  In order to characterize the mechanisms governing the diffusion of particles
in biological scenarios, it is essential to accurately determine their
diffusive properties. To do so, we propose a machine learning method to
characterize diffusion processes with time-dependent properties at the
experimental time resolution. Our approach operates at the single-trajectory
level predicting the properties of interest, such as the diffusion coefficient
or the anomalous diffusion exponent, at every time step of the trajectory. In
this way, changes in the diffusive properties occurring along the trajectory
emerge naturally in the prediction, and thus allow the characterization without
any prior knowledge or assumption about the system. We first benchmark the
method on synthetic trajectories simulated under several conditions. We show
that our approach can successfully characterize both abrupt and continuous
changes in the diffusion coefficient or the anomalous diffusion exponent.
Finally, we leverage the method to analyze experiments of single-molecule
diffusion of two membrane proteins in living cells: the pathogen-recognition
receptor DC-SIGN and the integrin $\alpha5\beta1$. The analysis allows us to
characterize physical parameters and diffusive states with unprecedented
accuracy, shedding new light on the underlying mechanisms.
