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Probability-based particle detection that enables threshold-free and robust in vivo single-molecule tracking

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 efficien...

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Detalles Bibliográficos
Autores principales: Smith, Carlas S., Stallinga, Sjoerd, Lidke, Keith A., Rieger, Bernd, Grunwald, David
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The American Society for Cell Biology 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4710236/
https://www.ncbi.nlm.nih.gov/pubmed/26424801
http://dx.doi.org/10.1091/mbc.E15-06-0448
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author Smith, Carlas S.
Stallinga, Sjoerd
Lidke, Keith A.
Rieger, Bernd
Grunwald, David
author_facet Smith, Carlas S.
Stallinga, Sjoerd
Lidke, Keith A.
Rieger, Bernd
Grunwald, David
author_sort Smith, Carlas S.
collection PubMed
description 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.
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spelling pubmed-47102362016-01-20 Probability-based particle detection that enables threshold-free and robust in vivo single-molecule tracking Smith, Carlas S. Stallinga, Sjoerd Lidke, Keith A. Rieger, Bernd Grunwald, David Mol Biol Cell Articles 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. The American Society for Cell Biology 2015-11-05 /pmc/articles/PMC4710236/ /pubmed/26424801 http://dx.doi.org/10.1091/mbc.E15-06-0448 Text en © 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). “ASCB®,” “The American Society for Cell Biology®,” and “Molecular Biology of the Cell®” are registered trademarks of The American Society for Cell Biology.
spellingShingle Articles
Smith, Carlas S.
Stallinga, Sjoerd
Lidke, Keith A.
Rieger, Bernd
Grunwald, David
Probability-based particle detection that enables threshold-free and robust in vivo single-molecule tracking
title Probability-based particle detection that enables threshold-free and robust in vivo single-molecule tracking
title_full Probability-based particle detection that enables threshold-free and robust in vivo single-molecule tracking
title_fullStr Probability-based particle detection that enables threshold-free and robust in vivo single-molecule tracking
title_full_unstemmed Probability-based particle detection that enables threshold-free and robust in vivo single-molecule tracking
title_short Probability-based particle detection that enables threshold-free and robust in vivo single-molecule tracking
title_sort probability-based particle detection that enables threshold-free and robust in vivo single-molecule tracking
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4710236/
https://www.ncbi.nlm.nih.gov/pubmed/26424801
http://dx.doi.org/10.1091/mbc.E15-06-0448
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