D. M. Owen

Single-molecule analysis of receptor-beta-arrestin interactions in living cells

This study uses single-molecule microscopy and simulations to show that beta-arrestin inserts into the lipid bilayer and interacts with receptors through lateral diffusion, challenging existing models of its function.

A framework for evaluating the performance of SMLM cluster analysis algorithms

The paper evaluates SMLM cluster analysis algorithms using ARI and IoU metrics across simulated scenarios to establish a standard framework for future algorithm development.

Correction of multiple-blinking artefacts in photoactivated localisation microscopy

This paper corrects multiple-blinking artefacts in PALM data using model-based clustering and calibration-free blinking dynamics estimation, enabling accurate quantification of fluorophore clustering.

Blinking Statistics and Molecular Counting in direct Stochastic Reconstruction Microscopy (dSTORM)

The paper develops a method to accurately count molecules in dSTORM microscopy by modeling photo-switching as a Markov process and demonstrates it on T cell LAT proteins.

Quantitative mapping of membrane nanoenvironments through single-molecule imaging of solvatochromic probes

The paper maps biophysical environments in mammalian cell membranes using solvatochromic probes and single-molecule imaging with nanoscale resolution.

Asymmetric phospholipids impart novel biophysical properties to lipid bilayers allowing environmental adaptation

This study shows that asymmetric phospholipids in yeast membranes maintain lipid order across a wider temperature range without needing unsaturated tails or sterols.

Three-dimensional total-internal reflection fluorescence nanoscopy with nanometric axial resolution by photometric localization of single molecules

This paper introduces SIMPLER, a method to achieve nanometric axial resolution in 3D single-molecule localization microscopy without hardware changes to TIRF microscopes.

Machine-learning for cluster analysis of localization microscopy data.

The paper develops a supervised machine-learning approach to cluster analysis of localization microscopy data, demonstrating its effectiveness on simulated and experimental datasets.