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.
Verfasserangaben: | Jakob Tómas BullerjahnORCiDGND, Gerhard HummerORCiD |
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URN: | urn:nbn:de:hebis:30:3-723129 |
DOI: | https://doi.org/10.1063/5.0038174 |
ISSN: | 1089-7690 |
Titel des übergeordneten Werkes (Englisch): | The journal of chemical physics |
Verlag: | American Institute of Physics |
Verlagsort: | Melville, NY |
Dokumentart: | Wissenschaftlicher Artikel |
Sprache: | Englisch |
Datum der Veröffentlichung (online): | 17.06.2021 |
Datum der Erstveröffentlichung: | 17.06.2021 |
Veröffentlichende Institution: | Universitätsbibliothek Johann Christian Senckenberg |
Datum der Freischaltung: | 15.06.2023 |
Jahrgang: | 154 |
Ausgabe / Heft: | 23, art. 234105 |
Aufsatznummer: | 234105 |
Seitenzahl: | 19 |
Erste Seite: | 1 |
Letzte Seite: | 19 |
HeBIS-PPN: | 510056881 |
Institute: | Physik |
Angeschlossene und kooperierende Institutionen / MPI für Biophysik | |
DDC-Klassifikation: | 5 Naturwissenschaften und Mathematik / 53 Physik / 530 Physik |
5 Naturwissenschaften und Mathematik / 54 Chemie / 540 Chemie und zugeordnete Wissenschaften | |
Sammlungen: | Universitätspublikationen |
Lizenz (Deutsch): | Creative Commons - CC BY - Namensnennung 4.0 International |