# ShapeSpaceExplorer: Analysis of morphological transitions in migrating cells using similarity-based shape space mapping

**Authors:** Samuel D. R. Jefferyes, Roswitha Gostner, Laura Cooper, Mohammed M. Abdelsamea, Elly Straube, Nasir Rajpoot, David B. A. Epstein, Anne Straube, Dimitrios Vavylonis, Jian Liu, Dimitrios Vavylonis, Jian Liu, Dimitrios Vavylonis, Jian Liu

PMC · DOI: 10.1371/journal.pcbi.1013864 · PLOS Computational Biology · 2026-01-05

## TL;DR

ShapeSpaceExplorer is a new tool that analyzes how cell shapes change during migration using machine learning and visualizes these changes in a simplified way.

## Contribution

The paper introduces a new, rapid, and landmark-free shape difference measure for unbiased analysis of cell morphologies during migration.

## Key findings

- ShapeSpaceExplorer learns the intrinsic low-dimensional structure of cell shape space from time-lapse images.
- The tool can predict cell turning based on dynamic shape changes and is applicable to various biological processes.
- The method enables unbiased analysis of diverse morphologies in migrating mesenchymal cells.

## Abstract

Here we describe the development of ShapeSpaceExplorer, an interactive software for the extraction, visualisation and analysis of complex 2D shape series. We also demonstrate its application to the analysis of cell morphology changes during cell migration. Cell migration is essential for many physiological and pathological processes. Intracellular force generation and transmission of these forces to extracellular structures or neighbouring cells drives cell migration. The emergent property of the processes driving cell migration is a change in cell shape. We describe a machine learning approach to understand the relationship of cell shape dynamics and cell migration behaviour. Our algorithm analyses cell shape from time-lapse images and learns the intrinsic low-dimensional structure of cell shape space. We use the resultant shape space map to visualise differences in cell shape distribution following perturbation experiments and to analyse the quantitative relationships between shape and migration behaviour. The core of our algorithm is a new, rapid, and landmark-free shape difference measure that allows unbiased analysis of the widely varying morphologies exhibited by migrating mesenchymal cells. We used our method to predict cell turning from dynamic cell shape information. ShapeSpaceExplorer can be applied widely to visualise and analyse cell morphology changes during development, the cell cycle and stress response, but also to the outlines of clusters, tissues and inanimate objects.

## Full-text entities

- **Genes:** KIF1C (kinesin family member 1C) [NCBI Gene 10749] {aka LTXS1, SATX2, SAX2, SPAX2, SPG58}
- **Diseases:** infection (MESH:D007239)
- **Chemicals:** Anita Estes (-)
- **Species:** Homo sapiens (human, species) [taxon 9606], Schizosaccharomyces pombe (fission yeast, species) [taxon 4896]
- **Cell lines:** RPE1 — Homo sapiens (Human), Telomerase immortalized cell line (CVCL_4388), RPE — Homo sapiens (Human), Spontaneously immortalized cell line (CVCL_0145)

## Full text

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## Figures

7 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12795464/full.md

## References

13 references — full list in the complete paper: https://tomesphere.com/paper/PMC12795464/full.md

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Source: https://tomesphere.com/paper/PMC12795464