An adaptive microscope for the imaging of biological surfaces

Scanning fluorescence microscopes are now able to image large biological samples at high spatial and temporal resolution. This comes at the expense of an increased light dose which is detrimental to fluorophore stability and cell physiology. To highly reduce the light dose, we designed an adaptive scanning fluorescence microscope with a scanning scheme optimized for the unsupervised imaging of cell sheets, which underly the shape of many embryos and organs. The surface of the tissue is first delineated from the acquisition of a very small subset (similar to 0.1%) of sample space, using a robust estimation strategy. Two alternative scanning strategies are then proposed to image the tissue with an improved photon budget, without loss in resolution. The first strategy consists in scanning only a thin shell around the estimated surface of interest, allowing high reduction of light dose when the tissue is curved. The second strategy applies when structures of interest lie at the cell periphery (e.g. adherens junctions). An iterative approach is then used to propagate scanning along cell contours. We demonstrate the benefit of our approach imaging live epithelia from Drosophila melanogaster. On the examples shown, both approaches yield more than a 20-fold reduction in light dose -and up to more than 80-fold- compared to a full scan of the volume. These smart-scanning strategies can be easily implemented on most scanning fluorescent imaging modality. The dramatic reduction in light exposure of the sample should allow prolonged imaging of the live processes under investigation. A smart-scanning fluorescence microscope adapts its scanning scheme to the curved surface of embryonic cell sheets. This results in a considerable reduction in light dose without deterioration of the images.

Automated summary

The smart-scanning fluorescence microscope adapts its scanning scheme to reduce light dose significantly while maintaining image quality when imaging large biological samples.