Accelerating multicolor spectroscopic single-molecule localization microscopy using deep learning.

Sunil Kumar Gaire,Yang Zhang,Hao F Zhang,Hongyu Li,Leslie Ying,Ray Yu

doi:10.1364/boe.391806

Abstract

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.

Highlights

Single-molecule localization microscopy (SMLM), including stochastic optical reconstruction microscopy (STORM) [1,2] and photoactivated localization microscopy (PALM) [3,4], have extended the imaging resolution of conventional optical fluorescence microscopy beyond the diffraction limit (∼ 250 nm)
The two-color imaging result of COS-7 cell (Cell 1) with the field-of-view (FOV) of 17.41 μm × 13.31 μm is shown in Fig. 6, where tubulin and mitochondria were labeled with Alexa Fluor 647 (AF647) and CF660C, respectively
The spectroscopic SMLM (sSMLM) two-color localization data for Cell 1 was taken from experiments previously published in [7]

Summary

Introduction

Single-molecule localization microscopy (SMLM), including stochastic optical reconstruction microscopy (STORM) [1,2] and photoactivated localization microscopy (PALM) [3,4], have extended the imaging resolution of conventional optical fluorescence microscopy beyond the diffraction limit (∼ 250 nm). Developed spectroscopic SMLM (sSMLM) simultaneously extracts the spatial locations as well as corresponding spectral information of single-molecule blinking events, offering simultaneous multicolor imaging of multi-stained samples [5,7,8,9,10,11,12,13]. Zhang et al.[7] developed a transmission diffraction grating element to obtain the spatial and spectral information of single-molecule blinking events simultaneously and obtain three-color super-resolution images of fixed cells using three dyes with highly overlapping emission spectra. A machine learning approach for the robust and accurate spectral classification has been reported for sSMLM [16]

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Conclusion