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Polarity of the Cytoskeleton01:18

Polarity of the Cytoskeleton

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The intrinsic polarity of cells can be primarily attributed to two factors- i) the asymmetric accumulation of mobile components such are regulatory molecules and subcellular components across the cell and ii) the orientation of polar cytoskeletal filaments that make up the cytoskeletal networks, specifically microfilaments, and microtubules arranged along the axis of polarity. Interactions between the cytoskeletal filaments are crucial for the establishment and maintenance of the polar nature...
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Related Experiment Video

Updated: May 24, 2025

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells
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A CNN-GNN Approach for Polarity Vectors Prediction in 3D Microscopy Images.

Diogo Moura, Hemaxi Narotamo, Margarida Silveira

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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    This study introduces a novel deep learning method using CNN and GNN for accurate nucleus-Golgi polarity vector detection in mouse retinas, improving upon traditional methods for cellular mechanism studies.

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    Area of Science:

    • Cell Biology
    • Bioimaging
    • Computational Biology

    Background:

    • Nucleus-Golgi polarity is vital for cell division, migration, signaling, and angiogenesis.
    • Traditional methods for predicting nucleus-Golgi polarity vectors are limited.
    • Advanced deep learning approaches can explore complex nucleus-Golgi interactions.

    Purpose of the Study:

    • To develop a novel deep learning approach for nucleus-Golgi polarity vector prediction.
    • To improve the accuracy and automation of polarity vector detection in 3D microscopy images.
    • To facilitate the study of cellular processes like angiogenesis.

    Main Methods:

    • A hybrid model combining Convolutional Neural Network (CNN) and Graph Neural Network (GNN).
    • CNN for detecting nuclei and Golgi centroids in 3D mouse retina images.
    • GNN for predicting nucleus-Golgi links and polarity vectors.

    Main Results:

    • The proposed CNN-GNN model achieved 66% detection of polarity vectors.
    • Significantly outperformed traditional bipartite matching algorithms.
    • Demonstrated the potential for automated and accurate polarity vector detection.

    Conclusions:

    • The novel CNN-GNN approach enables accurate nucleus-Golgi polarity vector prediction.
    • This method advances the understanding of cellular mechanisms, particularly angiogenesis.
    • Automated detection reduces manual annotation burden in biological research.