Learned adaptive multiphoton illumination microscopy

Multiphoton microscopy is a powerful technique for deep imaging in scattering samples. However, it requires precise, sample-dependent increases in excitation power with depth in order to maintain signal while minimizing photodamage. We show that cells with identical fluorescent labels imaged can be used to train a physics-based machine learning model that solves this problem. After this training has been performed, the correct illumination power can be predicted and adaptively adjusted at each point in a 3D volume on subsequent samples as a function of the sample’s shape, without the need for specialized fluorescent labelling. We use this technique for imaging of immune responses in mouse lymph nodes following vaccination, with imaging volumes 2-3 orders of magnitude larger than previously reported. We achieve visualization of physiologically realistic numbers of antigen-specific T cells for the first time, and demonstrate changes in the global organization and motility of dendritic cell networks during the early stages of the immune response.