Statistically unbiased prediction enables accurate denoising of voltage imaging data.

Here we report SUPPORT (statistically unbiased prediction utilizing spatiotemporal information in imaging data), a self-supervised learning method for removing Poisson-Gaussian noise in voltage imaging data. SUPPORT is based on the insight that a pixel value in voltage imaging data is highly dependent on its spatiotemporal neighboring pixels, even when its temporally adjacent frames alone do not provide useful information for statistical prediction. Such dependency is captured and used by a convolutional neural network with a spatiotemporal blind spot to accurately denoise voltage imaging data in which the existence of the action potential in a time frame cannot be inferred by the information in other frames. Through simulations and experiments, we show that SUPPORT enables precise denoising of voltage imaging data and other types of microscopy image while preserving the underlying dynamics within the scene.

Glycation-mediated tissue-level remodeling of brain meningeal membrane by aging.

Collagen is a prominent target of nonenzymatic glycation, which is a hallmark of aging and causes functional alteration of the matrix. Here, we uncover glycation-mediated structural and functional changes in the collagen-enriched meningeal membrane of the human and mouse brain. Using an in vitro culture platform mimicking the meningeal membrane composed of fibrillar collagen, we showed that the accumulation of advanced glycation end products (AGEs) in the collagen membrane is responsible for glycation-mediated matrix remodeling. These changes influence fibroblast-matrix interactions, inducing cell-mediated ECM remodeling. The adherence of meningeal fibroblasts to the glycated collagen membrane was mediated by the discoidin domain-containing receptor 2 (DDR2), whereas integrin-mediated adhesion was inhibited. A-kinase anchoring protein 12 (AKAP12)-positive meningeal fibroblasts in the meningeal membrane of aged mice exhibited substantially increased expression of DDR2 and depletion of integrin beta-1 (ITGB1). In the glycated collagen membrane, meningeal fibroblasts increased the expression of matrix metalloproteinase 14 (MMP14) and less tissue inhibitor of metalloproteinase-1 (TIMP1). In contrast, the cells exhibited decreased expression of type I collagen (COL1A1). These results suggest that glycation modification by meningeal fibroblasts is intimately linked to aging-related structural and functional alterations in the meningeal membrane.

Chitosan-coated mesoporous silica particles as a plastic-free platform for photochemical suppression and stabilization of organic ultraviolet filters.

Photochemical instability and reactivity of organic ultraviolet (UV) filters not only degrade the performance of sunscreen formulations but also generate toxic photodegradation products and reactive oxygen species (ROS). Although the encapsulation of organic UV filters into synthetic polymer particles has been widely investigated, synthetic plastics were recently banned for personal care and cosmetic products due to marine and coastal pollution issues. Here we present a plastic-free, photochemically stable and inactive UV filter platform based on chitosan-coated mesoporous silica microparticles, denoted 'mSOCPs', incorporating octyl methoxycinnamate (OMC) as a sunscreen agent. Sunlight induced the degradation of ∼80% free OMC in artificial sweat in 1 h at room temperature, while only 20% of OMC degraded for 3 h when encapsulated within mSOCPs. Moreover, mSOCPs efficiently suppressed the photochemical generation of ROS by about 99% through the combined effects of the mesoporous silica structure and chitosan coating. Accordingly, mSOCPs substantially increased the cell viability of fibroblasts exposed to UV irradiation. This work demonstrates that the biopolymer coatings of mesoporous inorganic particles can be a promising approach to the plastic-free encapsulation of organic UV filters for suppressing their photochemical reactivity and degradation.

Expansion microscopy imaging of various neuronal structures.

Visualization of the spatial distribution of biomolecules with nanoscale precision is essential to understanding the molecular mechanisms of biological phenomena and diseases. Among several state-of-the-art visualization techniques, expansion microscopy (ExM) is an attractive tool, as it can achieve sub-20-nm resolution imaging of biological specimens, even with conventional diffraction-limited microscopy. This chapter first introduces the concept of ExM and its variants and then provides practical guidelines for implementing expansion microscopy and related techniques.