Predicting placenta transcriptional regulatory interactions based on spatial gene expression data and convolutional neural network

<strong>Aims:</strong> 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. <strong>Methods and Results:</strong> In this study, first-trimester chorionic villus and decidua tissues were collected. Next, we present a machine-learning system to infer gene interaction networks of the human placenta from immunofluorescence images of trophoblast specific transcription factors obtained by a high-resolution scanner. <strong>Conclusions:</strong> The experimental results show that deep learning models reveal 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. 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.**研究目的：** 胎盘发育异常与妊娠缺陷及胎儿生长障碍密切相关。由于缺乏人体体内研究样本，人类胎盘发育的具体分子机制尚未阐明，而基于图像的基因调控网络（Gene Regulatory Network, GRN）重建技术仍极不成熟。**方法与结果：** 本研究收集了妊娠早期的绒毛膜绒毛与蜕膜组织。随后，我们提出了一种机器学习系统，可通过高分辨率扫描仪获取的滋养层特异性转录因子免疫荧光图像，推断人类胎盘的基因互作网络。**结论：** 实验结果表明，深度学习模型揭示了此前未被充分认知的调控功能。空间表达数据揭示了传统实验未能发现的全新调控关系，并助力构建基于基因表达空间分布的基因调控网络。我们验证了该方法在利用人类胎盘高分辨率图像构建基因调控网络时的有效性。本分析对于未来进一步探索胎盘发育及妊娠相关疾病的发生机制具有一定参考价值。本数据集与分析结果可为母胎界面领域及妊娠建立相关研究的科研人员提供宝贵的研究资源。