Meta-Analysis of the transcriptomic profile of human placenta

Aims: The establishment of a complex multi-scale model of biological tissue is of great significance for the study of related diseases, and the integration of relevant quantitative data is the premise to achieve this goal. Whereas, the systematic collation of data sets related to placental tissue is relatively lacking.

Methods and Results: In this study, first-trimester chorionic villus and decidua tissues were collected from 6 healthy women. Transcriptome data was acquired by RNA-seq and the expression levels of trophoblast specific transcription factors were identified by immunofluorescence and RNA-seq data analysis. Differentially expressed genes between chorionic villus and decidua tissues and its related biological functions were identified. Furthermore, 18 published transcriptomes (a total of 425 samples) datasets of human pregnancy-related tissues (including chorionic villus and decidua, term placenta, endometrium, in vitro cell lines, etc.) from public databases were collected and analyzed. We compared the most widely used dimensionality reduction (DR) methods to generate a 2D-map for visualization of these data. We also compared the effects of different parameter settings and commonly used manifold learning methods on the results.

Conclusions: The result indicates that the nonlinear method can better preserve the small differences between different subtypes of placental tissue than the linear method. It led the foundation for the study on accurate computational modeling of placental tissue development in the future. Although there are public RNA-seq data available for chorionic villus and decidua tissue, this is the first time that the RNA-seq data were obtained from the chorionic villus and decidua which derived from the same patient. The datasets and analysis provide a useful source for the researchers in the field of the maternal-fetal interface and the establishment of pregnancy.