Abstract
Single-particle tracking (SPT) has become an important method to bridge biochemistry and cell biology since it allows direct observation of protein binding and diffusion dynamics in live cells. However, accurately inferring information from SPT studies is challenging due to biases in both data analysis and experimental design. To address analysis bias, 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.
Highlights
Advances in imaging technologies, genetically encoded tags and fluorophore development have made single-particle tracking (SPT) an increasingly popular method for analyzing protein dynamics (Liu et al, 2015)
The first two biases must be minimized at the data acquisition stage and we describe an experimental Single-particle tracking (SPT)
Spot-On is a user-friendly web-interface that pedagogically guides the user through a series of quality-checks of uploaded datasets consisting of pooled single-molecule trajectories. It performs kinetic model-based analysis that leverages the histogram of molecular displacements over time to infer the fraction and diffusion constant of each subpopulation (Figure 2)
Summary
Genetically encoded tags and fluorophore development have made single-particle tracking (SPT) an increasingly popular method for analyzing protein dynamics (Liu et al, 2015). Recent biological application of SPT have revealed that transcription factors (TFs) bind mitotic chromosomes (Teves et al, 2016), how Polycomb interacts with chromatin (Zhen et al, 2016), that “pioneer factor” TFs bind chromatin dynamically (Swinstead et al, 2016), that TF binding time correlates with transcriptional activity (Loffreda et al, 2017) and that different nuclear proteins adopt distinct target search mechanisms (Izeddin et al, 2014; Rhodes et al, 2017). SPT makes it molecules over time in live cells and has provided clear evidence that proteins often exist in several subpopulations that can be characterized by their distinct diffusion coefficients (Mueller et al, 2013; Shen et al, 2017).
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