Cell-size control and homeostasis in bacteria

Colony context and size-dependent compensation mechanisms give rise to variations in nuclear growth trajectories

To investigate how cellular variations arise across spatiotemporal scales in a population of identical healthy cells, we performed a data-driven analysis of nuclear growth variations in hiPS cell colonies as a model system. We generated a 3D timelapse dataset of thousands of nuclei over multiple days and developed open-source tools for image and data analysis and feature-based timelapse data exploration. Together, these data, tools, and workflows comprise a framework for systematic quantitative analysis of dynamics at individual and population levels, and the analysis further highlights important aspects to consider when interpreting timelapse data. We found that individual nuclear volume growth trajectories arise from short-timescale variations attributable to their spatiotemporal context within the colony. We identified a time-invariant volume compensation relationship between nuclear growth duration and starting volume across the population. Notably, we discovered that inheritance plays a crucial role in determining these two key nuclear growth features while other growth features are determined by their spatiotemporal context and are not inherited.

A generalized adder for cell size homeostasis: Effects on stochastic clonal proliferation

Measurements of cell size dynamics have revealed phenomenological principles by which individual cells control their size across diverse organisms. One of the emerging paradigms of cell size homeostasis is the adder, where the cell cycle duration is established such that the cell size increase from birth to division is independent of the newborn cell size. We provide a mechanistic formulation of the adder, considering that cell size follows any arbitrary nonexponential growth law. Our results show that the main requirement to obtain an adder regardless of the growth law (the time derivative of cell size) is that cell cycle regulators are produced at a rate proportional to the growth law, and cell division is triggered when these molecules reach a prescribed threshold level. Among the implications of this generalized adder, we investigate fluctuations in the proliferation of single-cell-derived colonies. Considering exponential cell size growth, random fluctuations in clonal size show a transient increase and then eventually decay to zero over time (i.e., clonal populations become asymptotically more similar). In contrast, several forms of nonexponential cell size dynamics (with adder-based cell size control) yield qualitatively different results: clonal size fluctuations monotonically increase over time, reaching a nonzero value. These results characterize the interplay between cell size homeostasis at the single-cell level and clonal proliferation at the population level, explaining the broad fluctuations in clonal sizes seen in barcoded human cell lines.

Estimation of the lifetime distribution from fluctuations in Bellman-Harris processes

The growth of populations without interactions can often be modeled by branching processes where each individual evolves independently and with the same law. In Bellman-Harris processes, each individual lives a random time and is then replaced by a random number of offspring. We are interested in the estimation of the parameters of this model. Our motivation comes from the estimation of cell division time and we focus on Gamma distribution for lifetime and binary reproduction. The mean of the lifetime is closely related to the growth rate of the population. Going farther and describing lifetime variability from fixed time observations is a challenging task, due to the complexity of the fluctuations of non-Markovian branching processes. Using fine results on these fluctuations, we describe two time-asymptotic regimes and explain how to discriminate between them and estimate the parameters. Then, we consider simulations and biological data to validate and discuss our method. It allows to determine single-cell parameters from time-resolved measurements of populations without the need to track each individual or to know the details of the initial condition. The results can be extended to more general branching processes.

Unveiling microbial single-cell growth dynamics under rapid periodic oxygen oscillations

Microbial metabolism and growth are tightly linked to oxygen (O2). Microbes experience fluctuating O2 levels in natural environments; however, our understanding of how cells respond to fluctuating O2 over various time scales remains limited due to challenges in observing microbial growth at single-cell resolution under controlled O2 conditions and in linking individual cell growth with the specific O2 microenvironment. We performed time-resolved microbial growth analyses at single-cell resolution under a temporally controlled O2 supply. A multilayer microfluidic device was developed, featuring a gas supply above a cultivation layer, separated by a thin membrane enabling efficient gas transfer. This platform allows microbial cultivation under constant, dynamic, and oscillating O2 conditions. Automated time-lapse microscopy and deep-learning-based image analysis provide access to spatiotemporally resolved growth data at the single-cell level. O2 switching within tens of seconds, coupled with precise microenvironment monitoring, allows us to accurately correlate cellular growth with local O2 concentrations. Growing Escherichia coli microcolonies subjected to varying O2 oscillation periods show distinct growth dynamics characterized by response and recovery phases. The comprehensive growth data and insights gained from our unique platform are a crucial step forward to systematically study cell response and adaptation to fluctuating O2 environments at single-cell resolution.

Single-cell analysis of antimicrobial compound-induced cell death of bacterial cells

Due to the stochasticity of metabolic reactions and cell cycles of bacterial cells, it is necessary to examine the antibacterial activities of antimicrobial compounds (AMCs) such as antibiotics and antimicrobial peptides (AMPs) at the single-cell level. Here, we review recent studies of the bactericidal activities of AMCs at the single-cell level. First, we discuss recent investigations of the interaction of various AMPs with single bacterial cells, as monitored in real time using optical microscopy. This strategy provides information on AMP-induced membrane damage in single cells [e.g. the onset time of damage to the cell membrane (CM) and outer membrane of single cells]. The rate of AMP-induced CM damage is estimated as the fraction of cells with CM damage [Pdamage (t)] at a specific interaction time t. Second, we discuss the use of single-cell analysis of the bactericidal activity of AMCs. The fraction of dead cells after the exposure to AMCs for time t is determined as the fraction of the microcolonies containing only one cell [Psingle (t)]. For some AMPs, the Pdamage (t) and Psingle (t) values are similar, indicating that AMP-induced CM damage is the direct cause of cell death. Third, we discuss single-cell analysis of the processes and mechanisms of antibiotic-induced cell death. For example, fluoroquinolones and aminoglycosides are observed to induce cytoplasmic condensation and cell lysis, leading to cell death. Based on these studies, we provide our perspective on future investigations using single-cell analysis to assess the processes and the mechanisms of the bactericidal activities of AMCs.

Mechanistic basis for non-exponential bacterial growth

Bacterial populations typically exhibit exponential growth under resource-rich conditions, yet individual cells often deviate from this pattern. Recent work has shown that the elongation rates of Escherichia coli and Caulobacter crescentus increase throughout the cell cycle (super-exponential growth), while Bacillus subtilis displays a mid-cycle minimum (convex growth), and Mycobacterium tuberculosis grows linearly. Here, we develop a single-cell model linking gene expression, proteome allocation, and mass growth to explain these diverse growth trajectories. By calibrating model parameters with experimental data, we show that DNA-proportional mRNA transcription produces near-exponential growth, whereas deviations from this proportionality yield the observed non-exponential growth patterns. Analysis of gene expression perturbations reveals that ribosome expression primarily controls dry mass growth rate, whereas envelope expression more strongly affects cell elongation rate. Fitting our model to single-cell experimental data reproduces convex, super-exponential, and linear modes of growth, demonstrating how envelope and ribosome expression schedules drive cell-cycle-specific behaviors. These findings provide a mechanistic basis for non-exponential single-cell growth and offer insights into how bacterial cells dynamically regulate elongation rates within each generation.

Mitochondrial function regulates cell growth kinetics to actively maintain mitochondrial homeostasis

Mitochondria are not produced de novo in newly divided daughter cells, but are inherited from the mother cell during mitosis. While mitochondrial homeostasis is crucial for living cells, the feedback responses that maintain mitochondrial volume across generations of dividing cells remain elusive. Here, using a microfluidic yeast 'mother machine', we tracked several generations of fission yeast cells and observed that cell size and mitochondrial volume grew exponentially during the cell cycle. We discovered that while mitochondrial homeostasis relied on the 'sizer' mechanism of cell size maintenance, mitochondrial function was a critical determinant of the timing of cell division: cells born with lower than average amounts of mitochondria grew slower and thus added more mitochondria before they divided. Thus, mitochondrial addition during the cell cycle was tailored to the volume of mitochondria at birth, such that all cells ultimately contained the same mitochondrial volume at cell division. Quantitative modelling and experiments with mitochondrial DNA-deficient rho0 cells additionally revealed that mitochondrial function was essential for driving the exponential growth of cells. Taken together, we demonstrate a central role for mitochondrial activity in dictating cellular growth rates and ensuring mitochondrial volume homeostasis.

Evolutionary Advantage of Cell Size Control

We analyze the advantage of cell size control strategies in growing populations under mortality constraints and show that growth-dependent mortality can select for accurate size control. We determine how mortality, noise, and nongenetic heritability of cell size impact long-term population growth. We derive an analytical expression for the optimal cell size. We demonstrate that size heritability enables selection to act on the distribution of cell sizes in a population to avoid viability thresholds and adapt to size- and growth-dependent mortality landscapes.

Microscopic origin of the spatial and temporal precision in biological systems

All living systems display remarkable spatial and temporal precision, despite operating in intrinsically fluctuating environments. It is even more surprising given that biological phenomena are regulated by multiple chemical reactions that are also random. Although the underlying molecular mechanisms of surprisingly high precision in biology remain not well understood, a novel theoretical picture that relies on the coupling of relevant stochastic processes has recently been proposed and applied to explain different phenomena. To illustrate this approach, in this review, we discuss two systems that exhibit precision control: spatial regulation in bacterial cell size and temporal regulation in the timing of cell lysis by λ bacteriophage. In cell-size regulation, it is argued that a balance between stochastic cell growth and cell division processes leads to a narrow distribution of cell sizes. In cell lysis, it is shown that precise timing is due to the coupling of holin protein accumulation and the breakage of the cellular membrane. The stochastic coupling framework also allows us to explicitly evaluate dynamic properties for both biological systems, eliminating the need to utilize the phenomenological concept of thresholds. Excellent agreement with experimental observations is observed, supporting the proposed theoretical ideas. These observations also suggest that the stochastic coupling method captures the important aspects of molecular mechanisms of precise cellular regulation, providing a powerful new tool for more advanced investigations of complex biological phenomena.

Immobile lipopolysaccharides and outer membrane proteins differentially segregate in growing Escherichia coli

The outer membrane (OM) of gram-negative bacteria is a robust, impermeable barrier that excludes many classes of antibiotics. Contrary to the classical model of an asymmetric lipid bilayer, recent evidence suggests the OM is predominantly an asymmetric proteolipid membrane (APLM). Outer leaflet lipopolysaccharides (LPS) that surround integral β-barrel outer membrane proteins (OMPs) are shared with other OMPs to form a supramolecular network in which the levels of OMPs approach those of LPS. Some of the most abundant OMPs in the Escherichia coli OM are trimeric porins. How porins and LPS are incorporated into the OM of growing bacteria is poorly understood. Here, we use live-cell imaging and microfluidics to investigate how LPS, labeled using click chemistry, and the porin OmpF, labeled using the bacteriocin colicin N, are incorporated into the E. coli OM. Diffraction-limited fluorescence microscopy shows OmpF and LPS to be uniformly distributed and immobile. However, clustering of both macromolecules becomes evident by superresolution microscopy, which is also the case for their biogenesis proteins, BamA and LptD, respectively. Notwithstanding these common organizational features, OmpF insertion into the OM is cell-cycle-dependent leading to binary partitioning and strong polar accumulation of old OmpF. Old LPS on the other hand is diluted ~50% at each division cycle by new LPS, resulting in only mild polar accumulation of preexisting LPS. We conclude that although LPS and OMPs are destined to form the APLM their insertion dynamics are fundamentally different, which has major implications for understanding how the OM is assembled.

From noisy cell size control to population growth: When variability can be beneficial

Single-cell experiments revealed substantial variability in generation times, growth rates, but also in birth and division sizes between genetically identical cells. Understanding how these fluctuations determine the fitness of the population, i.e., its growth rate, is necessary in any quantitative theory of evolution. Here, we develop a biologically relevant model which accounts for the stochasticity in single-cell growth rates, birth sizes, and division sizes. We derive expressions for the population growth rate and mean birth size in the population in terms of single-cell fluctuations. Allowing division sizes to fluctuate reveals how the mechanism of cell size control (timer, sizer, and adder) influences population growth. Surprisingly, we find that fluctuations in single-cell growth rates can be beneficial for population growth when slow-growing cells tend to divide at smaller sizes than fast-growing cells. Our framework is not limited to exponentially growing cells like Escherichia coli, and we derive similar expressions for cells with linear and bilinear growth laws, such as Mycobacterium tuberculosis and fission yeast Schizosaccharomyces pombe, respectively.

The design of unit cells by combining the self-reproduction systems and metabolic cushioning loads

Recently, we published a comprehensive theoretical analysis of the self-reproduction processes in proto-cells (doubling of their components) composed of different combinations of cellular subsystems. In this paper, we extend the detailed analysis of structural and functional peculiarities of self-reproduction processes to unit cells of the Cooper-Helmstetter-Donachie cell cycle theory. We show that: 1. Our modelling framework allows to calculate physiological parameters (numbers of cell components, flux patterns, cellular composition, etc.) of unit cells, including also unit cell mass that determines the DNA replication initiation conditions. 2. Unit cells might have additional cell (cushioning) components that are responsible not only for carrying out various special functions, but also for regulating cell size and stabilizing the growth of cells. 3. The optimal productivity of the synthesis of cushioning components (useful cellular load) is observed at doubling time approximately two times longer than the minimal doubling time of the unit cells.

Diversification of single-cell growth dynamics under starvation influences subsequent reproduction in a clonal bacterial population

Most of the microbes in nature infrequently receive nutrients and are thus in slow- or non-growing states. How quickly they can resume their growth upon an influx of new resources is crucial to occupy environmental niches. Isogenic microbial populations are known to harbor only a fraction of cells with rapid growth resumption, yet little is known about the physiological characteristics of those cells and their emergence in the population. Here, we tracked growth of individual Escherichia coli cells in populations under fluctuating nutrient conditions. We found that shifting from high- to low-nutrient conditions caused stalling of cell growth with few cells continuing to divide extremely slowly, a process which was dependent on lipid turnover. Resuming high-nutrient inflow after low-nutrient conditions resulted in cells resuming growth and division, but with different lag times and leading to varying progeny. The history of cell growth during low-nutrient but not high-nutrient conditions was determinant for resumption of growth, which cellular genealogy analysis suggested to originate from inherited physiological differences. Our results demonstrate that cellular growth dynamics become diverse by nutrient limitations, under which a fraction of cells experienced a particular growth history can reproduce progeny with new resources in the future.

Single-cell data reveal heterogeneity of investment in ribosomes across a bacterial population

Ribosomes are responsible for the synthesis of proteins, the major component of cellular biomass. Classical experiments have established a linear relationship between the fraction of resources invested in ribosomal proteins and the rate of balanced growth of a microbial population. Very little is known, however, about how the investment in ribosomes varies over individual cells in a population. We therefore extended the study of ribosomal resource allocation from populations to single cells, using a combination of time-lapse fluorescence microscopy and statistical inference. We found a large variability of ribosome concentrations and growth rates in conditions of balanced growth of the model bacterium Escherichia coli in a given medium, which cannot be accounted for by the population-level growth law. A large variability in the allocation of resources to ribosomes was also found during the transition of the bacteria from a poor to a rich growth medium. While some cells immediately adapt their ribosome synthesis rate to the new environment, others do so only gradually. Our results thus reveal a range of strategies for investing resources in the molecular machines at the heart of cellular self-replication. This raises the fundamental question whether the observed variability is an intrinsic consequence of the stochastic nature of the underlying biochemical processes or whether it improves the fitness of Escherichia coli in its natural environment.

Genome concentration limits cell growth and modulates proteome composition in Escherichia coli

Defining the cellular factors that drive growth rate and proteome composition is essential for understanding and manipulating cellular systems. In bacteria, ribosome concentration is known to be a constraining factor of cell growth rate, while gene concentration is usually assumed not to be limiting. Here, using single-molecule tracking, quantitative single-cell microscopy, and modeling, we show that genome dilution in Escherichia coli cells arrested for DNA replication limits total RNA polymerase activity within physiological cell sizes across tested nutrient conditions. This rapid-onset limitation on bulk transcription results in sub-linear scaling of total active ribosomes with cell size and sub-exponential growth. Such downstream effects on bulk translation and cell growth are near-immediately detectable in a nutrient-rich medium, but delayed in nutrient-poor conditions, presumably due to cellular buffering activities. RNA sequencing and tandem-mass-tag mass spectrometry experiments further reveal that genome dilution remodels the relative abundance of mRNAs and proteins with cell size at a global level. Altogether, our findings indicate that chromosome concentration is a limiting factor of transcription and a global modulator of the transcriptome and proteome composition in E. coli. Experiments in Caulobacter crescentus and comparison with eukaryotic cell studies identify broadly conserved DNA concentration-dependent scaling principles of gene expression.

Single-cell imaging of the Mycobacterium tuberculosis cell cycle reveals linear and heterogenous growth

Difficulties in antibiotic treatment of Mycobacterium tuberculosis (Mtb) are partly thought to be due to heterogeneity in growth. Although the ability of bacterial pathogens to regulate growth is crucial to control homeostasis, virulence and drug responses, single-cell growth and cell cycle behaviours of Mtb are poorly characterized. Here we use time-lapse, single-cell imaging of Mtb coupled with mathematical modelling to observe asymmetric growth and heterogeneity in cell size, interdivision time and elongation speed. We find that, contrary to Mycobacterium smegmatis, Mtb initiates cell growth not only from the old pole but also from new poles or both poles. Whereas most organisms grow exponentially at the single-cell level, Mtb has a linear growth mode. Our data show that the growth behaviour of Mtb diverges from that of model bacteria, provide details into how Mtb grows and creates heterogeneity and suggest that growth regulation may also diverge from that in other bacteria.

Kinetic theories of state- and generation-dependent cell populations

We formulate a general, high-dimensional, partial integrodifferential equation (PIDE) kinetic theory describing the internal state (such as gene expression or protein levels) of cells in a stochastically evolving population. The resolution of our kinetic theory also allows one to track subpopulations associated with each generation. Both intrinsic noise of the cell's internal attribute and randomness in a cell's division times (demographic stochasticity) are fundamental to the development of our model. Using our framework, we are able to marginalize the high-dimensional PIDEs in a number of different ways to derive equations which can be PIDEs themselves) that describe the dynamics of marginalized or "macroscopic" quantities such as structured population densities, moments of generation-dependent cellular states, and moments of the total population. We also show how nonlinear "interaction" terms in lower-dimensional integrodifferential equations can arise from high-dimensional linear kinetic models that contain rate parameters of a cell (birth and death rates) that depend on variables associated with other cells, generating couplings in the dynamics. Our analysis provides a general, more complete mathematical framework that resolves the coevolution of cell populations and cell states. The approach may be tailored for studying, e.g., gene expression in developing tissues, or other more general particle systems which exhibit Brownian noise in individual attributes and population-level demographic noise.

(p)ppGpp Buffers Cell Division When Membrane Fluidity Decreases in Escherichia coli

Fluidity is an inherent property of biological membranes and its maintenance (homeoviscous adaptation) is important for optimal functioning of membrane-associated processes. The fluidity of bacterial cytoplasmic membrane increases with temperature or an increase in the proportion of unsaturated fatty acids and vice versa. We found that strains deficient in the synthesis of guanine nucleotide analogs (p)ppGpp and lacking FadR, a transcription factor involved in fatty acid metabolism exhibited a growth defect that was rescued by an increase in growth temperature or unsaturated fatty acid content. The strain lacking (p)ppGpp was sensitive to genetic or chemical perturbations that decrease the proportion of unsaturated fatty acids over saturated fatty acids. Microscopy showed that the growth defect was associated with cell filamentation and lysis and rescued by combined expression of cell division genes ftsQ, ftsA, and ftsZ from plasmid or the gain-of-function ftsA* allele but not over-expression of ftsN. The results implicate (p)ppGpp in positive regulation of cell division during membrane fluidity loss through enhancement of FtsZ proto-ring stability. To our knowledge, this is the first report of a (p)ppGpp-mediated regulation needed for adaptation to membrane fluidity loss in bacteria.

Determining the rate-limiting processes for cell division in Escherichia coli

A critical cell cycle checkpoint for most bacteria is the onset of constriction when the septal peptidoglycan synthesis starts. According to the current understanding, the arrival of FtsN to midcell triggers this checkpoint in Escherichia coli. Recent structural and in vitro data suggests that recruitment of FtsN to the Z-ring leads to a conformational switch in actin-like FtsA, which links FtsZ protofilaments to the cell membrane and acts as a hub for the late divisome proteins. Here, we investigate this putative pathway using in vivo measurements and stochastic cell cycle modeling at moderately fast growth rates. Quantitatively upregulating protein concentrations and determining the resulting division timings shows that FtsN and FtsA numbers are not rate-limiting for the division in E. coli. However, at higher overexpression levels, they affect divisions: FtsN by accelerating and FtsA by inhibiting them. At the same time, we find that the FtsZ numbers in the cell are one of the rate-limiting factors for cell divisions in E. coli. Altogether, these findings suggest that instead of FtsN, accumulation of FtsZ in the Z-ring is one of the main drivers of the onset of constriction in E. coli at faster growth rates.

Progressive decline in old pole gene expression signal enhances phenotypic heterogeneity in bacteria

Cell growth and gene expression are heterogeneous processes at the single-cell level, leading to the emergence of multiple physiological states within bacterial populations. Aging is a known deterministic driver of growth asymmetry; however, its role in gene expression heterogeneity remains elusive. Here, we show that aging mother cells undergo a progressive decline in old pole activity, generating asymmetry in protein partitioning, gene expression, and cell morphology. We demonstrate that mother cells, when compared to their daughters, exhibit lower product inheritance and gene expression rates independently of promoter dynamics. The declining activity of maternal old poles generates gene expression gradients that manifest as mother-daughter asymmetry upon division, showing that asymmetry is progressively built over time within the maternal intracellular environment. Moreover, old pole aging correlates with a gradual increase in cell length, leading to morphological asymmetry. These findings provide further evidence for aging as a mechanism to enhance phenotypic heterogeneity in bacterial populations, with possible consequences for stress response and survival.

Ecological Determinants of Altruism in Prokaryote Antivirus Defense

Prokaryote evolution is driven in large part by the incessant arms race with viruses. Genomic investments in antivirus defense can be coarsely classified into two categories, immune systems that abrogate virus reproduction resulting in clearance, and altruistic programmed cell death (PCD) systems. Prokaryotic defense systems are enormously diverse, as revealed by an avalanche of recent discoveries, but the basic ecological determinants of defense strategy remain poorly understood. Through mathematical modeling of defense against lytic virus infection, we identify two principal determinants of optimal defense strategy and, through comparative genomics, we test this model by measuring the genomic investment into immunity vs PCD among diverse bacteria and archaea. First, as viral pressure grows, immunity becomes the preferred defense strategy. Second, as host population size grows, PCD becomes the preferred strategy. We additionally predict that, although optimal strategy typically involves investment in both PCD and immunity, overinvestment in immunity can result in system antagonism, increasing the probability a PCD-competent cell will lyse due to infection. Together these findings indicate that, generally, PCD is preferred at low multiplicity of infection (MOI) and immunity is preferred at high MOI, and that the landscape of prokaryotic antivirus defense is substantially more complex than previously suspected.

Mechanics and Morphology of Proliferating Cell Collectives with Self-Inhibiting Growth

We study the dynamics of proliferating cell collectives whose microscopic constituents' growth is inhibited by macroscopic growth-induced stress. Discrete particle simulations of a growing collective show the emergence of concentric-ring patterns in cell size whose spatiotemporal structure is closely tied to the individual cell's stress response. Motivated by these observations, we derive a multiscale continuum theory whose parameters map directly to the discrete model. Analytical solutions of this theory show the concentric patterns arise from anisotropically accumulated resistance to growth over many cell cycles. This Letter shows how purely mechanical processes can affect the internal patterning and morphology of cell collectives, and provides a concise theoretical framework for connecting the micro- to macroscopic dynamics of proliferating matter.

Bacterial cell size modulation along the growth curve across nutrient conditions

Under stable growth conditions, bacteria maintain cell size homeostasis through coordinated elongation and division. However, fluctuations in nutrient availability result in dynamic regulation of the target cell size. Using microscopy imaging and mathematical modelling, we examine how bacterial cell volume changes over the growth curve in response to nutrient conditions. We find that two rod-shaped bacteria, Escherichia coli and Salmonella enterica, exhibit similar cell volume distributions in stationary phase cultures irrespective of growth media. Cell resuspension in rich media results in a transient peak with a five-fold increase in cell volume ≈ 2h after resuspension. This maximum cell volume, which depends on nutrient composition, subsequently decreases to the stationary phase cell size. Continuous nutrient supply sustains the maximum volume. In poor nutrient conditions, cell volume shows minimal changes over the growth curve, but a markedly decreased cell width compared to other conditions. The observed cell volume dynamics translate into non-monotonic dynamics in the ratio between biomass (optical density) and cell number (colony-forming units), highlighting their non-linear relationship. Our findings support a heuristic model comparing modulation of cell division relative to growth across nutrient conditions and providing novel insight into the mechanisms of cell size control under dynamic environmental conditions.

Metabolic interactions control the transfer and spread of plasmid-encoded antibiotic resistance during surface-associated microbial growth

Surface-associated microbial systems are hotspots for the spread of plasmid-encoded antibiotic resistance, but how surface association affects plasmid transfer and proliferation remains unclear. Surface association enables prolonged spatial proximities between different populations, which promotes plasmid transfer between them. However, surface association also fosters strong metabolic interactions between different populations, which can direct their spatial self-organization with consequences for plasmid transfer and proliferation. Here, we hypothesize that metabolic interactions direct the spatial self-organization of different populations and, in turn, regulate the spread of plasmid-encoded antibiotic resistance. We show that resource competition causes populations to spatially segregate, which represses plasmid transfer. In contrast, resource cross-feeding causes populations to spatially intermix, which promotes plasmid transfer. We further show that the spatial positionings that emerge from metabolic interactions determine the proliferation of plasmid recipients. Our results demonstrate that metabolic interactions are important regulators of both the transfer and proliferation of plasmid-encoded antibiotic resistance.

A Generalized Adder mechanism for Cell Size Homeostasis: Implications for Stochastic Dynamics of Clonal Proliferation

Measurements of cell size dynamics have revealed phenomenological principles by which individual cells control their size across diverse organisms. One of the emerging paradigms of cell size homeostasis is the adder, where the cell cycle duration is established such that the cell size increase from birth to division is independent of the newborn cell size. We provide a mechanistic formulation of the adder considering that cell size follows any arbitrary non-exponential growth law. Our results show that the main requirement to obtain an adder regardless of the growth law (the time derivative of cell size) is that cell cycle regulators are produced at a rate proportional to the growth law and cell division is triggered when these molecules reach a prescribed threshold level. Among the implications of this generalized adder, we investigate fluctuations in the proliferation of single-cell derived colonies. Considering exponential cell size growth, random fluctuations in clonal size show a transient increase and then eventually decay to zero over time (i.e., clonal populations become asymptotically more similar). In contrast, several forms of non-exponential cell size dynamics (with adder-based cell size control) yield qualitatively different results: clonal size fluctuations monotonically increase over time reaching a non-zero value. These results characterize the interplay between cell size homeostasis at the single-cell level and clonal proliferation at the population level, explaining the broad fluctuations in clonal sizes seen in barcoded human cell lines.

Quantitative microbiology with widefield microscopy: navigating optical artefacts for accurate interpretations

Time-resolved live-cell imaging using widefield microscopy is instrumental in quantitative microbiology research. It allows researchers to track and measure the size, shape, and content of individual microbial cells over time. However, the small size of microbial cells poses a significant challenge in interpreting image data, as their dimensions approache that of the microscope's depth of field, and they begin to experience significant diffraction effects. As a result, 2D widefield images of microbial cells contain projected 3D information, blurred by the 3D point spread function. In this study, we employed simulations and targeted experiments to investigate the impact of diffraction and projection on our ability to quantify the size and content of microbial cells from 2D microscopic images. This study points to some new and often unconsidered artefacts resulting from the interplay of projection and diffraction effects, within the context of quantitative microbiology. These artefacts introduce substantial errors and biases in size, fluorescence quantification, and even single-molecule counting, making the elimination of these errors a complex task. Awareness of these artefacts is crucial for designing strategies to accurately interpret micrographs of microbes. To address this, we present new experimental designs and machine learning-based analysis methods that account for these effects, resulting in accurate quantification of microbiological processes.

Dispensability of extrinsic DnaA regulators in Escherichia coli cell-cycle control

Investigating a long-standing conceptual question in bacterial physiology, we examine why DnaA, the bacterial master replication initiator protein, exists in both ATP and ADP forms, despite only the ATP form being essential for initiation. We engineered the Δ4 Escherichia coli strain, devoid of all known external elements facilitating the DnaA-ATP/ADP conversion and found that these cells display nearly wild-type behaviors under nonoverlapping replication cycles. However, during rapid growth with overlapping cycles, Δ4 cells exhibit initiation instability. This aligns with our model predictions, suggesting that the intrinsic ATPase activity of DnaA alone is sufficient for robust initiation control in E. coli and the DnaA-ATP/ADP conversion regulatory elements extend the robustness to multifork replication, indicating an evolutionary adaptation. Moreover, our experiments revealed constant DnaA concentrations during steady-state cell elongation in both wild-type and Δ4 cells. These insights not only advance our understanding of bacterial cell-cycle regulation and DnaA but also highlight a fundamental divergence from eukaryotic cell-cycle controls, emphasizing protein copy-number sensing in bacteria versus programmed protein concentration oscillations in eukaryotes.

Differential growth regulates asymmetric size partitioning in Caulobacter crescentus

Cell size regulation has been extensively studied in symmetrically dividing cells, but the mechanisms underlying the control of size asymmetry in asymmetrically dividing bacteria remain elusive. Here, we examine the control of asymmetric division in Caulobacter crescentus, a bacterium that produces daughter cells with distinct fates and morphologies upon division. Through comprehensive analysis of multi-generational growth and shape data, we uncover a tightly regulated cell size partitioning mechanism. We find that errors in division site positioning are promptly corrected early in the division cycle through differential growth. Our analysis reveals a negative feedback between the size of daughter cell compartments and their growth rates, wherein the larger compartment grows slower to achieve a homeostatic size partitioning ratio at division. To explain these observations, we propose a mechanistic model of differential growth, in which equal amounts of growth regulators are partitioned into daughter cell compartments of unequal sizes and maintained over time via size-independent synthesis.

Architectural underpinnings of stochastic intergenerational homeostasis

Living systems are naturally complex and adaptive and offer unique insights into the strategies for achieving and sustaining stochastic homeostasis in different conditions. Here we focus on homeostasis in the context of stochastic growth and division of individual bacterial cells. We take advantage of high-precision long-term dynamical data that have recently been used to extract emergent simplicities and to articulate empirical intra- and intergenerational scaling laws governing these stochastic dynamics. From these data, we identify the core motif in the mechanistic coupling between division and growth, which naturally yields these precise rules, thus also bridging the intra- and intergenerational phenomenologies. By developing and utilizing techniques for solving a broad class of first-passage processes, we derive the exact analytic necessary and sufficient condition for sustaining stochastic intergenerational cell-size homeostasis within this framework. Furthermore, we provide predictions for the precision kinematics of cell-size homeostasis and the shape of the interdivision time distribution, which are compellingly borne out by the high-precision data. Taken together, these results provide insights into the functional architecture of control systems that yield robust yet flexible stochastic homeostasis.

Deciphering the Relationship between Cell Growth and Cell Cycle in Individual Escherichia coli Cells by Flow Cytometry

Accurate coordination of chromosome replication and cell division is essential for cellular processes, yet the regulatory mechanisms governing the bacterial cell cycle remain contentious. The lack of quantitative data connecting key cell cycle players at the single-cell level across large samples hinders consensus. Employing high-throughput flow cytometry, we quantitatively correlated the expression levels of key cell cycle proteins (FtsZ, MreB, and DnaA) with DNA content in individual bacteria. Our findings reveal distinct correlations depending on the chromosome number (CN), specifically whether CN ≤2 or ≥4, unveiling a mixed regulatory scenario in populations where CN of 2 or 4 coexist. We observed function-dependent regulations for these key proteins across nonoverlapping division cycles and various nutrient conditions. Notably, a logarithmic relationship between total protein content and replication origin number across nutrient conditions suggests a unified mechanism governing cell cycle progression, confirming the applicability of Schaechter's growth law to cells with CN ≥4. For the first time, we established a proportional relationship between the synthesis rates of key cell cycle proteins and chromosome dynamics in cells with CN ≥4. Drug experiments highlighted CN 2 and 4 as pivotal turning points influencing cellular resource allocation. This high-throughput, single-cell analysis provides interconnected quantitative insights into key molecular events, facilitating a predictive understanding of the relationship between cell growth and cell cycle.

Body size: A hidden trait of the organisms that influences the distribution of antibiotic resistance genes in soil

Body size is a key life-history trait of organisms, which has important ecological functions. However, the relationship between soil antibiotic resistance gene (ARG) distribution and organisms' body size has not been systematically reported so far. Herein, the impact of organic fertilizer on the soil ARGs and organisms (bacteria, fungi, and nematode) at the aggregate level was analyzed. The results showed that the smaller the soil aggregate size, the greater the abundance of ARGs, and the larger the body size of bacteria and nematodes. Further analysis revealed significant positive correlations of ARG abundance with the body sizes of bacteria, fungi, and nematodes, respectively. Additionally, the structural equation model demonstrated that changes in soil fertility mainly regulate the ARG abundance by affecting bacterial body size. The random forest model revealed that total phosphorus was the primary soil fertility factor influencing the body size of organisms. Therefore, these findings proposed that excessive application of phosphate fertilizers could increase the risk of soil ARG transmission by increasing the body size of soil organisms. This study highlights the significance of organisms' body size in determining the distribution of soil ARGs and proposes a new disadvantage of excessive fertilization from the perspective of ARGs.

Colony context and size-dependent compensation mechanisms give rise to variations in nuclear growth trajectories

To investigate the fundamental question of how cellular variations arise across spatiotemporal scales in a population of identical healthy cells, we focused on nuclear growth in hiPS cell colonies as a model system. We generated a 3D timelapse dataset of thousands of nuclei over multiple days, and developed open-source tools for image and data analysis and an interactive timelapse viewer for exploring quantitative features of nuclear size and shape. We performed a data-driven analysis of nuclear growth variations across timescales. We found that individual nuclear volume growth trajectories arise from short timescale variations attributable to their spatiotemporal context within the colony. We identified a strikingly time-invariant volume compensation relationship between nuclear growth duration and starting volume across the population. Notably, we discovered that inheritance plays a crucial role in determining these two key nuclear growth features while other growth features are determined by their spatiotemporal context and are not inherited.

Stochastic Gene Expression in Proliferating Cells: Differing Noise Intensity in Single-Cell and Population Perspectives

Random fluctuations (noise) in gene expression can be studied from two complementary perspectives: following expression in a single cell over time or comparing expression between cells in a proliferating population at a given time. Here, we systematically investigated scenarios where both perspectives lead to different levels of noise in a given gene product. We first consider a stable protein, whose concentration is diluted by cellular growth, and the protein inhibits growth at high concentrations, establishing a positive feedback loop. For a stochastic model with molecular bursting of gene products, we analytically predict and contrast the steady-state distributions of protein concentration in both frameworks. Although positive feedback amplifies the noise in expression, this amplification is much higher in the population framework compared to following a single cell over time. We also study other processes that lead to different noise levels even in the absence of such dilution-based feedback. When considering randomness in the partitioning of molecules between daughters during mitosis, we find that in the single-cell perspective, the noise in protein concentration is independent of noise in the cell cycle duration. In contrast, partitioning noise is amplified in the population perspective by increasing randomness in cell-cycle time. Overall, our results show that the commonly used single-cell framework that does not account for proliferating cells can, in some cases, underestimate the noise in gene product levels. These results have important implications for studying the inter-cellular variation of different stress-related expression programs across cell types that are known to inhibit cellular growth.

An evolutionary optimum amid moderate heritability in prokaryotic cell size

We investigate the distribution and evolution of prokaryotic cell size based on a compilation of 5,380 species. Size spans four orders of magnitude, from 100 nm (Mycoplasma) to more than 1 cm (Thiomargarita); however, most species congregate heavily around the mean. The distribution approximates but is distinct from log normality. Comparative phylogenetics suggests that size is heritable, yet the phylogenetic signal is moderate, and the degree of heritability is independent of taxonomic scale (i.e., fractal). Evolutionary modeling indicates the presence of an optimal cell size to which most species gravitate. The size is equivalent to a coccus of 0.70 μm in diameter. Analyses of 1,361 species with sequenced genomes show that genomic traits contribute to size evolution moderately and synergistically. Given our results, scaling theory, and empirical evidence, we discuss potential drivers that may expand or shrink cells around the optimum and propose a stability landscape model for prokaryotic cell size.

Scaling of stochastic growth and division dynamics: A comparative study of individual rod-shaped cells in the Mother Machine and SChemostat platforms

Microfluidic platforms enable long-term quantification of stochastic behaviors of individual bacterial cells under precisely controlled growth conditions. Yet, quantitative comparisons of physiological parameters and cell behaviors of different microorganisms in different experimental and device modalities is not available due to experiment-specific details affecting cell physiology. To rigorously assess the effects of mechanical confinement, we designed, engineered, and performed side-by-side experiments under otherwise identical conditions in the Mother Machine (with confinement) and the SChemostat (without confinement), using the latter as the ideal comparator. We established a protocol to cultivate a suitably engineered rod-shaped mutant of Caulobacter crescentus in the Mother Machine and benchmarked the differences in stochastic growth and division dynamics with respect to the SChemostat. While the single-cell growth rate distributions are remarkably similar, the mechanically confined cells in the Mother Machine experience a substantial increase in interdivision times. However, we find that the division ratio distribution precisely compensates for this increase, which in turn reflects identical emergent simplicities governing stochastic intergenerational homeostasis of cell sizes across device and experimental configurations, provided the cell sizes are appropriately mean-rescaled in each condition. Our results provide insights into the nature of the robustness of the bacterial growth and division machinery.

Mechanisms of cell size regulation in slow-growing Escherichia coli cells: discriminating models beyond the adder

Under ideal conditions, Escherichia coli cells divide after adding a fixed cell size, a strategy known as the adder. This concept applies to various microbes and is often explained as the division that occurs after a certain number of stages, associated with the accumulation of precursor proteins at a rate proportional to cell size. However, under poor media conditions, E. coli cells exhibit a different size regulation. They are smaller and follow a sizer-like division strategy where the added size is inversely proportional to the size at birth. We explore three potential causes for this deviation: degradation of the precursor protein and two models where the propensity for accumulation depends on the cell size: a nonlinear accumulation rate, and accumulation starting at a threshold size termed the commitment size. These models fit the mean trends but predict different distributions given the birth size. To quantify the precision of the models to explain the data, we used the Akaike information criterion and compared them to open datasets of slow-growing E. coli cells in different media. We found that none of the models alone can consistently explain the data. However, the degradation model better explains the division strategy when cells are larger, whereas size-related models (power-law and commitment size) account for smaller cells. Our methodology proposes a data-based method in which different mechanisms can be tested systematically.

The purpose and ubiquity of turnover

Turnover-constant component production and destruction-is ubiquitous in biology. Turnover occurs across organisms and scales, including for RNAs, proteins, membranes, macromolecular structures, organelles, cells, hair, feathers, nails, antlers, and teeth. For many systems, turnover might seem wasteful when degraded components are often fully functional. Some components turn over with shockingly high rates and others do not turn over at all, further making this process enigmatic. However, turnover can address fundamental problems by yielding powerful properties, including regeneration, rapid repair onset, clearance of unpredictable damage and errors, maintenance of low constitutive levels of disrepair, prevention of stable hazards, and transitions. I argue that trade-offs between turnover benefits and metabolic costs, combined with constraints on turnover, determine its presence and rates across distinct contexts. I suggest that the limits of turnover help explain aging and that turnover properties and the basis for its levels underlie this fundamental component of life.

Synergistic and antagonistic effects of deterministic and stochastic cell-cell variations

By diversifying, cells in a clonal population can together overcome the limits of individuals. Diversity in single-cell growth rates allows the population to survive environmental stresses, such as antibiotics, and grow faster than the undiversified population. These functional cell-cell variations can arise stochastically, from noise in biochemical reactions, or deterministically, by asymmetrically distributing damaged components. While each of the mechanisms is well understood, the effect of the combined mechanisms is unclear. To evaluate the contribution of the deterministic component we developed a mathematical model by mapping the growing population to the Ising model. To analyze the combined effects of stochastic and deterministic contributions we introduced the analytical results of the Ising-mapping into an Euler-Lotka framework. Model results, confirmed by simulations and experimental data, show that deterministic cell-cell variations increase near-linearly with stress. As a consequence, we predict that the gain in population doubling time from cell-cell variations is primarily stochastic at low stress but may cross over to deterministic at higher stresses. Furthermore, we find that while the deterministic component minimizes population damage, stochastic variations antagonize this effect. Together our results may help identifying stress-tolerant pathogenic cells and thus inspire novel antibiotic strategies.

Tools and methods for high-throughput single-cell imaging with the mother machine

Despite much progress, image processing remains a significant bottleneck for high-throughput analysis of microscopy data. One popular platform for single-cell time-lapse imaging is the mother machine, which enables long-term tracking of microbial cells under precisely controlled growth conditions. While several mother machine image analysis pipelines have been developed in the past several years, adoption by a non-expert audience remains a challenge. To fill this gap, we implemented our own software, MM3, as a plugin for the multidimensional image viewer napari. napari-MM3 is a complete and modular image analysis pipeline for mother machine data, which takes advantage of the high-level interactivity of napari. Here, we give an overview of napari-MM3 and test it against several well-designed and widely used image analysis pipelines, including BACMMAN and DeLTA. Researchers often analyze mother machine data with custom scripts using varied image analysis methods, but a quantitative comparison of the output of different pipelines has been lacking. To this end, we show that key single-cell physiological parameter correlations and distributions are robust to the choice of analysis method. However, we also find that small changes in thresholding parameters can systematically alter parameters extracted from single-cell imaging experiments. Moreover, we explicitly show that in deep learning-based segmentation, 'what you put is what you get' (WYPIWYG) - that is, pixel-level variation in training data for cell segmentation can propagate to the model output and bias spatial and temporal measurements. Finally, while the primary purpose of this work is to introduce the image analysis software that we have developed over the last decade in our lab, we also provide information for those who want to implement mother machine-based high-throughput imaging and analysis methods in their research.

Mechanical control of cell proliferation patterns in growing epithelial monolayers

Cell proliferation plays a crucial role in regulating tissue homeostasis and development. However, our understanding of how cell proliferation is controlled in densely packed tissues is limited. Here we develop a computational framework to predict the patterns of cell proliferation in growing epithelial tissues, connecting single-cell behaviors and cell-cell interactions to tissue-level growth. Our model incorporates probabilistic rules governing cell growth, division, and elimination, also taking into account their feedback with tissue mechanics. In particular, cell growth is suppressed and apoptosis is enhanced in regions of high cell density. With these rules and model parameters calibrated using experimental data for epithelial monolayers, we predict how tissue confinement influences cell size and proliferation dynamics and how single-cell physical properties influence the spatiotemporal patterns of tissue growth. In this model, mechanical feedback between tissue confinement and cell growth leads to enhanced cell proliferation at tissue boundaries, whereas cell growth in the bulk is arrested, recapitulating experimental observations in epithelial tissues. By tuning cellular elasticity and contact inhibition of proliferation we can regulate the emergent patterns of cell proliferation, ranging from uniform growth at low contact inhibition to localized growth at higher contact inhibition. We show that the cell size threshold at G1/S transition governs the homeostatic cell density and tissue turnover rate, whereas the mechanical state of the tissue governs the dynamics of tissue growth. In particular, we find that the cellular parameters affecting tissue pressure play a significant role in determining the overall growth rate. Our computational study thus underscores the impact of cell mechanical properties on the spatiotemporal patterns of cell proliferation in growing epithelial tissues.

Effects of Molecular Noise on Cell Size Control

Cells employ control strategies to maintain a stable size. Dividing at a target size (the "sizer" strategy) is thought to produce the tightest size distribution. However, this result follows from phenomenological models that ignore the molecular mechanisms required to implement the strategy. Here we investigate a simple mechanistic model for exponentially growing cells whose division is triggered at a molecular abundance threshold. We find that size noise inherits the molecular noise and is consequently minimized not by the sizer but by the "adder" strategy, where a cell divides after adding a target amount to its birth size. We derive a lower bound on size noise that agrees with publicly available data from six microfluidic studies on Escherichia coli bacteria.

Tight coupling of cell width to nucleoid structure in Escherichia coli

Cell dimensions of rod-shaped bacteria such as Escherichia coli are connected to mass growth and chromosome replication. During their interdivision cycle (τ min), cells enlarge by elongation only, but at faster growth in richer media, they are also wider. Changes in width W upon nutritional shift-up (shortening τ) occur during the division process. The elusive signal directing the mechanism for W determination is likely related to the tightly linked duplications of the nucleoid (DNA) and the sacculus (peptidoglycan), the only two structures (macromolecules) existing in a single copy that are coupled, temporally and spatially. Six known parameters related to the nucleoid structure and replication are reasonable candidates to convey such a signal, all simple functions of the key number of replication positions n(=C/τ), the ratio between the rates of growth (τ-1) and of replication (C-1). The current analysis of available literature-recorded data discovered that, of these, nucleoid complexity NC[=(2n-1)/(n×ln2)] is by far the most likely parameter affecting cell width W. The exceedingly high correlations found between these two seemingly unrelated measures (NC and W) indicate that coupling between them is of major importance to the species' survival. As an exciting corollary, to the best of our knowledge, a new, indirect approach to estimate DNA replication rate is revealed. Potential involvement of DNA topoisomerases in W determination is also proposed and discussed.

Tools and methods for high-throughput single-cell imaging with the mother machine

Despite much progress, image processing remains a significant bottleneck for high-throughput analysis of microscopy data. One popular platform for single-cell time-lapse imaging is the mother machine, which enables long-term tracking of microbial cells under precisely controlled growth conditions. While several mother machine image analysis pipelines have been developed in the past several years, adoption by a non-expert audience remains a challenge. To fill this gap, we implemented our own software, MM3, as a plugin for the multidimensional image viewer napari. napari-MM3 is a complete and modular image analysis pipeline for mother machine data, which takes advantage of the high-level interactivity of napari. Here, we give an overview of napari-MM3 and test it against several well-designed and widely-used image analysis pipelines, including BACMMAN and DeLTA. Researchers often analyze mother machine data with custom scripts using varied image analysis methods, but a quantitative comparison of the output of different pipelines has been lacking. To this end, we show that key single-cell physiological parameter correlations and distributions are robust to the choice of analysis method. However, we also find that small changes in thresholding parameters can systematically alter parameters extracted from single-cell imaging experiments. Moreover, we explicitly show that in deep learning based segmentation, "what you put is what you get" (WYPIWYG) - i.e., pixel-level variation in training data for cell segmentation can propagate to the model output and bias spatial and temporal measurements. Finally, while the primary purpose of this work is to introduce the image analysis software that we have developed over the last decade in our lab, we also provide information for those who want to implement mother-machine-based high-throughput imaging and analysis methods in their research.

Apparent simplicity and emergent robustness in the control of the Escherichia coli cell cycle

To examine how bacteria achieve robust cell proliferation across diverse conditions, we developed a method that quantifies 77 cell morphological, cell cycle, and growth phenotypes of a fluorescently labeled Escherichia coli strain and >800 gene deletion derivatives under multiple nutrient conditions. This approach revealed extensive phenotypic plasticity and deviating mutant phenotypes were often nutrient dependent. From this broad phenotypic landscape emerged simple and robust unifying rules (laws) that connect DNA replication initiation, nucleoid segregation, FtsZ ring formation, and cell constriction to specific aspects of cell size (volume, length, or added length) at the population level. Furthermore, completion of cell division followed the initiation of cell constriction after a constant time delay across strains and nutrient conditions, identifying cell constriction as a key control point for cell size determination. Our work provides a population-level description of the governing principles by which E. coli integrates cell cycle processes and growth rate with cell size to achieve its robust proliferative capability. A record of this paper's transparent peer review process is included in the supplemental information.

Energy allocation theory for bacterial growth control in and out of steady state

Efficient allocation of energy resources to key physiological functions allows living organisms to grow and thrive in diverse environments and adapt to a wide range of perturbations. To quantitatively understand how unicellular organisms utilize their energy resources in response to changes in growth environment, we introduce a theory of dynamic energy allocation which describes cellular growth dynamics based on partitioning of metabolizable energy into key physiological functions: growth, division, cell shape regulation, energy storage and loss through dissipation. By optimizing the energy flux for growth, we develop the equations governing the time evolution of cell morphology and growth rate in diverse environments. The resulting model accurately captures experimentally observed dependencies of bacterial cell size on growth rate, superlinear scaling of metabolic rate with cell size, and predicts nutrient-dependent trade-offs between energy expended for growth, division, and shape maintenance. By calibrating model parameters with available experimental data for the model organism E. coli, our model is capable of describing bacterial growth control in dynamic conditions, particularly during nutrient shifts and osmotic shocks. The model captures these perturbations with minimal added complexity and our unified approach predicts the driving factors behind a wide range of observed morphological and growth phenomena.

Scarcity of fixed carbon transfer in a model microbial phototroph-heterotroph interaction

Although the green alga Chlamydomonas reinhardtii has long served as a reference organism, few studies have interrogated its role as a primary producer in microbial interactions. Here, we quantitatively investigated C. reinhardtii's capacity to support a heterotrophic microbe using the established coculture system with Mesorhizobium japonicum, a vitamin B12-producing α-proteobacterium. Using stable isotope probing and nanoscale secondary ion mass spectrometry (nanoSIMS), we tracked the flow of photosynthetic fixed carbon and consequent bacterial biomass synthesis under continuous and diurnal light with single-cell resolution. We found that more 13C fixed by the alga was taken up by bacterial cells under continuous light, invalidating the hypothesis that the alga's fermentative degradation of starch reserves during the night would boost M. japonicum heterotrophy. 15NH4 assimilation rates and changes in cell size revealed that M. japonicum cells reduced new biomass synthesis in coculture with the alga but continued to divide-a hallmark of nutrient limitation often referred to as reductive division. Despite this sign of starvation, the bacterium still synthesized vitamin B12 and supported the growth of a B12-dependent C. reinhardtii mutant. Finally, we showed that bacterial proliferation could be supported solely by the algal lysis that occurred in coculture, highlighting the role of necromass in carbon cycling. Collectively, these results reveal the scarcity of fixed carbon in this microbial trophic relationship (particularly under environmentally relevant light regimes), demonstrate B12 exchange even during bacterial starvation, and underscore the importance of quantitative approaches for assessing metabolic coupling in algal-bacterial interactions.

Coupling Cell Size Regulation and Proliferation Dynamics of C. glutamicum Reveals Cell Division Based on Surface Area

Single cells actively coordinate growth and division to regulate their size, yet how this size homeostasis at the single-cell level propagates over multiple generations to impact clonal expansion remains fundamentally unexplored. Classical timer models for cell proliferation (where the duration of the cell cycle is an independent variable) predict that the stochastic variation in colony size will increase monotonically over time. In stark contrast, implementing size control according to adder strategy (where on average a fixed size added from cell birth to division) leads to colony size variations that eventually decay to zero. While these results assume a fixed size of the colony-initiating progenitor cell, further analysis reveals that the magnitude of the intercolony variation in population number is sensitive to heterogeneity in the initial cell size. We validate these predictions by tracking the growth of isogenic microcolonies of Corynebacterium glutamicum in microfluidic chambers. Approximating their cell shape to a capsule, we observe that the degree of random variability in cell size is different depending on whether the cell size is quantified as per length, surface area, or volume, but size control remains an adder regardless of these size metrics. A comparison of the observed variability in the colony population with the predictions suggests that proliferation matches better with a cell division based on the cell surface. In summary, our integrated mathematical-experimental approach bridges the paradigms of single-cell size regulation and clonal expansion at the population levels. This innovative approach provides elucidation of the mechanisms of size homeostasis from the stochastic dynamics of colony size for rod-shaped microbes.

Peptides designed from a bacteriophage capsid protein function as synthetic transcription repressors

The bacteriophage capsid protein, Psu (polarity suppression), inhibits the bacterial transcription terminator, Rho. In an effort to find nontraditional antibacterial agents, we previously designed peptides from the Psu C terminus that function as inhibitors of Rho. Here, we demonstrated that these peptides have positive surface-charge densities, and they downregulate many genes in Escherichia coli. We hypothesized that these peptides could bind to nucleic acids and repress gene expression. One of these peptides, peptide 33, represses in vitro transcription from the T7A1 and Plac promoters efficiently by blocking the access of RNA polymerase to the promoter, a mode of transcription repression akin to many bacterial repressors. In vivo, expressions of the peptides reduce the total RNA level as well as transcription from Plac and Posm promoters significantly. However, they are less efficient in repressing transcription from the rRNA promoters with a very high turnover of RNA polymerase. The peptide 33 binds to both single and dsDNA as well as to RNA with dissociation constants ranging from 1 to 5 μM exhibiting preferences for the single-stranded DNA and RNAs. These interactions are salt-resistant and not sequence-specific. Interactions with dsDNA are entropy-driven, while it is enthalpy-driven for the ssDNA. This mode of interaction with nucleic acids is similar to many nonspecific ssDNA-binding proteins. Expression of peptide 33 induces cell elongation and impaired cell division, possibly due to the dislodging of the DNA-binding proteins. Overall, we surmised that these synthetic transcription repressors would function like bacterial nucleoid-associated proteins.

Maintenance of appropriate size scaling of the C. elegans pharynx by YAP-1

Even slight imbalance between the growth rate of different organs can accumulate to a large deviation from their appropriate size during development. Here, we use live imaging of the pharynx of C. elegans to ask if and how organ size scaling nevertheless remains uniform among individuals. Growth trajectories of hundreds of individuals reveal that pharynxes grow by a near constant volume per larval stage that is independent of their initial size, such that undersized pharynxes catch-up in size during development. Tissue-specific depletion of RAGA-1, an activator of mTOR and growth, shows that maintaining correct pharynx-to-body size proportions involves a bi-directional coupling between pharynx size and body growth. In simulations, this coupling cannot be explained by limitation of food uptake alone, and genetic experiments reveal an involvement of the mechanotransducing transcriptional co-regulator yap-1. Our data suggests that mechanotransduction coordinates pharynx growth with other tissues, ensuring body plan uniformity among individuals.

Protein aggregates act as a deterministic disruptor during bacterial cell size homeostasis

Mechanisms underlying deviant cell size fluctuations among clonal bacterial siblings are generally considered to be cryptic and stochastic in nature. However, by scrutinizing heat-stressed populations of the model bacterium Escherichia coli, we uncovered the existence of a deterministic asymmetry in cell division that is caused by the presence of intracellular protein aggregates (PAs). While these structures typically locate at the cell pole and segregate asymmetrically among daughter cells, we now show that the presence of a polar PA consistently causes a more distal off-center positioning of the FtsZ division septum. The resulting increased length of PA-inheriting siblings persists over multiple generations and could be observed in both E. coli and Bacillus subtilis populations. Closer investigation suggests that a PA can physically perturb the nucleoid structure, which subsequently leads to asymmetric septation.