ArticleNature communications2019
Information-rich localization microscopy through machine learning.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
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The trial behind it
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Who cites it
25 citing papers in PubMed.
- AI-empowered super-resolution microscopy: a revolution in nanoscale cellular imaging.Nature methods · 2026Review
- Neural Network-Based Depth Retrieval of Fluorescent Targets From Wide-Field Fluorescence Intensity Images in Scattering Tissues.Journal of biophotonics · 2026Article
- SIMBA: an agentic AI platform for single-molecule multidimensional imaging.bioRxiv : the preprint server for biology · 2026Article
- CC-DenseSTORM: deep learning enables colorimetry camera-based simultaneous two-color single-molecule localization microscopy with dense emitters.Biomedical optics express · 2026Article
- Case Study: Illuminating the Nanoscale World of Microbiology.Methods in molecular biology (Clifton, N.J.) · 2026Review
- 3D localization of retrovirus assembly in the presence of structured background with deep learning.Biophysical journal · 2025Article
- Enhancing ultra-high density single-molecule localization with deep spatiotemporal networks.Biomedical optics express · 2025Article
- Deep learning-based spectroscopic single-molecule localization microscopy.Journal of biomedical optics · 2024Article
- Large-FOV 3D localization microscopy by spatially variant point spread function generation.Science advances · 2024Article
- Computational drug development for membrane protein targets.Nature biotechnology · 2024Review
- Switchable and Functional Fluorophores for Multidimensional Single-Molecule Localization Microscopy.Chemical & biomedical imaging · 2023Review
- Machine-learning-powered extraction of molecular diffusivity from single-molecule images for super-resolution mapping.Communications biology · 2023Article
- Field-dependent deep learning enables high-throughput whole-cell 3D super-resolution imaging.Nature methods · 2023Article
- 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
- Robust emitter localization with enhanced harmonic analysis.Optics letters · 2021Article
- Tutorial: methods for three-dimensional visualization of archival tissue material.Nature protocols · 2021Review
- Single Molecules Are Your Quanta: A Bottom-Up Approach toward Multidimensional Super-resolution Microscopy.ACS nano · 2021Review
- Accelerating 3D single-molecule localization microscopy using blind sparse inpainting.Journal of biomedical optics · 2021Article
- Quantifying accuracy and heterogeneity in single-molecule super-resolution microscopy.Nature communications · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Recent years have witnessed the development of single-molecule localization microscopy as a generic tool for sampling diverse biologically relevant information at the super-resolution level. While current approaches often rely on the target-specific alteration of the point spread function to encode the multidimensional contents of single fluorophores, the details of the point spread function in an unmodified microscope already contain rich information. Here we introduce a data-driven approach in which artificial neural networks are trained to make a direct link between an experimental point spread function image and its underlying, multidimensional parameters, and compare results with alternative approaches based on maximum likelihood estimation. To demonstrate this concept in real systems, we decipher in fixed cells both the colors and the axial positions of single molecules in regular localization microscopy data.
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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.