Fortunate molecules boost signal to background ratio and localization precision in correlation based single molecule...

Single-molecule localization microscopy (SMLM) can decipher fine details that are otherwise impossible using diffraction-limited microscopy. Often, the reconstructed super-resolved images suffer from noise, strong background and are prone to false detections that may impact quantitative imaging. To overcome these limitations, we propose a technique (corrSMLM) that recognizes and detects fortunate molecules (molecules with long blinking cycles) from the recorded data. The method uses correlation between two or more consecutive frames to identify and isolate fortunate molecules that blink longer than the standard blinking period of a molecule. The corrSMLM is based on the fact that random fluctuations (noise) do not last longer (usually limited to a single frame). In contrast, fortunate molecules consistently fluoresce for extended periods and hence appear on more than one frame. Accordingly, strongly correlated spots (representing fortunate molecules) are compared in the consecutive frames, followed by data integration to determine their position and localization precision. The technique addresses two significant problems that plague existing SMLM : (1) false detection due to random noise that contributes to a strong background and (2) poor localization leading to overall low resolution. To demonstrate, corrSMLM is used for imaging fixed NIH3T3 cells (transfected with Dendra2-Actin, Dendra2-Tubulin, and mEos-Tom20 plasmid DNA). The super-resolved images show a significant reduction in background noise ( > 1.5 fold boost in SBR) and  > 2-fold improvement in localization precision as compared to standard SMLM. Intensity analysis based on the number of molecules suggests that corrSMLM better corroborates the raw data and preserves finer features (e.g., edges), which are wiped out in standard SMLM. Overall, an improvement is noted in the localization precision and spatial resolution. The proposed technique is anticipated to advance SMLM and is expected to contribute to a better understanding of single-molecule dynamics in a cellular environment.