Simulations of camera-based single-molecule fluorescence experiments

• Published in: PloS one Vol. 13; no. 4; p. e0195277
• Main Authors: Börner, Richard, Kowerko, Danny, Hadzic, Mélodie C A S, König, Sebastian L B, Ritter, Marc, Sigel, Roland K O
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 13.04.2018; Public Library of Science (PLoS)
• Subjects: Algorithms; Biophysics; Cameras; Computational biology; Computer and Information Sciences; Computer programs; Computer simulation; Data analysis; Data processing; Energy transfer; Engineering and Technology; Engineering research; Experiments; Fluorescence; Fluorescence microscopy; Fluorescence resonance energy transfer; Image processing; Methods; Microscopy; Noise; Physical Sciences; Research and Analysis Methods; Statistical analysis; Statistical tests; Test sets; Video; Switzerland; Germany
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0195277

Single-molecule microscopy has become a widely used technique in (bio)physics and (bio)chemistry. A popular implementation is single-molecule Förster Resonance Energy Transfer (smFRET), for which total internal reflection fluorescence microscopy is frequently combined with camera-based detection of surface-immobilized molecules. Camera-based smFRET experiments generate large and complex datasets and several methods for video processing and analysis have been reported. As these algorithms often address similar aspects in video analysis, there is a growing need for standardized comparison. Here, we present a Matlab-based software (MASH-FRET) that allows for the simulation of camera-based smFRET videos, yielding standardized data sets suitable for benchmarking video processing algorithms. The software permits to vary parameters that are relevant in cameras-based smFRET, such as video quality, and the properties of the system under study. Experimental noise is modeled taking into account photon statistics and camera noise. Finally, we survey how video test sets should be designed to evaluate currently available data analysis strategies in camera-based sm fluorescence experiments. We complement our study by pre-optimizing and evaluating spot detection algorithms using our simulated video test sets.