A novel phenotypic screening platform based on immunofluorescent imaging of histone modifications enables accurate identification of cell fates and environmental perturbations.

Easy-to-use image analysis software enables single cell quantitation of cell types and division rates in complex 3D tissues including living Drosophila brains, mouse embryos and Zebrafish organoids.

Image3C is a new image-based analysis of cell population composition for research organisms in which detailed cellular phenotypes are unknown or for which species-specific reagents are not available.

The application of deep learning fills a major gap in host–pathogen research providing for unbiased, multi-module high-throughput image analysis.

Advances in robotics-assisted plant handling and automated image analysis enables the characterization of root growth dynamics for Arabidopsis accessions at late stages of development and the association of these traits with genetic variation and climatic variables.

A computational strategy for extracting representative numerical features from 3D microscopy data enables in-depth quantitative analysis of cell and tissue organization through machine learning-driven data integration and context-guided visualization.

The presented primary epithelial acinus-based platform for studying interactions between normal and malignant cells will enable in depth analysis of the tumor initiation process on a molecular level.

Arc-shaped PFO oligomers consisting of at least four subunits can insert into the membrane in a kinetically controlled process opening a transmembrane pore, whereby post-insertion growth may ultimately lead to formation of a complete ring.

A comparison of different bioimage analysis pipelines reveals how deep learning can be used for automatized and reliable analysis of fluorescent features in biological datasets.

During embryonic development, tissue stiffness, which provides an important signal to motile cells, changes locally within tens of minutes in a well-controlled manner.