SpheroidPicker for automated 3D cell culture manipulation using deep learning

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Fig: Schematic representation of the SpheroidPicker. The system includes a stereo microscope, syringe, stage, and manipulator controller. The automatic screening function results in brightfield images of the spheroids. The segmentation and feature extraction steps are based on a deep learning model. After the selection of the spheroids, the spheroid picker automatically transfers the spheroids into the target plate.

Recent statistics report that more than 3.7 million new cases of cancer occur in Europe yearly, and the disease accounts for approximately 20% of all deaths. High-throughput screening of cancer cell cultures has dominated the search for novel, effective anticancer therapies in the past decades. Recently, functional assays with patient-derived ex vivo 3D cell culture have gained importance for drug discovery and precision medicine. We recently evaluated the major advancements and needs for the 3D cell culture screening, and concluded that strictly standardized and robust sample preparation is the most desired development. Here we propose an artificial intelligence-guided low-cost 3D cell culture delivery system. It consists of a light microscope, a micromanipulator, a syringe pump, and a controller computer. The system performs morphology-based feature analysis on spheroids and can select uniform sized or shaped spheroids to transfer them between various sample holders. It can select the samples from standard sample holders, including Petri dishes and microwell plates, and then transfer them to a variety of holders up to 384 well plates. The device performs reliable semi- and fully automated spheroid transfer. This results in highly controlled experimental conditions and eliminates non-trivial side effects of sample variability that is a key aspect towards next-generation precision medicine.

Grexa, I., Diosdi, A., Harmati, M. et al. SpheroidPicker for automated 3D cell culture manipulation using deep learning. Sci Rep 11, 14813 (2021). https://doi.org/10.1038/s41598-021-94217-1

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