Design of a custom-made device for real-time optical measurement of differential mineral concentrations in three-dimensional scaffolds for bone tissue engineering

Driving cell response through deep learning, a study in simulated 3D cell cultures

Computational simulations are becoming increasingly relevant in biomedical research, providing strategies to reproduce experimental results, improve the resolution of in-vitro experiments, and predict the system's behavior in untested conditions. Their use to determine the features associated with an extensive response to treatment and optimize treatment schedules has, however received little attention. To bridge this gap, we propose a deep learning framework capable of reliably classifying simulated time series data and identifying class-defining features. This information will be shown to be useful for the determination of which changes in treatment schedule elicit a more extensive cellular response. This analysis pipeline will be initially tested on a synthetic dataset created ad-hoc to identify its accuracy in identifying the most relevant portion of the signals. Successively this method will be applied to simulations describing the behaviors of populations of cancer cells treated with either one or two drugs in different concentrations. The proposed method will be shown to be effective in identifying which changes in the treatment protocol lead to a more extensive response to treatment. While lacking direct experimental validation, this result holds great potential for the integration of in-silico and in-vitro analyses and the effective optimization of experimental conditions in complex experimental setups.

Non-destructive monitoring of 3D cell cultures: new technologies and applications

3D cell cultures are becoming the new standard for cell-based in vitro research, due to their higher transferrability toward in vivo biology. The lack of established techniques for the non-destructive quantification of relevant variables, however, constitutes a major barrier to the adoption of these technologies, as it increases the resources needed for the experimentation and reduces its accuracy. In this review, we aim at addressing this limitation by providing an overview of different non-destructive approaches for the evaluation of biological features commonly quantified in a number of studies and applications. In this regard, we will cover cell viability, gene expression, population distribution, cell morphology and interactions between the cells and the environment. This analysis is expected to promote the use of the showcased technologies, together with the further development of these and other monitoring methods for 3D cell cultures. Overall, an extensive technology shift is required, in order for monolayer cultures to be superseded, but the potential benefit derived from an increased accuracy of in vitro studies, justifies the effort and the investment.