Cellular populations in both nature and the laboratory are composed of phenotypically heterogeneous individuals that compete with each other resulting in complex population dynamics. Predicting population growth characteristics based on knowledge of heterogeneous single-cell dynamics remains challenging. By observing groups of cells for hundreds of generations at single-cell resolution, we reveal that growth noise causes clonal populations of Escherichia coli to double faster than the mean doubling time of their constituent single cells across a broad set of balanced-growth conditions. We show that the population-level growth rate gain as well as age structures of populations and of cell lineages in competition are predictable. Furthermore, we theoretically reveal that the growth rate gain can be linked with the relative entropy of lineage generation time distributions. Unexpectedly, we find an empirical linear relation between the means and the variances of generation times across conditions, which provides a general constraint on maximal growth rates. Together, these results demonstrate a fundamental benefit of noise for population growth, and identify a growth law that sets a "speed limit" for proliferation.growth noise | age-structured population model | cell lineage analysis | growth law | microfluidics C ell growth is an important physiological process that underlies the fitness of organisms. In exponentially growing cell populations, proliferation is usually quantified using the bulk population growth rate, which is assumed to represent the average growth rate of single cells within a population. In addition, basic growth laws exist that relate ribosome function and metabolic efficiency, macromolecular composition, and cell size of the culture as a whole to the bulk population growth rate (1-3). Population growth rate is therefore a quantity of primary importance that reports cellular physiological states and fitness.However, at the single-cell level, growth-related parameters such as the division time interval and division cell size are heterogeneous even in a clonal population growing at a constant rate (4-9). Such "growth noise" causes concurrently living cells to compete within the population for representation among its future descendants. For example, if two sibling cells born from the same mother cell had different division intervals, the faster dividing sibling is likely to have more descendants in the future population compared with its slower dividing sister, despite the fact that progenies of both siblings may proliferate equally well (Fig. 1). Intrapopulation competition complicates single-cell analysis because any growth-correlated quantities measured over the population deviate from intrinsic single-cell properties (10-12). In the case of the toy model described in Fig. 1, cells are assumed to determine their generation times (division interval) randomly by roll of a dice. The mean of intrinsic cellular generation time is thus ð1 + 2 + ⋯ + 6Þ=6 = 3.5 h, but population doubling time, which is...

Recent advances in single-cell time-lapse microscopy have revealed non-genetic heterogeneity and temporal fluctuations of cellular phenotypes. While different phenotypic traits such as abundance of growth-related proteins in single cells may have differential effects on the reproductive success of cells, rigorous experimental quantification of this process has remained elusive due to the complexity of single cell physiology within the context of a proliferating population. We introduce and apply a practical empirical method to quantify the fitness landscapes of arbitrary phenotypic traits, using genealogical data in the form of population lineage trees which can include phenotypic data of various kinds. Our inference methodology for fitness landscapes determines how reproductivity is correlated to cellular phenotypes, and provides a natural generalization of bulk growth rate measures for single-cell histories. Using this technique, we quantify the strength of selection acting on different cellular phenotypic traits within populations, which allows us to determine whether a change in population growth is caused by individual cells’ response, selection within a population, or by a mixture of these two processes. By applying these methods to single-cell time-lapse data of growing bacterial populations that express a resistance-conferring protein under antibiotic stress, we show how the distributions, fitness landscapes, and selection strength of single-cell phenotypes are affected by the drug. Our work provides a unified and practical framework for quantitative measurements of fitness landscapes and selection strength for any statistical quantities definable on lineages, and thus elucidates the adaptive significance of phenotypic states in time series data. The method is applicable in diverse fields, from single cell biology to stem cell differentiation and viral evolution.

Bacterial persistence is a phenomenon in which a small fraction of isogenic bacterial cells survives a lethal dose of antibiotics. It is generally assumed that persistence is caused by growth-arrested dormant cells generated prior to drug exposure. However, evidence from direct observation is scarce due to extremely low frequencies of persisters, and is limited to high persistence mutants or to conditions that significantly increase persister frequencies. Here, utilizing a microfluidic device with a membrane-covered microchamber array, we visualize the responses of more than 106 individual cells of wildtype Escherichia coli to lethal doses of antibiotics, sampling cells from different growth phases and culture media. We show that preexisting dormant persisters constitute only minor fractions of persistent cell lineages in populations sampled from exponential phase, and that most persistent cell lineages grew actively before drug exposure. Actively growing persisters exhibit radical morphological changes in response to drug exposure, including L-form-like morphologies or filamentation depending on antibiotic type, and restore their rod-like shape after drug removal. Incubating cells under stationary phase conditions increases both the frequency and the probability of survival of dormant cells. While dormant cells in late stationary phase express a general stress response regulator, RpoS, at high levels, persistent cell lineages tended to show low to moderate RpoS expression among the dormant cells. These results demonstrate that heterogeneous survival pathways may coexist within bacterial populations to achieve persistence and that persistence does not necessarily require dormant cells.