Decoupling of Rates of Protein Synthesis from Cell Expansion Leads to Supergrowth

Eukaryotic cell size regulation and its implications for cellular function and dysfunction

Depending on cell type, environmental inputs, and disease, the cells in the human body can have widely different sizes. In recent years, it has become clear that cell size is a major regulator of cell function. However, we are only beginning to understand how the optimization of cell function determines a given cell's optimal size. Here, we review currently known size control strategies of eukaryotic cells and the intricate link of cell size to intracellular biomolecular scaling, organelle homeostasis, and cell cycle progression. We detail the cell size-dependent regulation of early development and the impact of cell size on cell differentiation. Given the importance of cell size for normal cellular physiology, cell size control must account for changing environmental conditions. We describe how cells sense environmental stimuli, such as nutrient availability, and accordingly adapt their size by regulating cell growth and cell cycle progression. Moreover, we discuss the correlation of pathological states with misregulation of cell size and how for a long time this was considered a downstream consequence of cellular dysfunction. We review newer studies that reveal a reversed causality, with misregulated cell size leading to pathophysiological phenotypes such as senescence and aging. In summary, we highlight the important roles of cell size in cellular function and dysfunction, which could have major implications for both diagnostics and treatment in the clinic.

Ribosome specialization in cancer: a spotlight on ribosomal proteins

In the past few decades, our view of ribosomes has changed substantially. Rather than passive machines without significant variability, it is now acknowledged that they are heterogeneous, and have direct regulatory capacity. This 'ribosome heterogeneity' comes in many flavors, including in both the RNA and protein components of ribosomes, so there are many paths through which ribosome specialization could arise. It is easy to imagine that specialized ribosomes could have wide physiological roles, through the translation of specific mRNA populations, and there is now evidence for this in several contexts. Translation is highly dysregulated in cancer, needed to support oncogenic phenotypes and to overcome cellular stress. However, the role of ribosome specialization in this is not clear. In this review we focus on specialized ribosomes in cancer. Specifically, we assess the impact that post-translational modifications and differential ribosome incorporation of ribosomal proteins (RPs) have in this disease. We focus on studies that have shown a ribosome-mediated change in translation of specific mRNA populations, and hypothesize how such a process could be driving other phenotypes. We review the impact of RP-mediated heterogeneity in both intrinsic and extrinsic oncogenic processes, and consider how this knowledge could be leveraged to benefit patients.

The Matrix Protein Tropoelastin Prolongs Mesenchymal Stromal Cell Vitality and Delays Senescence During Replicative Aging

Cellular senescence leads to the functional decline of regenerative cells such as mesenchymal stromal/stem cells (MSCs), which gives rise to chronic conditions and contributes to poor cell therapy outcomes. Aging tissues are associated with extracellular matrix (ECM) dysregulation, including loss of elastin. However, the role of the ECM in modulating senescence is underexplored. In this work, it is shown that tropoelastin, the soluble elastin precursor, is not only a marker of young MSCs but also actively preserves cell fitness and delays senescence during replicative aging. MSCs briefly exposed to tropoelastin exhibit upregulation of proliferative genes and concurrent downregulation of senescence genes. The seno-protective benefits of tropoelastin persist during continuous, long-term MSC culture, and significantly extend the MSC replicative lifespan. Tropoelastin-expanded MSCs further maintain youth-associated phenotype and function compared to age-matched controls, including preserved clonogenic potential, minimal senescence-associated beta-galactosidase activity, maintained cell sizes, reduced expression of senescence markers, suppressed secretion of senescence-associated factors, and increased production of youth-associated proteins. This work points to the utility of exogenously-supplemented tropoelastin for manufacturing MSCs that robustly maintain regenerative potential with age. It further reveals the active role of classical structural ECM proteins in driving cellular age-associated fitness, potentially leading to future interventions for aging-related pathologies.

Differential effects of translation inhibitors on Plasmodium berghei liver stage parasites

Increasing numbers of antimalarial compounds are being identified that converge mechanistically at inhibition of cytoplasmic translation, regardless of the molecular target or mechanism. A deeper understanding of how their effectiveness as liver stage translation inhibitors relates to their chemoprotective potential could prove useful. Here, we probed that relationship using the Plasmodium berghei-HepG2 liver stage infection model. After determining translation inhibition EC50s for five compounds, we tested them at equivalent effective concentrations to compare the parasite response to, and recovery from, a brief period of translation inhibition in early schizogony, followed by parasites to 120 h post-infection to assess antiplasmodial effects of the treatment. We show compound-specific heterogeneity in single parasite and population responses to translation inhibitor treatment, with no single metric strongly correlated to the release of hepatic merozoites for all compounds. We also demonstrate that DDD107498 is capable of exerting antiplasmodial effects on translationally arrested liver stage parasites and uncover unexpected growth dynamics during the liver stage. Our results demonstrate that translation inhibition efficacy does not determine antiplasmodial efficacy for these compounds.

Rapid adaptation of cellular metabolic rate to the MicroRNA complements of mammals and its relevance to the evolution of endothermy

The metabolic efficiency of mammalian cells depends on the attenuation of intrinsic translation noise by microRNAs. We devised a metric of cellular metabolic rate (cMR), rMR/Mexp optimally fit to the number of microRNA families (mirFam), that is robust to variation in mass and sensitive to body temperature (Tb), consistent with the heat dissipation limit theory of Speakman and Król (2010). Using mirFam as predictor, an Ornstein-Uhlenbeck process of stabilizing selection, with an adaptive shift at the divergence of Boreoeutheria, accounted for 95% of the variation in cMR across mammals. Branchwise rates of evolution of cMR, mirFam and Tb concurrently increased 6- to 7-fold at the divergence of Boreoeutheria, independent of mass. Cellular MR variation across placental mammals was also predicted by the sum of model conserved microRNA-target interactions, revealing an unexpected degree of integration of the microRNA-target apparatus into the energy economy of the mammalian cell.

Translation inhibition efficacy does not determine the Plasmodium berghei liver stage antiplasmodial efficacy of protein synthesis inhibitors

Protein synthesis is a core cellular process, necessary throughout the complex lifecycle of Plasmodium parasites, thus specific translation inhibitors would be a valuable class of antimalarial drugs, capable of both treating symptomatic infections in the blood and providing chemoprotection by targeting the initial parasite population in the liver, preventing both human disease and parasite transmission back to the mosquito host. As increasing numbers of antiplasmodial compounds are identified that converge mechanistically at inhibition of cytoplasmic translation, regardless of molecular target or mechanism, it would be useful to gain deeper understanding of how their effectiveness as liver stage translation inhibitors relates to their chemoprotective potential. Here, we probed that relationship using the P. berghei-HepG2 liver stage infection model. Using o-propargyl puromycin-based labeling of the nascent proteome in P. berghei-infected HepG2 monolayers coupled with automated confocal feedback microscopy to generate unbiased, single parasite image sets of P. berghei liver stage translation, we determined translation inhibition EC50s for five compounds, encompassing parasite-specific aminoacyl tRNA synthetase inhibitors, compounds targeting the ribosome in both host and parasite, as well as DDD107498, which targets Plasmodium eEF2, and is a leading antimalarial candidate compound being clinically developed as cabamiquine. Compounds were then tested at equivalent effective concentrations to compare the parasite response to, and recovery from, a brief period of translation inhibition in early schizogony, with parasites followed up to 120 hours post-infection to assess liver stage antiplasmodial effects of the treatment. Our data conclusively show that translation inhibition efficacy per se does not determine a translation inhibitor's antiplasmodial efficacy. DDD107498 was the least effective translation inhibitor, yet exerted the strongest antimalarial effects at both 5x- and 10x EC50 concentrations. We show compound-specific heterogeneity in single parasite and population responses to translation inhibitor treatment, with no single metric strongly correlated to release of hepatic merozoites for all compound, demonstrate that DDD107498 is capable of exerting antiplasmodial effects on translationally arrested liver stage parasites, and uncover unexpected growth dynamics during the liver stage. Our results demonstrate that translation inhibition efficacy cannot function as a proxy for antiplasmodial effectiveness, and highlight the importance of exploring the ultimate, as well as proximate, mechanisms of action of these compounds on liver stage parasites.

Hybrid machine-learning framework for volumetric segmentation and quantification of vacuoles in individual yeast cells using holotomography

The precise, quantitative evaluation of intracellular organelles in three-dimensional (3D) imaging data poses a significant challenge due to the inherent constraints of traditional microscopy techniques, the requirements of the use of exogenous labeling agents, and existing computational methods. To counter these challenges, we present a hybrid machine-learning framework exploiting correlative imaging of 3D quantitative phase imaging with 3D fluorescence imaging of labeled cells. The algorithm, which synergistically integrates a random-forest classifier with a deep neural network, is trained using the correlative imaging data set, and the trained network is then applied to 3D quantitative phase imaging of cell data. We applied this method to live budding yeast cells. The results revealed precise segmentation of vacuoles inside individual yeast cells, and also provided quantitative evaluations of biophysical parameters, including volumes, concentration, and dry masses of automatically segmented vacuoles.

Metabolomic rearrangement controls the intrinsic microbial response to temperature changes

Temperature is one of the key determinants of microbial behavior and survival, whose impact is typically studied under heat- or cold-shock conditions that elicit specific regulation to combat lethal stress. At intermediate temperatures, cellular growth rate varies according to the Arrhenius law of thermodynamics without stress responses, a behavior whose origins have not yet been elucidated. Using single-cell microscopy during temperature perturbations, we show that bacteria exhibit a highly conserved, gradual response to temperature upshifts with a time scale of ~1.5 doublings at the higher temperature, regardless of initial/final temperature or nutrient source. We find that this behavior is coupled to a temperature memory, which we rule out as being neither transcriptional, translational, nor membrane dependent. Instead, we demonstrate that an autocatalytic enzyme network incorporating temperature-sensitive Michaelis-Menten kinetics recapitulates all temperature-shift dynamics through metabolome rearrangement, which encodes a temperature memory and successfully predicts alterations in the upshift response observed under simple-sugar, low-nutrient conditions, and in fungi. This model also provides a mechanistic framework for both Arrhenius-dependent growth and the classical Monod Equation through temperature-dependent metabolite flux.

Optimal density of bacterial cells

A substantial fraction of the bacterial cytosol is occupied by catalysts and their substrates. While a higher volume density of catalysts and substrates might boost biochemical fluxes, the resulting molecular crowding can slow down diffusion, perturb the reactions' Gibbs free energies, and reduce the catalytic efficiency of proteins. Due to these tradeoffs, dry mass density likely possesses an optimum that facilitates maximal cellular growth and that is interdependent on the cytosolic molecule size distribution. Here, we analyze the balanced growth of a model cell, accounting systematically for crowding effects on reaction kinetics. Its optimal cytosolic volume occupancy depends on the nutrient-dependent resource allocation into large ribosomal vs. small metabolic macromolecules, reflecting a tradeoff between the saturation of metabolic enzymes, favoring larger occupancies with higher encounter rates, and the inhibition of the ribosomes, favoring lower occupancies with unhindered diffusion of tRNAs. Our predictions across growth rates are quantitatively consistent with the experimentally observed reduction in volume occupancy on rich media compared to minimal media in E. coli. Strong deviations from optimal cytosolic occupancy only lead to minute reductions in growth rate, which are nevertheless evolutionarily relevant due to large bacterial population sizes. In sum, cytosolic density variation in bacterial cells appears to be consistent with an optimality principle of cellular efficiency.

Bacterial growth - from physical principles to autolysins

For bacteria to increase in size, they need to enzymatically expand their cell envelopes, and more concretely their peptidoglycan cell wall. A major task of growth is to increase intracellular space for the accumulation of macromolecules, notably proteins, RNA, and DNA. Here, we review recent progress in our understanding of how cells coordinate envelope growth with biomass growth, focusing on elongation of rod-like bacteria. We first describe the recent discovery that surface area, but not cell volume, increases in proportion to mass growth. We then discuss how this relation could possibly be implemented mechanistically, reviewing the role of envelope insertion for envelope growth. Since cell-wall expansion requires the well-controlled activity of autolysins, we finally review recent progress in our understanding of autolysin regulation.

Mycobacterium tuberculosis grows linearly at the single-cell level with larger variability than model organisms

The ability of bacterial pathogens to regulate growth is crucial to control homeostasis, virulence, and drug response. Yet, we do not understand the growth and cell cycle behaviors of Mycobacterium tuberculosis (Mtb), a slow-growing pathogen, at the single-cell level. Here, we use time-lapse imaging and mathematical modeling to characterize these fundamental properties of Mtb. Whereas most organisms grow exponentially at the single-cell level, we find that Mtb exhibits a unique linear growth mode. Mtb growth characteristics are highly variable from cell-to-cell, notably in their growth speeds, cell cycle timing, and cell sizes. Together, our study demonstrates that growth behavior of Mtb diverges from what we have learned from model bacteria. Instead, Mtb generates a heterogeneous population while growing slowly and linearly. Our study provides a new level of detail into how Mtb grows and creates heterogeneity, and motivates more studies of growth behaviors in bacterial pathogens.

Super-exponential growth and stochastic size dynamics in rod-like bacteria

Proliferating bacterial cells exhibit stochastic growth and size dynamics, but the regulation of noise in bacterial growth and morphogenesis remains poorly understood. A quantitative understanding of morphogenetic noise control, and how it changes under different growth conditions, would provide better insights into cell-to-cell variability and intergenerational fluctuations in cell physiology. Using multigenerational growth and width data of single Escherichia coli and Caulobacter crescentus cells, we deduce the equations governing growth and size dynamics of rod-like bacterial cells. Interestingly, we find that both E. coli and C. crescentus cells deviate from exponential growth within the cell cycle. In particular, the exponential growth rate increases during the cell cycle irrespective of nutrient or temperature conditions. We propose a mechanistic model that explains the emergence of super-exponential growth from autocatalytic production of ribosomes coupled to the rate of cell elongation and surface area synthesis. Using this new model and statistical inference on large datasets, we construct the Langevin equations governing cell growth and size dynamics of E. coli cells in different nutrient conditions. The single-cell level model predicts how noise in intragenerational and intergenerational processes regulate variability in cell morphology and generation times, revealing quantitative strategies for cellular resource allocation and morphogenetic noise control in different growth conditions.

Physiological and regulatory convergence between osmotic and nutrient stress responses in microbes

Bacterial cells are regularly confronted with simultaneous changes in environmental nutrient supply and osmolarity. Despite the importance of osmolarity and osmoregulation in bacterial physiology, the relationship between the cellular response to osmotic perturbations and other stresses has remained largely unexplored. Bacteria cultured in hyperosmotic conditions and bacteria experiencing nutrient stress exhibit similar physiological changes, including metabolic shutdown, increased protein instability, dehydration, and condensation of chromosomal DNA. In this review, we highlight overlapping molecular players between osmotic and nutrient stresses. These connections between two seemingly disparate stress response pathways reinforce the importance of central carbon metabolism as a control point for diverse aspects of homeostatic regulation. We identify important open questions for future research, emphasizing the pressing need to develop and exploit new methods for probing how osmolarity affects phylogenetically diverse species.

Vast heterogeneity in cytoplasmic diffusion rates revealed by nanorheology and Doppelgänger simulations

The cytoplasm is a complex, crowded, actively driven environment whose biophysical characteristics modulate critical cellular processes such as cytoskeletal dynamics, phase separation, and stem cell fate. Little is known about the variance in these cytoplasmic properties. Here, we employed particle-tracking nanorheology on genetically encoded multimeric 40 nm nanoparticles (GEMs) to measure diffusion within the cytoplasm of individual fission yeast (Schizosaccharomyces pombe) cellscells. We found that the apparent diffusion coefficients of individual GEM particles varied over a 400-fold range, while the differences in average particle diffusivity among individual cells spanned a 10-fold range. To determine the origin of this heterogeneity, we developed a Doppelgänger simulation approach that uses stochastic simulations of GEM diffusion that replicate the experimental statistics on a particle-by-particle basis, such that each experimental track and cell had a one-to-one correspondence with their simulated counterpart. These simulations showed that the large intra- and inter-cellular variations in diffusivity could not be explained by experimental variability but could only be reproduced with stochastic models that assume a wide intra- and inter-cellular variation in cytoplasmic viscosity. The simulation combining intra- and inter-cellular variation in viscosity also predicted weak nonergodicity in GEM diffusion, consistent with the experimental data. To probe the origin of this variation, we found that the variance in GEM diffusivity was largely independent of factors such as temperature, the actin and microtubule cytoskeletons, cell-cyle stage, and spatial locations, but was magnified by hyperosmotic shocks. Taken together, our results provide a striking demonstration that the cytoplasm is not "well-mixed" but represents a highly heterogeneous environment in which subcellular components at the 40 nm size scale experience dramatically different effective viscosities within an individual cell, as well as in different cells in a genetically identical population. These findings carry significant implications for the origins and regulation of biological noise at cellular and subcellular levels.

Regulation of organelle size and organization during development

During early embryogenesis, as cells divide in the developing embryo, the size of intracellular organelles generally decreases to scale with the decrease in overall cell size. Organelle size scaling is thought to be important to establish and maintain proper cellular function, and defective scaling may lead to impaired development and disease. However, how the cell regulates organelle size and organization are largely unanswered questions. In this review, we summarize the process of size scaling at both the cell and organelle levels and discuss recently discovered mechanisms that regulate this process during early embryogenesis. In addition, we describe how some recently developed techniques and Xenopus as an animal model can be used to investigate the underlying mechanisms of size regulation and to uncover the significance of proper organelle size scaling and organization.

Segmentation, tracking and cell cycle analysis of live-cell imaging data with Cell-ACDC

BACKGROUND

High-throughput live-cell imaging is a powerful tool to study dynamic cellular processes in single cells but creates a bottleneck at the stage of data analysis, due to the large amount of data generated and limitations of analytical pipelines. Recent progress on deep learning dramatically improved cell segmentation and tracking. Nevertheless, manual data validation and correction is typically still required and tools spanning the complete range of image analysis are still needed.

RESULTS

We present Cell-ACDC, an open-source user-friendly GUI-based framework written in Python, for segmentation, tracking and cell cycle annotations. We included state-of-the-art deep learning models for single-cell segmentation of mammalian and yeast cells alongside cell tracking methods and an intuitive, semi-automated workflow for cell cycle annotation of single cells. Using Cell-ACDC, we found that mTOR activity in hematopoietic stem cells is largely independent of cell volume. By contrast, smaller cells exhibit higher p38 activity, consistent with a role of p38 in regulation of cell size. Additionally, we show that, in S. cerevisiae, histone Htb1 concentrations decrease with replicative age.

CONCLUSIONS

Cell-ACDC provides a framework for the application of state-of-the-art deep learning models to the analysis of live cell imaging data without programming knowledge. Furthermore, it allows for visualization and correction of segmentation and tracking errors as well as annotation of cell cycle stages. We embedded several smart algorithms that make the correction and annotation process fast and intuitive. Finally, the open-source and modularized nature of Cell-ACDC will enable simple and fast integration of new deep learning-based and traditional methods for cell segmentation, tracking, and downstream image analysis. Source code: https://github.com/SchmollerLab/Cell_ACDC.

Scaling of biosynthesis and metabolism with cell size

Cells adopt a size that is optimal for their function, and pushing them beyond this limit can cause cell aging and death by senescence or reduce proliferative potential. However, by increasing their genome copy number (ploidy), cells can increase their size dramatically and homeostatically maintain physiological properties such as biosynthesis rate. Recent studies investigating the relationship between cell size and rates of biosynthesis and metabolism under normal, polyploid, and pathological conditions are revealing new insights into how cells attain the best function or fitness for their size by tuning processes including transcription, translation, and mitochondrial respiration. A new frontier is to connect single-cell scaling relationships with tissue and whole-organism physiology, which promises to reveal molecular and evolutionary principles underlying the astonishing diversity of size observed across the tree of life.

Live cell microscopy: From image to insight

Live-cell microscopy is a powerful tool that can reveal cellular behavior as well as the underlying molecular processes. A key advantage of microscopy is that by visualizing biological processes, it can provide direct insights. Nevertheless, live-cell imaging can be technically challenging and prone to artifacts. For a successful experiment, many careful decisions are required at all steps from hardware selection to downstream image analysis. Facing these questions can be particularly intimidating due to the requirement for expertise in multiple disciplines, ranging from optics, biophysics, and programming to cell biology. In this review, we aim to summarize the key points that need to be considered when setting up and analyzing a live-cell imaging experiment. While we put a particular focus on yeast, many of the concepts discussed are applicable also to other organisms. In addition, we discuss reporting and data sharing strategies that we think are critical to improve reproducibility in the field.

Density fluctuations, homeostasis, and reproduction effects in bacteria

Single-cells grow by increasing their biomass and size. Here, we report that while mass and size accumulation rates of single Escherichia coli cells are exponential, their density and, thus, the levels of macromolecular crowding fluctuate during growth. As such, the average rates of mass and size accumulation of a single cell are generally not the same, but rather cells differentiate into increasing one rate with respect to the other. This differentiation yields a density homeostasis mechanism that we support mathematically. Further, we observe that density fluctuations can affect the reproduction rates of single cells, suggesting a link between the levels of macromolecular crowding with metabolism and overall population fitness. We detail our experimental approach and the “invisible” microfluidic arrays that enabled increased precision and throughput. Infections and natural communities start from a few cells, thus, emphasizing the significance of density-fluctuations when taking non-genetic variability into consideration.

Quantitative imaging, invisible microfluidics, and mathematical models demonstrate how the density of single E. coli cells fluctuates during the cell cycle, unmasking key homeostasis and population fitness effects.

Physical properties of the cytoplasm modulate the rates of microtubule polymerization and depolymerization

The cytoplasm is a crowded, visco-elastic environment whose physical properties change according to physiological or developmental states. How the physical properties of the cytoplasm impact cellular functions in vivo remains poorly understood. Here, we probe the effects of cytoplasmic concentration on microtubules by applying osmotic shifts to fission yeast, moss, and mammalian cells. We show that the rates of both microtubule polymerization and depolymerization scale linearly and inversely with cytoplasmic concentration; an increase in cytoplasmic concentration decreases the rates of microtubule polymerization and depolymerization proportionally, whereas a decrease in cytoplasmic concentration leads to the opposite. Numerous lines of evidence indicate that these effects are due to changes in cytoplasmic viscosity rather than cellular stress responses or macromolecular crowding per se. We reconstituted these effects on microtubules in vitro by tuning viscosity. Our findings indicate that, even in normal conditions, the viscosity of the cytoplasm modulates the reactions that underlie microtubule dynamic behaviors.

Fission yeast polycystin Pkd2p promotes cell size expansion and antagonizes the Hippo-related SIN pathway

Polycystins are conserved mechanosensitive channels whose mutations lead to the common human renal disorder autosomal dominant polycystic kidney disease (ADPKD). Previously, we discovered that the plasma membrane-localized fission yeast polycystin homolog Pkd2p is an essential protein required for cytokinesis; however, its role remains unclear. Here, we isolated a novel temperature-sensitive pkd2 mutant, pkd2-B42. Among the strong growth defects of this mutant, the most striking was that many mutant cells often lost a significant portion of their volume in just 5 min followed by a gradual recovery, a process that we termed 'deflation'. Unlike cell lysis, deflation did not result in plasma membrane rupture and occurred independently of cell cycle progression. The tip extension of pkd2-B42 cells was 80% slower than that of wild-type cells, and their turgor pressure was 50% lower. Both pkd2-B42 and the hypomorphic depletion mutant pkd2-81KD partially rescued mutants of the septation initiation network (SIN), a yeast Hippo-related signaling pathway, by preventing cell lysis, enhancing septum formation and doubling the number of Sid2p and Mob1p molecules at the spindle pole bodies. We conclude that Pkd2p promotes cell size expansion during interphase by regulating turgor pressure and antagonizes the SIN during cytokinesis. This article has an associated First Person interview with the first author of the paper.

In vitro cell cycle oscillations exhibit a robust and hysteretic response to changes in cytoplasmic density

Cells control the properties of the cytoplasm to ensure proper functioning of biochemical processes. Recent studies showed that cytoplasmic density varies in both physiological and pathological states of cells undergoing growth, division, differentiation, apoptosis, senescence, and metabolic starvation. Little is known about how cellular processes cope with these cytoplasmic variations. Here, we study how a cell cycle oscillator comprising cyclin-dependent kinase (Cdk1) responds to changes in cytoplasmic density by systematically diluting or concentrating cycling Xenopus egg extracts in cell-like microfluidic droplets. We found that the cell cycle maintains robust oscillations over a wide range of deviations from the endogenous density: as low as 0.2× to more than 1.22× relative cytoplasmic density (RCD). A further dilution or concentration from these values arrested the system in a low or high steady state of Cdk1 activity, respectively. Interestingly, diluting an arrested cytoplasm of 1.22× RCD recovers oscillations at lower than 1× RCD. Thus, the cell cycle switches reversibly between oscillatory and stable steady states at distinct thresholds depending on the direction of tuning, forming a hysteresis loop. We propose a mathematical model which recapitulates these observations and predicts that the Cdk1/Wee1/Cdc25 positive feedback loops do not contribute to the observed robustness, supported by experiments. Our system can be applied to study how cytoplasmic density affects other cellular processes.

Volume growth in animal cells is cell cycle dependent and shows additive fluctuations

The way proliferating animal cells coordinate the growth of their mass, volume, and other relevant size parameters is a long-standing question in biology. Studies focusing on cell mass have identified patterns of mass growth as a function of time and cell cycle phase, but little is known about volume growth. To address this question, we improved our fluorescence exclusion method of volume measurement (FXm) and obtained 1700 single-cell volume growth trajectories of HeLa cells. We find that, during most of the cell cycle, volume growth is close to exponential and proceeds at a higher rate in S-G2 than in G1. Comparing the data with a mathematical model, we establish that the cell-to-cell variability in volume growth arises from constant-amplitude fluctuations in volume steps rather than fluctuations of the underlying specific growth rate. We hypothesize that such 'additive noise' could emerge from the processes that regulate volume adaptation to biophysical cues, such as tension or osmotic pressure.

Characterizing non-exponential growth and bimodal cell size distributions in fission yeast: An analytical approach

Unlike many single-celled organisms, the growth of fission yeast cells within a cell cycle is not exponential. It is rather characterized by three distinct phases (elongation, septation, and reshaping), each with a different growth rate. Experiments also showed that the distribution of cell size in a lineage can be bimodal, unlike the unimodal distributions measured for the bacterium Escherichia coli. Here we construct a detailed stochastic model of cell size dynamics in fission yeast. The theory leads to analytic expressions for the cell size and the birth size distributions, and explains the origin of bimodality seen in experiments. In particular, our theory shows that the left peak in the bimodal distribution is associated with cells in the elongation phase, while the right peak is due to cells in the septation and reshaping phases. We show that the size control strategy, the variability in the added size during a cell cycle, and the fraction of time spent in each of the three cell growth phases have a strong bearing on the shape of the cell size distribution. Furthermore, we infer all the parameters of our model by matching the theoretical cell size and birth size distributions to those from experimental single-cell time-course data for seven different growth conditions. Our method provides a much more accurate means of determining the size control strategy (timer, adder or sizer) than the standard method based on the slope of the best linear fit between the birth and division sizes. We also show that the variability in added size and the strength of size control in fission yeast depend weakly on the temperature but strongly on the culture medium. More importantly, we find that stronger size homeostasis and larger added size variability are required for fission yeast to adapt to unfavorable environmental conditions.

Advances in microscopy enable us to follow single cells over long timescales from which we can understand how their size varies with time and the nature of innate strategies developed to control cell size. These data show that in many cell types, growth is exponential and the distribution of cell size has one peak, namely there is a single characteristic cell size. However data for fission yeast show remarkable differences: growth is non-exponential and the distribution of cell sizes has two peaks, corresponding to different growth phases. Here we construct a detailed stochastic mathematical model of this organism; by solving the model analytically, we show that it is able to predict the two peaked distributions of cell size seen in data and provide an explanation for each peak in terms of various growth phases of the single-celled organism. Furthermore, by fitting the model to the data, we infer values for the rates of all microscopic processes in our model. This method is shown to provide a much more reliable inference than current methods and shed light on how the strategy used by fission yeast cells to control their size varies with external conditions.

Control of nuclear size by osmotic forces in Schizosaccharomyces pombe

The size of the nucleus scales robustly with cell size so that the nuclear-to-cell volume ratio (N/C ratio) is maintained during cell growth in many cell types. The mechanism responsible for this scaling remains mysterious. Previous studies have established that the N/C ratio is not determined by DNA amount but is instead influenced by factors such as nuclear envelope mechanics and nuclear transport. Here, we developed a quantitative model for nuclear size control based upon colloid osmotic pressure and tested key predictions in the fission yeast Schizosaccharomyces pombe. This model posits that the N/C ratio is determined by the numbers of macromolecules in the nucleoplasm and cytoplasm. Osmotic shift experiments showed that the fission yeast nucleus behaves as an ideal osmometer whose volume is primarily dictated by osmotic forces. Inhibition of nuclear export caused accumulation of macromolecules in the nucleoplasm, leading to nuclear swelling. We further demonstrated that the N/C ratio is maintained by a homeostasis mechanism based upon synthesis of macromolecules during growth. These studies demonstrate the functions of colloid osmotic pressure in intracellular organization and size control.

Distinguishing different modes of growth using single-cell data

Collection of high-throughput data has become prevalent in biology. Large datasets allow the use of statistical constructs such as binning and linear regression to quantify relationships between variables and hypothesize underlying biological mechanisms based on it. We discuss several such examples in relation to single-cell data and cellular growth. In particular, we show instances where what appears to be ordinary use of these statistical methods leads to incorrect conclusions such as growth being non-exponential as opposed to exponential and vice versa. We propose that the data analysis and its interpretation should be done in the context of a generative model, if possible. In this way, the statistical methods can be validated either analytically or against synthetic data generated via the use of the model, leading to a consistent method for inferring biological mechanisms from data. On applying the validated methods of data analysis to infer cellular growth on our experimental data, we find the growth of length in E. coli to be non-exponential. Our analysis shows that in the later stages of the cell cycle the growth rate is faster than exponential.

Heterogeneous recruitment abilities to RNA polymerases generate nonlinear scaling of gene expression with cell volume

While most genes' expression levels are proportional to cell volumes, some genes exhibit nonlinear scaling between their expression levels and cell volume. Therefore, their mRNA and protein concentrations change as the cell volume increases, which often have crucial biological functions such as cell-cycle regulation. However, the biophysical mechanism underlying the nonlinear scaling between gene expression and cell volume is still unclear. In this work, we show that the nonlinear scaling is a direct consequence of the heterogeneous recruitment abilities of promoters to RNA polymerases based on a gene expression model at the whole-cell level. Those genes with weaker (stronger) recruitment abilities than the average ability spontaneously exhibit superlinear (sublinear) scaling with cell volume. Analysis of the promoter sequences and the nonlinear scaling of Saccharomyces cerevisiae's mRNA levels shows that motifs associated with transcription regulation are indeed enriched in genes exhibiting nonlinear scaling, in concert with our model.

Cell Length Growth in the Fission Yeast Cell Cycle: Is It (Bi)linear or (Bi)exponential?

Fission yeast is commonly used as a model organism in eukaryotic cell growth studies. To describe the cells’ length growth patterns during the mitotic cycle, different models have been proposed previously as linear, exponential, bilinear and biexponential ones. The task of discriminating among these patterns is still challenging. Here, we have analyzed 298 individual cells altogether, namely from three different steady-state cultures (wild-type, wee1-50 mutant and pom1Δ mutant). We have concluded that in 190 cases (63.8%) the bilinear model was more adequate than either the linear or the exponential ones. These 190 cells were further examined by separately analyzing the linear segments of the best fitted bilinear models. Linear and exponential functions have been fitted to these growth segments to determine whether the previously fitted bilinear functions were really correct. The majority of these growth segments were found to be linear; nonetheless, a significant number of exponential ones were also detected. However, exponential ones occurred mainly in cases of rather short segments (<40 min), where there were not enough data for an accurate model fitting. By contrast, in long enough growth segments (≥40 min), linear patterns highly dominated over exponential ones, verifying that overall growth is probably bilinear.

Robust surface-to-mass coupling and turgor-dependent cell width determine bacterial dry-mass density

Intracellular biomass density is an important variable for cellular physiology. It defines the crowded state of the cytoplasm and thus influences macromolecular interactions and transport. To control density during growth, bacteria must expand their cell volumes in synchrony with biomass. The regulation of volume growth and biomass density remain fundamentally not understood—in bacteria or any other organism. Using advanced microscopy, we demonstrate that cells control dry-mass density indirectly through two independent processes. First, cells expand surface area, rather than volume, in proportion with biomass growth. Second, cell width is controlled independently, with an important influence of turgor pressure. Our findings overturn a long-standing paradigm of mass-density constancy in bacteria and reveal fundamental determinants of dry-mass density and shape.

During growth, cells must expand their cell volumes in coordination with biomass to control the level of cytoplasmic macromolecular crowding. Dry-mass density, the average ratio of dry mass to volume, is roughly constant between different nutrient conditions in bacteria, but it remains unknown whether cells maintain dry-mass density constant at the single-cell level and during nonsteady conditions. Furthermore, the regulation of dry-mass density is fundamentally not understood in any organism. Using quantitative phase microscopy and an advanced image-analysis pipeline, we measured absolute single-cell mass and shape of the model organisms Escherichia coli and Caulobacter crescentus with improved precision and accuracy. We found that cells control dry-mass density indirectly by expanding their surface, rather than volume, in direct proportion to biomass growth—according to an empirical surface growth law. At the same time, cell width is controlled independently. Therefore, cellular dry-mass density varies systematically with cell shape, both during the cell cycle or after nutrient shifts, while the surface-to-mass ratio remains nearly constant on the generation time scale. Transient deviations from constancy during nutrient shifts can be reconciled with turgor-pressure variations and the resulting elastic changes in surface area. Finally, we find that plastic changes of cell width after nutrient shifts are likely driven by turgor variations, demonstrating an important regulatory role of mechanical forces for width regulation. In conclusion, turgor-dependent cell width and a slowly varying surface-to-mass coupling constant are the independent variables that determine dry-mass density.

Starvation induces shrinkage of the bacterial cytoplasm

Environmental fluctuations are a common challenge for single-celled organisms; enteric bacteria such as Escherichia coli experience dramatic changes in nutrient availability, pH, and temperature during their journey into and out of the host. While the effects of altered nutrient availability on gene expression and protein synthesis are well known, their impacts on cytoplasmic dynamics and cell morphology have been largely overlooked. Here, we discover that depletion of utilizable nutrients results in shrinkage of E. coli's inner membrane from the cell wall. Shrinkage was accompanied by an ∼17% reduction in cytoplasmic volume and a concurrent increase in periplasmic volume. Inner membrane retraction after sudden starvation occurred almost exclusively at the new cell pole. This phenomenon was distinct from turgor-mediated plasmolysis and independent of new transcription, translation, or canonical starvation-sensing pathways. Cytoplasmic dry-mass density increased during shrinkage, suggesting that it is driven primarily by loss of water. Shrinkage was reversible: upon a shift to nutrient-rich medium, expansion started almost immediately at a rate dependent on carbon source quality. A robust entry into and recovery from shrinkage required the Tol-Pal system, highlighting the importance of envelope coupling during shrinkage and recovery. Klebsiella pneumoniae also exhibited shrinkage when shifted to carbon-free conditions, suggesting a conserved phenomenon. These findings demonstrate that even when Gram-negative bacterial growth is arrested, cell morphology and physiology are still dynamic.

Variations of intracellular density during the cell cycle arise from tip-growth regulation in fission yeast

Intracellular density impacts the physical nature of the cytoplasm and can globally affect cellular processes, yet density regulation remains poorly understood. Here, using a new quantitative phase imaging method, we determined that dry-mass density in fission yeast is maintained in a narrow distribution and exhibits homeostatic behavior. However, density varied during the cell cycle, decreasing during G2, increasing in mitosis and cytokinesis, and dropping rapidly at cell birth. These density variations were explained by a constant rate of biomass synthesis, coupled to slowdown of volume growth during cell division and rapid expansion post-cytokinesis. Arrest at specific cell-cycle stages exacerbated density changes. Spatially heterogeneous patterns of density suggested links between density regulation, tip growth, and intracellular osmotic pressure. Our results demonstrate that systematic density variations during the cell cycle are predominantly due to modulation of volume expansion, and reveal functional consequences of density gradients and cell-cycle arrests.

Precise regulation of the relative rates of surface area and volume synthesis in bacterial cells growing in dynamic environments

The steady-state size of bacterial cells correlates with nutrient-determined growth rate. Here, we explore how rod-shaped bacterial cells regulate their morphology during rapid environmental changes. We quantify cellular dimensions throughout passage cycles of stationary-phase cells diluted into fresh medium and grown back to saturation. We find that cells exhibit characteristic dynamics in surface area to volume ratio (SA/V), which are conserved across genetic and chemical perturbations as well as across species and growth temperatures. A mathematical model with a single fitting parameter (the time delay between surface and volume synthesis) is quantitatively consistent with our SA/V experimental observations. The model supports that this time delay is due to differential expression of volume and surface-related genes, and that the first division after dilution occurs at a tightly controlled SA/V. Our minimal model thus provides insight into the connections between bacterial growth rate and cell shape in dynamic environments.

Bacterial cells actively change their size and shape in response to external environments. Here, Shi et al. explore how cells regulate their morphology during rapid environmental changes, showing that the characteristic dynamics of surface area-to-volume ratio are conserved across genetic and chemical perturbations, as well as across species and growth temperatures.

An image analysis method to survey the dynamics of polar protein abundance in the regulation of tip growth

Tip growth is critical for the lifestyle of many walled cells. In yeast and fungi, this process is typically associated with the polarized deposition of conserved tip factors, including landmarks, Rho GTPases, cytoskeleton regulators, and membrane and cell wall remodelers. Because tip growth speeds may vary extensively between life cycles or species, we asked whether the local amount of specific polar elements could determine or limit tip growth speeds. Using the model fission yeast, we developed a quantitative image analysis pipeline to dynamically correlate single tip elongation speeds and polar protein abundance in large data sets. We found that polarity landmarks are typically diluted by growth. In contrast, tip growth speed is positively correlated with the local amount of factors related to actin, secretion or cell wall remodeling, but, surprisingly, exhibits long saturation plateaus above certain concentrations of those factors. Similar saturation observed for Spitzenkörper components in much faster growing fungal hyphae suggests that elements independent of canonical surface remodelers may limit single tip growth. This work provides standardized methods and resources to decipher the complex mechanisms that control cell growth.This article has an associated First Person interview with Sarah Taheraly, joint first author of the paper.

Computationally enhanced quantitative phase microscopy reveals autonomous oscillations in mammalian cell growth

The fine balance of growth and division is a fundamental property of the physiology of cells, and one of the least understood. Its study has been thwarted by difficulties in the accurate measurement of cell size and the even greater challenges of measuring growth of a single cell over time. We address these limitations by demonstrating a computationally enhanced methodology for quantitative phase microscopy for adherent cells, using improved image processing algorithms and automated cell-tracking software. Accuracy has been improved more than twofold and this improvement is sufficient to establish the dynamics of cell growth and adherence to simple growth laws. It is also sufficient to reveal unknown features of cell growth, previously unmeasurable. With these methodological and analytical improvements, in several cell lines we document a remarkable oscillation in growth rate, occurring throughout the cell cycle, coupled to cell division or birth yet independent of cell cycle progression. We expect that further exploration with this advanced tool will provide a better understanding of growth rate regulation in mammalian cells.

SiCTeC: An inexpensive, easily assembled Peltier device for rapid temperature shifting during single-cell imaging

Single-cell imaging, combined with recent advances in image analysis and microfluidic technologies, have enabled fundamental discoveries of cellular responses to chemical perturbations that are often obscured by traditional liquid-culture experiments. Temperature is an environmental variable well known to impact growth and to elicit specific stress responses at extreme values; it is often used as a genetic tool to interrogate essential genes. However, the dynamic effects of temperature shifts have remained mostly unstudied at the single-cell level, due largely to engineering challenges related to sample stability, heatsink considerations, and temperature measurement and feedback. Additionally, the few commercially available temperature-control platforms are costly. Here, we report an inexpensive (<$110) and modular Single-Cell Temperature Controller (SiCTeC) device for microbial imaging-based on straightforward modifications of the typical slide-sample-coverslip approach to microbial imaging-that controls temperature using a ring-shaped Peltier module and microcontroller feedback. Through stable and precise (±0.15°C) temperature control, SiCTeC achieves reproducible and fast (1-2 min) temperature transitions with programmable waveforms between room temperature and 45°C with an air objective. At the device's maximum temperature of 89°C, SiCTeC revealed that Escherichia coli cells progressively shrink and lose cellular contents. During oscillations between 30°C and 37°C, cells rapidly adapted their response to temperature upshifts. Furthermore, SiCTeC enabled the discovery of rapid morphological changes and enhanced sensitivity to substrate stiffness during upshifts to nonpermissive temperatures in temperature-sensitive mutants of cell-wall synthesis enzymes. Overall, the simplicity and affordability of SiCTeC empowers future studies of the temperature dependence of single-cell physiology.

The principles of cellular geometry scaling

Cellular dimensions profoundly influence cellular physiology. For unicellular organisms, this has direct bearing on their ecology and evolution. The morphology of a cell is governed by scaling rules. As it grows, the ratio of its surface area to volume is expected to decrease. Similarly, if environmental conditions force proliferating cells to settle on different size optima, cells of the same type may exhibit size-dependent variation in cellular processes. In fungi, algae and plants where cells are surrounded by a rigid wall, division at smaller size often produces immediate changes in geometry, decreasing cell fitness. Here, we discuss how cells interpret their size, buffer against changes in shape and, if necessary, scale their polarity to maintain optimal shape at different cell volumes.

Relevance and Regulation of Cell Density

Cell density shows very little variation within a given cell type. For example, in humans variability in cell density among cells of a given cell type is 100 times smaller than variation in cell mass. This tight control indicates that maintenance of a cell type-specific cell density is important for cell function. Indeed, pathological conditions such as cellular senescence are accompanied by changes in cell density. Despite the apparent importance of cell-type-specific density, we know little about how cell density affects cell function, how it is controlled, and how it sometimes changes as part of a developmental process or in response to changes in the environment. The recent development of new technologies to accurately measure the cell density of single cells in suspension and in tissues is likely to provide answers to these important questions.