Department of Functional Neuroanatomy, Institute for Anatomy and Cell Biology, Heidelberg University, Heidelberg, Germany.
Institute of Physical and Theoretical Chemistry, Goethe University Frankfurt, Frankfurt, Germany.
Nat Commun. 2022 Aug 27;13(1):5047. doi: 10.1038/s41467-022-32626-0.
DNA points accumulation for imaging in nanoscale topography (DNA-PAINT) is a super-resolution technique with relatively easy-to-implement multi-target imaging. However, image acquisition is slow as sufficient statistical data has to be generated from spatio-temporally isolated single emitters. Here, we train the neural network (NN) DeepSTORM to predict fluorophore positions from high emitter density DNA-PAINT data. This achieves image acquisition in one minute. We demonstrate multi-colour super-resolution imaging of structure-conserved semi-thin neuronal tissue and imaging of large samples. This improvement can be integrated into any single-molecule imaging modality to enable fast single-molecule super-resolution microscopy.
DNA 点积累成像技术（DNA-PAINT）是一种具有相对易于实现的多目标成像的超分辨率技术。然而，由于必须从时空上分离的单个发射器中生成足够的统计数据，因此图像采集速度较慢。在这里，我们训练神经网络（NN）DeepSTORM 从高发射器密度 DNA-PAINT 数据中预测荧光团的位置。这可以在一分钟内实现图像采集。我们展示了结构保存的半薄神经元组织的多色超分辨率成像和大样本的成像。这种改进可以集成到任何单分子成像模式中，以实现快速的单分子超分辨率显微镜。
