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Maximum-likelihood model fitting for quantitative analysis of SMLM data

View ORCID ProfileYu-Le Wu, View ORCID ProfilePhilipp Hoess, View ORCID ProfileAline Tschanz, View ORCID ProfileUlf Matti, View ORCID ProfileMarkus Mund, View ORCID ProfileJonas Ries
doi: https://doi.org/10.1101/2021.08.30.456756
Yu-Le Wu
1European Molecular Biology Laboratory (EMBL), Cell Biology and Biophysics Unit, Heidelberg, Germany
3Candidate for Joint PhD Programme of EMBL and University of Heidelberg
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Philipp Hoess
1European Molecular Biology Laboratory (EMBL), Cell Biology and Biophysics Unit, Heidelberg, Germany
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Aline Tschanz
1European Molecular Biology Laboratory (EMBL), Cell Biology and Biophysics Unit, Heidelberg, Germany
3Candidate for Joint PhD Programme of EMBL and University of Heidelberg
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Ulf Matti
1European Molecular Biology Laboratory (EMBL), Cell Biology and Biophysics Unit, Heidelberg, Germany
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Markus Mund
1European Molecular Biology Laboratory (EMBL), Cell Biology and Biophysics Unit, Heidelberg, Germany
2University of Geneva, Department of Biochemistry, Geneva, Switzerland
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Jonas Ries
1European Molecular Biology Laboratory (EMBL), Cell Biology and Biophysics Unit, Heidelberg, Germany
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  • For correspondence: Jonas.ries@embl.de
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Abstract

Quantitative analysis is an important part of any single-molecule localization microscopy (SMLM) data analysis workflow to extract biological insights from the coordinates of the single fluorophores, but current approaches are restricted to simple geometries or do not work on heterogenous structures.

Here, we present LocMoFit (Localization Model Fit), an open-source framework to fit an arbitrary model directly to the localization coordinates in SMLM data. Using maximum likelihood estimation, this tool extracts the most likely parameters for a given model that best describe the data, and can select the most likely model from alternative models. We demonstrate the versatility of LocMoFit by measuring precise dimensions of the nuclear pore complex and microtubules. We also use LocMoFit to assemble static and dynamic multi-color protein density maps from thousands of snapshots. In case an underlying geometry cannot be postulated, LocMoFit can perform single-particle averaging of super-resolution structures without any assumption about geometry or symmetry. We provide extensive simulation and visualization routines to validate the robustness of LocMoFit and tutorials based on example data to enable any user to increase the information content they can extract from their SMLM data.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://www.rieslab.de

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted August 31, 2021.
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Maximum-likelihood model fitting for quantitative analysis of SMLM data
Yu-Le Wu, Philipp Hoess, Aline Tschanz, Ulf Matti, Markus Mund, Jonas Ries
bioRxiv 2021.08.30.456756; doi: https://doi.org/10.1101/2021.08.30.456756
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Maximum-likelihood model fitting for quantitative analysis of SMLM data
Yu-Le Wu, Philipp Hoess, Aline Tschanz, Ulf Matti, Markus Mund, Jonas Ries
bioRxiv 2021.08.30.456756; doi: https://doi.org/10.1101/2021.08.30.456756

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