Cell fate decisions emerge as a consequence of a complex set of gene regulatory networks. Models of these networks are known to have more parameters than data can determine. Recent work, inspired by Waddington's metaphor of a landscape, has instead tried to understand the geometry of gene regulatory networks. Here, we describe recent results on the appropriate mathematical framework for constructing these landscapes. This allows the construction of minimally parameterized models consistent with cell behavior.

Embryogenesis is guided by a limited set of signaling pathways dynamically expressed in different places. How a context-dependent signaling response is generated has been a central question of developmental biology, which can now be addressed with in vitro models of human embryos that are derived from embryonic stem cells (hESCs). Our previous work demonstrated that during early stages of hESC differentiation, cells chronicle signaling hierarchy.

Organoids are carrying the promise of modeling complex disease phenotypes and serving as a powerful basis for unbiased drug screens, potentially offering a more efficient drug-discovery route. However, unsolved technical bottlenecks of reproducibility and scalability have prevented the use of current organoids for high-throughput screening. Here, we present a method that overcomes these limitations by using deep-learning-driven analysis for phenotypic drug screens based on highly standardized micropattern-based neural organoids.

Our knowledge of the molecular mechanisms surrounding human embryo implantation and gastrulation is lacking, largely due to technical and ethical limitations of experimenting with human embryos. Alternatives to human embryos have been reported, in which 3D clusters of embryonic stem cells are differentiated in a stepwise manner to model aspects of human embryogenesis. Yet it remains challenging to model the events past attachment.

Embryonic cells grow in environments that provide a plethora of physical cues, including mechanical forces that shape the development of the entire embryo. Despite their prevalence, the role of these forces in embryonic development and their integration with chemical signals have been mostly neglected, and scrutiny in modern molecular embryology tilted, instead, towards the dissection of molecular pathways involved in cell fate determination and patterning.

Fate decisions in developing tissues involve cells transitioning between discrete cell states, each defined by distinct gene expression profiles. The Waddington landscape, in which the development of a cell is viewed as a ball rolling through a valley filled terrain, is an appealing way to describe differentiation. To construct and validate accurate landscapes, quantitative methods based on experimental data are necessary.

Understanding human organ formation is a scientific challenge with far-reaching medical implications1,2. Three-dimensional stem-cell cultures have provided insights into human cell differentiation3,4. However, current approaches use scaffold-free stem-cell aggregates, which develop non-reproducible tissue shapes and variable cell-fate patterns. This limits their capacity to recapitulate organ formation. Here we present a chip-based culture system that enables self-organization of micropatterned stem cells into precise three-dimensional cell-fate patterns and organ shapes.

Embryonic development leads to the reproducible and ordered appearance of complexity from egg to adult. The successive differentiation of different cell types that elaborate this complexity results from the activity of gene networks and was likened by Waddington to a flow through a landscape in which valleys represent alternative fates. Geometric methods allow the formal representation of such landscapes and codify the types of behaviors that result from systems of differential equations.

Using self-organizing human models of gastrulation, we previously showed that (1) BMP4 initiates the cascade of events leading to gastrulation, (2) BMP4 signal reception is restricted to the basolateral domain, and (3) in a human-specific manner, BMP4 directly induces the expression of NOGGIN. Here, we report the surprising discovery that in human epiblasts, NOGGIN and BMP4 were secreted into opposite extracellular spaces. Interestingly, apically presented NOGGIN could inhibit basally delivered BMP4.

Synthetic embryology endeavors to use stem cells to recapitulate the first steps of mammalian development that define the body axes and first stages of fate assignment. Well-engineered synthetic systems provide an unparalleled assay to disentangle and quantify the contributions of individual tissues as well as the molecular components driving embryogenesis. Experiments using a mixture of mouse embryonic and extraembryonic stem cell lines show a surprising degree of self-organization akin to certain milestones in the development of intact mouse embryos.