Multicell-Fold: geometric learning in folding multicellular life
(2407.07055)Abstract
During developmental processes such as embryogenesis, how a group of cells fold into specific structures, is a central question in biology that defines how living organisms form. Establishing tissue-level morphology critically relies on how every single cell decides to position itself relative to its neighboring cells. Despite its importance, it remains a major challenge to understand and predict the behavior of every cell within the living tissue over time during such intricate processes. To tackle this question, we propose a geometric deep learning model that can predict multicellular folding and embryogenesis, accurately capturing the highly convoluted spatial interactions among cells. We demonstrate that multicellular data can be represented with both granular and foam-like physical pictures through a unified graph data structure, considering both cellular interactions and cell junction networks. We successfully use our model to achieve two important tasks, interpretable 4-D morphological sequence alignment, and predicting local cell rearrangements before they occur at single-cell resolution. Furthermore, using an activation map and ablation studies, we demonstrate that cell geometries and cell junction networks together regulate local cell rearrangement which is critical for embryo morphogenesis. This approach provides a novel paradigm to study morphogenesis, highlighting a unified data structure and harnessing the power of geometric deep learning to accurately model the mechanisms and behaviors of cells during development. It offers a pathway toward creating a unified dynamic morphological atlas for a variety of developmental processes such as embryogenesis.
Overview
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The Multicell-Fold framework employs a geometric deep learning strategy to understand and predict multicellular folding processes, integrating granular and foam-like structures within a dual-graph representation.
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It features sophisticated data representations like nodes for cells, edges for cell-cell interactions, and tissue-level variables, all within a graph neural network to predict multicellular dynamics such as the alignment of 4-D morphological sequences and local cell rearrangements.
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The model has demonstrated high accuracy in aligning developmental stages of Drosophila embryos and predicting local cell rearrangements, with significant implications for large-scale morphological sequencing, drug screening, and synthetic biology.
Multicell-Fold: Geometric Learning in Folding Multicellular Life
This paper introduces a geometric deep learning approach, named Multicell-Fold, aimed at understanding and predicting the intricate processes of multicellular folding, such as those observed in embryogenesis. The authors propose a dual-graph data structure that represents the complex interactions and behaviors of cells, integrating both granular and foam-like physical perspectives. The study primarily addresses two critical tasks: the alignment of 4-D morphological sequences and the prediction of local cell rearrangements with single-cell resolution.
Geometric Deep Learning in Multicellular Folding
The authors begin by outlining the fundamental question in developmental biology regarding how groups of cells fold into specific structures during processes like embryogenesis. Traditional approaches face challenges in predicting cell behaviors over time due to the active and out-of-equilibrium nature of living tissues. Multicell-Fold leverages geometric deep learning to capture the spatial interactions between cells, encoded within a unified graph data structure that mirrors both granular and foam-like models of multicellular systems.
Data Representation and Graph Structure
The dual-graph representation incorporates:
- Nodes: Representing cells and vertices.
- Edges: Capturing cell-cell adjacency, cell edges, and cell-vertex adjacency.
- Tissue-Level Quantities: Embedding variables like developmental time, tissue stress, or sample types.
The combination of these elements within a graph neural network (GNN) framework allows for sophisticated predictions of multicellular dynamics. The study integrates both cell-specific and junction-specific attributes, facilitating a comprehensive understanding of cellular rearrangements and tissue morphogenesis.
Interpretable 4-D Morphological Sequence Alignment
To demonstrate the efficacy of their model, the authors perform an unsupervised alignment of two time-lapsed 3-D Drosophila embryo datasets. By training a model on one embryo and using it to align developmental stages of a second, the approach showcases high accuracy in defining developmental time. The activation maps generated reveal that vital morphological features, such as the ventral furrow, are inherently captured by the model, even without explicit training on these features.
Prediction of Local Cell Rearrangements
Another significant application of Multicell-Fold is in predicting local cell rearrangements before they occur. This capability is exemplified through its application to 3-D Drosophila embryo data during gastrulation. The model achieves over 82% accuracy in predicting the loss of cell-cell junctions within a minute into the future, highlighting its precision in capturing local cellular dynamics. The authors emphasize the importance of cell geometry, edge geometry, and dynamic information in making these predictions, supported by extensive ablation studies.
Implications and Future Directions
The implications of this research are twofold—practical and theoretical. Practically, the proposed model can facilitate large-scale morphological sequencing, enabling high-throughput analysis of developmental processes. In medical and biotechnological contexts, such a model could aid in drug screening and the design of synthetic multicellular structures. Theoretically, it bridges the gap between different physical representations of multicellular systems, offering a unified approach to study morphogenesis.
Furthermore, the model's accuracy in predicting complex behaviors at single-cell resolution indicates the potential for creating detailed dynamic morphological atlases. This achievement could significantly advance our understanding of morphogenesis and related developmental processes, potentially allowing for predictive modeling in other complex biological systems.
Conclusion
Multicell-Fold exemplifies a sophisticated geometric learning framework that addresses long-standing challenges in developmental biology. By unifying granular and foam-like representations within a dual-graph structure and leveraging the capabilities of GNNs, this approach opens pathways for precise, interpretable predictions of multicellular dynamics. Future work may extend this framework to other developmental systems or use it to integrate multi-omic data, further unraveling the complexities of life at the cellular level.
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