Embacher, Peter A;
Germanova, Tsvetelina E;
Roscioli, Emanuele;
McAinsh, Andrew D;
Burroughs, Nigel J;
(2022)
Bayesian inference of multi-point macromolecular architecture mixtures at nanometre resolution.
PLoS Computational Biology
, 18
(12)
, Article e1010765. 10.1371/journal.pcbi.1010765.
Preview |
PDF
journal.pcbi.1010765.pdf - Published Version Download (4MB) | Preview |
Abstract
Gaussian spot fitting methods have significantly extended the spatial range where fluorescent microscopy can be used, with recent techniques approaching nanometre (nm) resolutions. However, small inter-fluorophore distances are systematically over-estimated for typical molecular scales. This bias can be corrected computationally, but current algorithms are limited to correcting distances between pairs of fluorophores. Here we present a flexible Bayesian computational approach that infers the distances and angles between multiple fluorophores and has several advantages over these previous methods. Specifically it improves confidence intervals for small lengths, estimates measurement errors of each fluorophore individually and infers the correlations between polygon lengths. The latter is essential for determining the full multi-fluorophore 3D architecture. We further developed the algorithm to infer the mixture composition of a heterogeneous population of multiple polygon states. We use our algorithm to analyse the 3D architecture of the human kinetochore, a macro-molecular complex that is essential for high fidelity chromosome segregation during cell division. Using triple fluorophore image data we unravel the mixture of kinetochore states during human mitosis, inferring the conformation of microtubule attached and unattached kinetochores and their proportions across mitosis. We demonstrate that the attachment conformation correlates with intersister tension and sister alignment to the metaphase plate.
| Type: | Article |
|---|---|
| Title: | Bayesian inference of multi-point macromolecular architecture mixtures at nanometre resolution |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1371/journal.pcbi.1010765 |
| Publisher version: | https://doi.org/10.1371/journal.pcbi.1010765 |
| Language: | English |
| Additional information: | © 2022 Embacher et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/). |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10163075 |
Archive Staff Only
 |
View Item |
