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.

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.

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

100,000s of images from different growth conditions and genetic backgrounds can be integrated into proteome-scale analysis.

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

Junction Mapper is a powerful new semi-automated software that provides a fingerprint of cell–cell contact morphometry and receptor density alterations.

A conceptually simple algorithm can analyze previously uncharacterized 2D coiled postures of the nematode C. elegans and has uncovered new reorientation behaviors in large amplitude turns.

Building on previous work (Bai et al., 2013), we describe an algorithm that allows cryo-EM structure determination to near-atomic resolution for protein complexes as small as 170 kDa.

Applying big-data analytic techniques to brain images from 18,707 individuals is shedding light on the influence of aging on the brain.

Genetic interaction analysis by combinatorial genetic perturbation and high-throughput imaging maps time- and context-dependent crosstalk between signaling pathways.