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Related Experiment Video

Updated: Jan 14, 2026

Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy
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The Hitchhiker's guide to differential dynamic microscopy.

Enrico Lattuada1, Fabian Krautgasser1, Maxime Lavaud1

  • 1Faculty of Physics, University of Vienna, Boltzmanngasse 5, 1090 Vienna, Austria.

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|October 24, 2025
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Summary

Differential dynamic microscopy (DDM) analyzes microscopy images to reveal material dynamics. This tutorial and the fastDDM software simplify DDM experiments and data analysis for broader scientific use.

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Area of Science:

  • Soft matter physics
  • Biophysics
  • Materials science

Background:

  • Differential dynamic microscopy (DDM) is a key technique for analyzing dynamic correlation functions in time-lapse microscopy.
  • DDM applications span diverse fields including soft matter, active fluids, and biological systems.
  • Current challenges involve a widening user base and the need to reduce technical barriers for DDM implementation.

Purpose of the Study:

  • To provide a comprehensive, step-by-step tutorial for conducting Differential Dynamic Microscopy (DDM) experiments.
  • To introduce the open-source software package fastDDM for efficient DDM data processing.
  • To lower the technical barrier for DDM, promoting wider adoption and reproducibility.

Main Methods:

  • DDM analysis of image sequences from conventional microscopes with digital cameras.
  • Utilizing time- and wavevector-resolved information, analogous to dynamic light scattering.
  • Development and application of the fastDDM software package with optimized, parallel algorithms.

Main Results:

  • The fastDDM software reduces analysis times for large image datasets by up to four orders of magnitude.
  • Demonstrated efficiency gains enable high-throughput DDM workflows.
  • The tutorial provides a practical guide for both novice and experienced DDM users.

Conclusions:

  • DDM is a powerful tool for characterizing dynamic processes in various scientific disciplines.
  • The fastDDM software significantly enhances the efficiency and accessibility of DDM analysis.
  • This work facilitates broader adoption of DDM across research fields, improving reproducibility and throughput.