Single-molecule localization microscopy (SMLM) has had remarkable success in imaging cellular structures with nanometer resolution, but standard analysis algorithms require sparse emitters, which limits imaging speed and labeling density. Here, we overcome this major limitation using deep learning. We developed DECODE (deep context dependent), a computational tool that can localize single emitters at high density in three dimensions with highest accuracy for a large range of imaging modalities and conditions. In a public software benchmark competition, it outperformed all other fitters on 12 out of 12 datasets when comparing both detection accuracy and localization error, often by a substantial margin. DECODE allowed us to acquire fast dynamic live-cell SMLM data with reduced light exposure and to image microtubules at ultra-high labeling density. Packaged for simple installation and use, DECODE will enable many laboratories to reduce imaging times and increase localization density in SMLM.

单分子定位显微镜 (SMLM) 在以纳米分辨率成像细胞结构方面取得了显著成功，但标准分析算法需要稀疏的发射器，这限制了成像速度和标记密度。在这里，我们使用深度学习克服了这一主要限制。我们开发了 DECODE（依赖于深度上下文），这是一种计算工具，可以在大范围的成像模式和条件下以最高精度在三个维度上以高密度定位单个发射器。在公共软件基准竞赛中，在比较检测精度和定位误差时，它在 12 个数据集中的 12 个数据集上的表现优于所有其他拟合器，通常有很大的优势。DECODE 使我们能够在减少光照的情况下获取快速动态活细胞 SMLM 数据，并以超高标记密度对微管进行成像。