Prediction of the hypothalamus-pituitary organoid formation using machine learning

Multi-cellular organoids are self-assembly aggregates that mimic biological functions and developmental processes of many tissue types in vitro. They are widely employed for disease modeling and functional studies. Hypothalamus-pituitary organoids can be generated through differentiation induction from pluripotent stem cells. However, their maturation is time consuming and labor intensive, and the quality of the resulting organoids can vary. Here, we developed a machine learning model capable of accurately predicting the successful generation of high-quality hypothalamus-pituitary organoids based solely on phase-contrast images captured during the early stage of differentiation. The model achieved an accuracy of 79% using images from organoids on day 9 to predict pituitary cell differentiation at day 40. Moreover, the computational approach identified the shape of the organoid surface as a critical determining factor that significantly affected the prediction. This model can help to enhance the efficiency of organoid induction experiments and illuminate the molecular mechanisms involved in hypothalamus-pituitary differentiation.