Machine-learning based spectral classification for spectroscopic single-molecule localization microscopy.

Spectroscopic single-molecule localization microscopy (sSMLM) simultaneously captures the spatial locations and emission spectra of single molecular emissions and enables simultaneous multicolor super-resolution imaging. Existing sSMLM relies on extracting spectral signatures, such as weighted spectral centroids, to distinguish different molecular labels. However, the rich information carried by the complete spectral profiles is not fully utilized; thus, the misclassification rate between molecular labels can be high at low spectral analysis photon budget. We developed a machine learning (ML)-based method to analyze the full spectral profiles of each molecular emission and reduce the misclassification rate. We experimentally validated our method by imaging immunofluorescently labeled COS-7 cells using two far-red dyes typically used in sSMLM (AF647 and CF660) to resolve mitochondria and microtubules, respectively. We showed that the ML method achieved 10-fold reduction in misclassification and two-fold improvement in spectral data utilization comparing with the existing spectral centroid method.