Optimized Coiled-Coil Interactions for Multiplexed Peptide-PAINT

Super-resolution microscopy has revolutionized how researchers characterize samples in the life sciences in the last decades. Amongst methods employing single-molecule localization microscopy, DNA points accumulation for imaging in nanoscale topography (DNA-PAINT) is a relatively easy-to-implement method that uses the programmable and repetitive binding of dye-labeled DNA imager strands to their respective docking strands. Recently developed Peptide-PAINT replaces the interaction of oligonucleotides by short coiled-coil peptide sequences leading to an improved labeling scheme by reducing linkage errors to target proteins. However, only one coiled-coil pair is currently available for Peptide-PAINT, preventing multiplexed imaging. In this study, the initial Peptide-PAINT E/K coil is improved by modifying its length for optimized binding kinetics leading to improved localization precisions. Additionally, an orthogonal P3/P4 coil pair is introduced, enabling 2-plex Peptide-PAINT imaging and benchmarking its performance and orthogonality using single-molecule and DNA origami assays. Finally, the P3/P4 peptide pair is used to image the human epidermal growth factor receptors 2 (ErbB2/Her2) in 2D and 3D at the single receptor level using genetically encoded peptide tags.     

基于图像超分辨率的多尺度特征交互传播研究

该文构建了一种可对不同形式的多尺度结构进行归纳的统一框架,并基于该框架系统地探究了多尺度卷积的两个因素——特征传播和跨尺度交互,提出了简单而有效的多尺度卷积单元——多尺度-跨尺度-权重共享的卷积(MS3-Conv)网络.实验结果表明,与基于标准卷积的网络相比,基于MS3-Conv的网络可使用较少的参数和较低的计算成本实...     

通道注意力嵌入的Transformer图像超分辨率重构

目的 基于深度学习的图像超分辨率重构研究取得了重大进展，如何在更好提升重构性能的同时，有效降低重构模型的复杂度，以满足低成本及实时应用的需要，是该领域研究关注的重要问题。为此，提出了一种基于通道注意力（channel attention，CA）嵌入的Transformer图像超分辨率深度重构方法（image super-resolution with channelattention-embedded Transformer，CAET）。方法 提出将通道注意力自适应地嵌入Transformer变换特征及卷积运算特征，不仅可充分利用卷积运算与Transformer变换在图像特征提取的各自优势，而且将对应特征进行自适应增强与融合，有效改进网络的学习能力及超分辨率性能。结果 基于5个开源测试数据集，与6种代表性方法进行了实验比较，结果显示本文方法在不同放大倍数情形下均有最佳表现。具体在4倍放大因子时，比较先进的SwinIR （image restoration using swin Transformer）方法，峰值信噪比指标在Urban100数据集上得到了0.09 dB的提升，在Manga109数据集提升了0.30 dB，具有主观视觉质量的明显改善。结论 提出的通道注意力嵌入的Transformer图像超分辨率方法，通过融合卷积特征与Transformer特征，并自适应嵌入通道注意力特征增强，可以在较好地平衡网络模型轻量化同时，得到图像超分辨率性能的有效提升，在多个公共实验数据集的测试结果验证了本文方法的有效性。     

SRResNet Performance Enhancement Using Patch Inputs and Partial Convolution-Based Padding

Due to highly underdetermined nature of Single Image Super-Resolution (SISR) problem, deep learning neural networks are required to be more deeper to solve the problem effectively. One of deep neural networks successful in the Super-Resolution (SR) problem is ResNet which can render the capability of deeper networks with the help of skip connections. However, zero padding (ZP) scheme in the network restricts benefits of skip connections in SRResNet and its performance as the ratio of the number of pure input data to that of zero padded data increases. In this paper. we consider the ResNet with Partial Convolution based Padding (PCP) instead of ZP to solve SR problem. Since training of deep neural networks using patch images is advantageous in many aspects such as the number of training image data and network complexities, patch image based SR performance is compared with single full image based one. The experimental results show that patch based SRResNet SR results are better than single full image based ones and the performance of deep SRResNet with PCP is better than the one with ZP.     

Wide-Field and Real-Time Super-Resolution Optical Imaging By Titanium Dioxide Nanoparticle-Assembled Solid Immersion Lens

Super-resolution optical imaging techniques can break the optical diffraction limit, thus providing unique opportunities to visualize the microscopic world at the nanoscale. Although near-field optical microscopy techniques have been proven to achieve significantly improved imaging resolution, most near-field approaches still suffer from a narrow field of view (FOV) or difficulty in obtaining wide-field images in real time, which may limit their widespread and diverse applications. Here, the authors experimentally demonstrate an optical microscope magnification and image enhancement approach by using a submillimeter-sized solid immersion lens (SIL) assembled by densely-packed 15 nm TiO₂ nanoparticles through a silicone oil two-step dehydration method. This TiO₂ nanoparticle-assembled SIL can achieve both high transparency and high refractive index, as well as sufficient mechanical strength and easy-to-handle size, thus providing a fast, wide-field, real-time, non-destructive, and low-cost solution for improving the quality of optical microscopic observation of a variety of samples, including nanomaterials, cancer cells, and living cells or bacteria under conventional optical microscopes. This study provides an attractive alternative to simplify the fabrication and applications of high-performance SILs.     

前视成像声呐最大熵超分辨算法及验证

声呐方位向上的接收信息可以看作是目标散射信息和波束方向图卷积的结果,通过解卷积的方法可以恢复出目标的散射信息,但是反问题的求解存在着固有的"病态性"问题.为了使解卷积的"病态性"问题转换成"良性"问题求解,文章采用最大熵作为正则化约束项,不局限于原始场景中的目标分布,利用Frieden熵作为衡量最终解的标准,引入图像熵...     

用于多光谱和高光谱图像融合的联合自注意力Transformer

目的 将高光谱图像和多光谱图像进行融合，可以获得具有高空间分辨率和高光谱分辨率的光谱图像，提升光谱图像的质量。现有的基于深度学习的融合方法虽然表现良好，但缺乏对多源图像特征中光谱和空间长距离依赖关系的联合探索。为有效利用图像的光谱相关性和空间相似性，提出一种联合自注意力的Transformer网络来实现多光谱和高光谱图像融合超分辨。方法 首先利用联合自注意力模块，通过光谱注意力机制提取高光谱图像的光谱相关性特征，通过空间注意力机制提取多光谱图像的空间相似性特征，将获得的联合相似性特征用于指导高光谱图像和多光谱图像的融合；随后，将得到的融合特征输入到基于滑动窗口的残差Transformer深度网络中，探索融合特征的长距离依赖信息，学习深度先验融合知识；最后，特征通过卷积层映射为高空间分辨率的高光谱图像。结果 在CAVE和Harvard光谱数据集上分别进行了不同采样倍率下的实验，实验结果表明，与对比方法相比，本文方法从定量指标和视觉效果上，都取得了更好的效果。本文方法相较于性能第二的方法EDBIN （enhanced deep blind iterative network），在CAVE数据集上峰值信噪比提高了0.5 dB，在Harvard数据集上峰值信噪比提高了0.6 dB。结论 本文方法能够更好地融合光谱信息和空间信息，显著提升高光谱融合超分图像的质量。     

Single Molecule Localization Microscopy for Studying Small Extracellular Vesicles

Small extracellular vesicles (sEVs) are 30–200 nm nanovesicles enriched with unique cargoes of nucleic acids, lipids, and proteins. sEVs are released by all cell types and have emerged as a critical mediator of cell-to-cell communication. Although many studies have dealt with the role of sEVs in health and disease, the exact mechanism of sEVs biogenesis and uptake remain unexplored due to the lack of suitable imaging technologies. For sEVs functional studies, imaging has long relied on conventional fluorescence microscopy that has only 200–300 nm resolution, thereby generating blurred images. To break this resolution limit, recent developments in super-resolution microscopy techniques, specifically single-molecule localization microscopy (SMLM), expanded the understanding of subcellular details at the few nanometer level. SMLM success relies on the use of appropriate fluorophores with excellent blinking properties. In this review, the basic principle of SMLM is highlighted and the state of the art of SMLM use in sEV biology is summarized. Next, how SMLM techniques implemented for cell imaging can be translated to sEV imaging is discussed by applying different labeling strategies to study sEV biogenesis and their biomolecular interaction with the distant recipient cells.     

When Super-Resolution Microscopy Meets Microfluidics: Enhanced Biological Imaging and Analysis with Unprecedented Resolution

Super-resolution microscopy is rapidly developed in recent years, allowing biologists to extract more quantitative information on subcellular processes in live cells that is usually not accessible with conventional techniques. However, super-resolution imaging is not fully exploited because of the lack of an appropriate and multifunctional experimental platform. As an important tool in life sciences, microfluidics is capable of cell manipulation and the regulation of the cellular environment because of its superior flexibility and biocompatibility. The combination of microfluidics and super-resolution microscopy revolutionizes the study of complex cellular properties and dynamics, providing valuable insights into cellular structure and biological functions at the single-molecule level. In this perspective, an overview of the main advantages of microfluidic technology that are essential to the performance of super-resolution microscopy are offered. The main benefits of performing super-resolution imaging with microfluidic devices are highlighted and perspectives on the diverse applications that are facilitated by combining these two powerful techniques are provided.     

Accelerated MINFLUX Nanoscopy,through Spontaneously Fast-Blinking Fluorophores

The introduction of MINFLUX nanoscopy allows single molecules to be localized with one nanometer precision in as little as one millisecond. However, current applications have so far focused on increasing this precision by optimizing photon collection, rather than minimizing the localization time. Concurrently, commonly used fluorescent switches are specifically designed for stochastic methods (e.g., STORM), optimized for a high photon yield and rather long on-times (tens of milliseconds). Here, accelerated MINFLUX nanoscopy with up to a 30-fold gain in localization speed is presented. The improvement is attained by designing spontaneously blinking fluorescent markers with remarkably fast on-times, down to 1–3 ms, matching the iterative localization process used in a MINFLUX microscope. This design utilizes a silicon rhodamine amide core, shifting the spirocyclization equilibrium toward an uncharged closed form at physiological conditions and imparting intact live cell permeability, modified with a fused (benzo)thiophene spirolactam fragment. The best candidate for MINFLUX microscopy (also suitable for STORM imaging) is selected through detailed characterization of the blinking behavior of single fluorophores, bound to different protein tags. Finally, optimization of the localization routines, customized to the fast blinking times, renders a significant speed improvement on a commercial MINFLUX microscope.