Feedback-enhanced organoid maturation towards higher reproducibility for in-vitro drug testing
Co-PI: Assoc.Prof. Ioanna Miliou, Stockholm University | we are #hiring (postdoc)
Over the past decade, organoids have emerged as a potential breakthrough biological system to overcome major existing challenges in drug development – but their high inter- and intra-batch variability currently limits their practical application.
Organoids are miniaturized, self-assembled and self-organized cellular constructs. They can recapitulate key morphology, cellular composition, and biological function of human organs, improving greatly upon the simplistic mono-cellular models in use for early drug development. At the same time, organoids’ human origin avoids the species mismatch inherent to animal testing, which currently contributes significantly to poor translatability from drug candidates to human clinical trials. Last but not least, being derived from individual human donors’ cell samples, organoids can be used to model both fully personalized response as well as true population-level sampling.
Organoids are, however, sensitive to even small variations in their culture conditions over the often weeks-long course of their maturation, resulting in high variability of morphology, cell composition, and function. Mitigation approaches to date have focused on providing more homogenous conditions via micro-structured culture chambers (typically increasing the manual handling requirements), or via larger scale stirred bioreactors (which homogenize chemical exposure but add variation in physical forces).
We propose an entirely different approach, based on feedback-driven control of the chemical environment at the level of each individual organoid. We specifically hypothesize that, by independently adjusting growth media turnover and glucose levels – in response to that specific organoids’ current metabolic rate – we can balance out inherent variability in growth rates and thus achieve more homogenous endpoint outcomes.
To that end, we will initially work to adapt an open-source pipetting robot to achieve the requisite accuracy, micro-volume handling, and solution multiplexing for controlled organoid culture – with the critical inclusion also of metabolic sensing capabilities (Objective 1). We will further develop a machine learning-enhanced robot control algorithm that employs real-time sensor signals for feedback-guided media mixing and exchange (Objective 2). Finally, we will evaluate the impact of our strategy on brain organoid culture & maturation homogeneity (Objective 3). The ability to generate highly homogenous organoid populations can significantly advance early drug development by replacing both overly simplistic cell models as well as ethically and functionally suspect animal models with something more meaningful. We moreover expect that our approach for digital environmental control, applicable to any kind of medium/high-throughput cell culture (i.e., compatible with up to standard 384-well plates), can contribute significantly to addressing the ongoing biological reproducibility crisis.