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Nature Studies Present Single-Cell Map of Cell Cycle, Approach Identifies Cancer Treatment Resistance Pathways, More

By combining subcellular resolution proteomics with single-cell transcriptomics, a team led by scientists from the KTH Royal Institute of Technology in Sweden has created a comprehensive, spatiotemporally resolved map of the human cell cycle with single-cell resolution in unsynchronized human cells. As reported in Nature, the researchers use the atlas to show that around one-fifth of the human proteome displays cell-to-cell variability and identify hundreds of proteins as cell-cycle-dependent, most of which are regulated post-translationally rather than by transcript cycling. They also provide evidence that several of these proteins have oncogenic or anti-oncogenic functions. "This work paints a more complete picture of proteomic variations over space and time in individual human cells by providing a foundation for measuring and interpreting proteome variability between individual cells and at the subcellular level," the study's authors write. GenomeWeb has more on this, here.

Integrating single-cell RNA sequencing into clinical studies of cancer drugs can help identify treatment resistance pathways and potential therapeutic targets, according to a report appearing in Nature Medicine. In the study, researchers from Weizmann Institute and collaborators applied scRNA-seq in a trial evaluating a cocktail of chemotherapeutic agents in newly diagnosed multiple myeloma patients who either failed to respond to previous treatment or experienced early relapse. The team uncovers a novel molecular resistance signature that is highly correlated with poor clinical outcome and outperforms current risk stratifications based on FISH cytogenetics. Further analysis shows that this signature is present in more than five percent of newly diagnosed multiple myeloma patients and is more prevalent as treatment lines progress. The investigators also identify an enzyme involved in the protein-folding response pathway as a potential target for resistant disease. "Our study defines a roadmap for combining single-cell genomic profiling with clinical trials, and we anticipate that such studies will facilitate the design of molecularly informed diagnostics, personalized therapy selection, and detection of new targets for treatment of myeloma and treatment-resistant myeloma patients," the authors write.

A new computational method for the deconvolution of alternative RNA conformations from mutational profiling experiments is reported in Nature Methods this week. Called DRAGO, the approach is based on a combination of spectral clustering and fuzzy clustering. Its developers at the University of Groningen and the University of Turin demonstrate DRAGO with dimethyl sulfate mutational profiling with sequencing to analyze the SARS-CoV-2 genome, finding multiple regions that fold into two mutually exclusive conformations, including a conserved structural switch in the 3' untranslated region. "We anticipate that DRACO will enable exploration of the RNA structurome at unprecedented resolution, and the identification of transient and dynamic features of cellular transcriptomes," the scientists conclude.