Abstract
Single-molecule-localization-microscopy (SMLM) enables superresolution imaging of biological samples down to ~ 10–20 nm and in single molecule detail. However, common SMLM reconstruction largely disregards information embedded in the entire intensity trajectories of individual emitters. Here, we develop and demonstrate an approach, termed time-correlated-SMLM (tcSMLM), that uses such information for enhancing SMLM reconstruction. Specifically, tcSMLM is shown to increase the spatial resolution and fidelity of SMLM reconstruction of both simulated and experimental data; esp. upon acquisition under stringent conditions of low SNR, high acquisition rate and high density of emitters. We further provide detailed guidelines and optimization procedures for effectively applying tcSMLM to data of choice. Importantly, our approach can be readily added in tandem to multiple SMLM and related superresolution reconstruction algorithms. Thus, we expect that our approach will become an effective and readily accessible tool for enhancing SMLM and superresolution imaging.
Highlights
Single-molecule-localization-microscopy (SMLM) enables superresolution imaging of biological samples down to ~ 10–20 nm and in single molecule detail
We conclude that tcSMLM provides significantly better results on experimental data with frame rates above 32 fps, and is most useful when applied to high density samples, in which more than one PSF is acquired at each diffraction-limited area
We introduce an approach to employ information embedded in temporal correlations of single emitters to enhance SMLM reconstruction
Summary
Single-molecule-localization-microscopy (SMLM) enables superresolution imaging of biological samples down to ~ 10–20 nm and in single molecule detail. The single molecule reconstruction algorithms fit Gaussians to individual intensity peaks that are related to sparsely detected molecules in each frame of the acquired movie. These detected peaks can be grouped over space and time to estimate the location of the individual emitters. Most current SMLM techniques are performed on individually detected peaks in individual frames They do not consider information embedded in the time-dependent intensity of the emitters. In contrast to these techniques, methods such as Super-resolution Optical Fluctuation Imaging (SOFI)[12] use temporal intensity statistics, namely high-order moments, to provide super-resolved images. Only in principle, the efficiency of using temporal statistics to enhance identification of clustering a rtifacts[21], acquisition t ime[22], and counting p recision[8]
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