Decoding cell state through morphology: computational analyses of organelle and cellular architecture

Cell morphology embodies molecular information that reflects the functional state of a cell. The geometry of the nucleus, the organization of organelles, and the spatial architecture of cytoskeletal and membrane systems all change dynamically in response to physiological, developmental, and pathological signals. Recent large scale imaging efforts, such as systematic tagging and profiling of organelle structures in mammalian cells, demonstrate that quantitative morphological features can serve as rich readouts of molecular and functional states. These efforts have reshaped the field of cell biology by expanding morphology from descriptive observation to quantitative, data driven measurement. However, analyzing such high dimensional image data requires integrated computational frameworks capable of linking spatial structure to molecular function and ensuring reproducibility across studies.This Research Topic aims to advance quantitative methods that transform cellular and organelle morphology into interpretable, molecularly relevant data. The goal is to foster the development of imaging and computational approaches that integrate segmentation, modeling, and statistical inference to correlate morphological signatures with biochemical and genetic variation. By emphasizing how shape encodes molecular state, this collection will bridge cell biology, bioimaging, and data science, supporting a shift toward reproducible, morphology aware metrics of cell function and pathology.To gather further insights into quantitative morphology and spatial cell biology, we welcome articles addressing, but not limited to, the following themes:• Quantitative morphological profiling of organelles and subcellular structures from fluorescence or imaging based proteomics• Computational methods linking cell and organelle shape to molecular phenotypes and functional states• Image analysis frameworks for large scale, high content single cell datasets, including quality control and benchmarking• Machine learning and deep learning for morphology aware segmentation, phenotyping, and classification• Statistical and modeling approaches to describe cell to cell variation in intracellular organization• Reproducible, open source tools for quantitative morphological analysis• Applications in stem cell biology, organelle organization, disease modeling, and tissue architectureWe welcome Original Research, Methods, Review, and Technology and Code articles. Topic editors: Matheus Palhares Viana, Felix Yuran Zhou Source: https://www.frontiersin.org/research-topics/82742/decoding-cell-state-through-morphology-computational-analyses-of-organelle-and-cellular-architecture