Deep-learning-assisted spectroscopic single-molecule localization microscopy based on spectrum-to-spectrum denoising.

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.