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Cell tracking with accurate error prediction.

Max A Betjes1, Rutger N U Kok2, Sander J Tans3,4

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OrganoidTracker 2.0 introduces a novel algorithm for cell tracking, assigning error probabilities to each step. This enables fully automated analysis of cell dynamics in organoids, speeding up research and ensuring transparent reporting.

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Area of Science:

  • Developmental Biology
  • Bioinformatics
  • Computational Biology

Background:

  • Cell tracking is crucial for developmental studies using time-lapse imaging.
  • Current cell trackers lack confidence scores, hindering fully automated analysis and requiring manual curation.

Purpose of the Study:

  • To develop an advanced cell tracking algorithm with quantifiable error probabilities.
  • To enable fully automated, high-confidence cell tracking and analysis in organoid models.

Main Methods:

  • Integration of neural networks and statistical physics to predict cell tracks.
  • Assignment of error probabilities to individual track segments and derived features (e.g., cell cycles, lineage trees).
  • Development of OrganoidTracker 2.0 software.

Main Results:

  • The algorithm provides error probabilities analogous to P values for tracking data.
  • OrganoidTracker 2.0 significantly reduces manual curation by focusing on high-confidence track segments.
  • Demonstrated scalable analysis of cell cycles and differentiation in thousands of intestinal organoid cells.

Conclusions:

  • OrganoidTracker 2.0 facilitates fully automated cell tracking analysis with transparent, quantifiable results.
  • The method supports large-scale organoid screening based on cell dynamics.
  • Enables reliable reporting of cell-tracking data and scientific claims.