{'fraction_non_alphanumeric': 0.021018593371059015, 'fraction_numerical': 0.004042037186742118, 'mean_word_length': 5.7650273224043715, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 1, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Cellular Harmonics for the Morphology-invariant Analysis of Molecular Organization at the Cell Surface

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 framework’s 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.