ArticleBiomedical optics express2020
Accelerating multicolor spectroscopic single-molecule localization microscopy using deep learning.
Article in Biomedical optics express, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 39 citations in OpenAlex.
- SIMBA: an agentic AI platform for single-molecule multidimensional imaging.bioRxiv : the preprint server for biology · 2026Article
- Lightweight CycleGAN models for cross-modality image transformation and experimental quality assessment in fluorescence microscopy.Biomedical optics express · 2026Article
- DeepQR: single-molecule QR codes for optical gene-expression analysis.Nanophotonics (Berlin, Germany) · 2025Article
- Enhancing ultra-high density single-molecule localization with deep spatiotemporal networks.Biomedical optics express · 2025Article
- Surpassing the Diffraction Limit in Label-Free Optical Microscopy.ACS photonics · 2024Review
- Deep learning-based spectroscopic single-molecule localization microscopy.Journal of biomedical optics · 2024Article
- Switchable and Functional Fluorophores for Multidimensional Single-Molecule Localization Microscopy.Chemical & biomedical imaging · 2023Review
- Quantitative Single-Molecule Localization Microscopy.Annual review of biophysics · 2023Review
- Machine-learning-powered extraction of molecular diffusivity from single-molecule images for super-resolution mapping.Communications biology · 2023Article
- Spectroscopic single-molecule localization microscopy: applications and prospective.Nano convergence · 2023Review
- Enabling Spectrally Resolved Single-Molecule Localization Microscopy at High Emitter Densities.Nano letters · 2022Article
- Minimizing Molecular Misidentification in Imaging Low-Abundance Protein Interactions Using Spectroscopic Single-Molecule Localization Microscopy.Analytical chemistry · 2022Article
- Development of Deep-Learning-Based Single-Molecule Localization Image Analysis.International journal of molecular sciences · 2022Review
- Advancing biological super-resolution microscopy through deep learning: a brief review.Biophysics reports · 2021Article
- Parametric comparison between sparsity-based and deep learning-based image reconstruction of super-resolution fluorescence microscopy.Biomedical optics express · 2021Article
- Accelerating 3D single-molecule localization microscopy using blind sparse inpainting.Journal of biomedical optics · 2021Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.