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.

Biological systems consist of hierarchical protein structures, each of which has unique 3D geometries optimized for specific functions. In the past decades, the growth of inorganic materials on specific proteins has attracted considerable attention. However, the use of specific proteins as templates has only been demonstrated in relatively simple organisms, such as viruses, limiting the range of structures that can be used as scaffolds. This study proposes a method for synthesizing metallic structures that resemble the 3D assemblies of specific proteins in mammalian cells and animal tissues. Using 1.4 nm nanogold-conjugated antibodies, specific proteins within cells and ex vivo tissues are labeled, and then the nanogold acts as nucleation sites for growth of metal particles. As proof of concept, various metal particles are grown using microtubules in cells as templates. The metal-containing cells are applied as catalysts and show catalytic stability in liquid-phase reactions due to the rigid support provided by the microtubules. Finally, this method is used to produce metal structures that replicate the specific protein assemblies of neurons in the mouse brain or the extracellular matrices in the mouse kidney and heart. This new biotemplating approach can facilitate the conversion of specific protein structures into metallic forms in ex vivo multicellular organisms.

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.

Biological systems consist of hierarchical protein structures, each of which has unique 3D geometries optimized for specific functions. In the past decades, the growth of inorganic materials on specific proteins has attracted considerable attention. However, the use of specific proteins as templates has only been demonstrated in relatively simple organisms, such as viruses, limiting the range of structures that can be used as scaffolds. This study proposes a method for synthesizing metallic structures that resemble the 3D assemblies of specific proteins in mammalian cells and animal tissues. Using 1.4 nm nanogold-conjugated antibodies, specific proteins within cells and ex vivo tissues are labeled, and then the nanogold acts as nucleation sites for growth of metal particles. As proof of concept, various metal particles are grown using microtubules in cells as templates. The metal-containing cells are applied as catalysts and show catalytic stability in liquid-phase reactions due to the rigid support provided by the microtubules. Finally, this method is used to produce metal structures that replicate the specific protein assemblies of neurons in the mouse brain or the extracellular matrices in the mouse kidney and heart. This new biotemplating approach can facilitate the conversion of specific protein structures into metallic forms in ex vivo multicellular organisms.

Three-dimensional multiplexed fluorescence imaging is an indispensable technique in neuroscience. For two-dimensional multiplexed imaging, cyclic immunofluorescence, which involves repeating staining, imaging, and signal removal over multiple cycles, has been widely used. However, the application of cyclic immunofluorescence to three dimensions poses challenges, as a single staining process can take more than 12 hours for thick specimens, and repeating this process for multiple cycles can be prohibitively long. Here, we propose SEPARATE (Spatial Expression PAttern-guided paiRing And unmixing of proTEins), a method that reduces the number of cycles by half by imaging two proteins using a single fluorophore. This is achieved by labeling two proteins with the same fluorophores and unmixing their signals based on their three-dimensional spatial expression patterns, using a neural network. We employ a feature extraction network to quantify the spatial distinction between proteins, with these quantified values, termed feature-based distances, used to identify protein pairs. We then validate the feature extraction network with ten proteins, showing a high correlation between spatial pattern distinction and signal unmixing performance. We finally demonstrate the volumetric multiplexed imaging of six proteins using three fluorophores, pairing them based on feature-based distances and unmixing their signals through protein separation networks.

One of the key advantages of using a hydrogel is its superb control over elasticity obtained through variations of constituent polymer and water. The underlying molecular nature of a hydrogel is a fundamental origin of hydrogel mechanics. In this article, we report a Polyacrylamide (PAAm)-based hydrogel model using the MARTINI coarse-grained (CG) force field. The MARTINI hydrogel is molecularly developed through Iterative Boltzmann inversion (IBI) using all-atom molecular dynamics (AAMD), and its quality is evaluated through the experimental realization of the target hydrogel. The developed model offers a mechanically high-fidelity CG hydrogel that can access large-scale water-containing hydrogel behavior, which is difficult to explore through AAMD in practical time. With the modeled hydrogel, we reveal that the polymer conformation modulates the elasticity of the hydrogel from a folded state to a swollen state, confirmed by the Panyukov model. The results provide a robust bridge for linking the polymer conformations and alignment to their bulk deformation, enabling the multifaceted and material-specific predictions required for hydrogel applications.

Nanoscale imaging of whole vertebrates is essential for the systematic understanding of human diseases, yet this goal has not yet been achieved. Expansion microscopy (ExM) is an attractive option for accomplishing this aim; however, the expansion of even mouse embryos at mid- and late-developmental stages, which have fewer calcified body parts than adult mice, is yet to be demonstrated due to the challenges of expanding calcified tissues. Here, we introduce a state-of-the-art ExM technique, termed whole-body ExM, that utilizes cyclic digestion. This technique allows for the super-resolution, volumetric imaging of anatomical structures, proteins, and endogenous fluorescent proteins (FPs) within embryonic and neonatal mice by expanding them 4-fold. The key feature of whole-body ExM is the alternating application of two enzyme compositions repeated multiple times. Through the simple repetition of this digestion process with an increasing number of cycles, mouse embryos of various stages up to E18.5, and even neonatal mice, which display a dramatic difference in the content of calcified tissues compared to embryos, are expanded without further laborious optimization. Furthermore, the whole-body ExM's ability to retain FP signals allows the visualization of various neuronal structures in transgenic mice. Whole-body ExM could facilitate studies of molecular changes in various vertebrates.