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Spectroscopic single-molecule localization microscopy (sSMLM) generates super-resolution images of single molecules while simultaneously capturing the spectra of their fluorescence emissions. However, sSMLM splits photons from single-molecule emissions into a spatial channel and a spectral channel, reducing both channels’ precisions. It is also challenging in transmission grating-based sSMLM to achieve a large field-of-view (FOV) and avoid overlap between the spatial and spectral channels. The challenge in FOV has further significance in single-molecule tracking applications. In this work, we analyzed the correlation between the spatial and spectral channels in sSMLM to improve its spatial precision, and we developed a split-mirror assembly to enlarge its FOV. We demonstrate the benefits of these improvements by tracking quantum dots. We also show that we can reduce particle-identification ambiguity by tagging each particle with its unique spectral characteristics. 

Spectroscopic single-molecule localization microscopy (sSMLM) simultaneously provides spatial localization and spectral information of individual single-molecules emission, offering multicolor super-resolution imaging of multiple molecules in a single sample with the nanoscopic resolution. However, this technique is limited by the requirements of acquiring a large number of frames to reconstruct a super-resolution image. In addition, multicolor sSMLM imaging suffers from spectral cross-talk while using multiple dyes with relatively broad spectral bands that produce cross-color contamination. Here, we present a computational strategy to accelerate multicolor sSMLM imaging. Our method uses deep convolution neural networks to reconstruct high-density multicolor super-resolution images from low-density, contaminated multicolor images rendered using sSMLM datasets with much fewer frames, without compromising spatial resolution. High-quality, super-resolution images are reconstructed using up to 8-fold fewer frames than usually needed. Thus, our technique generates multicolor super-resolution images within a much shorter time, without any changes in the existing sSMLM hardware system. Two-color and three-color sSMLM experimental results demonstrate superior reconstructions of tubulin/mitochondria, peroxisome/mitochondria, and tubulin/mitochondria/peroxisome in fixed COS-7 and U2-OS cells with a significant reduction in acquisition time. 

Single‐molecule localization microscopy (SMLM) precisely localizes individual fluorescent molecules within the wide field of view (FOV). However, the localization precision is fundamentally limited to around 20 nm due to the physical photon limit of individual stochastic single‐molecule emissions. Using spectroscopic SMLM (sSMLM) to resolve their distinct fluorescence emission spectra, individual fluorophore is specifically distinguished and identified, even the ones of the same type. Consequently, the reported photon‐accumulation enhanced reconstruction (PACER) method accumulates photons over repeated stochastic emissions from the same fluorophore to significantly improve the localization precision. This work shows the feasibility of PACER by resolving quantum dots that are 6.1 nm apart with 1.7 nm localization precision. Next, a Monte Carlo simulation is used to investigate the success probability of the PACER's classification process for distance measurements under different conditions. Finally, PACER is used to resolve and measure the lengths of DNA origami nanorulers with an inter‐molecular spacing as small as 6 nm. Notably, the demonstrated sub‐2 nm localization precision bridges the detection range between Förster resonance energy transfer (FRET) and conventional SMLM. Fully exploiting the underlying imaging capability can potentially enable high‐throughput inter‐molecular distance measurements over a large FOV.