SCENIC enables simultaneous regulatory network inference and robust cell clustering from single-cell RNA-seq data. We present SCENIC, a computational method for simultaneous gene regulatory network reconstruction and cell-state identification from single-cell RNA-seq data ( http://scenic.aertslab.org ). On a compendium of single-cell data from tumors and brain, we demonstrate that cis-regulatory analysis can be exploited to guide the identification of transcription factors and cell states. SCENIC provides critical biological insights into the mechanisms driving cellular heterogeneity.

The diversity of cell types and regulatory states in the brain, and how these change during aging, remains largely unknown. We present a single-cell transcriptome atlas of the entire adult Drosophila melanogaster brain sampled across its lifespan. Cell clustering identified 87 initial cell clusters that are further subclustered and validated by targeted cell-sorting. Our data show high granularity and identify a wide range of cell types. Gene network analyses using SCENIC revealed regulatory heterogeneity linked to energy consumption. During aging, RNA content declines exponentially without affecting neuronal identity in old brains. This single-cell brain atlas covers nearly all cells in the normal brain and provides the tools to study cellular diversity alongside other Drosophila and mammalian single-cell datasets in our unique single-cell analysis platform: SCope (http://scope.aertslab.org). These results, together with SCope, allow comprehensive exploration of all transcriptional states of an entire aging brain.

Single-cell techniques are advancing rapidly and are yielding unprecedented insight into cellular heterogeneity. Mapping the gene regulatory networks (GRNs) underlying cell states provides attractive opportunities to mechanistically understand this heterogeneity. In this review, we discuss recently emerging methods to map GRNs from single-cell transcriptomics data, tackling the challenge of increased noise levels and data sparsity compared with bulk data, alongside increasing data volumes. Next, we discuss how new techniques for single-cell epigenomics, such as single-cell ATAC-seq and single-cell DNA methylation profiling, can be used to decipher gene regulatory programmes. We finally look forward to the application of single-cell multi-omics and perturbation techniques that will likely play important roles for GRN inference in the future.

Yeast and cancer cells share the unusual characteristic of favoring fermentation of sugar over respiration. We now reveal an evolutionary conserved mechanism linking fermentation to activation of Ras, a major regulator of cell proliferation in yeast and mammalian cells, and prime proto-oncogene product. A yeast mutant (tps1∆) with overactive influx of glucose into glycolysis and hyperaccumulation of Fru1,6bisP, shows hyperactivation of Ras, which causes its glucose growth defect by triggering apoptosis. Fru1,6bisP is a potent activator of Ras in permeabilized yeast cells, likely acting through Cdc25. As in yeast, glucose triggers activation of Ras and its downstream targets MEK and ERK in mammalian cells. Biolayer interferometry measurements show that physiological concentrations of Fru1,6bisP stimulate dissociation of the pure Sos1/H-Ras complex. Thermal shift assay confirms direct binding to Sos1, the mammalian ortholog of Cdc25. Our results suggest that the Warburg effect creates a vicious cycle through Fru1,6bisP activation of Ras, by which enhanced fermentation stimulates oncogenic potency.