Model-free machine learning-based 3D single molecule localisation microscopy.

Journal: Journal of microscopy
Published Date: May 8, 2025

Abstract

Single molecule localisation microscopy (SMLM) can provide two-dimensional super-resolved image data from conventional fluorescence microscopes, while three dimensional (3D) SMLM usually involves a modification of the microscope, for example, to engineer a predictable axial variation in the point spread function. Here we demonstrate a 3D SMLM approach (we call 'easyZloc') utilising a lightweight Convolutional Neural Network that is generally applicable, including with 'standard' (unmodified) fluorescence microscopes, and which we consider may be practically useful in a high throughput SMLM workflow. We demonstrate the reconstruction of nuclear pore complexes with comparable performance to previously reported methods but with a significant reduction in computational power and execution time. 3D reconstructions of the nuclear envelope and an actin sample over a larger axial range are also shown.

Authors

  • Miguel A Boland
    Department of Mathematics, Imperial College, London, UK.
  • Jonathan P E Lightley
    Department of Physics, Imperial College, London, UK.
  • Edwin Garcia
    Department of Physics, Imperial College, London, UK.
  • Sunil Kumar
    School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India.
  • Chris Dunsby
    Department of Physics, Imperial College, London, UK.
  • Seth Flaxman
    Department of Mathematics and Data Science Institute, Imperial College London, London, SW7 2AZ, UK.
  • Mark A A Neil
    Department of Physics, Imperial College, London, UK.
  • Paul M W French
    Department of Physics, Imperial College, London, UK.
  • Edward A K Cohen
    Department of Mathematics, Imperial College, London, UK.

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