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Shachar Schidorsky1, Xiyu Yi2, Yair Razvag1

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Superresolution optical fluctuation imaging (SOFI) assists single-molecule localization microscopy (SMLM) to improve biological sample imaging. This synergy enhances image quality and reduces reconstruction time, especially in low signal-to-noise conditions.

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Area of Science:

  • Biophysics
  • Optical Microscopy
  • Cell Biology

Background:

  • Single-molecule localization microscopy (SMLM) and superresolution optical fluctuation imaging (SOFI) are advanced techniques for imaging biological samples beyond the diffraction limit.
  • SOFI offers faster acquisition but lower resolution compared to SMLM.
  • Combining SMLM and SOFI algorithms could synergistically enhance overall imaging performance.

Purpose of the Study:

  • To develop a method for synergistically combining SMLM and SOFI algorithms.
  • To improve image reconstruction quality and efficiency in super-resolution microscopy.
  • To evaluate the performance of SOFI-assisted SMLM imaging.

Main Methods:

  • Defined an acquired signal-to-noise ratio (SNR) measure for SMLM and SOFI.
  • Optimized SOFI reconstruction parameters (time-window, time-lag) based on fluorophore properties.
  • Implemented SOFI-assisted SMLM for image reconstruction, focusing on background rejection and optical sectioning.
  • Validated the approach using realistic simulations and imaging of T-cell plasma membranes.

Main Results:

  • SOFI exhibited a 10- to 100-fold higher acquired SNR compared to SMLM.
  • SOFI-assisted SMLM effectively rejected background noise (out-of-focus emission, auto-fluorescence) under low SNR conditions.
  • The combined approach improved optical sectioning and reduced image reconstruction time.
  • Demonstrated enhanced SMLM performance on fixed and live T-cell plasma membranes.

Conclusions:

  • SOFI-assisted SMLM imaging significantly enhances SMLM performance, particularly under challenging imaging conditions.
  • This synergistic approach offers improved background rejection, optical sectioning, and faster reconstruction.
  • The method provides a framework for combining different super-resolution imaging techniques for superior results.