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Gastrulation

Gastrulation

Gastrulation establishes the three primary tissues of an embryo: the ectoderm, mesoderm, and endoderm. This developmental process relies on a series of intricate cellular movements, which in humans transforms a flat, “bilaminar disc” composed of two cell sheets into a three-tiered structure. In the resulting embryo, the endoderm serves as the bottom layer, and stacked directly above it is the intermediate mesoderm, and then the uppermost ectoderm. Respectively, these tissue strata...

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Related Experiment Video

Updated: Jul 18, 2025

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

Published on: April 30, 2019

insideOutside: an accessible algorithm for classifying interior and exterior points, with applications in embryology.

Stanley E Strawbridge^1,2, Agata Kurowski³, Elena Corujo-Simon^1,2,4

¹Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK.

|August 25, 2023

Summary

A new method called insideOutside uses spatial data to classify cells in early mouse embryos. This unsupervised machine learning approach accurately distinguishes interior from exterior cells, aiding developmental biology research.

Keywords:

Embryo Inner cell mass Machine learning Pre-implantation Quantitative biology Trophectoderm

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Last Updated: Jul 18, 2025

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Published on: April 30, 2019

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Area of Science:

Developmental Biology
Computational Biology
Machine Learning

Background:

Relating cell position to overall embryo geometry is key in embryology.
The first cell-fate decision in mouse embryos distinguishes interior (inner cell mass) from exterior (trophectoderm) cells.
Current methods rely on protein markers, which are not always available or reliable.

Purpose of the Study:

To develop a simple, robust method for classifying interior and exterior cells using only spatial information.
To address limitations of existing marker-based approaches in early mouse embryology.

Main Methods:

Developed a mathematical framework and unsupervised machine learning approach named insideOutside.
Applied the method to classify interior and exterior points from 3D point-cloud data of early mouse embryos.
Benchmarked insideOutside against existing methods.

Main Results:

The insideOutside method accurately classifies nuclei in pre-implantation mouse embryos.
Demonstrated superior accuracy compared to other methods, especially with complex geometries like surface concavities.
Provided freely available MATLAB and Python implementations.

Conclusions:

The insideOutside method offers a reliable, spatial-based approach for cell classification in embryology.
This tool is valuable for analyzing early mouse embryo development and has broader applications in life sciences.
Facilitates research where traditional markers are insufficient.