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February 2020 Statistical Molecule Counting in Super-Resolution Fluorescence Microscopy: Towards Quantitative Nanoscopy
Thomas Staudt, Timo Aspelmeier, Oskar Laitenberger, Claudia Geisler, Alexander Egner, Axel Munk
Statist. Sci. 35(1): 92-111 (February 2020). DOI: 10.1214/19-STS753

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. In this article, we discuss state of the art methods to count such fluorophores and statistical challenges that come along with it. In particular, we suggest a modeling scheme for time series generated by single-marker-switching (SMS) microscopy that makes it possible to quantify the number of markers in a statistically meaningful manner from the raw data. To this end, we model the entire process of photon generation in the fluorophore, their passage through the microscope, detection and photoelectron 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 timescales (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.

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Thomas Staudt. Timo Aspelmeier. Oskar Laitenberger. Claudia Geisler. Alexander Egner. Axel Munk. "Statistical Molecule Counting in Super-Resolution Fluorescence Microscopy: Towards Quantitative Nanoscopy." Statist. Sci. 35 (1) 92 - 111, February 2020. https://doi.org/10.1214/19-STS753

Information

Published: February 2020
First available in Project Euclid: 3 March 2020

MathSciNet: MR4071360
Digital Object Identifier: 10.1214/19-STS753

Rights: Copyright © 2020 Institute of Mathematical Statistics

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Vol.35 • No. 1 • February 2020
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