology.
By combining tissue clearing protocols with mesoSPIM’s millimetre-to-centimetre field of view and Oxxius combiners’ multicolour excitation, UCL’s platform delivers the throughput, spatial context and quantitative data required for computational pipelines performing cell segmentation, vascular network analysis and machine-learning-driven phenotyping across experimental conditions.
This combination transforms illumination from a technical specification into research infrastructure, enabling biologically faithful, high-throughput 3D imaging that reveals how tumours actually behave within their organ environments.