RNA Therapeutics Institute; Department of Biochemistry and Molecular Pharmacology
Biochemistry | Bioinformatics | Cell Biology | Molecular Biology
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.
Rights and Permissions
© 2015 Smith et al. This article is distributed by The American Society for Cell Biology under license from the author(s). Two months after publication it is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).
DOI of Published Version
Mol Biol Cell. 2015 Nov 5;26(22):4057-62. doi: 10.1091/mbc.E15-06-0448. Epub 2015 Sep 30. Link to article on publisher's site
Molecular biology of the cell
Smith C, Stallinga S, Lidke KA, Rieger B, Grunwald D. (2015). Probability-based particle detection that enables threshold-free and robust in vivo single-molecule tracking. University of Massachusetts Medical School Faculty Publications. https://doi.org/10.1091/mbc.E15-06-0448. Retrieved from https://escholarship.umassmed.edu/faculty_pubs/865
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.