Deep learning for bioimage analysis in developmental biology.

Deep learning has transformed the way large and complex image datasets can be processed, reshaping what is possible in bioimage analysis. As the complexity and size of bioimage data continues to grow, this new analysis paradigm is becoming increasingly ubiquitous. In this Review, we begin by introducing the concepts needed for beginners to understand deep learning.

Structural Redundancy in Supracellular Actomyosin Networks Enables Robust Tissue Folding.

Tissue morphogenesis is strikingly robust. Yet, how tissues are sculpted under challenging conditions is unknown. Here, we combined network analysis, experimental perturbations, and computational modeling to determine how network connectivity between hundreds of contractile cells on the ventral side of the Drosophila embryo ensures robust tissue folding.

Perspective: The promise of multi-cellular engineered living systems.

Recent technological breakthroughs in our ability to derive and differentiate induced pluripotent stem cells, organoid biology, organ-on-chip assays, and 3-D bioprinting have all contributed to a heightened interest in the design, assembly, and manufacture of living systems with a broad range of potential uses. This white paper summarizes the state of the emerging field of "multi-cellular engineered living systems," which are composed of interacting cell populations. Recent accomplishments are described, focusing on current and potential applications, as well as barriers to future advances, and the outlook for longer term benefits and potential ethical issues that need to be considered.

Quantitative analysis of cell shape and the cytoskeleton in developmental biology.

Computational approaches that enable quantification of microscopy data have revolutionized the field of developmental biology. Due to its inherent complexity, elucidating mechanisms of development requires sophisticated analysis of the structure, shape, and kinetics of cellular processes. This need has prompted the creation of numerous techniques to visualize, quantify, and merge microscopy data.

Controlling Confinement and Topology to Study Collective Cell Behaviors.

Confinement and substrate topology strongly affect the behavior of cell populations and, in particular, their collective migration. In vitro experiments dealing with these aspects require strategies of surface patterning that remain effective over long times (typically several days) and ways to control the surface topology in three dimensions. Here, we describe protocols addressing these two aspects.