Abstract
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.
Highlights
In drug discovery studies, cell-based assays with cell cultures have been widely adopted
Spheroid models can bridge the gap between conventional 2D in vitro testing and animal models, it is still a challenge to use these 3D cell cultures in a high-throughput system
We have shown that building a microscopy system that combines AI and robotic techniques could improve efficiency in laboratory work with spheroids
Summary
Cell-based assays with cell cultures have been widely adopted. Methods used for detection are threshold-based methods, which can lead to inappropriate feature calculation, e.g. regarding the size of the cell culture. These methods usually utilize machine learning only for cell classification, but not for segmentation. We show that our models can detect and segment spheroids with high reliability Using such accurate segmentation, the algorithm is able to extract features robustly and—based on the user’s criteria—decide which spheroids to manipulate. The algorithm is able to extract features robustly and—based on the user’s criteria—decide which spheroids to manipulate This system was developed to automate one of the most time-consuming processes during sample preparation, the preselection phase, where accuracy is crucial
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