Abstract

Analyzing single particle trajectories is a prominent issue in understanding complex dynamics such as nanoparticle-cell interactions. Existing methods treat data points as isolated \"atoms\" and use predefined mechanical models to \"frame\" their complicated relationship. Herein, we propose a \"historical evolution\" based model-free strategy. It allows spatiotemporal heterogeneity embedded in a trajectory to self-emerge as consecutive colored segments before any model assumption, provide both an overall picture and local state transitions on the particle movement with minimum information loss, and inspire further model-based investigation. We demonstrate with simulations and experiments that the underlying mechanisms of various time-series and motion states of single nanoparticles on live cell membranes could all be revealed successfully. Since complexity studies at different levels of molecules, particles, cells, human beings, vehicles, and even stars could all be reduced to analyzing spatiotemporal trajectories of \"single particles\", this presuppositionless approach will help fundamental researches on many important systems.\n\nImpact StatementA preprocessing strategy for single particle trajectory analysis is established by providing an intuitive global pattern from \"historical experiences\" of the particle without predefining any mechanical models.