Drug Derived Fluorescent Probes for the Specific Visualization of Cannabinoid Type 2 Receptor - A Toolbox Approach

Abstract

Cannabinoid type 2 receptor (CB2R) is a fundamental part of the endocannabinoid signaling system (eCB system), and is known to play an important role in tissue injury, inflammation, cancer and pain. In stark contrast to its significance, the underlying signaling mechanisms and tissue expression profiles are poorly understood. Due to its low expression in healthy tissue and lack of reliable chemical tools, CB2R visualization in live cells remains uncharted. Here we report the development of a drug derived toolbox of highly potent, CB2Rselective fluorescent probes based on reverse design. Extensive validation in several applications such as CB2R detection in flow cytometry and time-resolved imaging, and the development of a novel fluorescent-based TR-FRET assay to generate kinetic and equilibrium binding data demonstrate the high versatility of our toolbox. These probes are the first to preserve affinity and efficacy in both human and mouse CB2R, a crucial aspect for preclinical translatability, and to enable imaging of CB2R internalization in living cells using confocal microscopy.

Exploring group size for statistical analysis of real‐time signalling experiments

“…In this study, we emulate a real‐time experiment described by Thais et al (2019). In their work, they explore ligand receptor binding for SiR‐6 at the cannabinoid 2 receptor (CB 2 ).…”

“…The workflow of this approach is shown in Figure 2. First, we chose a set of parameter values and random effects that reflect those from the real‐world experiment referred to above (Thais et al, 2019). Second, we propose several experimental designs to be tested, each with varying characteristics, including group sizes ( n ).…”

“…We use the parameters derived from the binding experiments of the CB 2 receptor as the template (Thais et al, 2019), shown in Table 1. In addition, we included random error associated with the observations that represents error from sources such as operator error and measurement error.…”

“…Note that this random error does not include systematic bias, which means methodological validation should be performed first to exclude the possibility of this source of error. The random error was set according to (Thais et al, 2019) and included as shown in Equation (3). We use both an additive error (which reduces the information content at low effect levels) and a proportional error (which reduces the information content at high effect levels).…”

“…The right panel is the classical equilibrium concentration–effect curve, which only use the last time points of the real‐time assay, which are then fitted with an E max model. The data presented here are simulated and are based on the study of Thais et al (2019). The error bar in figure here is the standard error of the simulated data (10% proportional error and 1 additive error as stated in Table 1)…”