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Blinking statistics and molecular counting in direct stochastic reconstruction microscopy (dSTORM)

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Title: Blinking statistics and molecular counting in direct stochastic reconstruction microscopy (dSTORM)
Authors: Patel, L
David, W
Owen, D
Cohen, E
Item Type: Journal Article
Abstract: Motivation: Many recent advancements in single-molecule localization microscopy exploit the stochastic photoswitching of fluorophores to reveal complex cellular structures beyond the classical diffraction limit. However, this same stochasticity makes counting the number of molecules to high precision extremely challenging, preventing key insight into the cellular structures and processes under observation. Results: Modelling the photoswitching behaviour of a fluorophore as an unobserved continuous time Markov process transitioning between a single fluorescent and multiple dark states, and fully mitigating for missed blinks and false positives, we present a method for computing the exact probability distribution for the number of observed localizations from a single photoswitching fluorophore. This is then extended to provide the probability distribution for the number of localizations in a direct stochastic optical reconstruction microscopy experiment involving an arbitrary number of molecules. We demonstrate that when training data are available to estimate photoswitching rates, the unknown number of molecules can be accurately recovered from the posterior mode of the number of molecules given the number of localizations. Finally, we demonstrate the method on experimental data by quantifying the number of adapter protein linker for activation of T cells on the cell surface of the T-cell immunological synapse. Availability and implementation: Software and data available at https://github.com/lp1611/mol_count_dstorm. Supplementary information: Supplementary data are available at Bioinformatics online.
Issue Date: 1-Sep-2021
Date of Acceptance: 25-Feb-2021
URI: http://hdl.handle.net/10044/1/88246
DOI: 10.1093/bioinformatics/btab136
ISSN: 1367-4803
Publisher: Oxford University Press
Start Page: 2730
End Page: 2737
Journal / Book Title: Bioinformatics
Volume: 37
Issue: 17
Copyright Statement: © The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. This is a pre-copy-editing, author-produced version of an article accepted for publication in Bioinformatics following peer review. The definitive publisher-authenticated version, Patel, L., Williamson, D., Owen, D. M. et al., Bioinformatics, 27 February 2021, btab136, is available online at: https://doi.org/10.1093/bioinformatics/btab136
Keywords: Science & Technology
Life Sciences & Biomedicine
Physical Sciences
Biochemical Research Methods
Biotechnology & Applied Microbiology
Computer Science, Interdisciplinary Applications
Mathematical & Computational Biology
Statistics & Probability
Biochemistry & Molecular Biology
Computer Science
01 Mathematical Sciences
06 Biological Sciences
08 Information and Computing Sciences
Publication Status: Published
Article Number: btab136
Online Publication Date: 2021-02-27
Appears in Collections:Statistics
Faculty of Natural Sciences