Xu Dandan, Gu Yuanjie, Lu Jun, Xu Lei, Wang Wei, Dong Biqin
Academy for Engineering and Technology, Yiwu Research Institute, Fudan University, Shanghai 200433, China.
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China.
Nanoscale. 2024 Mar 14;16(11):5729-5736. doi: 10.1039/d3nr05870k.
Spectroscopic single-molecule localization microscopy (sSMLM) simultaneously captures spatial localizations and spectral signatures, providing the ability of multiplexed and functional subcellular imaging applications. However, extracting accurate spectral information in sSMLM remains challenging due to the poor signal-to-noise ratio (SNR) of spectral images set by a limited photon budget from single-molecule fluorescence emission and inherent electronic noise during the image acquisition using digital cameras. Here, we report a novel spectrum-to-spectrum (Spec2Spec) framework, a self-supervised deep-learning network that can significantly suppress the noise and accurately recover low SNR emission spectra from a single-molecule localization event. A training strategy of Spec2Spec was designed for sSMLM data by exploiting correlated spectral information in spatially adjacent pixels, which contain independent noise. By validating the qualitative and quantitative performance of Spec2Spec on simulated and experimental sSMLM data, we demonstrated that Spec2Spec can improve the SNR and the structure similarity index measure (SSIM) of single-molecule spectra by about 6-fold and 3-fold, respectively, further facilitating 94.6% spectral classification accuracy and nearly 100% data utilization ratio in dual-color sSMLM imaging.
光谱单分子定位显微镜（sSMLM）同时捕获空间定位和光谱特征，为多重和功能性亚细胞成像应用提供了可能。然而，由于单分子荧光发射的光子预算有限以及使用数码相机进行图像采集时固有的电子噪声导致光谱图像的信噪比（SNR）较差，在sSMLM中提取准确的光谱信息仍然具有挑战性。在此，我们报告了一种新颖的谱到谱（Spec2Spec）框架，这是一种自监督深度学习网络，它可以显著抑制噪声并从单分子定位事件中准确恢复低信噪比发射光谱。通过利用空间相邻像素中包含独立噪声的相关光谱信息，为sSMLM数据设计了Spec2Spec的训练策略。通过在模拟和实验sSMLM数据上验证Spec2Spec的定性和定量性能，我们证明Spec2Spec可以分别将单分子光谱的信噪比和结构相似性指数测量（SSIM）提高约6倍和3倍，进一步促进双色sSMLM成像中94.6%的光谱分类准确率和近100%的数据利用率。
