FreeTrace enables fractional Brownian motion-based single-molecule tracking and robust anomalous diffusion analysis

Abstract

Single-molecule tracking (SMT) in live cells reveals how biomolecules explore crowded intracellular environments, yet most tracking software assumes Brownian motion, an approximation that fails when anomalous diffusion dominates. This leads to biased trajectory reconstruction and loss of biophysical information, particularly for the short trajectories typical of intracellular experiments. We present FreeTrace, an SMT framework that reconstructs trajectories under fractional Brownian motion (fBm), incorporating temporal correlations directly into linking with minimal input parameters. A deep neural network estimates diffusion properties (Hurst exponent H and generalised diffusion coefficient K) for individual trajectories, while an analytical ensemble estimator accurately recovers H from trajectories as short as three frames, conditions where mean-squared displacement methods fail. Benchmarking on simulated data demonstrates superior performance across motion types and densities. Applications to chromatin-bound histones, DNA repair proteins in S.c. yeast and human cells reveal biologically meaningful diffusion subpopulations, with H values consistent with polymer models and confined motion. FreeTrace bridges theoretical anomalous diffusion models and routine biological experiments.