Convolutional Neural Network Transformer (CNNT) for Fluorescence Microscopy image Denoising with Improved Generalization and Fast Adaptation.

Deep neural networks have been applied to improve the image quality of fluorescence microscopy imaging. Previous methods are based on convolutional neural networks (CNNs) which generally require more time-consuming training of separate models for each new imaging experiment, impairing the applicability and generalization. Once the model is trained (typically with tens to hundreds of image pairs) it can then be used to enhance new images that are like the training data. In this study, we proposed a novel imaging-transformer based model, Convolutional Neural Network Transformer (CNNT), to outperform the CNN networks for image denoising. In our scheme we have trained a single CNNT based "backbone model" from pairwise high-low SNR images for one type of fluorescence microscope (instance structured illumination, iSim). Fast adaption to new applications was achieved by fine-tuning the backbone on only 5-10 sample pairs per new experiment. Results show the CNNT backbone and fine-tuning scheme significantly reduces the training time and improves the image quality, outperformed training separate models using CNN approaches such as - RCAN and Noise2Fast. Here we show three examples of the efficacy of this approach on denoising wide-field, two-photon and confocal fluorescence data. In the confocal experiment, which is a 5×5 tiled acquisition, the fine-tuned CNNT model reduces the scan time form one hour to eight minutes, with improved quality.

Vascular smooth muscle cell-derived nerve growth factor regulates sympathetic collateral branching to innervate blood vessels in embryonic skin.

Blood vessels serve as intermediate conduits for the extension of sympathetic axons towards target tissues, while also acting as crucial targets for their homeostatic processes encompassing the regulation of temperature, blood pressure, and oxygen availability. How sympathetic axons innervate not only blood vessels but also a wide array of target tissues is not clear. Here we show that in embryonic skin, after the establishment of co-branching between sensory nerves and blood vessels, sympathetic axons invade the skin alongside these sensory nerves and extend their branches towards these blood vessels covered by vascular smooth muscle cells (VSMCs). Our mosaic labeling technique for sympathetic axons shows that collateral branching predominantly mediates the innervation of VSMC-covered blood vessels by sympathetic axons. The expression of nerve growth factor (NGF), previously known to induce collateral axon branching in culture, can be detected in the vascular smooth muscle cell (VSMC)-covered blood vessels, as well as sensory nerves. Indeed, VSMC-specific Ngf knockout leads to a significant decrease of collateral branching of sympathetic axons innervating VSMC-covered blood vessels. These data suggest that VSMC-derived NGF serves as an inductive signal for collateral branching of sympathetic axons innervating blood vessels in the embryonic skin.