FISIK: Framework for the Inference of in Situ Interaction Kinetics from single-molecule imaging data

Abstract

Recent experimental and computational developments have been pushing the limits of live-cell single-molecule imaging, enabling the monitoring of inter-molecular interactions in their native environment with high spatiotemporal resolution. However, interactions are captured only for the labeled subset of molecules, which tends to be a small fraction. As a result, it has remained a challenge to calculate molecular interaction kinetics, in particular association rates, from single-molecule tracking data. To overcome this challenge, we developed a mathematical modeling-based Framework for the Inference of in Situ Interaction Kinetics from single-molecule imaging data (termed "FISIK"). FISIK consists of (1) devising a mathematical model of molecular movement and interactions, mimicking the biological system and data-acquisition setup, and (2) estimating the unknown model parameters, including molecular association and dissociation rates, by fitting the model to experimental single-molecule data. Due to the stochastic nature of the model and data, we adapted the method of indirect inference for model calibration. We validated FISIK using a series of tests, where we simulated trajectories of diffusing molecules that interact with each other, considering a wide range of model parameters, and including resolution limitations and tracking errors. We found that FISIK has the sensitivity to determine association and dissociation rates, where its accuracy depends on the labeled fraction of molecules and the extent of molecule tracking errors. For cases where the labeled fraction is relatively low (e.g. to afford accurate tracking), combining dynamic but sparse single-molecule imaging data with almost whole-population oligomer distribution data improves FISIKs performance. All in all, FISIK is a promising approach for the derivation of molecular interaction kinetics in their native environment from single-molecule imaging data.