Biology subjects

Hollo, K.

Publications and source records attributed to Hollo, K..

2 recordsLinked to original sources

bioRxiv2026

Repurposing dopamine D3 receptor fluorescent ligands for D2 receptor

The dopamine D2 receptor is a principal target of antipsychotic and antiparkinsonian drugs, yet well-characterized fluorescent ligands for the D2 receptor remain scarce compared with its relative, the D3 receptor. Here we show that fluorescent ligands developed for the D3 subtype can be repurposed as tools for studying the D2 receptor pharmacology. Using a machine-learning-assisted, live-cell fluorescence microscopy assay, we screened twelve fluorescent probes and identified eight ligands that bound specifically to the D2 receptor at 10 nM. Four ligands exhibited classical saturable binding, yielding Kd in the nanomolar range, whereas the remaining probes displayed unsaturated, time-dependent binding profiles. Kinetic analyses revealed substantial differences in association and dissociation behavior, highlighting the importance of time-resolved measurements. Competition binding with dopamine and haloperidol yielded affinity estimates consistent with literature values. These results expand the toolkit of validated D2 receptor fluorescent probes and support cross-receptor repurposing as a strategy for fluorescent ligand development.

biochemistry↗

bioRxiv2022

ArtSeg: Rapid Artifact Segmentation and Removal in Brightfield Cell Microscopy Images

Brightfield cell microscopy is a foundational tool in life sciences. The acquired images are prone to contain visual artifacts that hinder downstream analysis, and automatically removing them is therefore of great practical interest. Deep convolutional neural networks are state-of-the-art for image segmentation, but require pixel-level annotations, which are time-consuming to produce. Here, we propose ScoreCAM-U-Net, a pipeline to segment artifactual regions in brightfield images with limited user input. The model is trained using only image-level labels, so the process is faster by orders of magnitude compared to pixel-level annotation, but without substantially sacrificing the segmentation performance. We confirm that artifacts indeed exist with different shapes and sizes in three different brightfield microscopy image datasets, and distort downstream analyses such as nuclei segmentation, morphometry and fluorescence intensity quantification. We then demonstrate that our automated artifact removal ameliorates this problem. Such rapid cleaning of acquired images using the power of deep learning models is likely to become a standard step for all large scale microscopy experiments.

bioinformatics↗