Maximum likelihood estimates of diffusion coefficients from single-particle tracking experiments
- Single-molecule localization microscopy allows practitioners to locate and track labeled molecules in biological systems. When extracting diffusion coefficients from the resulting trajectories, it is common practice to perform a linear fit on mean-squared-displacement curves. However, this strategy is suboptimal and prone to errors. Recently, it was shown that the increments between the observed positions provide a good estimate for the diffusion coefficient, and their statistics are well-suited for likelihood-based analysis methods. Here, we revisit the problem of extracting diffusion coefficients from single-particle tracking experiments subject to static noise and dynamic motion blur using the principle of maximum likelihood. Taking advantage of an efficient real-space formulation, we extend the model to mixtures of subpopulations differing in their diffusion coefficients, which we estimate with the help of the expectation–maximization algorithm. This formulation naturally leads to a probabilistic assignment of trajectories to subpopulations. We employ the theory to analyze experimental tracking data that cannot be explained with a single diffusion coefficient. We test how well a dataset conforms to the assumptions of a diffusion model and determine the optimal number of subpopulations with the help of a quality factor of known analytical distribution. To facilitate use by practitioners, we provide a fast open-source implementation of the theory for the efficient analysis of multiple trajectories in arbitrary dimensions simultaneously.
Author: | Jakob Tómas BullerjahnORCiDGND, Gerhard HummerORCiD |
---|---|
URN: | urn:nbn:de:hebis:30:3-723129 |
DOI: | https://doi.org/10.1063/5.0038174 |
ISSN: | 1089-7690 |
Parent Title (English): | The journal of chemical physics |
Publisher: | American Institute of Physics |
Place of publication: | Melville, NY |
Document Type: | Article |
Language: | English |
Date of Publication (online): | 2021/06/17 |
Date of first Publication: | 2021/06/17 |
Publishing Institution: | Universitätsbibliothek Johann Christian Senckenberg |
Release Date: | 2023/06/15 |
Volume: | 154 |
Issue: | 23, art. 234105 |
Article Number: | 234105 |
Page Number: | 19 |
First Page: | 1 |
Last Page: | 19 |
HeBIS-PPN: | 510056881 |
Institutes: | Physik |
Angeschlossene und kooperierende Institutionen / MPI für Biophysik | |
Dewey Decimal Classification: | 5 Naturwissenschaften und Mathematik / 53 Physik / 530 Physik |
5 Naturwissenschaften und Mathematik / 54 Chemie / 540 Chemie und zugeordnete Wissenschaften | |
Sammlungen: | Universitätspublikationen |
Licence (German): | Creative Commons - CC BY - Namensnennung 4.0 International |