TY - JOUR T1 - Spot-On: robust model-based analysis of single-particle tracking experiments JF - bioRxiv DO - 10.1101/171983 SP - 171983 AU - Anders S Hansen AU - Maxime Woringer AU - Jonathan B Grimm AU - Luke D Lavis AU - Robert Tjian AU - Xavier Darzacq Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/08/22/171983.abstract N2 - Single-particle tracking (SPT) has become an important method to bridge biochemisty and cell biology since it allows direct observation of protein binding and diffusion dynamics in live cells. However, accurately inferring information from SPT approaches is challenging due to biases in both data analysis and experimental design. To address analysis biases, we introduce “Spot-On”, an intuitive web-interface. Spot-On implements a kinetic modeling framework that accounts for known biases, including molecules moving out-of-focus, and robustly infers diffusion constants and subpopulations from pooled single-molecule trajectories. To minimize inherent experimental biases, we implement and validate stroboscopic photo-activation SPT (spaSPT), which minimizes motion-blur bias and tracking errors. We validate Spot-On using experimentally realistic simulations and show that Spot-On outperforms other methods. We then apply Spot-On to spaSPT data from live mammalian cells spanning a wide range of nuclear dynamics and demonstrate that Spot-On consistently and robustly infers subpopulation fractions and diffusion constants. ER -