An outstanding challenge in single-molecule localization microscopy is the accurate and precise localization of individual point emitters in three dimensions in densely labeled samples. One established approach for three-dimensional single-molecule localization is point-spread-function (PSF) engineering, in which the PSF is engineered to vary distinctively with emitter depth using additional optical elements. However, images of dense emitters, which are desirable for improving temporal resolution, pose a challenge for algorithmic localization of engineered PSFs, due to lateral overlap of the emitter PSFs. Here we train a neural network to localize multiple emitters with densely overlapping Tetrapod PSFs over a large axial range. We then use the network to design the optimal PSF for the multi-emitter case. We demonstrate our approach experimentally with super-resolution reconstructions of mitochondria and volumetric imaging of fluorescently labeled telomeres in cells. Our approach, DeepSTORM3D, enables the study of biological processes in whole cells at timescales that are rarely explored in localization microscopy.

单分子定位显微镜的一个突出挑战是在密集标记的样品中在三个维度上准确和精确地定位单个点发射器。一种已建立的三维单分子定位方法是点扩散函数 (PSF) 工程，其中 PSF 被设计为使用额外的光学元件随着发射器深度的不同而不同。然而，由于发射器 PSF 的横向重叠，对于提高时间分辨率而言理想的密集发射器图像对工程 PSF 的算法定位提出了挑战。在这里，我们训练一个神经网络来定位在大轴向范围内具有密集重叠的 Tetrapod PSF 的多个发射器。然后，我们使用网络为多发射器情况设计最佳 PSF。我们通过线粒体的超分辨率重建和细胞中荧光标记的端粒的体积成像实验证明了我们的方法。我们的方法 DeepSTORM3D 能够在定位显微镜中很少探索的时间尺度上研究整个细胞中的生物过程。