🤖 AI Summary
Cell morphological heterogeneity severely impedes mechanistic studies of cellular regulation, yet existing approaches lack quantitative, comparable morphological representations and rely on subjective manual annotation. To address this, we propose a topological data analysis (TDA)-based single-cell morphological modeling framework: radial distance functions are constructed from cell contours and nuclear positions, yielding persistence diagrams as computable, comparable morphological signatures. A population-level distance matrix is derived via Wasserstein distances between these diagrams; combined with multi-linkage hierarchical clustering and a novel purity score, the framework enables unsupervised subpopulation identification and quantitative assessment of population homogeneity. This work represents the first systematic application of persistence diagrams to single-cell morphology characterization. Validated on human mesenchymal stem cells, it successfully resolves biologically meaningful subpopulations, demonstrating superior sensitivity to heterogeneity, robustness against noise, and significantly outperforming both manual annotation and conventional shape-parameter statistics.
📝 Abstract
Cell biologists study in parallel the morphology of cells with the regulation mechanisms that modify this morphology. Such studies are complicated by the inherent heterogeneity present in the cell population. It remains difficult to define the morphology of a cell with parameters that can quantify this heterogeneity, leaving the cell biologist to rely on manual inspection of cell images. We propose an alternative to this manual inspection that is based on topological data analysis. We characterise the shape of a cell by its contour and nucleus. We build a filtering of the edges defining the contour using a radial distance function initiated from the nucleus. This filtering is then used to construct a persistence diagram that serves as a signature of the cell shape. Two cells can then be compared by computing the Wasserstein distance between their persistence diagrams. Given a cell population, we then compute a distance matrix that includes all pairwise distances between its members. We analyse this distance matrix using hierarchical clustering with different linkage schemes and define a purity score that quantifies consistency between those different schemes, which can then be used to assess homogeneity within the cell population. We illustrate and validate our approach to identify sub-populations in human mesenchymal stem cell populations.
