Imaging three-dimensional, subcellular structures with high axial resolution has always been the core purpose of fluorescence microscopy. However, trade-offs exist between axial resolution and other important technical indicators, such as temporal resolution, optical power density, and imaging process complexity. We report a new imaging modality, fluorescence interference structured illumination microscopy (FI-SIM), which is based on three-dimensional structured illumination microscopy for wide-field lateral imaging and fluorescence interference for axial reconstruction. FI-SIM can acquire images quickly within the order of hundreds of milliseconds and exhibit even 30 nm axial resolution in half the wavelength depth range without z-axis scanning. Moreover, the relatively low laser power density relaxes the requirements for dyes and enables a wide range of applications for observing fixed and live subcellular structures.

成熟人红细胞膜骨架是由膜下多种蛋白组成的三角晶格网状结构，在维持红细胞形态、变形性、运动和代谢等功能方面扮演着重要角色。单分子定位超分辨成像（SMLM）技术在解析骨架超微结构方面展现出了强大的能力，但分辨率的提升对成像分析手段提出了更高要求。作为一种常用的空间分析方法，Vorono?分割在SMLM图像聚类分析中已被广泛应用。笔者利用自主搭建的SMLM超分辨成像系统获得红细胞膜蛋白和骨架蛋白的超分辨点簇图像，对点簇质心进行Vorono?分割，并对Vorono?多边形面积分布进行伽马函数拟合，发现自由膜蛋白CD59的伽马分布峰值对应的x轴坐标x_peak为0.78。结合模拟结果，验证了自由膜蛋白CD59呈随机分布。进一步，肌动蛋白、血影蛋白N端和原肌球蛋白的Vorono?分析结果显示它们的x_peak均为0.86，而锚蛋白的x_peak为0.84，说明骨架膜蛋白呈相对均匀的分布状态，但锚蛋白较其他骨架蛋白更具随机性。Vorono?方法可助力阐释红细胞膜骨架蛋白的空间分布特性，同时也为点簇状SMLM超分辨图像数据的深入提取提供了新思路和新方法。

Various super-resolution microscopy techniques have been presented to explore fine structures of biological specimens. However, the super-resolution capability is often achieved at the expense of reducing imaging speed by either point scanning or multiframe computation. The contradiction between spatial resolution and imaging speed seriously hampers the observation of high-speed dynamics of fine structures. To overcome this contradiction, here we propose and demonstrate a temporal compressive super-resolution microscopy (TCSRM) technique. This technique is to merge an enhanced temporal compressive microscopy and a deep-learning-based super-resolution image reconstruction, where the enhanced temporal compressive microscopy is utilized to improve the imaging speed, and the deep-learning-based super-resolution image reconstruction is used to realize the resolution enhancement. The high-speed super-resolution imaging ability of TCSRM with a frame rate of 1200 frames per second (fps) and spatial resolution of 100 nm is experimentally demonstrated by capturing the flowing fluorescent beads in microfluidic chip. Given the outstanding imaging performance with high-speed super-resolution, TCSRM provides a desired tool for the studies of high-speed dynamical behaviors in fine structures, especially in the biomedical field.

荧光显微镜具有对样品损伤小、可特异性成像等优点，是生物医学研究的主流成像手段。随着人工智能技术的快速发展，深度学习在逆问题求解中取得了巨大成功，被广泛应用于诸多领域。近年来，深度学习在荧光显微成像中的应用掀起了一个研究热潮，为荧光显微技术发展提供了性能上的突破与新思路。基于此，首先介绍了深度学习的基本网络模型，然后对基于深度学习的荧光显微成像技术在荧光显微的空间分辨率、图像采集及重建速度、成像通量和成像质量提升方面的应用进行阐述。最后，对目前深度学习在荧光显微成像中的研究进行总结与展望。

观察细胞器间动态相互作用，深入分析作用规律，对于揭示生理病理过程现象背后的机制具有十分重要的意义。传统光学显微镜受到由光波波长和孔径造成的衍射极限的限制，无法观测细胞器纳米级精细结构及细胞器间相互作用的动态变化规律。超分辨显微成像技术的出现为细胞器相互作用研究提供了重要手段，在深入揭示细胞器相互作用规律，阐明生理病理现象深层的机制研究中发挥了重要的作用。文中介绍了受激发射损耗（Stimulated emission depletion, STED）显微成像、结构光照明显微成像（Structured illumination microscopy, SIM）、单分子定位显微成像（Single molecule localization microscopy, SMLM）技术，并总结了这三类超分辨显微成像技术在细胞器相互作用中的应用与现状，为超分辨显微成像技术在细胞器相互作用研究中的应用提供思路拓展。最后，对超分辨显微成像技术在细胞器相互作用研究中的优势与不足进行分析总结，展望了超分辨显微成像技术在活细胞内细胞器相互作用成像中的需求发展趋势，为光学与医学及生物学的交叉融合发展提供一定的参考。

亚细胞器是细胞的重要组成单位，其形态结构与动力学特性直接反映了细胞的生理状态。21世纪初新兴的结构光照明显微技术、受激发射损耗显微技术和单分子定位成像技术等超分辨显微成像技术，巧妙地绕过了光学衍射极限对成像分辨率的限制，目前已被广泛应用于活细胞亚细胞器精细结构的观察及其动力学过程的监测上。本文首先介绍了上述三种超分辨显微成像技术的基本原理和特点，然后介绍了活细胞中细胞核、细胞骨架、线粒体、内质网等亚细胞器的超分辨精细结构和动力学特性，最后讨论了亚细胞器超分辨精细结构成像与机器学习、图像处理相结合的发展潜力。

Stimulated emission depletion (STED) nanoscopy is one of the most well-developed nanoscopy techniques that can provide subdiffraction spatial resolution imaging. Here, we introduce dual-modulation difference STED microscopy (dmdSTED) to suppress the background noise in traditional STED imaging. By applying respective time-domain modulations to the two continuous-wave lasers, signals are distributed discretely in the frequency spectrum and thus are obtained through lock-in demodulation of the corresponding frequencies. The background signals can be selectively eliminated from the effective signal without compromise of temporal resolution. We used nanoparticle, fixed cell, and perovskite coating experiments, as well as theoretical demonstration, to confirm the effectiveness of this method. We highlight dmdSTED as an idea and approach with simple implementation for improving the imaging quality, which substantially enlarges the versatility of STED nanoscopy.

二次谐波产生(SHG)成像技术是一种针对非中心对称生物组织的免标记成像技术,已经成为生命科学研究的重要手段。衍射极限使得SHG技术无法分辨衍射极限以下的精细结构。虽然超分辨显微技术取得了突破性进展,但是SHG的相干非线性过程限制了SHG超分辨显微技术的发展。提出了一种点扫描结构光照明SHG超分辨显微(SHG-psSIM)技术,实现了氧化锌颗粒和小鼠尾腱的超分辨SHG显微成像。在传统的SHG显微系统的激发光路中引入电光调制器,通过对激发光正弦调制产生点扫描结构照明图案。基于点扫描结构照明图案与样本结构相互作用产生的莫尔条纹效应,将原本不可探测的样本高频信息搬移到显微镜通频带内,并利用光电倍增管探测。最后,利用软件重构出超分辨率图像。对比传统SHG系统,SHG-psSIM分辨率提高了1.86倍。

荧光超分辨显微成像技术的发展使得人们有望以前所未有的时空分辨率来实现细胞内动态生命活动的纳米尺度探测。传统超分辨显微成像技术需要较强的激发光源或采集数量较多的原始数据用于图像重建，因此在活细胞动态实时成像应用中存在不足。深度学习驱动的超分辨成像新技术的研究，有望在多个方面突破现有超分辨成像技术的发展瓶颈。从超分辨成像技术的原理出发，分析了传统超分辨成像技术的不足，总结阐述了深度学习技术在超分辨显微成像领域中的最新应用及未来的研究方向与面临的挑战。