Congrats to Juliana. She created a computational (stochastic kinetic) framework to predict how the image of sensors looks like in response to signalling molecules released by cells. Highly important to understand cell signalling. @solvationsci.bsky.social @ruhr-uni-bochum.de tinyurl.com/yvs6e9n8
Simulation of Neurotransmitter Release and Its Imaging by Fluorescent Sensors
Cells release signaling molecules such as neurotransmitters that diffuse through the extracellular space and bind to receptors. These signaling molecules can be detected by fluorescent sensors and probes to provide images of the signaling process. Such images are not equivalent to a concentration because diffusion and sensor kinetics affect (convolute) them. Therefore, computational approaches are necessary to disentangle these contributions and allow the interpretation of fluorescent sensor-based images. Here, we present a kinetic Monte Carlo framework (fluorescent sensor imaging kinetic simulation, FLIKS) that simulates signaling molecules undergoing cellular release, stochastic diffusion, and reversible binding to sensors in realistic cellular (2D or 3D) geometries. We apply it to model neurotransmitter (dopamine) release in synaptic clefts and for paracrine signaling by immune cells. We also show how sensor location, sensor kinetics, and release location affect fluorescence images. For example, we show how sensor sensitivity depends on the distance from the synaptic cleft and changes when dopamine transporters (DATs) clear dopamine. The approach also allows us to compare the performance of membrane-bound (genetically encoded) sensors versus artificial sensors such as nanosensors placed outside under or around the cells. As an example, we also demonstrate how the images of catecholamine release by immune cells can be modeled and compared with experimental data to better understand the release pattern. This framework provides a quantitative basis for analyzing and interpreting the fluorescent sensor imaging data.
tinyurl.com
