Predicting placenta transcriptional regulatory interactions based on immunohistochemistry images data and convolutional neural network

Abstract

Aims: The dysfunction of placenta development is correlated to the defects of pregnancy and fetal growth. The detailed molecular mechanism of placenta development is not identified in human due to the lack of material in vivo. Image-based reconstructions of GRN are still very underdeveloped. Methods and Results: In this study, immunohistochemistry images of different TFs in chorionic villus were obtained by a high-resolution scanner. Next, we used a convolutional neural network and machine learning method to infer gene interaction networks of human placenta from these images based on the transfer learning technique. The experimental results show that deep learning models reveals regulatory roles that have not yet been fully recognized. The spatial expression data reveal new regulatory relationships that traditional experiments have failed to recognize, and has allowed the development of gene regulation networks based on the spatial distribution of gene expression. Conclusions: We demonstrate the effectiveness of this approach in building networks using high-resolution images of the human placenta. Our analysis is of certain significance for further exploration of the development of the placenta and the occurrence of pregnancy-related diseases in the future. The datasets and analysis provide a useful source for the researchers in the field of the maternal-fetal interface and the establishment of pregnancy.