We present a protocol for building and operating an automated fluidic system for continuous culture that we call the 'morbidostat'. The morbidostat is used to follow the evolution of microbial drug resistance in real time. Instead of exposing bacteria to predetermined drug environments, the morbidostat constantly measures the growth rates of evolving microbial populations and dynamically adjusts drug concentrations inside culture vials in order to maintain a constant drug-induced inhibition. The growth rate measurements are done using an optical detection system that is based on measuring the intensity of back-scattered light from bacterial cells suspended in the liquid culture. The morbidostat can additionally be used as a chemostat or a turbidostat. The whole system can be built from readily available components within 2-3 weeks by biologists with some electronics experience or engineers familiar with basic microbiology.
Genetically identical cells sharing an environment can display markedly different phenotypes. It is often unclear how much of this variation derives from chance, external signals, or attempts by individual cells to exert autonomous phenotypic programs. By observing thousands of cells for hundreds of consecutive generations under constant conditions, we dissect the stochastic decision between a solitary, motile state and a chained, sessile state in Bacillus subtilis. We show that the motile state is 'memoryless', exhibiting no autonomous control over the time spent in the state. In contrast, the time spent as connected chains of cells is tightly controlled, enforcing coordination among related cells in the multicellular state. We show that the three-protein regulatory circuit governing the decision is modular, as initiation and maintenance of chaining are genetically separable functions. As stimulation of the same initiating pathway triggers biofilm formation, we argue that autonomous timing allows a trial commitment to multicellularity that external signals could extend.
Plasmid loss rate measurements are standard in microbiology and key to understanding plasmid stabilization mechanisms. The conventional assays eliminate selection for plasmids at the beginning of the experiment and screen for the appearance of plasmid-free cells over long-term population growth. However, it has been long appreciated in plasmid biology that the growth rate differential between plasmid-free and plasmid-containing cells at some point overshadows the effect of primary loss events, such that the assays can greatly over-estimate inherent loss rates. The standard solutions to this problem are to either consider the very early phase of loss where the fraction of plasmid-free cells increases linearly, or to measure the growth rate difference either by following the population for longer time or by measuring growth rates separately. Here we mathematically show that in all these cases, seemingly small experimental errors in the growth rate estimates can overshadow the estimates of the loss rates. For many plasmids, loss rates may thus be much lower than previously thought, and for some plasmids, the estimated loss rate may have nothing to do with actual loss rates. We further modify two independent experimental methods to separate inherent losses from growth differences and apply them to the same plasmids. First we use a high-throughput microscopy-based approach to screen for plasmid-free cells at extremely short time scales--tens of minutes rather than tens of generations--and apply it to a par⁻ version of mini-R1. Second we modify a counterselection-based plasmid loss assay inspired by the Luria-Delbrück fluctuation test that completely separates losses from growth, and apply it to various R1 and pSC101 derivatives. Concordant results from the two assays suggest that plasmids are lost at a lower frequency than previously believed. In fact, for par⁻ mini-R1 the observed loss rate of about 10⁻³ per cell and generation seems to be so low as to be inconsistent with what we know about the R1 stabilization mechanisms, suggesting these well characterized plasmids may have some additional and so far unknown stabilization mechanisms, for example improving copy number control or partitioning at cell division.
The life of plasmids is a constant battle against fluctuations: failing to correct copy number fluctuations can increase the plasmid loss rate by many orders of magnitude, as can a failure to more evenly divide the copies between daughters at cell division. Plasmids are therefore long-standing model systems for stochastic processes in cells, much thanks to the efforts of Kurt Nordström to whose memory this issue is dedicated. Here we analyze a range of experimental methods for measuring plasmid copy numbers in single cells, focusing on challenges, trade-offs, and necessary experimental controls. In particular we analyze published and unpublished strategies to infer copy numbers from expression of plasmid-encoded reporters, direct labeling of plasmids with fluorescent probes or DNA binding proteins fused to fluorescent reporters, PCR based methods applied to single cell lysates, and plasmid-specific replication arrest. We conclude that no method currently exists to measure plasmid copy numbers in single cells, and that most methods are overwhelmed by various types of experimental noise. We also discuss how accurate methods can be developed.
Studies of stochastic biological dynamics typically compare observed fluctuations to theoretically predicted variances, sometimes after separating the intrinsic randomness of the system from the enslaving influence of changing environments. But variances have been shown to discriminate surprisingly poorly between alternative mechanisms, while for other system properties no approaches exist that rigorously disentangle environmental influences from intrinsic effects. Here, we apply the theory of generalized random walks in random environments to derive exact rules for decomposing time series and higher statistics, rather than just variances. We show for which properties and for which classes of systems intrinsic fluctuations can be analyzed without accounting for extrinsic stochasticity and vice versa. We derive two independent experimental methods to measure the separate noise contributions and show how to use the additional information in temporal correlations to detect multiplicative effects in dynamical systems.
We introduce a nonintrusive method exploiting single-cell variability after cell division to validate protein localization. We found that Clp proteases, widely reported to form biologically relevant foci, were uniformly distributed in Escherichia coli cells, and that many commonly used fluorescent proteins caused severe mislocalization when fused to homo-oligomers. Retagging five other reportedly foci-forming proteins with the most monomeric fluorescent protein tested suggests that the foci were caused by the fluorescent tags.
Gene expression involves inherently probabilistic steps that create fluctuations in protein abundances. The results from many in-depth analyses and genome-scale surveys have suggested how such fluctuations arise and spread, often in ways consistent with stochastic models of transcription and translation. But fluctuations also arise during cell division when molecules are partitioned stochastically between the two daughters. Here we mathematically demonstrate how stochastic partitioning contributes to the non-genetic heterogeneity. Our results show that partitioning errors are hard to correct, and that the resulting noise profiles are remarkably difficult to separate from gene expression noise. By applying these results to common experimental strategies and distinguishing between creation versus transmission of noise, we hypothesize that much of the cell-to-cell heterogeneity that has been attributed to various aspects of gene expression instead comes from random segregation at cell division. We propose experiments to separate between these two types of fluctuations and discuss future directions.