title: Probability-based particle detection that enables threshold-free and robust in vivo single-molecule tracking.
abstract: Single-molecule detection in fluorescence nanoscopy has become a powerful tool in cell biology but can present vexing issues in image analysis, such as limited signal, unspecific background, empirically set thresholds, image filtering, and false-positive detection limiting overall detection efficiency. Here we present a framework in which expert knowledge and parameter tweaking are replaced with a probability-based hypothesis test. Our method delivers robust and threshold-free signal detection with a defined error estimate and improved detection of weaker signals. The probability value has consequences for downstream data analysis, such as weighing a series of detections and corresponding probabilities, Bayesian propagation of probability, or defining metrics in tracking applications. We show that the method outperforms all current approaches, yielding a detection efficiency of >70% and a false-positive detection rate of <5% under conditions down to 17 photons/pixel background and 180 photons/molecule signal, which is beneficial for any kind of photon-limited application. Examples include limited brightness and photostability, phototoxicity in live-cell single-molecule imaging, and use of new labels for nanoscopy. We present simulations, experimental data, and tracking of low-signal mRNAs in yeast cells.
authors.affiliation: RNA Therapeutics Institute, University of Massachusetts Medical School, Worcester, MA 01605.
authors.forename: Carlas S
authors.initials: CS
authors.lastname: Smith
authors.affiliation: Quantitative Imaging Group, Department of Imaging Science and Technology, Faculty of Applied Sciences, Delft University of Technology, 2628 CJ Delft, Netherlands.
authors.forename: Sjoerd
authors.initials: S
authors.lastname: Stallinga
authors.affiliation: Department of Physics and Astronomy, University of New Mexico, Albuquerque, NM 87131.
authors.forename: Keith A
authors.initials: KA
authors.lastname: Lidke
authors.affiliation: Quantitative Imaging Group, Department of Imaging Science and Technology, Faculty of Applied Sciences, Delft University of Technology, 2628 CJ Delft, Netherlands.
authors.forename: Bernd
authors.initials: B
authors.lastname: Rieger
authors.affiliation: RNA Therapeutics Institute, University of Massachusetts Medical School, Worcester, MA 01605 David.Grunwald@umassmed.edu.
authors.forename: David
authors.initials: D
authors.lastname: Grunwald
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation; D008856:Microscopy, Fluorescence / Q000379:methods*; D057054:Molecular Imaging / Q000379:methods*; D017785:Photons; D011336:Probability; D012333:RNA, Messenger / Q000032:analysis; D012441:Saccharomyces cerevisiae / Q000737:chemistry
chemical_list: D012333:RNA, Messenger