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Protein Dynamics in Living Cells

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Related Experiment Video

Updated: Jun 12, 2026

Cortical Actin Flow in T Cells Quantified by Spatio-temporal Image Correlation Spectroscopy of Structured Illumination Microscopy Data

Cortical Actin Flow in T Cells Quantified by Spatio-temporal Image Correlation Spectroscopy of Structured Illumination Microscopy Data

Published on: December 17, 2015

Event ordering in live-cell imaging determined from temporal cross-correlation asymmetry.

Daniel R Sisan¹, Defne Yarar, Clare M Waterman

¹Department of Physics, Georgetown University, Washington, District of Columbia, USA. dan.sisan@gmail.com

Biophysical Journal

|June 2, 2010

Summary

This study introduces a new method using cross-correlation to determine the order of cellular events in live-cell imaging. This technique accurately analyzes noisy biological data, revealing hidden temporal sequences in cellular processes.

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Area of Science:

Cell Biology
Biophysics
Biochemistry

Background:

Live-cell imaging generates complex, often noisy, data.
Determining the precise temporal order of cellular events is crucial for understanding biological pathways.
Stochasticity and noise can obscure event sequencing in raw data.

Purpose of the Study:

To develop a quantitative method for determining the temporal ordering of spatially localized cellular events.
To provide a robust analysis for noisy and stochastic live-cell imaging data.
To enable statistical interpretation of cellular signaling dynamics.

Main Methods:

Utilizing the temporal asymmetry of the cross-correlation function.
Applying the method to simulations of biophysical models with known temporal order.
Analyzing multichannel fluorescence imaging data from live cells.

Main Results:

The cross-correlation method successfully determined temporal ordering in simulated data.
The technique accurately identified the sequence of actin, sorting nexin 9, and clathrin during endocytosis.
The approach demonstrated robustness in noisy, stochastic systems.

Conclusions:

The temporal asymmetry of cross-correlation is a powerful tool for deciphering cellular event order.
This method offers an automated, quantitative approach to analyze complex live-cell imaging data.
The technique is broadly applicable to non-equilibrium biochemical reactions and cellular signaling.