ArticleNature computational science2023
Cellular harmonics for the morphology-invariant analysis of molecular organization at the cell surface.
Article in Nature computational science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Data modeling the interplay between single-cell shape, single-cell protein expression, and tissue state.Cell reports methods · 2026Article
- Universal consensus 3D segmentation of cells from 2D segmented stacks.Nature methods · 2025Article
- CellMet: Extracting 3D shape and topology metrics from confluent cells within tissues.PLoS computational biology · 2025Article
- Caveolin-1 regulates context-dependent signaling and survival in Ewing sarcoma.bioRxiv : the preprint server for biology · 2025Article
- Spherical harmonics texture extraction for versatile analysis of biological objects.PLoS computational biology · 2025Article
- Cellular harmonics for the morphology-invariant analysis of molecular organization at the cell surface.Nature computational science · 2023Article
- Surface-guided computing to analyze subcellular morphology and membrane-associated signals in 3D.bioRxiv : the preprint server for biology · 2023Article
- Surface-guided computing to analyze subcellular morphology and membrane-associated signals in 3D.ArXiv · 2023Article
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Authors and funding
5 authors.
Funding
Abstract
The spatiotemporal organization of membrane-associated molecules is central to the regulation of cellular signals. Powerful new microscopy techniques enable the three-dimensional visualization of localization and activation of these molecules; however, the quantitative interpretation and comparison of molecular organization on the three-dimensional cell surface remains challenging because cells themselves vary greatly in morphology. Here we introduce u-signal3D, a framework to assess the spatial scales of molecular organization at the cell surface in a cell-morphology-invariant manner. We validated the framework by analyzing synthetic signaling patterns painted onto observed cell morphologies, as well as measured distributions of cytoskeletal and signaling molecules. To demonstrate the framework's versatility, we further compared the spatial organization of cell surface signals both within, and between, cell populations, and powered an upstream machine-learning-based analysis of signaling motifs.
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Registered trials
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