Enhanced neuroimaging with a calcium sensor in ex-vivo Drosophila melanogaster brains using closed-loop adaptive optics light-sheet fluorescence microscopy

Significance

Adaptive optics (AO) has been implemented on several microscopy setups and has proven its ability to increase both signal and resolution. However, reported configurations are not suited for fast imaging of live samples or are based on an invasive or complex implementation method.

Aim

Provide a fast aberration correction method with an easy to implement AO module compatible with light-sheet fluorescence microscopy (LSFM) for enhanced imaging of live samples.

Approach

Development of an AO add-on module for LSFM based on direct wavefront sensing without requiring a guide star using an extended-scene Shack-Hartmann wavefront sensor. The enhanced setup uses a two-color sample labeling strategy to optimize the photon budget.

Results

Fast AO correction of in-depth aberrations in an ex-vivo adult Drosophila brain enables doubling the contrast when imaging with either cell reporters or calcium sensors for functional imaging. We quantify the gain in terms of image quality on different functional domains of sleep neurons in the Drosophila brain at various depths and discuss the optimization of key parameters driving AO.

Conclusion

We developed a compact AO module that can be integrated into most of the reported light-sheet microscopy setups, provides significant improvement of image quality and is compatible with fast imaging requirements such as calcium imaging.

Active image optimization for lattice light sheet microscopy in thick samples

Lattice light-sheet microscopy (LLSM) is a very efficient technique for high resolution 3D imaging of dynamic phenomena in living biological samples. However, LLSM imaging remains limited in depth due to optical aberrations caused by sample-based refractive index mismatch. Here, we propose a simple and low-cost active image optimization (AIO) method to recover high resolution imaging inside thick biological samples. AIO is based on (1) a light-sheet autofocus step (AF) followed by (2) an adaptive optics image-based optimization. We determine the optimum AIO parameters to provide a fast, precise and robust aberration correction on biological samples. Finally, we demonstrate the performances of our approach on sub-micrometric structures in brain slices and plant roots.