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Field-dependent deep learning enables high-throughput whole-cell 3D super-resolution imaging.

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Summary

We developed a deep-learning method (FD-DeepLoc) for precise single-molecule localization microscopy over a large field of view. This significantly increases imaging throughput for subcellular structures without hardware scanning.

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Area of Science:

  • Biophysics
  • Microscopy
  • Computational Biology

Background:

  • Wide-field single-molecule localization microscopy (SMLM) offers super-resolution imaging of subcellular structures.
  • Field-dependent aberrations limit the field of view (FOV) in conventional SMLM to tens of micrometers.

Purpose of the Study:

  • To develop a deep-learning method (FD-DeepLoc) for precise localization of point emitters across a large FOV.
  • To overcome the FOV limitations of traditional SMLM and enable imaging of entire cells.

Main Methods:

  • Utilized a deep-learning approach (FD-DeepLoc) for precise emitter localization.
  • Employed a GPU-based vectorial point spread function (PSF) fitter to model spatially variant PSFs.
  • Integrated deformable mirror-based PSF engineering for optimal imaging.

Main Results:

  • Demonstrated high-accuracy 3D SMLM over a large volume (~180 × 180 × 5 μm³).
  • Successfully imaged mitochondria and nuclear pore complexes in entire cells in a single cycle.
  • Achieved a 100-fold increase in imaging throughput compared to existing methods.

Conclusions:

  • FD-DeepLoc enables large-FOV, high-accuracy 3D SMLM.
  • The method significantly enhances imaging throughput for cellular structures.
  • This advancement facilitates comprehensive cellular imaging without hardware scanning.