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Related Experiment Video

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Author Spotlight: Understanding Disease Mechanisms Through Real-Time Analysis of T-Cell Migration
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Automated cell tracking using StarDist and TrackMate.

Elnaz Fazeli1, Nathan H Roy2, Gautier Follain3,4

  • 1Laboratory of Biophysics, Institute of Biomedicine, Faculty of Medicine, University of Turku, Turku, Finland.

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|November 23, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces an automated cell tracking pipeline using deep learning (StarDist) and TrackMate software. This open-source, no-code method enables quantitative analysis of cell migration in live-cell imaging, benefiting researchers in various biological fields.

Keywords:
Automated trackingCell migrationDeep-learningImage analysisStarDistTrackMate

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Area of Science:

  • Cell Biology
  • Biophysics
  • Bioimage Analysis

Background:

  • Cell migration is crucial for development, immunity, and healing.
  • Manual cell tracking is common despite computational tools, due to setup complexity.
  • Automated tracking requires robust, user-friendly solutions.

Purpose of the Study:

  • To present a novel, automated 2D cell tracking pipeline.
  • To integrate deep learning (StarDist) with existing software (TrackMate).
  • To provide a no-code, open-source solution for quantitative cell migration analysis.

Main Methods:

  • Utilized the deep learning network StarDist for cell segmentation.
  • Combined StarDist with TrackMate software for cell tracking.
  • Developed a pipeline compatible with fluorescent and widefield microscopy images.
  • Ensured all software used is freely available and open-source (ZeroCostDL4Mic, Fiji).

Main Results:

  • Successfully demonstrated automated 2D cell tracking for cancer cells and T cells.
  • Achieved quantitative readouts of cell migration dynamics.
  • Pipeline is compatible with both fluorescent and brightfield imaging.
  • Protocol requires no coding knowledge, enhancing accessibility.

Conclusions:

  • The proposed pipeline offers a versatile and powerful tool for automated cell tracking.
  • This no-code, open-source solution simplifies quantitative analysis of cell migration.
  • Facilitates large-scale analysis and robust experimental design in cell biology research.