The mechanisms underlying the development of biological tissues remain largely unknown. To better understand these mechanisms, analyzing the spatiotemporal changes of cells and molecules through mathematical models constructed from cellular behavior data can be effective. However, there are still challenges concerning the reproducibility of results due to variations in experimental conditions. This variability is particularly pronounced in three-dimensional culture systems, making it difficult to precisely align experimental and simulation conditions. Obtaining high signal-to-noise (SN) ratio data sets is essential for decoding the rule underlying cellular behavior.

We achieved high SN ratio data sets by strictly controlling the initial shape of cell populations in two dimensions, thereby significantly enhancing the reproducibility of cellular behavior. This study aims to establish techniques for controlling and measuring the environment of three-dimensional cultures using engineering methods. As an experimental platform, we employed a simple cube-shaped culture device we developed, and utilized 3D bioprinting, microfluidic chips, and biomaterials technology to establish new techniques to control the three-dimensional initial positions of cell populations, ECM localization, and the concentration gradients of soluble factors within the cube. Furthermore, by continuously rotating the cube under a microscope, we enabled scanning with lasers from three orthogonal planes, making it possible to conduct time-lapse observations while capturing the overall view of millimeter-sized samples.

The data obtained with these technologies can be integrated with information technology in the future, allowing for more efficient and quantitative representation of overall trends, and is expected to facilitate the validation, improvement, and optimization of mathematical models.