This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Mitochondria—tiny structures that convert nutrients into energy—are often depicted as discrete kidney bean-shaped objects. But in reality, they form a dynamic, interconnected network throughout the entire cell, rapidly splitting and fusing as they're transported to where energy is needed most.
Because mitochondrial networks change shape based on cellular health, they can be used as markers of disease or to test new treatments. However, understanding how these changes affect cell function has been difficult because mitochondria have mostly been imaged as flat, still snapshots. Now, researchers at the University of California San Diego have employed two different approaches to quantify these morphological changes by creating "virtual cells"—digital models that mimic the dynamic biological processes of real cells.
Both approaches use 4D lattice light-sheet microscopy, an advanced technique that captures how mitochondria and other structures move in three dimensions over time. One approach trained a deep-learning artificial intelligence (AI) model on 40,000 4D movies of drug-treated cells to predict cellular health from mitochondrial shape alone. The other built a "digital twin" of a living cell from a 4D movie by defining a set of rules about how its organelles (tiny internal structures) behave and implementing those rules in a physics-based model.
The researchers found that the way virtual mitochondrial networks responded to drugs closely matched real cells. Together, these studies, both published in Cell, could reduce dependence on time-consuming lab experiments and accelerate drug discovery for a variety of diseases, including cancer, diabetes, Alzheimer's and pediatric mitochondrial disorders. The researchers treated cancer cells with 25 different compounds known to perturb mitochondria through different mechanisms, producing 40,000 single-cell 4D movies.
They used this library to train a deep-learning model called MitoSpace. Unlike most AI models that require humans to manually label images, MitoSpace found patterns on its own, learning what makes the mitochondria of one cell distinct from those of another. The results were striking: Without knowing which drug was used on each cell, the model produced an organized map that grouped cells that respond in similar ways.
Furthermore, the model was able to predict the energetic state of the cell based solely on the shape and movement of its mitochondria across 26 drug conditions. "For a century we have believed that mitochondrial form reflects function; this shows the relationship is strong enough that a model can learn it without ever being shown the answer," said corresponding author Johannes Schöneberg, Ph.D., Roger Tsien Chancellor's Faculty Fellow and associate professor in the Department of Pharmacology at UC San Diego School of Medicine and in the Department of Biochemistry and Molecular Biophysics. When trained on the 4D movies, the model distinguished between drugs and grouped them by mechanism with 75% accuracy, compared with 56% accuracy when trained on the flat 2D images common in large-scale drug screens today.
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