Pitfalls in the quantitative analysis of random walks and the mapping of single-molecule dynamics at the cellular scale

TL;DR

This paper discusses common pitfalls in the quantitative analysis of single-molecule trajectories, emphasizing the importance of avoiding biases to accurately infer diffusion, force, and potential fields at cellular scales using Bayesian inference.

Contribution

It highlights potential pitfalls in current single-molecule analysis methods and provides guidance to obtain unbiased, reliable results in cellular-scale studies.

Findings

Identification of key biases in Bayesian inference for single-molecule data

Emphasis on the importance of avoiding analysis pitfalls for accurate field mapping

Guidance on best practices for unbiased single-molecule trajectory analysis

Abstract

In recent years Bayesian Inference has become an efficient tool to analyse single molecule trajectories. Recently, high density single molecule tagging, Langevin Equation modelling and Bayesian Inference [10] have been used to infer diffusion, force and potential fields at the full cell scale. In this short comment, we point out pitfalls [1, 2] to avoid in single molecule analysis in order to get unbiased results and reliable fields at various scales.