The use of bioluminescence as a reporter for physiology in neuroscience is as old as the discovery of the calcium-dependent photon emission of aequorin. Over the years, luciferases have been largely replaced by fluorescent reporters, but recently, the field has seen a renaissance of bioluminescent probes, catalyzed by unique developments in imaging technology, bioengineering, and biochemistry to produce luciferases with previously unseen colors and intensity. This is not surprising as the advantages of bioluminescence make luciferases very attractive for noninvasive, longitudinal in vivo observations without the need of an excitation light source. Here, we review how the development of dedicated and specific sensor-luciferases afforded, among others, transcranial imaging of calcium and neurotransmitters, or cellular metabolites and physical quantities such as forces and membrane voltage. Further, the increased versatility and light output of luciferases have paved the way for a new field of functional bioluminescence optogenetics, in which the photon emission of the luciferase is coupled to the gating of a photosensor, e.g., a channelrhodopsin and we review how they have been successfully used to engineer synthetic neuronal connections. Finally, we provide a primer to consider important factors in setting up functional bioluminescence experiments, with a particular focus on the genetic model Caenorhabditis elegans, and discuss the leading challenges that the field needs to overcome to regain a competitive advantage over fluorescence modalities. Together, our paper caters to experienced users of bioluminescence as well as novices who would like to experience the advantages of luciferases in their own hand.
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.
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