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Abstract The morphology and morphodynamics of cells as important biomarkers of the cellular state are widely appreciated in both fundamental research and clinical applications. Quantification of cell morphology often requires a large number of geometric measures that form a high-dimensional feature vector. This mathematical representation creates barriers to communicating, interpreting, and visualizing data. Here, we develop a deep learning-based algorithm to project 13-dimensional (13D) morphological feature vectors into 2-dimensional (2D) morphological latent space (MLS). We show that the projection has less than 5% information loss and separates the different migration phenotypes of metastatic breast cancer cells. Using the projection, we demonstrate the phenotype-dependent motility of breast cancer cells in the 3D extracellular matrix, and the continuous cell state change upon drug treatment. We also find that dynamics in the 2D MLS quantitatively agrees with the morphodynamics of cells in the 13D feature space, preserving the diffusive power and the Lyapunov exponent of cell shape fluctuations even though the dimensional reduction projection is highly nonlinear. Our results suggest that MLS is a powerful tool to represent and understand the cell morphology and morphodynamics.
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This content will become publicly available on March 17, 2026
Generative AI extracts ecological meaning from the complex three dimensional shapes of bird bills
Data on the three dimensional shape of organismal morphology is becoming increasingly available, and forms part of a new revolution in high-throughput phenomics that promises to help understand ecological and evolutionary processes that influence phenotypes at unprecedented scales. However, in order to meet the potential of this revolution we need new data analysis tools to deal with the complexity and heterogeneity of large-scale phenotypic data such as 3D shapes. In this study we explore the potential of generative Artificial Intelligence to help organize and extract meaning from complex 3D data. Specifically, we train a deep representational learning method known as DeepSDF on a dataset of 3D scans of the bills of 2,020 bird species. The model is designed to learn a continuous vector representation of 3D shapes, along with a ’decoder’ function, that allows the transformation from this vector space to the original 3D morphological space. We find that approach successfully learns coherent representations: particular directions in latent space are associated with discernible morphological meaning (such as elongation, flattening, etc.). More importantly, learned latent vectors have ecological meaning as shown by their ability to predict the trophic niche of the bird each bill belongs to with a high degree of accuracy. Unlike existing 3D morphometric techniques, this method has very little requirements for human supervised tasks such as landmark placement, increasing it accessibility to labs with fewer labour resources. It has fewer strong assumptions than alternative dimension reduction techniques such as PCA. Once trained, 3D morphology predictions can be made from latent vectors very computationally cheaply. The trained model has been made publicly available and can be used by the community, including for finetuning on new data, representing an early step toward developing shared, reusable AI models for analyzing organismal morphology.
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- Award ID(s):
- 2329701
- PAR ID:
- 10583024
- Editor(s):
- D'Andrea, Rafael
- Publisher / Repository:
- Public Library of Science
- Date Published:
- Journal Name:
- PLOS Computational Biology
- Volume:
- 21
- Issue:
- 3
- ISSN:
- 1553-7358
- Page Range / eLocation ID:
- e1012887
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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