R. F. Laine

CLEM-Reg automates 3D alignment of vCLEM datasets, achieving sub-micron correlation of fluorescent signals with EM structures and enabling identification of TGN46-positive transport carriers.

The paper improves the center-of-mass method for fluorescence lifetime imaging by enhancing photon efficiency and robustness to background noise across large dynamic ranges.

This paper introduces eSRRF, an enhanced super-resolution imaging method that improves image fidelity and extends to 3D imaging using multi-focus microscopy.

DeepBacs uses open-source deep learning to analyze bacterial microscopy images, offering tools for segmentation, classification, and phenotyping with applications in antibiotic research.

This paper introduces two deep learning models for improving temporal resolution in bioimaging by interpolating frames between image pairs, reducing phototoxicity and enabling long-term live-cell imaging.

This study uses single-molecule super-resolution imaging to quantify nanoscale CD4 redistribution on CD4+ T cells after HIV-1 binding, revealing cluster size and molecule number changes.

This paper presents an automated cell tracking pipeline using StarDist and TrackMate for 2D live-cell imaging, applicable to fluorescent and brightfield images without coding knowledge.

This paper introduces a method to improve traction force microscopy resolution and throughput using super-resolution microscopy based on fluorophore fluctuation analysis.

OptiJ is an open-source OPT system for 3D imaging of large transparent samples, offering low-cost, modular hardware and software with improved reconstruction capabilities.

This study uses fast TG-FLIM to analyze α-synuclein and polyglutamine aggregation in aging C. elegans, revealing differences in their aggregation kinetics and amyloid-like features.

This study shows that age-dependent protein aggregation in C. elegans forms amyloid-like structures and accelerates tissue functional decline, suggesting a role for protein aggregation in aging.

The paper combines structured illumination microscopy with machine learning to analyze and classify virus structures efficiently.

The paper introduces NanoJ-Fluidics, an open-source system for automating multimodal microscopy workflows, demonstrating its ability to perform complex imaging experiments with high reproducibility.