Single-cell heterogeneity in ribosome content and the consequences for the growth laws

ABSTRACT

Previous work has suggested that the ribosome content of a cell is optimized to maximize growth given the nutrient availability. The resulting correlation between ribosome number and growth rate appears to be independent of the rate limiting nutrient and has been reported in many organisms. The robustness and universality of this observation has given it the classification of a “growth law.” These laws have had powerful impacts on many biological disciplines. They have fueled predictions about how organisms evolve to maximize reproduction, and informed models about how cells regulate growth. Due to methodological limitations, this growth law has rarely been studied at the level of individual cells. While populations of fast-growing cells tend to have more ribosomes than populations of slow-growing cells, it is unclear if individual cells tightly regulate their ribosome content to match their environment. Here, we use recent ground-breaking single-cell RNA sequencing techniques to study this growth law at the single-cell level in two different microbes, S. cerevisiae (a single-celled yeast and eukaryote) and B. subtilis (a bacterium and prokaryote). In both species, we find enormous variation in the ribosomal content of single cells that is not predictive of growth rate. Fast-growing populations include cells showing transcriptional signatures of slow growth and stress, as do cells with the highest ribosome content we survey. Broadening our focus to the levels of non-ribosomal transcripts reveals subpopulations of cells in unique transcriptional states suggestive of divergent growth strategies. These results suggest that single-cell ribosome levels are not finely tuned to match population growth rates or nutrient availability, at least not in a way that can be captured by a unifying law that applies to all cell types. Overall, this work encourages the expansion of these “laws” and other models that predict how growth rates are regulated or how they evolve to consider single-cell heterogeneity.