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. 2021 Mar;288(5):1514-1532.
doi: 10.1111/febs.15519. Epub 2020 Sep 17.

Quantitative analysis of fluorescent ligand binding to dopamine D3 receptors using live-cell microscopy

Anni Allikalt  1   2 , Tõnis Laasfeld  1 , Mihkel Ilisson  1 , Sergei Kopanchuk  1 , Ago Rinken  1
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Free article

Quantitative analysis of fluorescent ligand binding to dopamine D3 receptors using live-cell microscopy

Anni Allikalt et al. FEBS J. 2021 Mar.
Free article

Abstract

Dopamine receptors are G protein-coupled receptors that have several essential functions in the central nervous system. A better understanding of the regulatory mechanisms of ligand binding to the receptor may open new possibilities to affect the downstream signal transduction pathways. The majority of the available ligand binding assays use either membrane preparations, cell suspensions, or genetically modified receptors, which may give at least partially incorrect understanding of ligand binding. In this study, we implemented an assay combining fluorescence and bright-field microscopy to measure ligand binding to dopamine D3 receptors in live mammalian cells. For membrane fluorescence intensity quantification from microscopy images, we developed a machine learning-based user-friendly software membrane tools and incorporated it into a data management software aparecium that has been previously developed in our workgroup. For the experiments, a fluorescent ligand NAPS-Cy3B was synthesized by conjugating a dopaminergic antagonist N-(p-aminophenethyl)spiperone with a fluorophore Cy3B. The subnanomolar affinity of NAPS-Cy3B makes it a suitable ligand for the characterization of D3 receptors in live HEK293 cells. Using a microplate compatible automated widefield fluorescence microscope, together with the membrane tools software, enables the detection and quantification of ligand binding with a high-throughput. The live cell assay is suitable for the characterization of fluorescent ligand binding and also in the competition experiments for the screening of novel unlabeled dopaminergic ligands. We propose that this simple yet more native-like approach is feasible in GPCR research, as it enables the detection of ligand binding in an environment containing more components involved in the signal transduction cascade.

Keywords: G protein-coupled receptors; dopamine D3 receptor; fluorescence microscopy; ligand binding; machine learning.

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