■光谱单分子定位显微镜(sSMLM)利用了纳米显微镜和光谱学的优势,使亚10nm分辨率以及多标记样品的同时多色成像。使用深度学习重建原始sSMLM数据是一种在纳米级可视化亚细胞结构的有前途的方法。
■开发一种新的计算方法,利用深度学习来重建无标记和荧光标记的sSMLM成像数据。
■我们开发了一种基于双网络模型的深度学习算法,称为DsSMLM,来重建sSMLM数据。通过对不同样品进行成像实验来评估DsSMLM的有效性,包括无标记单链DNA(ssDNA)纤维,COS-7和U2OS细胞上的荧光标记组蛋白标记,和合成DNA折纸纳米探针的同时多色成像。
■对于无标签成像,在ssDNA纤维上获得6.22nm的空间分辨率;对于荧光标记成像,DsSMLM揭示了由细胞核上的组蛋白标记物定义的富含染色质和缺乏染色质的区域的分布,并同时提供了纳米探针样品的多色成像,区分在三个发射点标记的两种染料,分离距离为40nm。有了DsSMLM,我们观察到增强的光谱轮廓,单色成像的定位检测提高了8.8%,同时双色成像的定位检测提高了5.05%.
■我们证明了适用于无标记和荧光标记的sSMLM成像数据的基于深度学习的sSMLM成像重建的可行性。我们预计我们的技术将是高质量超分辨率成像的有价值的工具,用于更深入地了解DNA分子的光物理,并将有助于研究多个纳米细胞结构及其相互作用。
UNASSIGNED: Spectroscopic single-molecule localization microscopy (sSMLM) takes advantage of nanoscopy and spectroscopy, enabling sub-10 nm resolution as well as simultaneous multicolor imaging of multi-labeled samples. Reconstruction of raw sSMLM data using deep learning is a promising approach for visualizing the subcellular structures at the nanoscale.
UNASSIGNED: Develop a novel computational approach leveraging deep learning to reconstruct both label-free and fluorescence-labeled sSMLM imaging data.
UNASSIGNED: We developed a two-network-model based deep learning algorithm, termed DsSMLM, to reconstruct sSMLM data. The effectiveness of DsSMLM was assessed by conducting imaging experiments on diverse samples, including label-free single-stranded DNA (ssDNA) fiber, fluorescence-labeled histone markers on COS-7 and U2OS cells, and simultaneous multicolor imaging of synthetic DNA origami nanoruler.
UNASSIGNED: For label-free imaging, a spatial resolution of 6.22 nm was achieved on ssDNA fiber; for fluorescence-labeled imaging, DsSMLM revealed the distribution of chromatin-rich and chromatin-poor regions defined by histone markers on the cell nucleus and also offered simultaneous multicolor imaging of nanoruler samples, distinguishing two dyes labeled in three emitting points with a separation distance of 40 nm. With DsSMLM, we observed enhanced spectral profiles with 8.8% higher localization detection for single-color imaging and up to 5.05% higher localization detection for simultaneous two-color imaging.
UNASSIGNED: We demonstrate the feasibility of deep learning-based reconstruction for sSMLM imaging applicable to label-free and fluorescence-labeled sSMLM imaging data. We anticipate our technique will be a valuable tool for high-quality super-resolution imaging for a deeper understanding of DNA molecules\' photophysics and will facilitate the investigation of multiple nanoscopic cellular structures and their interactions.