Project Leader Fraunhofer Institute for manufacturing engineering and automation iPA Stuttgart, Baden-Wurttemberg, Germany
Abstract: 3D cell based models show promising potential to be a gold standard for many fields of applied life sciences. Due to the inherited complexity of the biological system, individuals may vary dramatically between different production batches and even within the same batch. Screening for specific morphological structures is inevitable to determine the suitability for further culturing or assay preparation. We developed a novel analysis and sorting technology using image recognition to assess specific morphological properties during culture and select them based on the desired profile. We optimized our technology for different biological systems and applications, ranging from fertility determination of Zebrafish eggs to determination of the differentiation state of neural hiPSC-derived cerebral organoids. In summary, we demonstrate a technology development process for quality control and sorting of 3D cell based models, according to their morphology. In the future, we aim to further adapt and test this workflow with other model systems and introduce a machine learning algorithm to fully-automate the process of decision making. Quality 3D sorting feasibility is a major interest for robust and unbiased high-throughput downstream applications such as biomanufacturing, drug testing or toxicology.
