Placental single cell transcriptomics: Opportunities for endocrine disrupting chemical toxicology

The placenta performs essential biologic functions for fetal development throughout pregnancy. Placental dysfunction is at the root of multiple adverse birth outcomes such as intrauterine growth restriction, preeclampsia, and preterm birth. Exposure to endocrine disrupting chemicals during pregnancy can cause placental dysfunction, and many prior human studies have examined molecular changes in bulk placental tissues. Placenta-specific cell types, including cytotrophoblasts, syncytiotrophoblasts, extravillous trophoblasts, and placental resident macrophage Hofbauer cells play unique roles in placental development, structure, and function. Toxicant-induced changes in relative abundance and/or impairment of these cell types likely contribute to placental pathogenesis. Although gene expression insights gained from bulk placental tissue RNA-sequencing data are useful, their interpretation is limited because bulk analysis can mask the effects of a chemical on individual populations of placental cells. Cutting-edge single cell RNA-sequencing technologies are enabling the investigation of placental cell-type specific responses to endocrine disrupting chemicals. Moreover, in situ bioinformatic cell deconvolution enables the estimation of cell type proportions in bulk placental tissue gene expression data. These emerging technologies have tremendous potential to provide novel mechanistic insights in a complex heterogeneous tissue with implications for toxicant contributions to adverse pregnancy outcomes.

Decoding cell-type contributions to the cfRNA transcriptomic landscape of liver cancer

BACKGROUND

Liquid biopsy, particularly cell-free RNA (cfRNA), has emerged as a promising non-invasive diagnostic tool for various diseases, including cancer, due to its accessibility and the wealth of information it provides. A key area of interest is the composition and cellular origin of cfRNA in the blood and the alterations in the cfRNA transcriptomic landscape during carcinogenesis. Investigating these changes can offer insights into the manifestations of tissue alterations in the blood, potentially leading to more effective diagnostic strategies. However, the consistency of these findings across different studies and their clinical utility remains to be fully elucidated, highlighting the need for further research in this area.

RESULTS

In this study, we analyzed over 350 blood samples from four distinct studies, investigating the cell type contributions to the cfRNA transcriptomic landscape in liver cancer. We found that an increase in hepatocyte proportions in the blood is a consistent feature across most studies and can be effectively utilized for classifying cancer and healthy samples. Moreover, our analysis revealed that in addition to hepatocytes, liver endothelial cell signatures are also prominent in the observed changes. By comparing the classification performance of cellular proportions to established markers, we demonstrated that cellular proportions could distinguish cancer from healthy samples as effectively as existing markers and can even enhance classification when used in combination with these markers.

CONCLUSIONS

Our comprehensive analysis of liver cell-type composition changes in blood revealed robust effects that help classify cancer from healthy samples. This is especially noteworthy, considering the heterogeneous nature of datasets and the etiological distinctions of samples. Furthermore, the observed differences in results across studies underscore the importance of integrative and comparative approaches in the future research to determine the consistency and robustness of findings. This study contributes to the understanding of cfRNA composition in liver cancer and highlights the potential of cellular deconvolution in liquid biopsy.

Accurate estimation of rare cell type fractions from tissue omics data via hierarchical deconvolution

Bulk transcriptomics in tissue samples reflects the average expression levels across different cell types and is highly influenced by cellular fractions. As such, it is critical to estimate cellular fractions to both deconfound differential expression analyses and infer cell type-specific differential expression. Since experimentally counting cells is infeasible in most tissues and studies, in silico cellular deconvolution methods have been developed as an alternative. However, existing methods are designed for tissues consisting of clearly distinguishable cell types and have difficulties estimating highly correlated or rare cell types. To address this challenge, we propose Hierarchical Deconvolution (HiDecon) that uses single-cell RNA sequencing references and a hierarchical cell type tree, which models the similarities among cell types and cell differentiation relationships, to estimate cellular fractions in bulk data. By coordinating cell fractions across layers of the hierarchical tree, cellular fraction information is passed up and down the tree, which helps correct estimation biases by pooling information across related cell types. The flexible hierarchical tree structure also enables estimating rare cell fractions by splitting the tree to higher resolutions. Through simulations and real data applications with the ground truth of measured cellular fractions, we demonstrate that HiDecon significantly outperforms existing methods and accurately estimates cellular fractions.