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.
Original language | English (US) |
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Pages (from-to) | 2705-2721 |
Number of pages | 17 |
Journal | Biomedical Optics Express |
Volume | 11 |
Issue number | 5 |
DOIs | |
State | Published - May 1 2020 |
Funding
Directorate for Engineering (CBET-1604531, CBET-1706642, EFMA-1830969); National Institutes of Health (R01EY026078, R01EY029121).
ASJC Scopus subject areas
- Biotechnology
- Atomic and Molecular Physics, and Optics