Certain uncertainty: using pointwise error estimates in super-resolution microscopy

TL;DR

This paper introduces a method to accurately estimate point-wise localization uncertainty in super-resolution microscopy directly from imaging data, enhancing downstream analysis and surpassing traditional error bounds by modeling fluorophore movement.

Contribution

It presents a novel approach using a Laplace approximation constrained by microscope properties to estimate localization uncertainty from data, improving analysis accuracy.

Findings

Estimated localization uncertainty improves diffusion constant calculations.

Method enhances detection of molecular motion changes.

Localization accuracy exceeds traditional bounds by modeling fluorophore dynamics.

Abstract

Point-wise localization of individual fluorophores is a critical step in super-resolution microscopy and single particle tracking. Although the methods are limited by the accuracy in localizing individual flourophores, this point-wise accuracy has so far only been estimated by theoretical best case approximations, disregarding for example motional blur, out of focus broadening of the point spread function and time varying changes in the fluorescence background. Here, we show that pointwise localization uncertainty can be accurately estimated directly from imaging data using a Laplace approximation constrained by simple mircoscope properties. We further demonstrate that the estimated localization uncertainty can be used to improve downstream quantitative analysis, such as estimation of diffusion constants and detection of changes in molecular motion patterns. Most importantly, the accuracy of actual point localizations in live cell super-resolution microscopy can be improved beyond the information theoretic lower bound for localization errors in individual images, by modeling the fluorophores' movement and accounting for their point-wise localization uncertainty.