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OrganoidTracker: Efficient cell tracking using machine learning and manual error correction.

Rutger N U Kok¹, Laetitia Hebert², Guizela Huelsz-Prince¹

¹AMOLF, Amsterdam, The Netherlands.

|October 22, 2020

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This summary is machine-generated.

Automated cell tracking in organoids is challenging due to high cell density. This study introduces a semi-automated tracker using machine learning and a min-cost flow solver, significantly speeding up analysis while maintaining data quality.

Area of Science:

* Developmental Biology
* Cell Biology
* Bioengineering

Background:

* Time-lapse microscopy is crucial for studying cell division and differentiation in organoids.
* Manual cell tracking in dense, dynamic organoid cultures is time-consuming and impractical.
* Automated solutions are needed to enable single-cell level analysis of organoid growth and homeostasis.

Purpose of the Study:

* To develop a semi-automated cell tracking tool for organoids.
* To overcome challenges posed by high cell density and rapid cell movement.
* To provide a faster alternative to manual cell tracking without compromising data quality.

Main Methods:

* Nuclei detection using a machine learning approach based on convolutional neural networks.

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* Cell trajectory reconstruction via a min-cost flow solver linking detections across time points.

* Implementation of a warning system to flag potential tracking errors for manual review.

Main Results:

* The developed semi-automated tracker significantly accelerates cell tracking in organoids.
* A warning system identifies potential errors like rapid volume/position changes and nucleus division/appearance/disappearance.
* With optimized warnings, over 98% of detected nuclei positions require no manual analysis, yielding high-quality lineage trees.

Conclusions:

* The semi-automated cell tracker offers a substantial speed improvement over manual methods.
* The tool provides high-quality, single-cell level tracking data for organoid research.
* This approach facilitates more efficient study of organoid growth, development, and homeostasis.