Parameter-free rendering of single-molecule localization microscopy data for parameter-free resolution estimation

Descloux et al. introduce a parameter-free modified histogram rendering method for resolution estimation of localization microscopy datasets compatible with decorrelation analysis. The proposed bilinear histogram rendering and processing pipeline convey the localization information into the image accurately, making the resolution estimate dependent on the localization precision and the localization density. Localization microscopy is a super-resolution imaging technique that relies on the spatial and temporal separation of blinking fluorescent emitters. These blinking events can be individually localized with a precision significantly smaller than the classical diffraction limit. This sub-diffraction localization precision is theoretically bounded by the number of photons emitted per molecule and by the sensor noise. These parameters can be estimated from the raw images. Alternatively, the resolution can be estimated from a rendered image of the localizations. Here, we show how the rendering of localization datasets can influence the resolution estimation based on decorrelation analysis. We demonstrate that a modified histogram rendering, termed bilinear histogram, circumvents the biases introduced by Gaussian or standard histogram rendering. We propose a parameter-free processing pipeline and show that the resolution estimation becomes a function of the localization density and the localization precision, on both simulated and state-of-the-art experimental datasets.

Automated summary

Descloux et al. introduce a parameter-free modified histogram rendering method for resolution estimation in localization microscopy datasets, accurately conveying localization information and depending on precision and density of localizations. Localization microscopy is a super-resolution imaging technique that relies on spatial and temporal separation of blinking fluorescent emitters, achieving sub-diffraction precision bounded by photon emission and sensor noise. The proposed bilinear histogram rendering pipeline avoids biases from Gaussian or standard rendering, demonstrating resolution estimation dependence on localization density and precision.