Human placenta is a complex and heterogeneous organ interfacing between the mother and the fetus that supports fetal development. Alterations to placental structural components are associated with various pregnancy complications. To reveal the heterogeneity among various placenta cell types in normal and diseased placentas, as well as elucidate molecular interactions within a population of placental cells, a new genomics technology called single cell RNA-seq (or scRNA-seq) has been employed in the last couple of years. Here we review the principles of scRNA-seq technology, and summarize the recent human placenta studies at scRNA-seq level across gestational ages as well as in pregnancy complications, such as preterm birth and preeclampsia. We list the computational analysis platforms and resources available for the public use. Lastly, we discuss the future areas of interest for placenta single cell studies, as well as the data analytics needed to accomplish them.
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Hui Li, Qianhui Huang, Yu Liu, and Lana X Garmire
Renjie Wang, Wei Pan, Lei Jin, Yuehan Li, Yudi Geng, Chun Gao, Gang Chen, Hui Wang, Ding Ma, and Shujie Liao
Artificial intelligence (AI) has experienced rapid growth over the past few years, moving from the experimental to the implementation phase in various fields, including medicine. Advances in learning algorithms and theories, the availability of large datasets and improvements in computing power have contributed to breakthroughs in current AI applications. Machine learning (ML), a subset of AI, allows computers to detect patterns from large complex datasets automatically and uses these patterns to make predictions. AI is proving to be increasingly applicable to healthcare, and multiple machine learning techniques have been used to improve the performance of assisted reproductive technology (ART). Despite various challenges, the integration of AI and reproductive medicine is bound to give an essential direction to medical development in the future. In this review, we discuss the basic aspects of AI and machine learning, and we address the applications, potential limitations and challenges of AI. We also highlight the prospects and future directions in the context of reproductive medicine.
Yu-chen Zhang, Xiao-li Qin, Xiao-ling Ma, Hui-qin Mo, Shi Qin, Cheng-xi Zhang, Xiao-wei Wei, Xue-qing Liu, Yan Zhang, Fu-ju Tian, and Yi Lin
Preeclampsia is a gestational hypertensive disease; however, preeclampsia remains poorly understood. Bioinformatics analysis was applied to find novel genes involved in the pathogenesis of preeclampsia and identified CLDN1 as one of the most differentially expressed genes when comparing patients with preeclampsia and healthy controls. The results of the qRT-PCR, Western blotting and immunohistochemistry experiments demonstrated that CLDN1 was significantly downregulated in the chorionic villi in samples from patients with preeclampsia. Furthermore, knockdown of CLDN1 in HTR-8/SVneo cells resulted in the inhibition of proliferation and induction of apoptosis, and overexpression of CLDN1 reversed these effects. In addition, RNA-seq assays demonstrated that the gene BIRC3 is potentially downstream of CLDN1 and is involved in the regulation of apoptosis. Knockdown of CLDN1 confirmed that the expression level of BIRC3 was obviously decreased and was associated with a significant increase in cleaved PARP. Interestingly, the apoptotic effect in CLDN1 knockdown cells was rescued after BIRC3 overexpression. Overall, these results indicate that a decrease in CLDN1 inhibits BIRC3 expression and increases cleaved PARP levels thus participating in the pathogenesis of preeclampsia.