Probabilistic Nearest Neighbor Estimation of Diffusion Constants from Single Molecular Measurement without Explicit Tracking

TL;DR

This paper introduces a novel algorithm for estimating molecular diffusion constants directly from single-molecule measurements, avoiding the limitations of traditional tracking methods especially in high-density conditions.

Contribution

The proposed algorithm estimates diffusion constants without explicit particle tracking, improving accuracy in high-density or inhomogeneous environments.

Findings

Accurately estimates diffusion constants without tracking.

Performs well under high particle density conditions.

Enables visualization of molecular dynamics over time.

Abstract

Time course measurement of single molecules on a cell surface provides detailed information on the dynamics of the molecules, which is otherwise inaccessible. To extract the quantitative information, single particle tracking (SPT) is typically performed. However, trajectories extracted by SPT inevitably have linking errors when the diffusion speed of single molecules is high compared to the scale of the particle density. To circumvent this problem we developed an algorithm to estimate diffusion constants without relying on SPT. We demonstrated that the proposed algorithm provides reasonable estimation of diffusion constants even when other methods fail due to high particle density or inhomogeneous particle distribution. In addition, our algorithm can be used for visualization of time course data from single molecular measurements.