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Fluorescence microscopy imaging speed is fundamentally limited by the measurement signal-to-noise ratio (SNR). To improve image SNR for a given image acquisition rate, computational denoising techniques can be used to suppress noise. However, common techniques to estimate a denoised image from a single frame either are computationally expensive or rely on simple noise statistical models. These models assume Poisson or Gaussian noise statistics, which are not appropriate for many fluorescence microscopy applications that contain quantum shot noise and electronic Johnson–Nyquist noise, therefore a mixture of Poisson and Gaussian noise. In this paper, we show convolutional neural networks (CNNs) trained on mixed Poisson and Gaussian noise images to overcome the limitations of existing image denoising methods. The trained CNN is presented as an open-source ImageJ plugin that performs real-time image denoising (within tens of milliseconds) with superior performance (SNR improvement) compared to conventional fluorescence microscopy denoising methods. The method is validated on external datasets with out-of-distribution noise, contrast, structure, and imaging modalities from the training data and consistently achieves high-performance ($><\#comment/>8\mathrm{d}\mathrm{B}$) denoising in less time than other fluorescence microscopy denoising methods.

 

Traditional fluorescence microscopy is blind to molecular microenvironment information that is present in a fluorescence lifetime, which can be measured by fluorescence lifetime imaging microscopy (FLIM). However, most existing FLIM techniques are slow to acquire and process lifetime images, difficult to implement, and expensive. Here we present instant FLIM, an analog signal processing method that allows real-time streaming of fluorescence intensity, lifetime, and phasor imaging data through simultaneous image acquisition and instantaneous data processing. Instant FLIM can be easily implemented by upgrading an existing two-photon microscope using cost-effective components and our open-source software. We further improve the functionality, penetration depth, and resolution of instant FLIM using phasor segmentation, adaptive optics, and super-resolution techniques. We demonstrate through-skull intravital 3D FLIM of mouse brains to depths of 300 µm and present the firstin vivo4D FLIM of microglial dynamics in intact and injured zebrafish and mouse brains for up to 12 h.

 

Super-resolution microscopy is broadening our in-depth understanding of cellular structure. However, super-resolution approaches are limited, for numerous reasons, from utilization in longer-term intravital imaging. We devised a combinatorial imaging technique that combines deconvolution with stepwise optical saturation microscopy (DeSOS) to circumvent this issue and image cells in their native physiological environment. Other than a traditional confocal or two-photon microscope, this approach requires no additional hardware. Here, we provide an open-access application to obtain DeSOS images from conventional microscope images obtained at low excitation powers. We show that DeSOS can be used in time-lapse imaging to generate super-resolution movies in zebrafish. DeSOS was also validated in live mice. These movies uncover that actin structures dynamically remodel to produce a single pioneer axon in a “top-down” scaffolding event. Further, we identify an F-actin population – stable base clusters – that orchestrate that scaffolding event. We then identify that activation of Rac1 in pioneer axons destabilizes stable base clusters and disrupts pioneer axon formation. The ease of acquisition and processing with this approach provides a universal technique for biologists to answer questions in living animals.