ArticleOptics communications2023
Combining deep learning approaches and point spread function engineering for simultaneous 3D position and 3D orientation measurements of fluorescent single molecules.
Article in Optics communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Single Fluorogens and Orientation-Localization Microscopy for Quantifying Chemical and Biomolecular Dynamics at the Nanoscale.Accounts of chemical research · 2026Article
- Fused deep learning enables 6D single-molecule localization in polarization-resolved microscopy.Methods and applications in fluorescence · 2026Article
- 4polar3D single molecule imaging of 3D orientation in dense actin networks using ratiometric polarization splitting.Nature communications · 2026Article
- Single-Molecule Imaging and Spectroscopy Enables Quantification of Location-Dependent Light-Matter Interactions on Nanoantennas.Small science · 2026Article
- Single molecule orientation and localization microscopy.Nature photonics · 2025Article
- Paired-objectives photon enhancement (POPE) microscopy: enhanced photon collection for fluorescence imaging.Communications engineering · 2025Article
- Resolving the Orientations of and Angular Separation Between a Pair of Dipole Emitters.Physical review letters · 2025Article
- Super-Resolution Goes Viral: T4 Virus Particles as Versatile 3D-Bio-NanoRulers.Advanced materials (Deerfield Beach, Fla.) · 2025Article
- Single-molecule orientation-localization microscopy: Applications and approaches.Quarterly reviews of biophysics · 2024Review
- Neural network-based processing and reconstruction of compromised biophotonic image data.Light, science & applications · 2024Review
- Fundamental Limits in Measuring the Anisotropic Rotational Diffusion of Single Molecules.The journal of physical chemistry. A · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Point Spread Function (PSF) engineering is an effective method to increase the sensitivity of single-molecule fluorescence images to specific parameters. Classical phase mask optimization approaches have enabled the creation of new PSFs that can achieve, for example, localization precision of a few nanometers axially over a capture range of several microns with bright emitters. However, for complex high-dimensional optimization problems, classical approaches are difficult to implement and can be very time-consuming for computation. The advent of deep learning methods and their application to single-molecule imaging has provided a way to solve these problems. Here, we propose to combine PSF engineering and deep learning approaches to obtain both an optimized phase mask and a neural network structure to obtain the 3D position and 3D orientation of fixed fluorescent molecules. Our approach allows us to obtain an axial localization precision around 30 nanometers, as well as an orientation precision around 5 degrees for orientations and positions over a one micron depth range for a signal-to-noise ratio consistent with what is typical in single-molecule cellular imaging experiments.
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