Statistical Molecule Counting in Super-Resolution Fluorescence Microscopy: Towards Quantitative Nanoscopy

TL;DR

This paper introduces a comprehensive statistical model for quantifying the number of fluorophores in super-resolution microscopy, enabling absolute intensity measurement and kinetic analysis from raw fluorescence data.

Contribution

It presents a novel hidden Markov model operating on two time scales to estimate fluorophore counts and transition rates from microscopy data.

Findings

Accurate fluorophore number estimation demonstrated on simulated data.

Model captures fluorophore dynamics and transition rates.

Provides a framework for quantitative analysis in super-resolution microscopy.

Abstract

Super-resolution microscopy is rapidly gaining importance as an analytical tool in the life sciences. A compelling feature is the ability to label biological units of interest with fluorescent markers in living cells and to observe them with considerably higher resolution than conventional microscopy permits. The images obtained this way, however, lack an absolute intensity scale in terms of numbers of fluorophores observed. We provide an elaborate model to estimate this information from the raw data. To this end we model the entire process of photon generation in the fluorophore, their passage trough the microscope, detection and photo electron amplification in the camera, and extraction of time series from the microscopic images. At the heart of these modeling steps is a careful description of the fluorophore dynamics by a novel hidden Markov model that operates on two time scales (HTMM). Besides the fluorophore number, information about the kinetic transition rates of the fluorophore's internal states is also inferred during estimation. We comment on computational issues that arise when applying our model to simulated or measured fluorescence traces and illustrate our methodology on simulated data.