C-S.B.39: MorphoMapper: 3D Segmentation-Driven Feature Mapping of Stress-Perturbed Mitochondrial Remodeling
Authors
Sadia S. Tamanna
Molecular botany, RPTU Kaiserslautern-Landau
Simon Foellinger
Computational Systems Biology, RPTU Kaiserslautern-Landau
Sophie Pompejus
Molecular botany, RPTU Kaiserslautern-Landau
Stefanie Mueller-Schuessele
Molecular botany, RPTU Kaiserslautern-Landau
David Zimmer
Computational Systems Biology, RPTU Kaiserslautern-Landau
Timo Muehlhaus
Computational Systems Biology, RPTU Kaiserslautern-Landau
Keywords
3D image segmentation, morphology analysis, stress response
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Organelle morphology provides a sensitive and integrative readout of cellular state, reflecting changes in metabolism, signaling, and environmental perturbations such as stress. While three-dimensional (3D) fluorescence microscopy enables detailed observation of organelle structure, translating volumetric image data into biologically meaningful conclusions remains challenging. Existing approaches often address only individual analysis steps and rely on summary statistics of morphometric descriptors, limiting their ability to capture population-level heterogeneity and perturbation-dependent remodeling.
Here, we present Morphomapper, a FAIR end-to-end workflow for quantitative analysis of organelle morphology from 3D confocal fluorescence imaging. Morphomapper integrates automated deep learning-based volumetric segmentation, extraction of geometric features, dimensionality reduction, and statistical inference into a reproducible and FAIR-compliant pipeline. We benchmark automated 3D segmentation against expert human annotations, demonstrating robust reconstruction of complex organelle shapes. Using the resulting segmentations, we systematically compare two-dimensional and three-dimensional morphometric descriptors and show that 3D features substantially improve the discrimination of stress perturbations. Importantly, Morphomapper treats organelles as populations rather than isolated objects. We introduce an optimal transport-based framework to compare distributions of organelle morphologies across perturbations, enabling principled quantification of stress-induced shifts in population structure. By operating on full morphological distributions instead of mean descriptors alone, Morphomapper captures heterogeneity as a primary signal and links structural remodeling to environmental perturbations. This framework bridges the gap between volumetric segmentation and biologically actionable inference, providing a scalable approach for identifying morphology-based biomarkers of stress perturbation states from 3D imaging data.
Co-authors: Sadia S. Tamanna, Simon Foellinger, Sophie Pompejus, Stefanie Mueller-Schuessele, David Zimmer, Timo Muehlhaus
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