Live-cell microscopy or fluorescence anisotropy with budded baculoviruses—which way to go with measuring ligand binding to M ₄ muscarinic receptors?

Abstract

M 4 muscarinic acetylcholine receptor is a G protein-coupled receptor (GPCR) that has been associated with alcohol and cocaine abuse, Alzheimer's disease, and schizophrenia which makes it an interesting drug target. For many GPCRs, the high-affinity fluorescence ligands have expanded the options for high-throughput screening of drug candidates and serve as useful tools in fundamental receptor research. Here, we explored two TAMRA-labelled fluorescence ligands, UR-MK342 and UR-CG072, for development of assays for studying ligand-binding properties to M 4 receptor. Using budded baculovirus particles as M 4 receptor preparation and fluorescence anisotropy method, we measured the affinities and binding kinetics of both fluorescence ligands. Using the fluorescence ligands as reporter probes, the binding affinities of unlabelled ligands could be determined. Based on these results, we took a step towards a more natural system and developed a method using live CHO-K1-hM 4 R cells and automated fluorescence microscopy suitable for the routine determination of unlabelled ligand affinities. For quantitative image analysis, we developed random forest and deep learning-based pipelines for cell segmentation. The pipelines were integrated into the user-friendly open-source Aparecium software. Both image analysis methods were suitable for measuring fluorescence ligand saturation binding and kinetics as well as for screening binding affinities of unlabelled ligands.

Synthesis, Characterization, and Application of Muscarinergic M3 Receptor Ligands Linked to Fluorescent Dyes

“…In the field of muscarinergic ligands, only very few conjugates to fluorescent dyes have so far been described (Figure ). − The conjugate 1 recently reported by Keller et al is based on a dibenzodiazepinone pharmacophore (blue) and comprises a Cy5-type dye (red) with two sulfonate groups for increased solubility in water. As conjugate 1 and its derivatives show strong preference for binding to the M 2 receptor over M 3 (10–500-fold), they are primarily suited as tools for investigations of the M 2 subtype.…”

Fluorescence based HTS-compatible ligand binding assays for dopamine D3 receptors in baculovirus preparations and live cells

“…For fine-tuning the U-Net cell segmentation model for HEK293 cells, the fluorescence ground-truth masks were generated as described in ( Tahk et al, 2022 ). Briefly, HEK293-D3R cells were seeded with a density of 20,000 cells/well into a µ-Plate 96 well Black well plate (Ibidi, Gräfelfing, Germany) and incubated for 2.5 h. One mM DiI (Invitrogen, Eugene, Oregon, United States) in DMSO kept at -20°C was thawed and, to disrupt aggregates, kept in an ultrasound bath for 5 min.…”

“…Image analysis was performed as described in ( Tahk et al, 2022 ) with some modifications. For training the final U-Net2D image segmentation model in the ZeroCostDL4Mic ( Ronneberger et al, 2015 ; von Chamier et al, 2021 ) environment, U-Net2D notebook with minor modifications was used to allow compatibility with Aparecium and Matlab Keras framework model import.…”

“…Therefore, fluorescence polarization assays and other fluorescence based methods allowing kinetic measurements help reduce this fundamental knowledge gap. Fluorescence anisotropy (FA) assay has been used to study the ligand binding kinetics of many GPCRs - 5-HT 1A ( Tõntson et al, 2014 ), melanocortin MC 4 ( Link et al, 2017 ), dopamine D 1 ( Allikalt et al, 2018 ), neuropeptide Y Y 1 ( Müller et al, 2022 ), muscarinic acetylcholine M 2 ( Grätz et al, 2021 ) and M 4 ( Tahk et al, 2022 ) receptors. FA is based on measuring the change in rotational freedom of the fluorescent label upon receptor binding and, therefore, does not require the physical separation of the bound and free ligand ( Rinken et al, 2018 ).…”

“…The advantage of this approach is that no additional fluorescence channels are needed for cell and cell contour detection reducing both photo and chemical toxicity while also allowing for the analysis of different areas of the cell depending on the cell type or the goal of the analysis. The method has been further developed to allow for kinetic measurements and reduced data volumes by employing deep convolutional neural networks, which regularly surpass more classical machine learning models such as random forests ( Caicedo et al, 2019 ; Tahk et al, 2022 ). Nowadays, many machine learning models can be developed in almost any available high-performance cluster or even on a reasonably powerful PC, but it is also possible to use freely available open-source cloud-based tools for the task.…”