Abstract
Single-molecule localization microscopy can decipher fine details that are otherwise not possible using diffraction-limited microscopy. Often the reconstructed super-resolved image contains unwanted noise, random background and is prone to false detections. This cause spurious data that necessitates several trials, multiple experimentations, and repeated preparation of specimens. Moreover, this is not suitable for experiments that require time-lapse imaging and real-time microscopy. To overcome these limitations, we propose a technique (corrSMLM) that can recognize and detect fortunate molecules (molecules with long fluorescence cycles) from the recorded data. The technique uses correlation between two or more consecutive frames to extract fortunate molecules that blink for longer than the standard blinking time. Accordingly, strongly-correlated spots (single molecule signatures) are compared in consecutive frames, followed by data integration (mean centroid position and the total number of photons) and estimation of critical parameters (position and localization precision). The technique addresses two major problems that plague SMLM : (1) random noise due to false detection that contributes to strong background, and (2) poor localization precision offered by standard SMLM techniques. On the brighter side, corrSMLM allows only fortunate molecules contribute to the super-resolved image, thereby suppressing the background and improving localization precision by a factor of 2-4 times as compared to standard SMLM. To substantiate, corrSMLM is used for imaging fixed cell samples (Dendra2-Actin and Dendra2-Tubulin transfected NIH3T3 cells). Results show multi-fold reduction in noise and localization precision with a marked improvement in overall resolution and SBR. We anticipate corrSMLM to improve overall image quality and offer a better understanding of single molecule dynamics in cell biology.
Competing Interest Statement
The authors have declared no competing interest.
