Mapping and exploring the organoid state space using synthetic biology

The functional relevance of an organoid is dependent on the differentiation, morphology, cell arrangement and biophysical properties, which collectively define the state of an organoid. For an organoid culture, an individual organoid or the cells that compose it, these state variables can be characterised, most easily by transcriptomics and by high-content image analysis. Their states can be compared to their in vivo counterparts. Current evidence suggests that organoids explore a wider state space than organs in vivo due to the lack of niche signalling and the variability of boundary conditions in vitro. Using data-driven state inference and in silico modelling, phase di-agrams can be constructed to systematically sort organoids along biochemical or biophysical axes. These phase diagrams allow us to identify control strategies to modulate organoid state. To do so, the biochemical and biophysical environment, as well as the cells that seed organoids, can be manipulated.

Automated summary

The functional relevance of an organoid depends on its differentiation, morphology, cell arrangement, and biophysical properties, which collectively define its state. Transcriptomics and high-content image analysis are the most effective methods for characterizing the state variables of an organoid or the cells that compose it, and comparing them to their in vivo counterparts. Organoids explore a wider state space than in vivo organs due to the lack of niche signaling and the variability of boundary conditions in vitro. By using data-driven state inference and in silico modeling, phase diagrams can be constructed to systematically sort organoids based on biochemical or biophysical axes, providing strategies for modulating their state by manipulating the biochemical and biophysical environment and the cells used for seeding.