OmniSegger: A time-lapse image analysis pipeline for bacterial cells.

Time-lapse microscopy is a powerful tool for studying the cell biology of bacterial cells. The development of pipelines that facilitate the automated analysis of these datasets is a long-standing goal of the field. In this paper, we describe , an updated version of our pipeline, developed as an open-source, modular, and holistic suite of algorithms whose input is raw microscopy images and whose output is a wide range of quantitative cellular analyses, including dynamical cell cytometry data and cellular visualizations.

Protein overabundance is driven by growth robustness.

Protein expression levels optimize cell fitness: Too low an expression level of essential proteins will slow growth by compromising essential processes; whereas overexpression slows growth by increasing the metabolic load. This trade-off naïvely predicts that cells maximize their fitness by sufficiency, expressing just enough of each essential protein for function. We test this prediction in the naturally-competent bacterium by characterizing the proliferation dynamics of essential-gene knockouts at a single-cell scale (by imaging) as well as at a genome-wide scale (by TFNseq).

Protein overabundance is driven by growth robustness.

Protein expression levels optimize cell fitness: Too low an expression level of essential proteins will slow growth by compromising essential processes; whereas overexpression slows growth by increasing the metabolic load. This trade-off naïvely predicts that cells maximize their fitness by sufficiency, expressing just enough of each essential protein for function. We test this prediction in the naturally-competent bacterium by characterizing the proliferation dynamics of essential-gene knockouts at a single-cell scale (by imaging) as well as at a genome-wide scale (by TFNseq).

Noise robustness and metabolic load determine the principles of central dogma regulation.

The processes of gene expression are inherently stochastic, even for essential genes required for growth. How does the cell maximize fitness in light of noise? To answer this question, we build a mathematical model to explore the trade-off between metabolic load and growth robustness. The model provides insights for principles of central dogma regulation: Optimal protein expression levels for many genes are in vast overabundance.

Noise robustness and metabolic load determine the principles of central dogma regulation.

The processes of gene expression are inherently stochastic, even for essential genes required for growth. How does the cell maximize fitness in light of noise? To answer this question, we build a mathematical model to explore the trade-off between metabolic load and growth robustness. The model predicts novel principles of central dogma regulation: Optimal protein expression levels for many genes are in vast overabundance.

Noise robustness and metabolic load determine the principles of central dogma regulation.

The processes of gene expression are inherently stochastic, even for essential genes required for growth. How does the cell maximize fitness in light of noise? To answer this question, we build a mathematical model to explore the trade-off between metabolic load and growth robustness. The model predicts novel principles of central dogma regulation: Optimal protein expression levels for many genes are in vast overabundance.

The one-message-per-cell-cycle rule: A conserved minimum transcription level for essential genes.

The inherent stochasticity of cellular processes leads to significant cell-to-cell variation in protein abundance. Although this noise has already been characterized and modeled, its broader implications and significance remain unclear. In this paper, we revisit the noise model and identify the number of messages transcribed per cell cycle as the critical determinant of noise.

The one-message-per-cell-cycle rule: A conserved minimum transcription level for essential genes.

The inherent stochasticity of cellular processes leads to significant cell-to-cell variation in protein abundance. Although this noise has already been characterized and modeled, its broader implications and significance remain unclear. In this paper, we revisit the noise model and identify the number of messages transcribed per cell cycle as the critical determinant of noise.

The in vivo measurement of replication fork velocity and pausing by lag-time analysis.

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. To capture these dynamics, we develop lag-time analysis, a method for measuring in vivo dynamics. Using this approach, we provide quantitative locus-specific measurements of fork velocity, in units of kilobases per second, as well as replisome pause durations, some with the precision of seconds.

Omnipose: a high-precision morphology-independent solution for bacterial cell segmentation.

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.

Genome-wide protein-DNA interaction site mapping in bacteria using a double-stranded DNA-specific cytosine deaminase.

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.

Characterizing stochastic cell-cycle dynamics in exponential growth.

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.

An interbacterial DNA deaminase toxin directly mutagenizes surviving target populations.

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 .

Correspondence between thermodynamics and inference.

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.

Essential gene deletions producing gigantic bacteria.

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.

Bifunctional Immunity Proteins Protect Bacteria against FtsZ-Targeting ADP-Ribosylating Toxins.

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.

Strong disorder leads to scale invariance in complex biological systems.

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.

The bacterial replisome has factory-like localization.

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.

Type VI Secretion System Dynamics Reveals a Novel Secretion Mechanism in Pseudomonas aeruginosa.

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 T6SS. It has been proposed that the type VI secretion process is driven by a conformational-change-induced contraction of the T6SS sheath.

Noise in a phosphorelay drives stochastic entry into sporulation in .

Entry into sporulation in 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 σ, a hallmark for entry into sporulation. Here, we have used a microfluidics-based platform to investigate the activation of Spo0A and σ in individual cells held under constant, sporulation-inducing conditions.

The Replisomes Remain Spatially Proximal throughout the Cell Cycle in Bacteria.

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.

Transcription leads to pervasive replisome instability in bacteria.

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 reconstitution and ensemble experiments. Here, we characterize the replisome-complex stoichiometry and dynamics with single-molecule resolution in bacterial cells.

Cytoplasmic RNA-Protein Particles Exhibit Non-Gaussian Subdiffusive Behavior.

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.

Probing bacterial cell biology using image cytometry.

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.

SuperSegger: robust image segmentation, analysis and lineage tracking of bacterial cells.

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.