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Area of Science:

  • Cellular and Molecular Imaging
  • Biophysics
  • Optical Microscopy

Background:

  • Stimulated emission depletion (STED) microscopy offers high resolution for intracellular structures.
  • Increasing STED beam power enhances resolution but causes significant photodamage and phototoxicity.
  • This limits practical applications of STED microscopy in live cells.

Purpose of the Study:

  • To enhance STED image resolution while reducing STED beam power.
  • To overcome limitations of photodamage and phototoxicity in STED imaging.
  • To develop a novel approach for STED imaging with limited photon budgets.

Main Methods:

  • Implemented the separation of photons by a lifetime tuning (SPLIT) scheme.
  • Utilized a deep learning-based phasor analysis algorithm, flimGANE (fluorescence lifetime imaging based on a generative adversarial network).
  • Applied the combined SPLIT and flimGANE approach to STED microscopy.

Main Results:

  • Achieved up to 1.45-fold improvement in STED image resolution.
  • Reduced STED beam power by 50% compared to conventional methods.
  • Demonstrated enhanced imaging capabilities under limited photon budget conditions.

Conclusions:

  • The combination of SPLIT and flimGANE offers a powerful strategy for high-resolution STED imaging.
  • This approach significantly mitigates photodamage and phototoxicity issues.
  • Provides a viable solution for STED microscopy in photon-limited scenarios, enabling new biological discoveries.