2019/03/24 by Thomas Staudt, Timo Aspelmeier, Staudt, Thomas +9
Biochemistry, Genetics and Molecular Biology · Engineering · #60J10 #62M05 (primary) #62P10 #62P35 (secondary) #Advanced Fluorescence Microscopy Techniques #Applications (stat.AP) #Cell Image Analysis Techniques #FOS: Computer and information sciences #Photoacoustic and Ultrasonic Imaging
paper · pdf · doi:10.48550/arxiv.1903.11577
openalex publication_date 2019/03/24 · openalex created_date 2022/07/29 · openalex updated_date 2026/07/28
Super-resolution microscopy is rapidly gaining importance as an analytical\ntool in the life sciences. A compelling feature is the ability to label\nbiological units of interest with fluorescent markers in living cells and to\nobserve them with considerably higher resolution than conventional microscopy\npermits. The images obtained this way, however, lack an absolute intensity\nscale in terms of numbers of fluorophores observed. We provide an elaborate\nmodel to estimate this information from the raw data. To this end we model the\nentire process of photon generation in the fluorophore, their passage trough\nthe microscope, detection and photo electron amplification in the camera, and\nextraction of time series from the microscopic images. At the heart of these\nmodeling steps is a careful description of the fluorophore dynamics by a novel\nhidden Markov model that operates on two time scales (HTMM). Besides the\nfluorophore number, information about the kinetic transition rates of the\nfluorophore's internal states is also inferred during estimation. We comment on\ncomputational issues that arise when applying our model to simulated or\nmeasured fluorescence traces and illustrate our methodology on simulated data.\n