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Blinking Statistics and Molecular Counting in direct Stochastic Reconstruction Microscopy (dSTORM)

Research output: Contribution to journalArticlepeer-review

Lekha Patel, David Williamson, Dylan M Owen, Edward A K Cohen

Original languageEnglish
Pages (from-to)2730-2737
Number of pages8
JournalBioinformatics (Oxford, England)
Issue number17
Early online date27 Feb 2021
E-pub ahead of print27 Feb 2021
Published1 Sep 2021

Bibliographical note

Funding Information: This work was supported by the Laboratory Directed Research and Development program at Sandia National Laboratories, a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525. Publisher Copyright: © 2021 The Author(s). Published by Oxford University Press. All rights reserved. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

King's Authors


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.

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