Open AccessMachine-learning based spectral classification for spectroscopic single-molecule localization microscopy
Author(s) -
Zheyuan Zhang,
Yang Zhang,
Leslie Ying,
Cheng Sun,
Hao F. Zhang
Publication year - 2019
Publication title -
optics letters/optics index
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.524
H-Index - 272
eISSN - 1071-2763
pISSN - 0146-9592
DOI - 10.1364/ol.44.005864
Subject(s) - centroid , spectral imaging , microscopy , spectral resolution , optics , spectral signature , pattern recognition (psychology) , materials science , spectral line , computer science , artificial intelligence , physics , quantum mechanics , astronomy
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.