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An important step towards understanding the mechanistic basis of the central dogma is the quantitative characterization of the dynamics of nucleic-acid-bound molecular motors in the context of the living cell, where a crowded cytoplasm as well as competing and potentially antagonistic processes may significantly affect their rapidity and reliability. To capture these dynamics, we develop a novel method, lag-time analysis, for measuring in vivo dynamics. The approach uses exponential growth as the stopwatch to resolve dynamics in an asynchronous culture and therefore circumvents the difficulties and potential artifacts associated with synchronization or fluorescent labeling. Although lag-time analysis has the potential to be widely applicable to the quantitative analysis of in vivo dynamics, we focus on an important application: characterizing replication dynamics. To benchmark the approach, we analyze replication dynamics in three different species and a collection of mutants. We provide the first quantitative locus-specific measurements of fork velocity, in units of kb per second, as well as replisome-pause durations, some with the precision of seconds. The measured fork velocity is observed to be both locus and time dependent, even in wild-type cells. In addition to quantitatively characterizing known phenomena, we detect brief, locus-specific pauses at rDNA in wild-type cells for the first time. We also observe temporal fork velocity oscillations in three highly-divergent bacterial species. Lag-time analysis not only has great potential to offer new insights into replication, as demonstrated in the paper, but also has potential to provide quantitative insights into other important processes., Comment: 37 pages, 24 figures

Advances in microscopy hold great promise for allowing quantitative and precise measurement of morphological and molecular phenomena at the single-cell level in bacteria; however, the potential of this approach is ultimately limited by the availability of methods to faithfully segment cells independent of their morphological or optical characteristics. Here, we present Omnipose, a deep neural network image-segmentation algorithm. Unique network outputs such as the gradient of the distance field allow Omnipose to accurately segment cells on which current algorithms, including its predecessor, Cellpose, produce errors. We show that Omnipose achieves unprecedented segmentation performance on mixed bacterial cultures, antibiotic-treated cells and cells of elongated or branched morphology. Furthermore, the benefits of Omnipose extend to non-bacterial subjects, varied imaging modalities and three-dimensional objects. Finally, we demonstrate the utility of Omnipose in the characterization of extreme morphological phenotypes that arise during interbacterial antagonism. Our results distinguish Omnipose as a powerful tool for characterizing diverse and arbitrarily shaped cell types from imaging data. This work was supported by the National Institutes of Health (AI080609 to J.D.M., GM128191 to P.A.W., R01-GM128191 to T.W.L. and T32-GM008268 to K.J.C.). L.R. and N.S. were funded by the European Research Council under the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement no. 852201), the Spanish Ministry of Economy, Industry and Competitiveness to the EMBL partnership, the Centro de Excelencia Severo Ochoa (CEX2020-001049-S, MCIN/AEI /10.13039/501100011033), the CERCA Programme/Generalitat de Catalunya and the Spanish Ministry of Economy, Industry and Competitiveness Excelencia award PID2020-115189GB-I00. C.S. was funded by the Howard Hughes Medical Institute at the Janelia Research Campus. J.D.M. is an HHMI Investigator.

Advances in microscopy hold great promise for allowing quantitative and precise readouts of morphological and molecular phenomena at the single cell level in bacteria. However, the potential of this approach is ultimately limited by the availability of methods to perform unbiased cell segmentation, defined as the ability to faithfully identify cells independent of their morphology or optical characteristics. In this study, we present a new algorithm, Omnipose, which accurately segments samples that present significant challenges to current algorithms, including mixed bacterial cultures, antibiotic-treated cells, and cells of extended or branched morphology. We show that Omnipose achieves generality and performance beyond leading algorithms and its predecessor, Cellpose, by virtue of unique neural network outputs such as the gradient of the distance field. Finally, we demonstrate the utility of Omnipose in the characterization of extreme morphological phenotypes that arise during interbacterial antagonism and on the segmentation of non-bacterial objects. Our results distinguish Omnipose as a uniquely powerful tool for answering diverse questions in bacterial cell biology.

Two powerful and complementary experimental approaches are commonly used to study the cell cycle and cell biology: One class of experiments characterizes the statistics (or demographics) of an unsynchronized exponentially-growing population, while the other captures cell cycle dynamics, either by time-lapse imaging of full cell cycles or in bulk experiments on synchronized populations. In this paper, we study the subtle relationship between observations in these two distinct experimental approaches. We begin with an existing model: a single-cell deterministic description of cell cycle dynamics where cell states (i.e. periods or phases) have precise lifetimes. We then generalize this description to a stochastic model in which the states have stochastic lifetimes, as described by arbitrary probability distribution functions. Our analyses of the demographics of an exponential culture reveal a simple and exact correspondence between the deterministic and stochastic models: The corresponding state lifetimes in the deterministic model are equal to the exponential mean of the lifetimes in the stochastic model. An important implication is therefore that the demographics of an exponential culture will be well-fit by a deterministic model even if the state timing is stochastic. Although we explore the implications of the models in the context of the Escherichia coli cell cycle, we expect both the models as well as the significance of the exponential-mean lifetimes to find many applications in the quantitative analysis of cell cycle dynamics in other biological systems.

Two powerful and complementary experimental approaches are commonly used to study the cell cycle and cell biology: One class of experiments characterizes the statistics (or demographics) of an unsynchronized exponentially-growing population, while the other captures cell cycle dynamics, either by time-lapse imaging of full cell cycles or in bulk experiments on synchronized populations. In this paper, we study the subtle relationship between observations in these two distinct experimental approaches. We begin with an existing model: a single-cell deterministic description of cell cycle dynamics where cell states (i.e. periods or phases) have precise lifetimes. We then generalize this description to a stochastic model in which the states have stochastic lifetimes, as described by arbitrary probability distribution functions. Our analyses of the demographics of an exponential culture reveal a simple and exact correspondence between the deterministic and stochastic models: The corresponding state lifetimes in the deterministic model are equal to the exponential mean of the lifetimes in the stochastic model. An important implication is therefore that the demographics of an exponential culture will be well-fit by a deterministic model even if the state timing is stochastic. Although we explore the implications of the models in the context of the Escherichia coli cell cycle, we expect both the models as well as the significance of the exponential-mean lifetimes to find many applications in the quantitative analysis of cell cycle dynamics in other biological systems.

DNA–protein interactions (DPIs) are central to such fundamental cellular processes as transcription and chromosome maintenance and organization. The spatiotemporal dynamics of these interactions dictate their functional consequences; therefore, there is great interest in facile methods for defining the sites of DPI within cells. Here, we present a general method for mapping DPI sites in vivo using the double stranded DNA-specific cytosine deaminase toxin DddA. Our approach, which we term DddA-sequencing (3D-seq), entails generating a translational fusion of DddA to a DNA binding protein of interest, inactivating uracil DNA glycosylase, modulating DddA activity via its natural inhibitor protein, and DNA sequencing for genome-wide DPI detection. We successfully applied this method to three Pseudomonas aeruginosa transcription factors that represent divergent protein families and bind variable numbers of chromosomal locations. 3D-seq offers several advantages over existing technologies including ease of implementation and the possibility to measure DPIs at single-cell resolution.

DNA–protein interactions are central to fundamental cellular processes, yet widely implemented technologies for measuring these interactions on a genome scale in bacteria are laborious and capture only a snapshot of binding events. We devised a facile method for mapping DNA–protein interaction sites in vivo using the double-stranded DNA-specific cytosine deaminase toxin DddA. In 3D-seq (DddA-sequencing), strains containing DddA fused to a DNA-binding protein of interest accumulate characteristic mutations in DNA sequence adjacent to sites occupied by the DNA-bound fusion protein. High-depth sequencing enables detection of sites of increased mutation frequency in these strains, yielding genome-wide maps of DNA–protein interaction sites. We validated 3D-seq for four transcription regulators in two bacterial species, Pseudomonas aeruginosa and Escherichia coli. We show that 3D-seq offers ease of implementation, the ability to record binding event signatures over time and the capacity for single-cell resolution.

Wide-field magnetometry can be realized by imaging the optically-detected magnetic resonance of diamond nitrogen vacancy (NV) center ensembles. However, NV ensemble inhomogeneities significantly limit the magnetic-field sensitivity of these measurements. We demonstrate a double-double quantum (DDQ) driving technique to facilitate wide-field magnetic imaging of dynamic magnetic fields at a micron scale. DDQ imaging employs four-tone radio frequency pulses to suppress inhomogeneity-induced variations of the NV resonant response. As a proof-of-principle, we use the DDQ technique to image the dc magnetic field produced by individual magnetic-nanoparticles tethered by single DNA molecules to a diamond sensor surface. This demonstrates the efficacy of the diamond NV ensemble system in high-frame-rate magnetic microscopy, as well as single-molecule biophysics applications.

When bacterial cells come in contact, antagonism mediated by the delivery of toxins frequently ensues. The potential for such encounters to have long-term beneficial consequences in recipient cells has not been investigated. Here, we examined the effects of intoxication by DddA, a cytosine deaminase delivered via the type VI secretion system (T6SS) of Burkholderia cenocepacia. Despite its killing potential, we observed that several bacterial species resist DddA and instead accumulate mutations. These mutations can lead to the acquisition of antibiotic resistance, indicating that even in the absence of killing, interbacterial antagonism can have profound consequences on target populations. Investigation of additional toxins from the deaminase superfamily revealed that mutagenic activity is a common feature of these proteins, including a representative we show targets single-stranded DNA and displays a markedly divergent structure. Our findings suggest that a surprising consequence of antagonistic interactions between bacteria could be the promotion of adaptation via the action of directly mutagenic toxins.

Entry into sporulation in Bacillus subtilis is governed by a phosphorelay in which phosphoryl groups from a histidine kinase are successively transferred via relay proteins to the response regulator Spo0A. Spo0A~P, in turn, sets in motion events that lead to asymmetric division and activation of the cell-specific transcription factor σF, a hallmark for entry into sporulation. Here, we have used a microfluidics-based platform to investigate the activation of Spo0A and σF in individual cells held under constant, sporulation-inducing conditions. The principal conclusions were that: (i) activation of σF occurs with an approximately constant probability after adaptation to conditions of nutrient limitation; (ii) activation of σF is tightly correlated with, and preceded by, Spo0A~P reaching a high threshold level; (iii) activation of Spo0A takes place abruptly just prior to asymmetric division; and (iv) the primary source of noise in the activation of Spo0A is the phosphorelay. We propose that cells exhibit a constant probability of attaining a high threshold level of Spo0A~P due to fluctuations in the flux of phosphoryl groups through the phosphorelay.

Advances in automated fluorescence microscopy have made snapshot and time-lapse imaging of bacterial cells commonplace, yet fundamental challenges remain in analysis. The vast quantity of data collected in high-throughput experiments requires a fast and reliable automated method to analyze fluorescence intensity and localization, cell morphology and proliferation as well as other descriptors. Inspired by effective yet tractable methods of population-level analysis using flow cytometry, we have developed a framework and tools for facilitating analogous analyses in image cytometry. These tools can both visualize and gate (generate subpopulations) more than 70 cell descriptors, including cell size, age and fluorescence. The method is well suited to multi-well imaging, analysis of bacterial cultures with high cell density (thousands of cells per frame) and complete cell cycle imaging. We give a brief description of the analysis of four distinct applications to emphasize the broad applicability of the tool.

We expand upon a natural analogy between Bayesian statistics and statistical physics in which sample size corresponds to inverse temperature. This analogy motivates the definition of two statistical quantities: a learning capacity and a Gibbs entropy. The analysis of the learning capacity, corresponding to the heat capacity in thermal physics, leads to insight into the mechanism of learning and explains why some models have anomalously high learning performance. We explore the properties of the learning capacity in a number of examples, including a sloppy model. Next, we propose that the Gibbs entropy provides a natural device for counting distinguishable distributions in the context of Bayesian inference. We use this device to define a generalized principle of indifference in which every distinguishable model is assigned equal a priori probability. This principle results in a solution to a long-standing problem in Bayesian inference: the definition of an objective or uninformative prior. A key characteristic of this approach is that it can be applied to analyses where the model dimension is unknown and circumvents the automatic rejection of higher-dimensional models in Bayesian inference.

Many quantitative cell biology questions require fast yet reliable automated image segmentation to identify and link cells from frame-to-frame, and characterize the cell morphology and fluorescence. We present SuperSegger, an automated MATLAB-based image processing package well-suited to quantitative analysis of high-throughput live-cell fluorescence microscopy of bacterial cells. SuperSegger incorporates machine-learning algorithms to optimize cellular boundaries and automated error resolution to reliably link cells from frame-to-frame. Unlike existing packages, it can reliably segment micro-colonies with many cells, facilitating the analysis of cell-cycle dynamics in bacteria as well as cell-contact mediated phenomena. This package has a range of built-in capabilities for characterizing bacterial cells, including the identification of cell division events, mother, daughter, and neighboring cells, and computing statistics on cellular fluorescence, the location and intensity of fluorescent foci. SuperSegger provides a variety of post-processing data visualization tools for single cell and population level analysis, such as histograms, kymographs, frame mosaics, movies, and consensus images. Finally, we demonstrate the power of the package by analyzing lag phase growth with single cell resolution. This article is protected by copyright. All rights reserved.

ADP-ribosylation of proteins can profoundly impact their function and serves as an effective mechanism by which bacterial toxins impair eukaryotic cell processes. Here, we report the discovery that bacteria also employ ADP-ribosylating toxins against each other during interspecies competition. We demonstrate that one such toxin from Serratia proteamaculans interrupts the division of competing cells by modifying the essential bacterial tubulin-like protein, FtsZ, adjacent to its protomer interface, blocking its capacity to polymerize. The structure of the toxin in complex with its immunity determinant revealed two distinct modes of inhibition: active site occlusion and enzymatic removal of ADP-ribose modifications. We show that each is sufficient to support toxin immunity; however, the latter additionally provides unprecedented broad protection against non-cognate ADP-ribosylating effectors. Our findings reveal how an interbacterial arms race has produced a unique solution for safeguarding the integrity of bacterial cell division machinery against inactivating post-translational modifications.

The type VI secretion system (T6SS) inhibits the growth of neighboring bacterial cells through a contact-mediated mechanism. Here, we describe a detailed characterization of the protein localization dynamics in the Pseudomonas aeruginosa T6SS. It has been proposed that the type VI secretion process is driven by a conformational-change-induced contraction of the T6SS sheath. However, although the contraction of an optically resolvable TssBC sheath and the subsequent localization of ClpV are observed in Vibrio cholerae , coordinated assembly and disassembly of TssB and ClpV are observed without TssB contraction in P. aeruginosa . These dynamics are inconsistent with the proposed contraction sheath model. Motivated by the phenomenon of dynamic instability, we propose a new model in which ATP hydrolysis, rather than conformational change, generates the force for secretion. IMPORTANCE The type VI secretion system (T6SS) is widely conserved among Gram-negative bacteria and is a central determinant of bacterial fitness in polymicrobial communities. The secretion system targets bacteria and secretes effectors that inhibit the growth of neighboring cells, using a contact-mediated-delivery system. Despite significant homology to the previously characterized Vibrio cholerae T6SS, our analysis reveals that effector secretion is driven by a distinct force generation mechanism in Pseudomonas aeruginosa . The presence of two distinct force generation mechanisms in T6SS represents an example of the evolutionary diversification of force generation mechanisms.

DNA replication is essential to cellular proliferation. The cellular-scale organization of the replication machinery (replisome) and the replicating chromosome has remained controversial. Two competing models describe the replication process: In the track model, the replisomes translocate along the DNA like a train on a track. Alternately, in the factory model, the replisomes form a stationary complex through which the DNA is pulled. We summarize the evidence for each model and discuss a number of confounding aspects that complicate interpretation of the observations. We advocate a factory-like model for bacterial replication where the replisomes form a relatively stationary and weakly associated complex that can transiently separate.

This article describes the application of a change-point algorithm to the analysis of stochastic signals in biological systems whose underlying state dynamics consist of transitions between discrete states. Applications of this analysis include molecular-motor stepping, fluorophore bleaching, electrophysiology, particle and cell tracking, detection of copy number variation by sequencing, tethered-particle motion, etc. We present a unified approach to the analysis of processes whose noise can be modeled by Gaussian, Wiener, or Ornstein-Uhlenbeck processes. To fit the model, we exploit explicit, closed-form algebraic expressions for maximum-likelihood estimators of model parameters and estimated information loss of the generalized noise model, which can be computed extremely efficiently. We implement change-point detection using the frequentist information criterion (which, to our knowledge, is a new information criterion). The frequentist information criterion specifies a single, information-based statistical test that is free from ad hoc parameters and requires no prior probability distribution. We demonstrate this information-based approach in the analysis of simulated and experimental tethered-particle-motion data.

During the life of a cell, numerous essential cellular processes must be coordinated both spatially and temporally, from DNA replication and chromosome segregation to gene expression and cytokinesis. In order to analyze these inherently dynamic and cell-cycle-dependent processes, it is essential to observe the dynamic localization of the cellular machinery throughout the entire cell cycle. Although some coarse features of cell-cycle dynamics can be captured in snapshot imaging, where cellular size or morphology can be used as a proxy for cell-cycle phase, the inherently stochastic nature of ultrastructures in the cell makes the direct visualization of subcellular dynamics an essential tool to differentiate between structural differences that are the result of biologically relevant dynamics versus cell-to-cell variation. With these goals in mind, we have developed a unique high-throughput imaging approach, and have recently applied this to characterize the cell-cycle localization of nearly every protein in the bacterial cell (Kuwada in Mol Microbiol, 95(1), 64-79, 2015). This approach combines large-format sample preparation with automated image capture, processing, and analysis to quantitatively characterize proteome localization of tens of thousands of complete cell cycles.

It has been proposed that forces resulting from the physical exclusion of macromolecules from the bacterial nucleoid play a central role in organizing the bacterial cell, yet this proposal has not been quantitatively tested. To investigate this hypothesis, we mapped the generic motion of large protein complexes in the bacterial cytoplasm through quantitative analysis of thousands of complete cell-cycle trajectories of fluorescently tagged ectopic MS2-mRNA complexes. We find the motion of these complexes in the cytoplasm is strongly dependent on their spatial position along the long axis of the cell, and that their dynamics are consistent with a quantitative model that requires only nucleoid exclusion and membrane confinement. This analysis also reveals that the nucleoid increases the mobility of MS2-mRNA complexes, resulting in a fourfold increase in diffusion coefficients between regions of the lowest and highest nucleoid density. These data provide strong quantitative support for two modes of nucleoid action: the widely accepted mechanism of nucleoid exclusion in organizing the cell and a newly proposed mode, in which the nucleoid facilitates rapid motion throughout the cytoplasm.

Despite the innate complexity of the cell, emergent scale-invariant behavior is observed in many biological systems. We investigate one example of this phenomenon: the dynamics of large complexes in the bacterial cytoplasm. The observed dynamics of these complexes is scale invariant in three measures of dynamics: mean-squared displacement (MSD), velocity autocorrelation function, and the step-size distribution. To investigate the physical mechanism for this emergent scale invariance, we explore minimal models in which mobility is modeled as diffusion on a rough free-energy landscape in one dimension. We discover that all three scale-invariant characteristics emerge generically in the strong disorder limit. (Strong disorder is defined by the divergence of the ensemble-averaged hop time between lattice sites.) In particular, we demonstrate how the scale invariance of the relative step-size distribution can be understood from the perspective of extreme-value theory in statistics (EVT). We show that the Gumbel scale parameter is simply related to the MSD scaling parameter. The EVT mechanism of scale invariance is expected to be generic to strongly disordered systems and therefore a powerful tool for the analysis of other systems in biology and beyond.

Entry into sporulation in Bacillus subtilis is governed by a phosphorelay in which phosphoryl groups from a histidine kinase are successively transferred via relay proteins to the response regulator Spo0A. Spo0A~P, in turn, sets in motion events that lead to asymmetric division and activation of the cell‐specific transcription factor σF, a hallmark for entry into sporulation. Here, we have used a microfluidics‐based platform to investigate the activation of Spo0A and σF in individual cells held under constant, sporulation‐inducing conditions. The principal conclusions were that: (i) activation of σF occurs with an approximately constant probability after adaptation to conditions of nutrient limitation; (ii) activation of σF is tightly correlated with, and preceded by, Spo0A~P reaching a high threshold level; (iii) activation of Spo0A takes place abruptly just prior to asymmetric division; and (iv) the primary source of noise in the activation of Spo0A is the phosphorelay. We propose that cells exhibit a constant probability of attaining a high threshold level of Spo0A~P due to fluctuations in the flux of phosphoryl groups through the phosphorelay.

The positioning of the DNA replication machinery (replisome) has been the subject of several studies. Two conflicting models for replisome localization have been proposed: In the Factory Model, sister replisomes remain spatially co-localized as the replicating DNA is translocated through a stationary replication factory. In the Track Model, sister replisomes translocate independently along a stationary DNA track and the replisomes are spatially separated for the majority of the cell cycle. Here, we used time-lapse imaging to observe and quantify the position of fluorescently labeled processivity-clamp (DnaN) complexes throughout the cell cycle in two highly-divergent bacterial model organisms: Bacillus subtilis and Escherichia coli. Because DnaN is a core component of the replication machinery, its localization patterns should be an appropriate proxy for replisome positioning in general. We present automated statistical analysis of DnaN positioning in large populations, which is essential due to the high degree of cell-to-cell variation. We find that both bacteria show remarkably similar DnaN positioning, where any potential separation of the two replication forks remains below the diffraction limit throughout the majority of the replication cycle. Additionally, the localization pattern of several other core replisome components is consistent with that of DnaN. These data altogether indicate that the two replication forks remain spatially co-localized and mostly function in close proximity throughout the replication cycle. The conservation of the observed localization patterns in these highly divergent species suggests that the subcellular positioning of the replisome is a functionally critical feature of DNA replication., Author Summary Cell proliferation depends on efficient replication of the genome. Bacteria typically have a single origin of replication on a circular chromosome. After replication initiation, two replisomes assemble at the origin and each copy one of the two arms of the chromosome until they reach the terminus. There have been conflicting reports about the subcellular positioning and putative co-localization of the two replication forks during this process. It has remained controversial whether the two replisomes remain relatively close to each other with the DNA being pulled through, or separate as they translocate along the DNA like a track. Existing studies have relied heavily on snapshot images and these experiments cannot unambiguously distinguish between these two models: i.e. two resolvable forks versus two pairs of co-localized forks. The ability of replication to re-initiate before cell division in bacterial cells further complicates the interpretation of these types of imaging studies. In this paper, we use a combination of snapshot imaging, time-lapse imaging, and quantitative analysis to measure the fraction of time forks are co-localized during each cell cycle. We find that the forks are co-localized for the majority (80%) of the replication cycle in two highly-divergent model organisms: B. subtilis and E. coli. Our observations are consistent with proximal localization of the two forks, but also some transient separations of sister forks during replication. The conserved behavior of sub-cellular positioning of the replisomes in these two highly divergent species implies a potential functional relevance of this feature.

The canonical model of DNA replication describes a highly-processive and largely continuous process by which the genome is duplicated. This continuous model is based upon in vitro reconstitution and in vivo ensemble experiments. Here, we characterize the replisome-complex stoichiometry and dynamics with single-molecule resolution in bacterial cells. Strikingly, the stoichiometries of the replicative helicase, DNA polymerase, and clamp loader complexes are consistent with the presence of only one active replisome in a significant fraction of cells (>40%). Furthermore, many of the observed complexes have short lifetimes (

Bacterial cells display both spatial and temporal organization, and this complex structure is known to play a central role in cellular function. Although nearly one-fifth of all proteins in Escherichia coli localize to specific subcellular locations, fundamental questions remain about how cellular-scale structure is encoded at the level of molecular-scale interactions. One significant limitation to our understanding is that the localization behavior of only a small subset of proteins has been characterized in detail. As an essential step toward a global model of protein localization in bacteria, we capture and quantitatively analyze spatial and temporal protein localization patterns throughout the cell cycle for nearly every protein in E. coli that exhibits nondiffuse localization. This genome-scale analysis reveals significant complexity in patterning, notably in the behavior of DNA-binding proteins. Complete cell-cycle imaging also facilitates analysis of protein partitioning to daughter cells at division, revealing a broad and robust assortment of asymmetric partitioning behaviors.

To characterize the consequences of eliminating essential functions needed for peptidoglycan synthesis, we generated deletion mutations of Acinetobacter baylyi by natural transformation and visualized the resulting microcolonies of dead cells. We found that loss of genes required for peptidoglycan precursor synthesis or polymerization led to the formation of polymorphic giant cells with diameters that could exceed ten times normal. Treatment with antibiotics targeting early or late steps of peptidoglycan synthesis also produced giant cells. The giant cells eventually lysed, although they were partially stabilized by osmotic protection. Genome-scale transposon mutant screening (Tn-seq) identified mutations that blocked or accelerated giant cell formation. Among the mutations that blocked the process were those inactivating a function predicted to cleave murein glycan chains (the MltD murein lytic transglycosylase), suggesting that giant cell formation requires MltD hydrolysis of existing peptidoglycan. Among the mutations that accelerated giant cell formation after ß-lactam treatment were those inactivating an enzyme that produces unusual 3->3 peptide cross-links in peptidoglycan (the LdtG L,D-transpeptidase). The mutations may weaken the sacculus and make it more vulnerable to further disruption. Although the study focused on A. baylyi, we found that a pathogenic relative (A. baumannii) also produced giant cells with genetic dependencies overlapping those of A. baylyi. Overall, the analysis defines a genetic pathway for giant cell formation conserved in Acinetobacter species in which independent initiating branches converge to create the unusual cells., Author summary Although essential genes control the most basic functions of bacterial life, they are difficult to study genetically because mutants lacking the functions die. We have developed a simple procedure for creating bacteria in which different essential genes have been completely deleted, making it possible to analyze the roles of the missing functions based on the features of the dead cells that result. When genes needed for the production of the cell wall were inactivated, the bacteria formed bizarre giant cells. It was possible to identify the functions responsible for forming the giant cells, and to formulate a model for how they form. Since cell wall synthesis is one of the most important antibiotic targets, understanding how bacteria respond to its disruption may ultimately help in developing procedures to overcome antibiotic resistant bacterial infections.

The mechanism responsible for the accurate partitioning of newly replicated Escherichia coli chromosomes into daughter cells remains a mystery. In this article, we use automated cell cycle imaging to quantitatively analyse the cell cycle dynamics of the origin of replication (oriC) in hundreds of cells. We exploit the natural stochastic fluctuations of the chromosome structure to map both the spatial and temporal dependence of the motional bias segregating the chromosomes. The observed map is most consistent with force generation by an active mechanism, but one that generates much smaller forces than canonical molecular motors, including those driving eukaryotic chromosome segregation.

Membranes allow the compartmentalization of biochemical processes and are therefore fundamental to life. The conservation of the cellular membrane, combined with its accessibility to secreted proteins, has made it a common target of factors mediating antagonistic interactions between diverse organisms. Here we report the discovery of a diverse superfamily of bacterial phospholipase enzymes. Within this superfamily, we defined enzymes with phospholipase A1 (PLA1) and A2 (PLA2) activity, which are common in host cell-targeting bacterial toxins and the venoms of certain insects and reptiles1,2. However, we find that the fundamental role of the superfamily is to mediate antagonistic bacterial interactions as effectors of the type VI secretion system (T6SS) translocation apparatus; accordingly, we name these proteins type VI lipase effectors (Tle). Our analyses indicate that PldA of Pseudomonas aeruginosa, a eukaryotic-like phospholipase D (PLD)3, is a member of the Tle superfamily and the founding substrate of the haemolysin co-regulated protein secretion island II T6SS (H2-T6SS). While prior studies have specifically implicated PldA and the H2-T6SS in pathogenesis3–5, we uncovered a specific role for the effector and its secretory machinery in intra- and inter-species bacterial interactions. Furthermore we find that this effector achieves its antibacterial activity by degrading phosphatidylethanolamine (PE), the major component of bacterial membranes. The surprising finding that virulence-associated phospholipases can serve as specific antibacterial effectors suggests that interbacterial interactions are a relevant factor driving the ongoing evolution of pathogenesis.

Advances in automated fluorescence microscopy have made snap-shot and time-lapse imaging of bacterial cells commonplace, yet fundamental challenges remain in analysis. The vast quantity of data collected in high-throughput experiments requires a fast and reliable automated method to analyze fluorescence intensity and localization, cell morphology and proliferation as well as other descriptors. Inspired by effective yet tractable methods of population-level analysis using flow cytometry, we have developed a framework and tools for facilitating analogous analyses in image cytometry. These tools can both visualize and gate (generate sub-populations) more than 70 cell descriptors, including cell size, age, fluorescence, etc. The method is well suited to multi-well imaging, analysis of bacterial cultures with high cell density (thousands of cells per frame), and complete cell cycle imaging. We give a brief description of the analysis of four distinct applications to emphasize the broad applicability of the tool.

The cellular cytoplasm is a complex, heterogeneous environment (both spatially and temporally) that exhibits viscoelastic behavior. To further develop our quantitative insight into cellular transport, we analyze data sets of mRNA molecules fluorescently labeled with MS2-GFP tracked in real time in live Escherichia coli and Saccharomyces cerevisiae cells. As shown previously, these RNA-protein particles exhibit subdiffusive behavior that is viscoelastic in its origin. Examining the ensemble of particle displacements reveals a Laplace distribution at all observed timescales rather than the Gaussian distribution predicted by the central limit theorem. This ensemble non-Gaussian behavior is caused by a combination of an exponential distribution in the time-averaged diffusivities and non-Gaussian behavior of individual trajectories. We show that the non-Gaussian behavior is a consequence of significant heterogeneity between trajectories and dynamic heterogeneity along single trajectories. Informed by theory and simulation, our work provides an in-depth analysis of the complex diffusive behavior of RNA-protein particles in live cells.

The structure of the Escherichia coli chromosome is inherently dynamic over the duration of the cell cycle. Genetic loci undergo both stochastic motion around their initial positions and directed motion to opposite poles of the rod-shaped cell during segregation. We developed a quantitative method to characterize cell-cycle dynamics of the E. coli chromosome to probe the chromosomal steady-state mobility and segregation process. By tracking fluorescently labeled chromosomal loci in thousands of cells throughout the entire cell cycle, our method allows for the statistical analysis of locus position and motion, the step-size distribution for movement during segregation, and the locus drift velocity. The robust statistics of our detailed analysis of the wild-type E. coli nucleoid allow us to observe loci moving toward midcell before segregation occurs, consistent with a replication factory model. Then, as segregation initiates, we perform a detailed characterization of the average segregation velocity of loci. Contrary to origin-centric models of segregation, which predict distinct dynamics for oriC-proximal versus oriC-distal loci, we find that the dynamics of loci were universal and independent of genetic position.

Summary Bacterial genomes are organized by a plethora of chromatin proteins and physical mechanisms. This organization appears to be hierarchical with DNA folding events at the nm scale influencing higher levels of chromosome organization. Besides acting in shaping the genome these factors also play important regulatory roles in numerous DNA transactions. While DNA folding mechanisms operating at the nm scale are fairly well understood, it has been hard to translate this knowledge into accurate models that describe the complete dynamics of the genome. In recent years new techniques have evolved that are key to filling the current gaps in understanding. Particularly insightful in this light appear techniques that probe architectural properties of chromatin proteins on single molecules, techniques that map the binding of protein components and spatial structure on a genome-wide basis and improved imaging techniques that provide resolutions capable of resolving substructures/heterogeneities in the nucleoid. Moreover, bioinformatic and polymer physics approaches are starting to provide novel insights. In our opinion, an important aim in the field is to generate an accurate and complete description of the nucleoid and its dynamics at all scales. A first step towards this aim has now been set by bringing together people from diverse disciplinary backgrounds at the Lorentz centre workshop ‘Biology and Physics of Bacterial Genome Organization’ in Leiden, the Netherlands from 18 to 22 June 2012.

Pseudomonas aeruginosa responds to growth on agar surfaces to produce cyclic-di-GMP, which stimulates biofilm formation. This is mediated by an alternative cellular function chemotaxis-like system called Wsp. The receptor protein WspA, is bioinformatically indistinguishable from methyl-accepting chemotaxis proteins. However, unlike standard chemoreceptors, WspA does not form stable clusters at cell poles. Rather, it forms dynamic clusters at both polar and lateral subcellular locations. To begin to study the mechanism of Wsp signal transduction in response to surfaces, we carried out a structure-function study of WspA and found that its C-terminus is important for its lateral subcellular localization and function. When this region was replaced with that of a chemoreceptor for amino acids, WspA became polarly localized. In addition, introduction of mutations in the C-terminal region of WspA that rendered this protein able to form more stable receptor-receptor interactions, also resulted in a WspA protein that was less capable of activating signal transduction. Receptor chimeras with a WspA C-terminus and N-terminal periplasmic domains from chemoreceptors that sense amino acids or malate responded to surfaces to produce c-di-GMP. Thus, the amino acid sequence of the WspA periplasmic region did not need to be conserved for the Wsp system to respond to surfaces.

What physical mechanism leads to organization of a highly condensed and confined circular chromosome? Computational modeling shows that confinement-induced organization is able to overcome the chromosome's propensity to mix by the formation of topological domains. The experimentally observed high precision of separate subcellular positioning of loci (located on different chromosomal domains) in Escherichia coli naturally emerges as a result of entropic demixing of such chromosomal loops. We propose one possible mechanism for organizing these domains: regulatory control defined by the underlying E. coli gene regulatory network requires the colocalization of transcription factor genes and target genes. Investigating this assumption, we find the DNA chain to self-organize into several topologically distinguishable domains where the interplay between the entropic repulsion of chromosomal loops and their compression due to the confining geometry induces an effective nucleoid filament-type of structure. Thus, we propose that the physical structure of the chromosome is a direct result of regulatory interactions. To reproduce the observed precise ordering of the chromosome, we estimate that the domain sizes are distributed between 10 and 700 kb, in agreement with the size of topological domains identified in the context of DNA supercoiling.

Current models of stem cell biology assume that normal and neoplastic stem cells reside at the apices of hierarchies and differentiate into nonstem progeny in a unidirectional manner. Here we identify a subpopulation of basal-like human mammary epithelial cells that departs from that assumption, spontaneously dedifferentiating into stem-like cells. Moreover, oncogenic transformation enhances the spontaneous conversion, so that nonstem cancer cells give rise to cancer stem cell (CSC)-like cells in vitro and in vivo. We further show that the differentiation state of normal cells-of-origin is a strong determinant of posttransformation behavior. These findings demonstrate that normal and CSC-like cells can arise de novo from more differentiated cell types and that hierarchical models of mammary stem cell biology should encompass bidirectional interconversions between stem and nonstem compartments. The observed plasticity may allow derivation of patient-specific adult stem cells without genetic manipulation and holds important implications for therapeutic strategies to eradicate cancer.

The stochasticity of chromosome organization was investigated by fluorescently labeling genetic loci in live Escherichia coli cells. In spite of the common assumption that the chromosome is well modeled by an unstructured polymer, measurements of the locus distributions reveal that the E. coli chromosome is precisely organized into a nucleoid filament with a linear order. Loci in the body of the nucleoid show a precision of positioning within the cell of better than 10% of the cell length. The precision of interlocus distance of genomically-proximate loci was better than 4% of the cell length. The measured dependence of the precision of interlocus distance on genomic distance singles out intranucleoid interactions as the mechanism responsible for chromosome organization. From the magnitude of the variance, we infer the existence of an as-yet uncharacterized higher-order DNA organization in bacteria. We demonstrate that both the stochastic and average structure of the nucleoid is captured by a fluctuating elastic filament model.

Protein-mediated bridging is ubiquitous and essential for shaping cellular structures in all organisms. Here we dissect this mechanism for a model system: the Histone-like Nucleoid-Structuring protein (H-NS). We present data from two complementary single-molecule assays that probe the H-NS-DNA interaction: a dynamic optical-trap-driven unzipping assay and an equilibrium H-NS-mediated DNA looping scanning force microscopy imaging assay. To quantitatively analyze and compare these assays, we employ what we consider a novel theoretical framework that describes the bridging motif. The interplay between the experiments and our theoretical model not only infers the effective interaction free energy, the bridging conformation and the duplex-duplex spacing, but also reveals a second, unresolved, cis-binding mode that challenges our current understanding of the role of bridging proteins in chromatin structure. We expect that this theoretical framework for describing protein-mediated bridging will be applicable to proteins acting in chromatin and cytoskeletal organization.

Bacterial cells exhibit complex, cell-cycle-dependent subcellular organization despite the lack of membrane-bound organelles. One of the most popular proposed mechanisms for this cellular ultrastructure is physical exclusion from the dense bacterial nucleoid. To quantitatively investigate this hypothesis, we visualized and mapped the motion of fluorescently-tagged ectopic MS2-mRNA complexes in thousands of growing E. coli cells. We find that the the molecular complexes’ motion strongly depends on their spatial position along the long-axis of the cell and that their dynamics are well characterized by a quantitative model that requires only two physical contributions: nucleoid exclusion and membrane confinement. Strikingly, we also find that the mobility of the molecular complexes is highest in regions of high nucleoid density, and that perturbations to nucleoid structure tend to increase cytoplasmic mobility. These results provide strong quantitative support for two modes of nucleoid action: (1) organizing the cell through physical exclusion forces and (2) as a facilitator of rapid motion throughout the cytoplasm. These results have potentially important biological implications and suggest that the nucleoid may play a much more direct role, than previously thought, in the organization and transport of subcellular components, including large protein complexes and plasmids.