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This Week in Genome Biology: Sep 7, 2016

A team led by investigators at Morgridge Institute for Research explores factors behind human embryonic stem cell differentiation with single-cell transcriptome data. Using single-cell RNA sequencing patterns in nearly 1,800 individual cells, including undifferentiated human embryonic stems cells and stem-cell derived progenitor cells, the researchers narrowed in on a transcriptional signature and regulatory features that seems to denote differentiation to endoderm tissue during a particular developmental time frame. "Our strategy of combining single-cell analysis and genetic approaches can be applied to uncover novel regulators governing cell fate decisions in a variety of systems," the study's authors write.

Researchers from the Dana Farber Cancer Institute, Harvard University, and elsewhere report on findings from an analysis of tumor-infiltrating cell features in samples from the Cancer Genome Atlas. Using RNA sequencing data for more than 10,000 samples representing almost two dozen cancer types, the team computationally teased out immune components, before looking at their relationship to clinical features. It also explored tumor-immune interactions that might impact response to cancer vaccine or checkpoint blockade immunotherapy. In melanoma samples with high CTLA4 levels, for example, the investigators detected two groups with distinct CD8 T cell infiltration patterns.

A UK-led team takes a look at genetic and epigenetic features of schizophrenia using a multi-stage epigenome-wide association study approach involving more than 1,700 individuals. The researchers began by comparing array-based methylation profiles in 675 individuals with or without schizophrenia, identifying candidate regions with differential DNA methylation. Apparent differentially methylated regions from that analysis were subsequently tested in hundreds more individuals, including 96 monozygotic twin pairs, leading them to 343 differentially methylated regions with ties to schizophrenia in a meta-analysis of the available data. GenomeWeb has more on the study, here.