Assignment of Focus Position with Convolutional Neural Networks in Adaptive Lens Based Axial Scanning for Confocal Microscopy

Adaptive lenses offer axial scanning without mechanical translation and thus are promising to replace mechanical-movement-based axial scanning in microscopy. The scan is accomplished by sweeping the applied voltage. However, the relation between the applied voltage and the resulting axial focus position is not unambiguous. Adaptive lenses suffer from hysteresis effects, and their behaviour depends on environmental conditions. This is especially a hurdle when complex adaptive lenses are used that offer additional functionalities and are controlled with more degrees of freedom. In such case, a common approach is to iterate the voltage and monitor the adaptive lens. Here, we introduce an alternative approach which provides a single shot estimation of the current axial focus position by a convolutional neural network. We use the experimental data of our custom confocal microscope for training and validation. This leads to fast scanning without photo bleaching of the sample and opens the door to automatized and aberration-free smart microscopy. Applications in different types of laser-scanning microscopes are possible. However, maybe the training procedure of the neural network must be adapted for some use cases.

Targeted volumetric single-molecule localization microscopy of defined presynaptic structures in brain sections

Revealing the molecular organization of anatomically precisely defined brain regions is necessary for refined understanding of synaptic plasticity. Although three-dimensional (3D) single-molecule localization microscopy can provide the required resolution, imaging more than a few micrometers deep into tissue remains challenging. To quantify presynaptic active zones (AZ) of entire, large, conditional detonator hippocampal mossy fiber (MF) boutons with diameters as large as 10 µm, we developed a method for targeted volumetric direct stochastic optical reconstruction microscopy (dSTORM). An optimized protocol for fast repeated axial scanning and efficient sequential labeling of the AZ scaffold Bassoon and membrane bound GFP with Alexa Fluor 647 enabled 3D-dSTORM imaging of 25 µm thick mouse brain sections and assignment of AZs to specific neuronal substructures. Quantitative data analysis revealed large differences in Bassoon cluster size and density for distinct hippocampal regions with largest clusters in MF boutons.

Large-scale calcium imaging with a head-mount axial scanning 3D fluorescence microscope

Miniaturized fluorescence microscopes are becoming more important for deciphering the neural codes underlying various brain functions. Using gradient index (GRIN) lenses, these devices enable the recording of neuronal activity in deep brain structures. However, to minimize any damage to brain tissue and local circuits, the diameter of the GRIN lens should be 0.5–1 mm, which results in a small field of view. Considering the three-dimensional (3D) structure of neural circuits in the brain, volumetric imaging capability would increase the number of neurons imaged through the lenses. To observe 3D calcium dynamics, we developed a miniaturized microscope with an electrically tunable lens. Using this microscope, we performed 3D calcium imaging in behaving mice and were able to image approximately twice the number of cells as could be recorded using a 2D imaging technique. This simple low-cost 3D microscope will improve the efficiency of calcium imaging in behaving animals.