Synergizing superresolution optical fluctuation imaging with single molecule localization microscopy

TL;DR

This paper demonstrates how combining superresolution optical fluctuation imaging (SOFI) with single molecule localization microscopy (SMLM) enhances imaging resolution, background rejection, and convergence speed, especially under challenging conditions.

Contribution

It introduces a synergistic approach where SOFI assists SMLM, improving image quality and convergence speed, particularly in low signal-to-background scenarios.

Findings

SOFI converges faster than SMLM.

Combining SOFI with SMLM improves background rejection.

The method enhances SMLM performance in demanding conditions.

Abstract

Single molecule localization microscopy (SMLM) techniques enable imaging biological samples well beyond the diffraction limit of light, but they vary significantly in their spatial and temporal resolutions. High-order statistical analysis of temporal fluctuations as in superresolution optical fluctuation imaging (SOFI) also enable imaging beyond diffraction limit, but usually at a lower resolution as compared to SMLM. Since the same data format is acquired for both methods, their algorithms can be applied to the same data set, and thus may be combined synergistically to improve overall imaging performance. Here, we find that SOFI converges much faster than SMLM, provides additive information to SMLM, and can efficiently reject background. We then show how SOFI-assisted SMLM imaging can improve SMLM image reconstruction by rejecting common sources of background, especially under low signal-to-background conditions. The performance of our approach was evaluated using a realistic simulation of fluorescence imaging we developed and further demonstrated on experimental SMLM images of the plasma membrane of activated fixed and live T cells. Our approach significantly enhances SMLM performance under demanding imaging conditions and could set an example for synergizing additional imaging techniques.