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Article

4D spatiotemporal landscape of mitochondrial phenotypes across cellular states unlocked through representation learning.

Cell - 17 Sept 2026

Agarwal Dhruv, Wang Zichen, Arkfeld Eric, Modolo Andre, Natekar Parth, Hakozaki Hiroyuki, Arora Mehul, McMahon Gillian, Nahar Siddharth, Doshi Manav, Schöneberg Johannes

Abstract excerpt

Mitochondria are four-dimensional (4D: x, y, z, and time) organelles essential for cellular function. Characterizing their 4D phenotypic landscape across diverse cellular states requires both 4D imaging and analytical frameworks. We present MitoSpace, a self-supervised deep learning model trained without labels on terabytes of single-cell lattice light-sheet microscopy data of mitochondria under mechanistically...

Read the complete abstract on PubMed

Topics

Mitochondria
Humans
Phenotype
Representation Machine Learning
Deep Learning
Membrane Potential, Mitochondrial

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Animals

Organoids

Imaging, Three-Dimensional

Microscopy

Source

Identifiers

PMID: 42721963
DOI: 10.1016/j.cell.2026.08.028

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