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Updated: Jun 6, 2026
12:06
Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
Published on: March 3, 2023
Deep learning of functional perturbations from condensate morphology
Anita Donlic1, Troy J Comi2, Sofia A Quinodoz3
1Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544, USA; Omenn-Darling Bioengineering Institute, Princeton University, Princeton, NJ 08544, USA.
Cell
|June 4, 2026
Summary
Deep-Phase, a new AI tool, analyzes cell images to link molecular changes within biomolecular condensates to their structure. This reveals how drugs affect cellular organization and identifies new biological pathways.
Area of Science:
- Cell Biology
- Biophysics
- Artificial Intelligence
Background:
- Biomolecular condensates organize cellular functions but linking molecular interactions to mesoscale organization is challenging.
- Understanding condensate dynamics is crucial for deciphering cellular complexity and disease mechanisms.
Purpose of the Study:
- To develop a computational framework for directly measuring condensate morphology changes.
- To connect molecular perturbations to mesoscale organization in cellular compartments.
Main Methods:
- Developed Deep-Phase, a neural network framework analyzing microscopy images.
- Quantified structural changes in the multiphase nucleolus under pharmacological treatments.
- Applied Deep-Phase in a chemical screen to identify novel morphologies and pathways.
Main Results:
- Deep-Phase precisely quantifies time- and concentration-dependent perturbations in nucleolar structure.
- Nucleolar morphology changes correlate with drug potencies inhibiting ribosomal RNA (rRNA) transcription and processing.
- Identified a unique nucleolar morphology and a role for DNA topoisomerase in rRNA processing.
Conclusions:
- Deep-Phase provides a powerful platform for linking molecular pathways to cellular mesoscale organization.
- The framework is adaptable to various cell lines, labeling techniques, and condensate types.
- Revealed insights into nucleolar sub-compartment interface maintenance and rRNA processing.
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