Improved analysis of anomalous diffusion data in single particle tracking experiments

The Mean Square Displacement is a central tool in the analysis of Single Particle Tracking experiments, shedding light on various biophysical phenomena. However, as we show, it suffers from two systematic errors when analysing tracks of anomalous diffusing particles. The first is significant at short time differences and is induced by measurement errors. The second arises from the natural heterogeneity in biophysical systems. We show how to estimate and correct these two errors and improve the estimation of the anomalous parameters for the whole particle distribution. As a consequence we manage to characterise ensembles of heterogeneous particles even at very short and noisy measurements where regular time averaged mean square displacement analysis fails. This procedure has the potential to improve experimental accuracy while maintaining lower experimental costs and complexity.