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Real-time single-molecule 3D tracking in E. coli based on cross-entropy minimization
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Molecular Systems Biology.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology. Uppsala University, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0002-5870-2457
Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Molecular Systems Biology.ORCID iD: 0000-0001-8811-2629
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2023 (English)In: Nature Communications, E-ISSN 2041-1723, Vol. 14, no 1, article id 1336Article in journal (Refereed) Published
Abstract [en]

Reaching sub-millisecond 3D tracking of individual molecules in living cells would enable direct measurements of diffusion-limited macromolecular interactions under physiological conditions. Here, we present a 3D tracking principle that approaches the relevant regime. The method is based on the true excitation point spread function and cross-entropy minimization for position localization of moving fluorescent reporters. Tests on beads moving on a stage reaches 67 nm lateral and 109 nm axial precision with a time resolution of 0.84 ms at a photon count rate of 60 kHz; the measurements agree with the theoretical and simulated predictions. Our implementation also features a method for microsecond 3D PSF positioning and an estimator for diffusion analysis of tracking data. Finally, we successfully apply these methods to track the Trigger Factor protein in living bacterial cells. Overall, our results show that while it is possible to reach sub-millisecond live-cell single-molecule tracking, it is still hard to resolve state transitions based on diffusivity at this time scale.

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Springer Nature, 2023. Vol. 14, no 1, article id 1336
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URN: urn:nbn:se:uu:diva-506957DOI: 10.1038/s41467-023-36879-1ISI: 001001718000019PubMedID: 36906676OAI: oai:DiVA.org:uu-506957DiVA, id: diva2:1779303
Funder
EU, European Research Council, BIGGER:885360EU, European Research Council, SMACK:947747Swedish Research Council, 2016.06213Swedish Research Council, 2019.03714Swedish Research Council, 2018.03958Knut and Alice Wallenberg Foundation, 2016.0077Knut and Alice Wallenberg Foundation, 2017.0291Knut and Alice Wallenberg Foundation, 2019.0439Swedish National Infrastructure for Computing (SNIC)Available from: 2023-07-04 Created: 2023-07-04 Last updated: 2025-12-05Bibliographically approved
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Amselem, EliasJohansson, MagnusElf, Johan

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