Many cellular behaviors, responses, and decisions are executed through regulatory networks controlling gene expression.  Systems-level approaches traditionally reconstruct these networks by profiling populations of cells under different experimental conditions and inferring groups of co-regulated genes. However, this strategy has limitations in heterogeneous samples where the population average is a poor representation of individual cells. Particularly in complex tissues, like the immune system, new single cell methods are needed to better understand the regulatory circuits driving cellular diversity and behavior.

In this talk, I will explore the use of single cell transcriptomics to investigate cellular variability in the response of mouse dendritic cells to pathogenic components. Even when starting with a population of 'identical' cells, our single cell measurements reveal widespread heterogeneity across the responding transcriptome, with cell-to-cell variability approaching 1,000 fold in extreme cases. I will discuss how we can harness these extensive differences to decipher heterogeneity in cell state and the activation of cell circuits, identify master regulators and rare subpopulations, and understand how expression variability is dynamically regulated during immune response.