RT Journal Article
SR Electronic
T1 Maximum-likelihood model fitting for quantitative analysis of SMLM data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.08.30.456756
DO 10.1101/2021.08.30.456756
A1 Yu-Le Wu
A1 Philipp Hoess
A1 Aline Tschanz
A1 Ulf Matti
A1 Markus Mund
A1 Jonas Ries
YR 2021
UL http://biorxiv.org/content/early/2021/08/31/2021.08.30.456756.abstract
AB 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 StatementThe authors have declared no competing interest.