The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands-or even millions-of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.

The regulation of messenger RNA levels in mammalian cells can be achieved by the modulation of synthesis and degradation rates. Metabolic RNA-labeling experiments in bulk have quantified these rates using relatively homogeneous cell populations. However, to determine these rates during complex dynamical processes, for instance during cellular differentiation, single-cell resolution is required. Therefore, we developed a method that simultaneously quantifies metabolically labeled and preexisting unlabeled transcripts in thousands of individual cells. We determined synthesis and degradation rates during the cell cycle and during differentiation of intestinal stem cells, revealing major regulatory strategies. These strategies have distinct consequences for controlling the dynamic range and precision of gene expression. These findings advance our understanding of how individual cells in heterogeneous populations shape their gene expression dynamics.