Using a newly developed integrated spatial genomics approach, scientists from the California Institute of Technology and Mount Sinai have created a resource that combines images of thousands of genomic loci along with RNAs and epigenetic markers simultaneously in individual cells. As they report in Science, the investigators applied the technology to the mouse brain and find that that cell type-specific association and scaffolding of DNA loci around nuclear bodies organize the nuclear architecture and correlate with differential expression levels in different cell types. At the submegabase level, meantime, active and inactive X chromosomes access similar domain structures in single cells despite distinct epigenetic and expression states. The approach, they write, can be applied to a range of biological systems to further explore the diversity and invariant in single-cell nuclear architecture. It could also be scaled up using signal amplification methods to allow larger tissue sections to be imaged, while super-resolution imaging of epigenetic marks overlaid on the super-resolved DNA seqFISH+ data to provide finer resolution in single-cell chromatin profiles.
An analysis of a compendium of cancer protein interactions is presented in this week's Science, revealing a number of protein systems involved in tumor mutations. In the study, a team led by researchers from the University of California, San Diego, built a comprehensive map of cancer protein systems integrating both new and published multi-omic interaction data at multiple scales of analysis. They then developed a statistical model to identify a set of systems that best explains the gene mutation frequencies observed in tumors, pinpointing 395 specific systems under mutational selection across 13 cancer types. The systems, the authors say, can be used as clinical biomarkers and implicate a total of 548 genes in cancer evolution and progression. GenomeWeb has more on this and related studies, here.