Jia Zhe, Zhou Lingxiao, Li Haoyu, Ni Jielei, Chen Danni, Guo Dongfei, Cao Bo, Liu Gang, Liang Guotao, Zhou Qianwen, Yuan Xiaocong, Ni Yanxiang
Nanophotonics Research Center, Institute of Microscale Optoelectronics & State Key Laboratory of Radio Frequency Heterogeneous Integration, College of Physics and Optoelectronic Engineering & Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Shenzhen University, Shenzhen 518060, China.
Nanophotonics. 2024 Jun 6;13(19):3647-3661. doi: 10.1515/nanoph-2023-0936. eCollection 2024 Aug.
Precisely pinpointing the positions of emitters within the diffraction limit is crucial for quantitative analysis or molecular mechanism investigation in biomedical research but has remained challenging unless exploiting single molecule localization microscopy (SMLM). Via integrating experimental spot dataset with deep learning, we develop a new approach, Digital-SMLM, to accurately predict emitter numbers and positions for sub-diffraction-limit spots with an accuracy of up to 98 % and a root mean square error as low as 14 nm. Digital-SMLM can accurately resolve two emitters at a close distance, e.g. 30 nm. Digital-SMLM outperforms Deep-STORM in predicting emitter numbers and positions for sub-diffraction-limited spots and recovering the ground truth distribution of molecules of interest. We have validated the generalization capability of Digital-SMLM using independent experimental data. Furthermore, Digital-SMLM complements SMLM by providing more accurate event number and precise emitter positions, enabling SMLM to closely approximate the natural state of high-density cellular structures.
在生物医学研究中，精确确定发射器在衍射极限内的位置对于定量分析或分子机制研究至关重要，但除非采用单分子定位显微镜（SMLM），否则一直具有挑战性。通过将实验光斑数据集与深度学习相结合，我们开发了一种新方法Digital-SMLM，用于准确预测亚衍射极限光斑的发射器数量和位置，准确率高达98%，均方根误差低至14nm。Digital-SMLM能够精确分辨近距离（如30nm）的两个发射器。在预测亚衍射极限光斑的发射器数量和位置以及恢复感兴趣分子的真实分布方面，Digital-SMLM优于Deep-STORM。我们使用独立实验数据验证了Digital-SMLM的泛化能力。此外，Digital-SMLM通过提供更准确的事件数量和精确的发射器位置对SMLM进行补充，使SMLM能够更接近高密度细胞结构的自然状态。
