ArticleNature protocols2021
A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei.
Article in Nature protocols, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers.
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Who cites it
73 citing papers in PubMed.
- Impacts of Morphology and Elasticity on Cancer Cell Deformation in Shear Flows.Annals of biomedical engineering · 2026Article
- Emerging contaminants follow class-specific mechanistic regimes in sediment-water partitioning.Environmental science and ecotechnology · 2026Article
- KSTITCH links cellular morphology and gene expression in spatial transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- Behavioral and Functional Profiling ofbioRxiv : the preprint server for biology · 2026Article
- CODAvision: best practices and a user-friendly interface for rapid, customizable segmentation of medical images.Nature protocols · 2026Review
- Single-cell morphodynamics predict cell fate decisions during mucociliary epithelial differentiation.Molecular systems biology · 2026Article
- Target identification and assessment in the era of AI.Nature reviews. Drug discovery · 2026Review
- Cell shapes decode molecular phenotypes in image-based spatial proteomics.Cell systems · 2026Article
- Review of Progress of AI in Biomimetics: From Biological Patterns to Closed-Loop Discovery.Biomimetics (Basel, Switzerland) · 2026Review
- Cell-nanoplastics association impacts cell proliferation and motility.bioRxiv : the preprint server for biology · 2026Article
- A synthetic cell with integrated DNA self-replication and lipid biosynthesis.Nature communications · 2026Article
- Integration and harmonization of cell shape images for generative modeling.bioRxiv : the preprint server for biology · 2026Article
- Self-supervised pretraining with NuSPIRe unlocks nuclear morphology-driven insights in spatial omics.Genome biology · 2026Article
- Leveraging AI for cell biology discovery.Biochemical Society transactions · 2026Review
- Quantifying the Single-Cell Morphological Landscape of Cellular Transdifferentiation through Force Field Reconstruction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Label-Free Detection of Nuclear Envelope Nucleoporation using 2D Morphological Embeddings and Machine Learning.Small (Weinheim an der Bergstrasse, Germany) · 2025Article
- Single-cell morphodynamics predict cell fate decisions during mucociliary epithelial differentiation.bioRxiv : the preprint server for biology · 2025Article
- Deep Disentangled Representation Learning Reveals Neuron Subtype-Specific Nuclear Morphologies Across Aging in Mice and Humans.bioRxiv : the preprint server for biology · 2025Article
- Curved Magnetic Hydrogels for Understanding Cancer Initiation.ACS applied materials & interfaces · 2025Article
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13 more citing papers are in PubMed but not listed here.
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5 authors.
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Abstract
Cell morphology encodes essential information on many underlying biological processes. It is commonly used by clinicians and researchers in the study, diagnosis, prognosis, and treatment of human diseases. Quantification of cell morphology has seen tremendous advances in recent years. However, effectively defining morphological shapes and evaluating the extent of morphological heterogeneity within cell populations remain challenging. Here we present a protocol and software for the analysis of cell and nuclear morphology from fluorescence or bright-field images using the VAMPIRE algorithm ( https://github.com/kukionfr/VAMPIRE_open ). This algorithm enables the profiling and classification of cells into shape modes based on equidistant points along cell and nuclear contours. Examining the distributions of cell morphologies across automatically identified shape modes provides an effective visualization scheme that relates cell shapes to cellular subtypes based on endogenous and exogenous cellular conditions. In addition, these shape mode distributions offer a direct and quantitative way to measure the extent of morphological heterogeneity within cell populations. This protocol is highly automated and fast, with the ability to quantify the morphologies from 2D projections of cells seeded both on 2D substrates or embedded within 3D microenvironments, such as hydrogels and tissues. The complete analysis pipeline can be completed within 60 minutes for a dataset of ~20,000 cells/2,400 images.
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