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Nucleic Acids Research Papers Explore Regulatory Networks, Cellular Senescence, More

Researchers from the US and the UK present a framework for putting together a chromatin interaction network from 127 human reference epigenomes. Using a matrix factorization framework designed for integrating physical and functional interaction profiles, the MIT-led team attempted to tease out the chromatin interaction edges for more than 20,000 promoters and 1.8 million enhancers, demonstrating that the approach could produce unbiased regulatory element networks from sets of data with variable quality. "We show that the unbiased integration of independent data sources suggestive of regulatory interactions produces meaningful associations supported by existing functional and physical evidence," the authors write, "correlating with expected independent biological features."

A National Institute on Aging team describes a cellular senescence signature established through RNA sequencing on human fibroblast, umbilical vein endothelial, or alveolar endothelial cells representing eight distinct senescence models, prompted by everything from ionizing radiation exposure to oncogene expression. Based on the RNA-seq profiles produced in these senescence models together with bioinformatic analyses and RT-qPCR-based validation, the researchers narrowed in on transcripts with altered expression across the senescence models, including 50 transcripts with enhanced expression and 18 transcripts that are muted in senescence. "We propose that these shared transcriptome profiles will enable the identification of senescent cells in vivo, the investigation of their roles in aging and malignancy, and the development of strategies to target senescent cells therapeutically," they say.

Finally, a team in the Netherlands introduces an algorithm for classifying cell types, particularly in tumor samples, using single-cell RNA sequence data. The selective, hierarchical approach — known as "Characterization of Cell Types Aided by Hierarchical Classification," or CHETAH — hinges on scRNA-seq reference data reported in the past, along with a confidence score to account for variable gene expression in a given cell type, the researchers say. By applying CHETAH to published pancreatic cell datasets, for example, they found that the algorithm is especially suited for identifying new, intermediate, or unassigned cell types. "Having the possibility of unassigned and intermediate cell types is pivotal for preventing misclassification," the authors note, "and can yield important biological information for previously unexplored tissues."