The invasive trophoblast cell lineage in rat and human share crucial responsibilities in establishing the uterine-placental interface of the hemochorial placenta. These observations have led to the rat becoming an especially useful animal model to study hemochorial placentation. However, our understanding of similarities or differences between regulatory mechanisms governing rat and human invasive trophoblast cell populations is limited. In this study, we generated single-nucleus (sn) ATAC-seq data from gestation day (gd) 15.5 and 19.5 rat uterine-placental interface tissues and integrated the data with single-cell RNA-seq data generated at the same stages. We determined the chromatin accessibility profiles of invasive trophoblast, natural killer, macrophage, endothelial, and smooth muscle cells, and compared invasive trophoblast chromatin accessibility to extravillous trophoblast (EVT) cell accessibility. In comparing chromatin accessibility profiles between species, we found similarities in patterns of gene regulation and groups of motifs enriched in accessible regions. Finally, we identified a conserved gene regulatory network in invasive trophoblast cells. Our data, findings and analysis will facilitate future studies investigating regulatory mechanisms essential for the invasive trophoblast cell lineage.

Background Genome-wide gene function annotations are useful for hypothesis generation and for prioritizing candidate genes potentially responsible for phenotypes of interest. We functionally annotated the genes of 18 crop plant genomes across 14 species using the GOMAP pipeline. Results By comparison to existing GO annotation datasets, GOMAP-generated datasets cover more genes, contain more GO terms, and are similar in quality (based on precision and recall metrics using existing gold standards as the basis for comparison). From there, we sought to determine whether the datasets across multiple species could be used together to carry out comparative functional genomics analyses in plants. To test the idea and as a proof of concept, we created dendrograms of functional relatedness based on terms assigned for all 18 genomes. These dendrograms were compared to well-established species-level evolutionary phylogenies to determine whether trees derived were in agreement with known evolutionary relationships, which they largely are. Where discrepancies were observed, we determined branch support based on jackknifing then removed individual annotation sets by genome to identify the annotation sets causing unexpected relationships. Conclusions GOMAP-derived functional annotations used together across multiple species generally retain sufficient biological signal to recover known phylogenetic relationships based on genome-wide functional similarities, indicating that comparative functional genomics across species based on GO data holds promise for generating novel hypotheses about comparative gene function and traits.

The assay for transposase-accessible chromatin with sequencing (ATAC-seq) is a common assay to identify chromatin accessible regions by using a Tn5 transposase that can access, cut, and ligate adapters to DNA fragments for subsequent amplification and sequencing. These sequenced regions are quantified and tested for enrichment in a process referred to as "peak calling". Most unsupervised peak calling methods are based on simple statistical models and suffer from elevated false positive rates. Newly developed supervised deep learning methods can be successful, but they rely on high quality labeled data for training, which can be difficult to obtain. Moreover, though biological replicates are recognized to be important, there are no established approaches for using replicates in the deep learning tools, and the approaches available for traditional methods either cannot be applied to ATAC-seq, where control samples may be unavailable, or are post-hoc and do not capitalize on potentially complex, but reproducible signal in the read enrichment data. Here, we propose a novel peak caller that uses unsupervised contrastive learning to extract shared signals from multiple replicates. Raw coverage data are encoded to obtain low-dimensional embeddings and optimized to minimize a contrastive loss over biological replicates. These embeddings are passed to another contrastive loss for learning and predicting peaks and decoded to denoised data under an autoencoder loss. We compared our Replicative Contrastive Learner (RCL) method with other existing methods on ATAC-seq data, using annotations from ChromHMM genome and transcription factor ChIP-seq as noisy truth. RCL consistently achieved the best performance.

The assay for transposase-accessible chromatin with sequencing (ATAC-seq) is a common assay to identify chromatin accessible regions by using a Tn5 transposase that can access, cut, and ligate adapters to DNA fragments for subsequent amplification and sequencing. These sequenced regions are quantified and tested for enrichment in a process referred to as "peak calling". Most unsupervised peak calling methods are based on simple statistical models and suffer from elevated false positive rates. Newly developed supervised deep learning methods can be successful, but they rely on high quality labeled data for training, which can be difficult to obtain. Moreover, though biological replicates are recognized to be important, there are no established approaches for using replicates in the deep learning tools, and the approaches available for traditional methods either cannot be applied to ATAC-seq, where control samples may be unavailable, or are post-hoc and do not capitalize on potentially complex, but reproducible signal in the read enrichment data. Here, we propose a novel peak caller that uses unsupervised contrastive learning to extract shared signals from multiple replicates. Raw coverage data are encoded to obtain low-dimensional embeddings and optimized to minimize a contrastive loss over biological replicates. These embeddings are passed to another contrastive loss for learning and predicting peaks and decoded to denoised data under an autoencoder loss. We compared our Replicative Contrastive Learner (RCL) method with other existing methods on ATAC-seq data, using annotations from ChromHMM genome and transcription factor ChIP-seq as noisy truth. RCL consistently achieved the best performance.

The placenta serves as a connection between the mother and the fetus during pregnancy, providing the fetus with oxygen, nutrients, and growth hormones. However, the regulatory mechanisms and dynamic gene interaction networks underlying early placental development are understudied. Here, we generated RNA-sequencing data from mouse fetal placenta at embryonic days 7.5, 8.5, and 9.5 to identify genes with timepoint-specific expression, then inferred gene interaction networks to analyze highly connected network modules. We determined that timepoint-specific gene network modules were associated with distinct developmental processes, and with similar expression profiles to specific human placental cell populations. From each module, we identified hub genes and their direct neighboring genes, which were predicted to govern placental functions. We confirmed that four novel candidate regulators identified through our analyses regulate cell migration in the HTR-8/SVneo cell line. Overall, we predicted several novel regulators of placental development expressed in specific placental cell types using network analysis of bulk RNA-sequencing data. Our findings and analysis approaches will be valuable for future studies investigating the transcriptional landscape of early development.