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For cells, location is key to their fate and behavior, but studying expression patterns across complex tissues is difficult. A team led by ӳý visiting scientist Rahul Satija (of ӳý core member Aviv Regev's lab) and Jeff Farrell (a postdoc in associate member Alex Schier's lab) recently reported on Seurat, their new computational strategy to map single cells by integrating single-cell RNA-seq data with RNA patterns from tissues. Their team used the tool, named after the 19th century pointillist painter, to generate a transcriptome-wide map of spatial patterning in zebrafish embryos, demonstrating Seurat's utility for mapping cells within complex patterned tissues. Their paper appears online in .

A in this month’s issue of the journal Nature Methods discusses the exponential growth of cancer “omics” data, the need for interoperability between data integration tools, and the approaches researchers are taking to address these issues. One of the helpful technologies highlighted in the piece is , a platform that bridges commonly used bioinformatics tools, facilitating interoperability for biomedical investigators with little to no computer programming experience.

How do cancer cells survive the low blood supply of the tumor microenvironment? A team led by ӳý senior associate member David Sabatini, a member of the Whitehead Institute for Biomedical Research and professor of biology at MIT, recently found that brain cancer cells express high levels of metabolic enzymes SHMT2 and GLDC, and the resulting reduced oxygen consumption gives the cells a survival advantage in poorly vascularized tumor regions. Inhibiting GLDC in SHMT2-overexpressing cells leads to toxic glycine accumulation and may be a possible new avenue for therapeutics. The study was published online by the journal .