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Updated: Apr 8, 2026

A Protocol for Real-time 3D Single Particle Tracking

A Protocol for Real-time 3D Single Particle Tracking

Published on: January 3, 2018

Detecting directed motion and confinement in single-particle trajectories using hidden variables.

François Simon¹, Guillaume Ramadier¹, Inès Fonquernie^1,2

¹Department of Engineering Physics, Polytechnique Montréal, Montréal, Canada.

|April 7, 2026

Summary

This summary is machine-generated.

We developed aTrack, a software tool to classify particle motion, distinguishing Brownian, confined, and directed movement. This tool accurately analyzes individual and populations of tracks for biophysical insights.

Keywords:

E. coli S. cerevisiae Single-particle tracking anomalous diffusion confined motion directed motion molecular biophysics physics of living systems state-space model structural biology track analysis

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Last Updated: Apr 8, 2026

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Area of Science:

Biophysics
Cell Biology
Software Development

Background:

Single-particle tracking (SPT) is crucial for studying protein dynamics and cellular microenvironments.
Analyzing nanoscale particle motion, especially deviations from Brownian diffusion, offers significant biophysical insights.
Classifying non-Brownian motion is challenging due to inherent stochasticity and localization errors in SPT data.

Purpose of the Study:

To introduce aTrack, a versatile software tool for classifying particle track behaviors.
To accurately estimate motion parameters for Brownian, confined, or directed particle movement.
To enable robust analysis of individual and populations of tracks to identify distinct motion states.

Main Methods:

aTrack employs algorithms to classify particle trajectories based on their movement patterns.
The software estimates key motion parameters, including diffusion coefficients and confinement sizes.
It can determine the optimal number of motion states within a population of tracks.

Main Results:

aTrack demonstrates high accuracy and speed in classifying simulated particle tracks.
The tool successfully characterizes particle motion in biological systems like yeast and bacteria.
Applications include analyzing intracellular transport and biosensing in microbial systems.

Conclusions:

aTrack provides a robust and user-friendly solution for analyzing single-particle tracking data.
The software facilitates the identification and quantification of diverse particle motion behaviors.
aTrack enhances the utility of SPT for biophysical research and biotechnological applications.