Cellular Harmonics for the Morphology-invariant Analysis of Molecular Organization at the Cell Surface
Mazloom-Farsibaf, H.; Zou, Q.; Hsieh, R.; Danuser, G.; Driscoll, M.
Show abstract
The spatiotemporal organization of membrane-associated molecules is central to the regulation of the vast signaling network that control cellular functions. Powerful new microscopy techniques enable the 3D visualization of the localization and activation of these molecules. However, quantitatively interpreting and comparing the spatial organization of molecules on the 3D cell surface remains challenging because cells themselves vary greatly in their 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 our framework by analyzing both synthetic polka dot patterns painted onto observed cell morphologies, as well as measured distributions of cytoskeletal and signaling molecules. To demonstrate the frameworks versatility, we further compared the spatial organization of cell surface signaling both within and between cell populations and powered an upstream machine-learning based analysis of signaling motifs. U-signal3D is open source and is available at https://github.com/DanuserLab/u-signal3D.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Emergence of synchronized multicellular mechanosensing from spatiotemporal integration of heterogeneous single-cell information transfer 95%
- Interpretable deep learning of label-free live cell images uncovers functional hallmarks of highly-metastatic melanoma 94%
- A synthetic gene circuit for imaging-free detection of dynamic cell signaling 94%
Similar papers in this journal
- Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning 95%
- Density-Preserving Data Visualization Unveils Dynamic Patterns of Single-Cell Transcriptomic Variability 95%
- Three-dimensional structured illumination microscopy with enhanced axial resolution 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.