ArticleMolecular biology of the cell2022
DynaMorph: self-supervised learning of morphodynamic states of live cells.
Article in Molecular biology of the cell, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Understanding cell fate decisions with temporal attention.PLoS computational biology · 2026Article
- Single-cell morphodynamical trajectories enable prediction of gene expression accompanying cell state change.Cell systems · 2026Article
- Cracking the Code: Computational Image Analysis Tools for Histopathological and Morphometric Insights.Journal of imaging · 2026Review
- AI-Driven Innovations for Quality Control and Standardization: Future Strategies in Adipose-Derived Stem Cell Manufacturing.International journal of molecular sciences · 2026Review
- A self-supervised machine learning pipeline for extracting information from live cell images at multiple doses and timepoints.Scientific reports · 2026Article
- Generalizable morphological profiling of cells by interpretable unsupervised learning.Nature communications · 2025Article
- A deep learning approach for time-consistent cell cycle phase prediction from microscopy data.PLoS computational biology · 2025Article
- Classification of differentially activated groups of fibroblasts using morphodynamic and motile features.APL bioengineering · 2025Article
- Automated prediction of fibroblast phenotypes using mathematical descriptors of cellular features.Nature communications · 2025Article
- Self-Supervised Learning to Unveil Brain Dysfunctional Signatures in Brain Disorders: Methods and Applications.Health data science · 2025Review
- Interpretable Fine-Grained Phenotypes of Subcellular Dynamics via Unsupervised Deep Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- AI analysis of super-resolution microscopy: Biological discovery in the absence of ground truth.The Journal of cell biology · 2024Review
- Perspective on quantitative phase imaging to improve precision cancer medicine.Journal of biomedical optics · 2024Article
- Morphological profiling for drug discovery in the era of deep learning.Briefings in bioinformatics · 2024Review
- Machine learning inference of continuous single-cell state transitions during myoblast differentiation and fusion.Molecular systems biology · 2024Article
- Orientation-invariant autoencoders learn robust representations for shape profiling of cells and organelles.Nature communications · 2024Article
- Particulate matter composition drives differential molecular and morphological responses in lung epithelial cells.PNAS nexus · 2024Article
- Bridging live-cell imaging and next-generation cancer treatment.Nature reviews. Cancer · 2023Review
- Morphodynamical cell state description via live-cell imaging trajectory embedding.Communications biology · 2023Article
- Deep-Manager: a versatile tool for optimal feature selection in live-cell imaging analysis.Communications biology · 2023Article
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9 authors.
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Abstract
A cell's shape and motion represent fundamental aspects of cell identity and can be highly predictive of function and pathology. However, automated analysis of the morphodynamic states remains challenging for most cell types, especially primary human cells where genetic labeling may not be feasible. To enable automated and quantitative analysis of morphodynamic states, we developed DynaMorph-a computational framework that combines quantitative live cell imaging with self-supervised learning. To demonstrate the robustness and utility of this approach, we used DynaMorph to annotate morphodynamic states observed with label-free measurements of optical density and anisotropy of live microglia isolated from human brain tissue. These cells show complex behavior and have varied responses to disease-relevant perturbations. DynaMorph generates quantitative morphodynamic representations that can be used to compare the effects of the perturbations. Using DynaMorph, we identify distinct morphodynamic states of microglia polarization and detect rare transition events between states. The concepts and the methods presented here can facilitate automated discovery of functional states of diverse cellular systems.
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