A repetitive gait cycle is an archetypical component within the behavioural repertoire of many if not all animals including humans. It originates from mechanical feedback within proprioceptors to adjust the motorprogram during locomotion and thus leads to a periodic orbit in a low dimensional space. Here, we investigate the mechanics, molecules and neurons responsible for proprioception in Caenorhabditis (C.) elegans to gain insight into how mechanosensation shapes the orbital trajectory to a well-defined limit cycle. We used genome editing, force spectroscopy and multiscale modeling and found that alternating tension and compression with the spectrin network of a single proprioceptor encodes body posture and informs TRP-4/NOMPC and TWK-16/TREK2 homologs of mechanosensitive ion channels during locomotion. In contrast to a widely accepted model of proprioceptive ‘stretch’ reception, we found that proprioceptors activated under compressive stresses in vivo and in vitro, and speculate that this property is conserved across function and species.

The application of genetically encoded fluorophores for microscopy has afforded one of the biggest revolutions in the biosciences. Bioluminescence microscopy is an appealing alternative to fluorescence microscopy, because it does not depend on external illumination, and consequently does neither produce spurious background autofluorescence, nor perturb intrinsically photosensitive processes in living cells and animals. The low quantum yield of known luciferases, however, limit the acquisition of high signal-noise images of fast biological dynamics. To increase the versatility of bioluminescence microscopy, we present an improved low-light microscope in combination with deep learning methods to increase the signal to noise ratio in extremely photon-starved samples at millisecond exposures for timelapse and volumetric imaging. We apply our method to image subcellular dynamics in mouse embryonic stem cells, the epithelial morphology during zebrafish development, and DAF-16 FoxO transcription factor shuttling from the cytoplasm to the nucleus under external stress. Finally, we concatenate neural networks for denoising and light-field deconvolution to resolve intracellular calcium dynamics in three dimensions of freely moving Caenorhabditis elegans with millisecond exposure times. This technology is cost-effective and has the potential to replace standard optical microscopy where external illumination is prohibitive.

In fluorescence microscopy, an external source of excitation light is required for photon emission and thereby sample visualization. Even though fluorescence imaging has provided a paradigm shift for cell biology and other disciplines, the sample might suffer due to high excitation light intensities, and spurious signals originating from autofluorescence. Bioluminescence imaging, on the contrary, does not need an external source of light for photon emission and visualization, bypassing the effects of autofluorescence, phototoxicity and photobleaching. This renders bioluminescence microscopy as an ideal tool for long term imaging. A major limitation of bioluminescence, compared to fluorescence imaging, is the low quantum yield of the bioluminescent proteins, which requires long exposure times and large collecting wells. Here, we work towards universal tools to overcome the main limitations of bioluminescence imaging: low signal/noise (SNR) imaging. To enhance spatiotemporal resolution, we have designed an optimized setup that boosts the optical efficiency and combine the photon starved, low SNR output with deep learning based content aware reconstruction methods. We trained a UNet architecture neural network with augmented fluorescent experimental data to denoise low SNR bioluminescent images. In addition, we trained a subpixel convolutional network with synthetic light field data to perform 3D reconstruction from a single photographic exposure without the presence of autofluorescence. Furthermore, we compare the reconstruction time and quality improvement with classical deconvolution methods.