Direct Observation of the Dynamics of Single-Cell Metabolic Activity during Microbial Diauxic Growth

Population-level analyses are rapidly becoming inadequate to answer many of biomedical science and microbial ecology's most pressing questions. The role of microbial populations within ecosystems and the evolutionary selective pressure on individuals depend fundamentally on the metabolic activity of single cells. Yet, many existing single-cell technologies provide only indirect evidence of metabolic specialization because they rely on correlations between transcription and phenotype established at the level of the population to infer activity. In this study, we take a top-down approach using isotope labels and secondary ion mass spectrometry to track the uptake of carbon and nitrogen atoms from different sources into biomass and directly observe dynamic changes in anabolic specialization at the level of single cells. We investigate the classic microbiological phenomenon of diauxic growth at the single-cell level in the model methylotroph Methylobacterium extorquens. In nature, this organism inhabits the phyllosphere, where it experiences diurnal changes in the available carbon substrates, necessitating an overhaul of central carbon metabolism. We show that the population exhibits a unimodal response to the changing availability of viable substrates, a conclusion that supports the canonical model but has thus far been supported by only indirect evidence. We anticipate that the ability to monitor the dynamics of anabolism in individual cells directly will have important applications across the fields of ecology, medicine, and biogeochemistry, especially where regulation downstream of transcription has the potential to manifest as heterogeneity that would be undetectable with other existing single-cell approaches. IMPORTANCE Understanding how genetic information is realized as the behavior of individual cells is a long-term goal of biology but represents a significant technological challenge. In clonal microbial populations, variation in gene regulation is often interpreted as metabolic heterogeneity. This follows the central dogma of biology, in which information flows from DNA to RNA to protein and ultimately manifests as activity. At present, DNA and RNA can be characterized in single cells, but the abundance and activity of proteins cannot. Inferences about metabolic activity usually therefore rely on the assumption that transcription reflects activity. By tracking the atoms from which they build their biomass, we make direct observations of growth rate a