Single-cell atlases of human fetal gene expression and fetal chromatin accessibility are published by a University of Washington School of Medicine-led team in Science this week. In the first study, the researchers applied three-level single-cell combinatorial indexing for gene expression to 121 human fetal samples ranging from an estimated 79 days to 129 days post-conception and representing 15 organs, ultimately profiling around 4 million individual cells. Using the literature and other atlases to build a framework for quantifying cell type specificity, identifying 657 cell subtypes both within and across tissues. In the second study, the scientists used the sample indexing approach with 59 human fetal samples ranging from 89 to 125 days in estimated post-conception age and representing 15 organs, profile around 800,000 single cells in total. They annotated these data and cataloged hundreds of thousands of candidate regulatory elements that exhibit cell type-specific chromatin accessibility. "These data represent a rich resource for the exploration of in vivo human gene regulation in diverse tissues and cell types," the scientists write of the work.
An examination of the prevalence of antibodies against SARS-CoV-2 in Kenya suggests that exposure to the virus may be far more extensive there than previously reported. In the study, which appears in Science this week, a group led by scientists from the KEMRI-Wellcome Trust Research Programme analyzed 3,174 blood transfusion samples from Kenyans aged 15 to 66 years old, determining that the crude seroprevalence of anti-SARS-CoV-2 immunoglobulin G was 5.6 percent. Adjusting for the age-sex structure of Kenya, the overall seroprevalence was estimated to be 4.3 percent. The findings indicate that SARS-CoV-2 exposure in Kenya is greater than indicated by case-based surveillance, peaking in individuals 35 to 44 years old. The study's authors offer possible explanations for the divergence in the ratio of observed cases or deaths to serologically defined infections in the study including the steep demographic age-pyramid of Kenya, which results in a smaller vulnerable age group, and the possibility of alternative mechanisms of immunity to SARS-CoV-2, potentially from endemic human coronaviruses in the nation.
A machine learning tool for identifying oncogenes (OGs) and tumor suppressor genes (TSGs) based on genetic and epigenetic data is reported in Science Advances this week. While most cancer driver genes are identified based on genetic alterations also, epigenetic alterations are important for tumor initiation and progression. To take advantage of both, a team led by scientists from the University of California, Irvine, developed DORGE — short for discovery of oncogenes and tumor suppressor genes using genetic and epigenetic features — that integrates genetic and epigenetic features to identify OGs and TSGs. Using DORGE, the investigators identify histone modifications as strong predictors for TSGs, as well as missense mutations, super enhancers, and methylation differences as strong predictors for OGs. "We also found that DORGE-predicted dual-functional genes (both TSGs and OGs) are enriched at hubs in protein-protein interaction and drug-gene networks," they write. "DORGE will serve as an essential resource for cancer biology, particularly in the development of targeted therapeutics and personalized medicine for cancer treatment."