Single-cell growth and division dynamics showing epigenetic correlations

Capturing of extracellular vesicles derived from single cells of Escherichia coli

Bacteria secrete extracellular vesicles (EVs), also referred to as bacterial membrane vesicles, which carry, among other compounds, lipids, nucleic acids and virulence factors. Recent studies highlight the role of EVs in the emergence of antibiotic resistance, e.g. as carrier and absorbent particles of the drug to protect the cells, or as a pathway to disseminate resistance elements. In this study, we are interested in characterizing the secretion of EVs at the single bacterial level to ultimately understand how cells respond to antibiotic treatment. We introduce a microfluidic device that enables culture of single bacterial cells and capture of EVs secreted from these individuals. The device incorporates parallel, narrow winding channels to trap single rod-shaped E. coli cells at their entrances. The daughter cells are immediately removed by continuous flow on the open side of the trap, so that the trap contains always only a single cell. Cells grew in these traps over 24 h with a doubling time of 25 minutes. Under antibiotic treatment, the doubling time did not change, but we observed small changes in the cell length of the trapped cells (decrease from 4.0 μm to 3.6 μm for 0 and 250 ng mL-1 polymyxin B, respectively), and cells stopped growing within hours, depending on the drug concentration. Compared to bulk culture, the results indicate a higher susceptibility of on-chip-cultured cells (250 ng mL-1vs. >500 ng mL-1 in bulk), which may be caused, among other reasons, by the space limitation in the cell trap and shear forces. During the culture, EVs secreted by the trapped cells entered the winding channel. We developed a procedure to selectively coat these channels with poly-L-lysine resulting in a positively charged surface, which enabled electrostatic capture of negatively charged EVs. Subsequently, the immobilized EVs were stained with a lipophilic dye and detected by fluorescence microscopy. Our findings confirm large variations of EV secretion among individual bacteria and indicate a relative high rate of EV secretion under antibiotic treatment. The proposed method can be extended to the detection of other secreted substances of interest and may facilitate the elucidation of unknown heterogeneities in bacteria.

Methods to monitor bacterial growth and replicative rates at the single-cell level

The heterogeneity of bacterial growth and replicative rates within a population was proposed a century ago notably to explain the presence of bacterial persisters. The term "growth rate" at the single-cell level corresponds to the increase in size or mass of an individual bacterium while the "replicative rate" refers to its division capacity within a defined temporality. After a decades long hiatus, recent technical innovative approaches allow population growth and replicative rates heterogeneity monitoring at the single-cell level resuming in earnest. Among these techniques, the oldest and widely used is time-lapse microscopy, most recently combined with microfluidics. We also discuss recent fluorescence dilution methods informing only on replicative rates and best suited. Some new elegant single cell methods so far only sporadically used such as buoyant mass measurement and stable isotope probing have emerged. Overall, such tools are widely used to investigate and compare the growth and replicative rates of bacteria displaying drug-persistent behaviors to that of bacteria growing in specific ecological niches or collected from patients. In this review, we describe the current methods available, discussing both the type of queries these have been used to answer and the specific strengths and limitations of each method.

A unified framework for measuring selection on cellular lineages and traits

Intracellular states probed by gene expression profiles and metabolic activities are intrinsically noisy, causing phenotypic variations among cellular lineages. Understanding the adaptive and evolutionary roles of such variations requires clarifying their linkage to population growth rates. Extending a cell lineage statistics framework, here we show that a population's growth rate can be expanded by the cumulants of a fitness landscape that characterize how fitness distributes in a population. The expansion enables quantifying the contribution of each cumulant, such as variance and skewness, to population growth. We introduce a function that contains all the essential information of cell lineage statistics, including mean lineage fitness and selection strength. We reveal a relation between fitness heterogeneity and population growth rate response to perturbation. We apply the framework to experimental cell lineage data from bacteria to mammalian cells, revealing distinct levels of growth rate gain from fitness heterogeneity across environments and organisms. Furthermore, third or higher order cumulants' contributions are negligible under constant growth conditions but could be significant in regrowing processes from growth-arrested conditions. We identify cellular populations in which selection leads to an increase of fitness variance among lineages in retrospective statistics compared to chronological statistics. The framework assumes no particular growth models or environmental conditions, and is thus applicable to various biological phenomena for which phenotypic heterogeneity and cellular proliferation are important.

Analytics and visualization tools to characterize single-cell stochasticity using bacterial single-cell movie cytometry data

Background

Time-lapse microscopy live-cell imaging is essential for studying the evolution of bacterial communities at single-cell resolution. It allows capturing detailed information about the morphology, gene expression, and spatial characteristics of individual cells at every time instance of the imaging experiment. The image analysis of bacterial "single-cell movies" (videos) generates big data in the form of multidimensional time series of measured bacterial attributes. If properly analyzed, these datasets can help us decipher the bacterial communities' growth dynamics and identify the sources and potential functional role of intra- and inter-subpopulation heterogeneity. Recent research has highlighted the importance of investigating the role of biological "noise" in gene regulation, cell growth, cell division, etc. Single-cell analytics of complex single-cell movie datasets, capturing the interaction of multiple micro-colonies with thousands of cells, can shed light on essential phenomena for human health, such as the competition of pathogens and benign microbiome cells, the emergence of dormant cells (“persisters”), the formation of biofilms under different stress conditions, etc. However, highly accurate and automated bacterial bioimage analysis and single-cell analytics methods remain elusive, even though they are required before we can routinely exploit the plethora of data that single-cell movies generate.

Results

We present visualization and single-cell analytics using R (ViSCAR), a set of methods and corresponding functions, to visually explore and correlate single-cell attributes generated from the image processing of complex bacterial single-cell movies. They can be used to model and visualize the spatiotemporal evolution of attributes at different levels of the microbial community organization (i.e., cell population, colony, generation, etc.), to discover possible epigenetic information transfer across cell generations, infer mathematical and statistical models describing various stochastic phenomena (e.g., cell growth, cell division), and even identify and auto-correct errors introduced unavoidably during the bioimage analysis of a dense movie with thousands of overcrowded cells in the microscope's field of view.

Conclusions

ViSCAR empowers researchers to capture and characterize the stochasticity, uncover the mechanisms leading to cellular phenotypes of interest, and decipher a large heterogeneous microbial communities' dynamic behavior. ViSCAR source code is available from GitLab at https://gitlab.com/ManolakosLab/viscar.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04409-9.

Intrinsic growth heterogeneity of mouse leukemia cells underlies differential susceptibility to a growth-inhibiting anticancer drug

Cancer cell populations consist of phenotypically heterogeneous cells. Growing evidence suggests that pre-existing phenotypic differences among cancer cells correlate with differential susceptibility to anticancer drugs and eventually lead to a relapse. Such phenotypic differences can arise not only externally driven by the environmental heterogeneity around individual cells but also internally by the intrinsic fluctuation of cells. However, the quantitative characteristics of intrinsic phenotypic heterogeneity emerging even under constant environments and their relevance to drug susceptibility remain elusive. Here we employed a microfluidic device, mammalian mother machine, for studying the intrinsic heterogeneity of growth dynamics of mouse lymphocytic leukemia cells (L1210) across tens of generations. The generation time of this cancer cell line had a distribution with a long tail and a heritability across generations. We determined that a minority of cell lineages exist in a slow-cycling state for multiple generations. These slow-cycling cell lineages had a higher chance of survival than the fast-cycling lineages under continuous exposure to the anticancer drug Mitomycin C. This result suggests that heritable heterogeneity in cancer cells’ growth in a population influences their susceptibility to anticancer drugs.

Non-genetic inheritance restraint of cell-to-cell variation

Heterogeneity in physical and functional characteristics of cells (e.g. size, cycle time, growth rate, protein concentration) proliferates within an isogenic population due to stochasticity in intracellular biochemical processes and in the distribution of resources during divisions. Conversely, it is limited in part by the inheritance of cellular components between consecutive generations. Here we introduce a new experimental method for measuring proliferation of heterogeneity in bacterial cell characteristics, based on measuring how two sister cells become different from each other over time. Our measurements provide the inheritance dynamics of different cellular properties, and the 'inertia' of cells to maintain these properties along time. We find that inheritance dynamics are property specific and can exhibit long-term memory (∼10 generations) that works to restrain variation among cells. Our results can reveal mechanisms of non-genetic inheritance in bacteria and help understand how cells control their properties and heterogeneity within isogenic cell populations.

Magnetic self-assembly of toroidal hepatic microstructures for micro-tissue fabrication

In this study, we developed a procedure for assembling hepatic microstructures into tube shapes using magnetic self-assembly for in vitro 3D micro-tissue fabrication. To this end, biocompatible hydrogels, which have a toroidal shape, were made using the micro-patterned electrodeposition method. Ferrite particles were used to coat the fabricated toroidal hydrogel microcapsules using a poly-L-lysine membrane. The microcapsules were then magnetized with a 3 T magnetic field, and assembled using a magnetic self-assembly process. During electrodeposition, hepatic cells were trapped inside the microcapsules, and they were cultured to construct tissue-like structures. The magnetized toroidal microstructures then automatically assembled to form tube structures. Shaking was used to enhance the assembly process, and the shaking speed was experimentally optimized to achieve the high-speed assembly of longer tube structures. The flow velocity inside the dish during shaking was measured by particle image velocimetry. Hepatic functions were evaluated to check for side-effects of the magnetized ferrite particles on the microstructures. Collectively, our findings indicated that the developed method can achieve the high-speed assembly of a large number of microstructures to form tissue-like hepatic structures.

Dominant rule of community effect in synchronized beating behavior of cardiomyocyte networks

Exploiting the combination of latest microfabrication technologies and single cell measurement technologies, we can measure the interactions of single cells, and cell networks from "algebraic" and "geometric" perspectives under the full control of their environments and interactions. However, the experimental constructive single cell-based approach still remains the limitations regarding the quality and condition control of those cells. To overcome these limitations, mathematical modeling is one of the most powerful complementary approaches. In this review, we first explain our on-chip experimental methods for constructive approach, and we introduce the results of the "community effect" of beating cardiomyocyte networks as an example of this approach. On-chip analysis revealed that (1) synchronized interbeat intervals (IBIs) of cell networks were followed to the more stable beating cells even their IBIs were slower than the other cells, which is against the conventional faster firing regulation or "overdrive suppression," and (2) fluctuation of IBIs of cardiomyocyte networks decreased according to the increase of the number of connected cells regardless of their geometry. The mathematical simulation of this synchronous behavior of cardiomyocyte networks also fitted well with the experimental results after incorporating the fluctuation-dissipation theorem into the oscillating stochastic phase model, in which the concept of spatially arranged cardiomyocyte networks was involved. The constructive experiments and mathematical modeling indicated the dominant rule of synchronization behavior of beating cardiomyocyte networks is a kind of stability-oriented synchronization phenomenon as the "community effect" or a fluctuation-dissipation phenomenon. Finally, as a practical application of this approach, the predictive cardiotoxicity is introduced.

Biophysics at Waseda University

Biophysics in Waseda University was started in 1965 as one of the three key research areas that constitute the Physics Department. In the biophysics group, one theoretical lab and two experimental labs are now working on the cutting-edge themes on biophysics, disseminating the ideas and knowledge of biophysics to undergraduate and graduate students from the viewpoint of physics.

Cell Size Regulation Induces Sustained Oscillations in the Population Growth Rate

We study the effect of correlations in generation times on the dynamics of population growth of microorganisms. We show that any nonzero correlation that is due to cell-size regulation, no matter how small, induces long-term oscillations in the population growth rate. The population only reaches its steady state when we include the often-neglected variability in the growth rates of individual cells. We discover that the relaxation timescale of the population to its steady state is determined by the distribution of single-cell growth rates and is surprisingly independent of details of the division process such as the noise in the timing of division and the mechanism of cell-size regulation. We validate the predictions of our model using existing experimental data and propose an experimental method to measure single-cell growth variability by observing how long it takes for the population to reach its steady state or balanced growth.

Heterogeneity of single cell inactivation: Assessment of the individual cell time to death and implications in population behavior

A direct microscopic time-lapse method, using appropriate staining for cell viability in a confocal scanning laser microscope, was used for the direct assessment of Salmonella Agona individual cell inactivation in small two-dimensional colonies exposed to osmotic stress. Individual cell inactivation times were fitted to a variety of continuous distributions using @Risk software. The best fitted distribution (LogLogistic) was further used to predict the inactivation of Salmonella populations of various initial levels using Monte Carlo simulation. The simulation results showed that the variability in inactivation kinetics is negligible for concentrations down to 100 cells and the population behavior can be described with a deterministic model. As the concentration decreases below 100 cells, however, the variability increases significantly indicating that the traditional D-value used in deterministic first order kinetic models is not valid. At a second stage, single cell behavior was monitored in larger three dimensional colonies. The results showed that colony size can affect the inactivation pattern. The effect of colony size on microbial inactivation was confirmed with validation experiments which showed a higher inactivation rate for populations consisting of single cells or small colonies compared to those consisting of cells organized in larger colonies.

Heritable stress response dynamics revealed by single-cell genealogy

Cells often respond to environmental stimuli by activating specific transcription factors. Upon exposure to glucose limitation stress, it is known that yeast Saccharomyces cerevisiae cells dephosphorylate the general stress response factor Msn2, leading to its nuclear localization, which in turn activates the expression of many genes. However, the precise dynamics of Msn2 nucleocytoplasmic translocations and whether they are inherited over multiple generations in a stress-dependent manner are not well understood. Tracking Msn2 localization events in yeast lineages grown on a microfluidic chip, here we report how cells modulate the amplitude, duration, frequency, and dynamic pattern of the localization events in response to glucose limitation stress. Single yeast cells were found to modulate the amplitude and frequency of Msn2 nuclear localization, but not its duration. Moreover, the Msn2 localization frequency was epigenetically inherited in descendants of mother cells, leading to a decrease in cell-to-cell variation in localization frequency. An analysis of the time dynamic patterns of nuclear localizations between genealogically related cell pairs using an information theory approach found that the magnitude of pattern similarity increased with stress intensity and was strongly inherited by the descendant cells at the highest stress level. By dissecting how general stress response dynamics is contributed by different modulation schemes over long time scales, our work provides insight into which scheme evolution might have acted on to optimize fitness in stressful environments.

Image analysis driven single-cell analytics for systems microbiology

BACKGROUND

Time-lapse microscopy is an essential tool for capturing and correlating bacterial morphology and gene expression dynamics at single-cell resolution. However state-of-the-art computational methods are limited in terms of the complexity of cell movies that they can analyze and lack of automation. The proposed Bacterial image analysis driven Single Cell Analytics (BaSCA) computational pipeline addresses these limitations thus enabling high throughput systems microbiology.

RESULTS

BaSCA can segment and track multiple bacterial colonies and single-cells, as they grow and divide over time (cell segmentation and lineage tree construction) to give rise to dense communities with thousands of interacting cells in the field of view. It combines advanced image processing and machine learning methods to deliver very accurate bacterial cell segmentation and tracking (F-measure over 95%) even when processing images of imperfect quality with several overcrowded colonies in the field of view. In addition, BaSCA extracts on the fly a plethora of single-cell properties, which get organized into a database summarizing the analysis of the cell movie. We present alternative ways to analyze and visually explore the spatiotemporal evolution of single-cell properties in order to understand trends and epigenetic effects across cell generations. The robustness of BaSCA is demonstrated across different imaging modalities and microscopy types.

CONCLUSIONS

BaSCA can be used to analyze accurately and efficiently cell movies both at a high resolution (single-cell level) and at a large scale (communities with many dense colonies) as needed to shed light on e.g. how bacterial community effects and epigenetic information transfer play a role on important phenomena for human health, such as biofilm formation, persisters' emergence etc. Moreover, it enables studying the role of single-cell stochasticity without losing sight of community effects that may drive it.

Individual cell heterogeneity in Predictive Food Microbiology: Challenges in predicting a "noisy" world

Gene expression is a fundamentally noisy process giving rise to a significant cell to cell variability at the phenotype level. The phenotypic noise is manifested in a wide range of microbial traits. Heterogeneous behavior of individual cells is observed at the growth, survival and inactivation responses and should be taken into account in the context of Predictive Food Microbiology (PMF). Recent methodological advances can be employed for the study and modeling of single cell dynamics leading to a new generation of mechanistic models which can provide insight into the link between phenotype, gene-expression, protein and metabolic functional units at the single cell level. Such models however, need to deal with an enormous amount of interactions and processes that influence each other, forming an extremely complex system. In this review paper, we discuss the importance of noise and present the future challenges in predicting the "noisy" microbial responses in foods.

Noise and Epigenetic Inheritance of Single-Cell Division Times Influence Population Fitness

The fitness effect of biological noise remains unclear. For example, even within clonal microbial populations, individual cells grow at different speeds. Although it is known that the individuals' mean growth speed can affect population-level fitness, it is unclear how or whether growth speed heterogeneity itself is subject to natural selection. Here, we show that noisy single-cell division times can significantly affect population-level growth rate. Using time-lapse microscopy to measure the division times of thousands of individual S. cerevisiae cells across different genetic and environmental backgrounds, we find that the length of individual cells' division times can vary substantially between clonal individuals and that sublineages often show epigenetic inheritance of division times. By combining these experimental measurements with mathematical modeling, we find that, for a given mean division time, increasing heterogeneity and epigenetic inheritance of division times increases the population growth rate. Furthermore, we demonstrate that the heterogeneity and epigenetic inheritance of single-cell division times can be linked with variation in the expression of catabolic genes. Taken together, our results reveal how a change in noisy single-cell behaviors can directly influence fitness through dynamics that operate independently of effects caused by changes to the mean. These results not only allow a better understanding of microbial fitness but also help to more accurately predict fitness in other clonal populations, such as tumors.

Noise-driven growth rate gain in clonal cellular populations

Cellular populations in both nature and the laboratory are composed of phenotypically heterogeneous individuals that compete with each other resulting in complex population dynamics. Predicting population growth characteristics based on knowledge of heterogeneous single-cell dynamics remains challenging. By observing groups of cells for hundreds of generations at single-cell resolution, we reveal that growth noise causes clonal populations of Escherichia coli to double faster than the mean doubling time of their constituent single cells across a broad set of balanced-growth conditions. We show that the population-level growth rate gain as well as age structures of populations and of cell lineages in competition are predictable. Furthermore, we theoretically reveal that the growth rate gain can be linked with the relative entropy of lineage generation time distributions. Unexpectedly, we find an empirical linear relation between the means and the variances of generation times across conditions, which provides a general constraint on maximal growth rates. Together, these results demonstrate a fundamental benefit of noise for population growth, and identify a growth law that sets a "speed limit" for proliferation.

Individuality and universality in the growth-division laws of single E. coli cells

The mean size of exponentially dividing Escherichia coli cells in different nutrient conditions is known to depend on the mean growth rate only. However, the joint fluctuations relating cell size, doubling time, and individual growth rate are only starting to be characterized. Recent studies in bacteria reported a universal trend where the spread in both size and doubling times is a linear function of the population means of these variables. Here we combine experiments and theory and use scaling concepts to elucidate the constraints posed by the second observation on the division control mechanism and on the joint fluctuations of sizes and doubling times. We found that scaling relations based on the means collapse both size and doubling-time distributions across different conditions and explain how the shape of their joint fluctuations deviates from the means. Our data on these joint fluctuations highlight the importance of cell individuality: Single cells do not follow the dependence observed for the means between size and either growth rate or inverse doubling time. Our calculations show that these results emerge from a broad class of division control mechanisms requiring a certain scaling form of the "division hazard rate function," which defines the probability rate of dividing as a function of measurable parameters. This "model free" approach gives a rationale for the universal body-size distributions observed in microbial ecosystems across many microbial species, presumably dividing with multiple mechanisms. Additionally, our experiments show a crossover between fast and slow growth in the relation between individual-cell growth rate and division time, which can be understood in terms of different regimes of genome replication control.

Single cell growth rate and morphological dynamics revealing an "opportunistic" persistence

Bacteria persistence is a well-known phenomenon, where a small fraction of cells in an isogenic population are able to survive high doses of antibiotic treatment. Since the persistence is often associated with single cell behaviour, the ability to study the dynamic response of individual cells to antibiotics is critical. In this work, we developed a gradient microfluidic system that enables long-term tracking of single cell morphology under a wide range of inhibitor concentrations. From time-lapse images, we calculated bacterial growth rates based on the variations in cell mass and in cell number. Using E. coli and Comamonas denitrificans to amoxicillin inhibition as model systems, we found the IC50 determined via both methods are in a good agreement. Importantly, the growth rates together with morphological dynamics of individual cells has led to the discovery of a new form of persistence to amoxicillin. Normal cells that are sensitive to amoxicillin gain persistence or recover from the killing process, if they have had an opportunity to utilise the cytoplasm released from lysed cells close-by. We term this acquired persistence in normal growing cells "opportunistic persistence". This finding might shed new insights into biofilm resistance and the effect of antibiotics on environmental microbes.

Survival probability of beneficial mutations in bacterial batch culture

The survival of rare beneficial mutations can be extremely sensitive to the organism's life history and the trait affected by the mutation. Given the tremendous impact of bacteria in batch culture as a model system for the study of adaptation, it is important to understand the survival probability of beneficial mutations in these populations. Here we develop a life-history model for bacterial populations in batch culture and predict the survival of mutations that increase fitness through their effects on specific traits: lag time, fission time, viability, and the timing of stationary phase. We find that if beneficial mutations are present in the founding population at the beginning of culture growth, mutations that reduce the mortality of daughter cells are the most likely to survive drift. In contrast, of mutations that occur de novo during growth, those that delay the onset of stationary phase are the most likely to survive. Our model predicts that approximately fivefold population growth between bottlenecks will optimize the occurrence and survival of beneficial mutations of all four types. This prediction is relatively insensitive to other model parameters, such as the lag time, fission time, or mortality rate of the population. We further estimate that bottlenecks that are more severe than this optimal prediction substantially reduce the occurrence and survival of adaptive mutations.

A constant size extension drives bacterial cell size homeostasis

Cell size control is an intrinsic feature of the cell cycle. In bacteria, cell growth and division are thought to be coupled through a cell size threshold. Here, we provide direct experimental evidence disproving the critical size paradigm. Instead, we show through single-cell microscopy and modeling that the evolutionarily distant bacteria Escherichia coli and Caulobacter crescentus achieve cell size homeostasis by growing, on average, the same amount between divisions, irrespective of cell length at birth. This simple mechanism provides a remarkably robust cell size control without the need of being precise, abating size deviations exponentially within a few generations. This size homeostasis mechanism is broadly applicable for symmetric and asymmetric divisions, as well as for different growth rates. Furthermore, our data suggest that constant size extension is implemented at or close to division. Altogether, our findings provide fundamentally distinct governing principles for cell size and cell-cycle control in bacteria.

Bacteria in solitary confinement

Even in clonal bacterial cultures, individual bacteria can show substantial stochastic variation, leading to pitfalls in the interpretation of data derived from millions of cells in a culture. In this issue of the Journal of Bacteriology, as part of their study on osmoadaptation in a cyanobacterium, Nanatani et al. describe employing an ingenious microfluidic device that gently cages individual cells (J Bacteriol 197:676–687, 2015, http://dx.doi.org/10.1128/JB.02276-14). The device is a welcome addition to the toolkit available to probe the responses of individual cells to environmental cues.

Comparative analysis of kdp and ktr mutants reveals distinct roles of the potassium transporters in the model cyanobacterium Synechocystis sp. strain PCC 6803

Photoautotrophic bacteria have developed mechanisms to maintain K(+) homeostasis under conditions of changing ionic concentrations in the environment. Synechocystis sp. strain PCC 6803 contains genes encoding a well-characterized Ktr-type K(+) uptake transporter (Ktr) and a putative ATP-dependent transporter specific for K(+) (Kdp). The contributions of each of these K(+) transport systems to cellular K(+) homeostasis have not yet been defined conclusively. To verify the functionality of Kdp, kdp genes were expressed in Escherichia coli, where Kdp conferred K(+) uptake, albeit with lower rates than were conferred by Ktr. An on-chip microfluidic device enabled monitoring of the biphasic initial volume recovery of single Synechocystis cells after hyperosmotic shock. Here, Ktr functioned as the primary K(+) uptake system during the first recovery phase, whereas Kdp did not contribute significantly. The expression of the kdp operon in Synechocystis was induced by extracellular K(+) depletion. Correspondingly, Kdp-mediated K(+) uptake supported Synechocystis cell growth with trace amounts of external potassium. This induction of kdp expression depended on two adjacent genes, hik20 and rre19, encoding a putative two-component system. The circadian expression of kdp and ktr peaked at subjective dawn, which may support the acquisition of K(+) required for the regular diurnal photosynthetic metabolism. These results indicate that Kdp contributes to the maintenance of a basal intracellular K(+) concentration under conditions of limited K(+) in natural environments, whereas Ktr mediates fast potassium movements in the presence of greater K(+) availability. Through their distinct activities, both Ktr and Kdp coordinate the responses of Synechocystis to changes in K(+) levels under fluctuating environmental conditions.

A new toolbox for assessing single cells

Unprecedented access to the biology of single cells is now feasible, enabled by recent technological advancements that allow us to manipulate and measure sparse samples and achieve a new level of resolution in space and time. This review focuses on advances in tools to study single cells for specific areas of biology. We examine both mature and nascent techniques to study single cells at the genomics, transcriptomics, and proteomics level. In addition, we provide an overview of tools that are well suited for following biological responses to defined perturbations with single-cell resolution. Techniques to analyze and manipulate single cells through soluble and chemical ligands, the microenvironment, and cell-cell interactions are provided. For each of these topics, we highlight the biological motivation, applications, methods, recent advances, and opportunities for improvement. The toolbox presented in this review can function as a starting point for the design of single-cell experiments.

Concerted control of Escherichia coli cell division

The coordination of cell growth and division is a long-standing problem in biology. Focusing on Escherichia coli in steady growth, we quantify cell division control using a stochastic model, by inferring the division rate as a function of the observable parameters from large empirical datasets of dividing cells. We find that (i) cells have mechanisms to control their size, (ii) size control is effected by changes in the doubling time, rather than in the single-cell elongation rate, (iii) the division rate increases steeply with cell size for small cells, and saturates for larger cells. Importantly, (iv) the current size is not the only variable controlling cell division, but the time spent in the cell cycle appears to play a role, and (v) common tests of cell size control may fail when such concerted control is in place. Our analysis illustrates the mechanisms of cell division control in E. coli. The phenomenological framework presented is sufficiently general to be widely applicable and opens the way for rigorous tests of molecular cell-cycle models.

Fluorescent-Based Temperature Measurement with Simple Compensation of Photo-Degradation Using Hydrogel-Tool and Color Space Conversion

We developed a method to obtain stable and longlifetime temperature measurements using a fluorescence micromeasurement system. A hydrogel tool containing nano-semiconductor quantum dots (Q-dots) was developed as a fluorescent temperature indicator. We used image processing to convert RGB information to other color information to compensate for photodegradation. The temperature was calibrated using the hydrogel tool in several color spaces, includingRGB(R: red,G: green,B: blue),HSV(H: hue,S: saturation,V: value (brightness)), andYCrCb(Y: brightness,Cr: red color difference,Cb: blue color difference). The calibration results showed thatR,G,B,Y, andCrdecreased monotonically with increasing temperature, whereasHandCbdid not decrease monotonically. The photodegradation analysis showed thatCrwas robust against the brightness fluctuation; however,R,G, andBstrongly affected the brightness fluctuation because these values included the brightness information. These results show that temperature measurements based onCrvalues are suitable to compensate for photodegradation and have a sensitivity of -1.3%/K and an accuracy of 0.3 K. These values are the same as those obtained using the fluorescence intensity method.

Sex allocation pattern of the diatom Cyclotella meneghiniana

Sex allocation is one of the most successful applications of evolutionary game theory. This theory has usually been applied to multicellular organisms; however, conditional sex allocation in unicellular organisms remains an unexplored field of research. Observations at the cellular level are indispensable for an understanding of the phenotypic sex allocation strategy among individuals within clonal unicellular organisms. The diatom Cyclotella meneghiniana, in which the sexes are generated from vegetative cells, is suitable for investigating effects of phenotypic plasticity factors on sex allocation while excluding genetic differences. We designed a microfluidic system that allowed us to trace the fate of individual cells. Sex allocation by individual mother cells was affected by cell lineage, cell size and cell density. Sibling cell pairs tended to differentiate into the same fates (split sex ratio). We found a significant negative correlation between the cell area of the mother cell and sex ratio of the two sibling cells. The male-biased sex ratio declined with higher local cell population density, supporting the fertility insurance hypothesis. Our results characterize multiple non-genetic factors that affect the phenotypic single cell-level sex allocation. Sex allocation in diatoms may provide a model system for testing evolutionary game theory in unicellular organisms.

Stochasticity in colonial growth dynamics of individual bacterial cells

Conventional bacterial growth studies rely on large bacterial populations without considering the individual cells. Individual cells, however, can exhibit marked behavioral heterogeneity. Here, we present experimental observations on the colonial growth of 220 individual cells of Salmonella enterica serotype Typhimurium using time-lapse microscopy videos. We found a highly heterogeneous behavior. Some cells did not grow, showing filamentation or lysis before division. Cells that were able to grow and form microcolonies showed highly diverse growth dynamics. The quality of the videos allowed for counting the cells over time and estimating the kinetic parameters lag time (λ) and maximum specific growth rate (μmax) for each microcolony originating from a single cell. To interpret the observations, the variability of the kinetic parameters was characterized using appropriate probability distributions and introduced to a stochastic model that allows for taking into account heterogeneity using Monte Carlo simulation. The model provides stochastic growth curves demonstrating that growth of single cells or small microbial populations is a pool of events each one of which has its own probability to occur. Simulations of the model illustrated how the apparent variability in population growth gradually decreases with increasing initial population size (N(0)). For bacterial populations with N(0) of >100 cells, the variability is almost eliminated and the system seems to behave deterministically, even though the underlying law is stochastic. We also used the model to demonstrate the effect of the presence and extent of a nongrowing population fraction on the stochastic growth of bacterial populations.

Hydrodynamic trapping of Tetrahymena thermophila for the long-term monitoring of cell behaviors

Microfluidic trapping technology has been widely applied for single-cell observation in order to reveal characteristic cell behaviors. However, this strategy has yet to be tested for monitoring highly motile cells, which are often biologically important. In this paper, we seek the conditions that enable effective and long-term trapping of a prominent model ciliate Tetrahymena thermophila within a hydrodynamic microfluidic device. Although motility and flexibility of T. thermophila make it difficult to avoid escaping from the trap, we show that tuning some key parameters in the hydrodynamic circuit was effective to achieve approximately 40 h cell retention, which is long enough to monitor cell behaviors over several generations. Here, we demonstrate the real-time observation of cell division and phagocytic digestion, revealing interesting phenomena such as a wide distribution in doubling time in a poor synthetic medium and heterogeneous time courses in digestion processes. Our results present a strategy for trapping highly motile ciliate cells in order to study the dynamic behaviors of single cells.

Reproduction-time statistics and segregation patterns in growing populations

Pattern formation in microbial colonies of competing strains under purely space-limited population growth has recently attracted considerable research interest. We show that the reproduction time statistics of individuals has a significant impact on the sectoring patterns. Generalizing the standard Eden growth model, we introduce a simple one-parameter family of reproduction time distributions indexed by the variation coefficient δ∈[0,1], which includes deterministic (δ=0) and memory-less exponential distribution (δ=1) as extreme cases. We present convincing numerical evidence and heuristic arguments that the generalized model is still in the Kardar-Parisi-Zhang (KPZ) universality class, and the changes in patterns are due to changing prefactors in the scaling relations, which we are able to predict quantitatively. With the example of Saccharomyces cerevisiae, we show that our approach using the variation coefficient also works for more realistic reproduction time distributions.

Optimal lineage principle for age-structured populations

We present a formulation of branching and aging processes that allows age distributions along lineages to be studied within populations, and provides a new interpretation of classical results in the theory of aging. We establish a variational principle for the stable age distribution along lineages. Using this optimal lineage principle, we show that the response of a population's growth rate to age-specific changes in mortality and fecundity--a key quantity that was first calculated by Hamilton--is given directly by the age distribution along lineages. We apply our method also to the Bellman-Harris process, in which both mother and progeny are rejuvenated at each reproduction event, and show that this process can be mapped to the classic aging process such that age statistics in the population and along lineages are identical. Our approach provides both a theoretical framework for understanding the statistics of aging in a population, and a new method of analytical calculations for populations with age structure. We discuss generalizations for populations with multiple phenotypes, and more complex aging processes. We also provide a first experimental test of our theory applied to bacterial populations growing in a microfluidics device.

Origin of lognormal-like distributions with a common width in a growth and division process

Lognormal statistical distributions are observed in a variety of scientific fields. The widths of these distributions in the log scale are often similar, but the underlying mechanism that maintains these widths within a small range has not been well explained. We show that a stochastic process of halving followed by addition can yield a stationary distribution that resembles the universal lognormal distribution with a certain width. The mechanism that we propose here would provide insight into the essence of why lognormal-like distributions in many systems have a common width.

Nanoliters Discharge/Suction by Thermoresponsive Polymer Actuated Probe and Applied for Single Cell Manipulation

We propose the Thermoresponsive Polymer Actuated (TPA) probe which uses thermoresponsive polymer poly (N-isopropylacrylamide) (PNIPAAm) volume change as an actuator. The proposed probe is applicable to single cell analysis, especially single cell manipulation. The TPA probe can discharge and suck solution in several nanoliters (nl) using the volume change. Normally, it is difficult to realize solution discharge and suction less than several dozen nl by the conventional air- or oil-pressure-actuated probe. We designed the TPA probe for low-cost fabrication and disposable use. The probe also takes in and ejects on a nl order by simply switching a heater on and off. PNIPAAm solution volume change was evaluated in this paper. The manipulation of single microbead and the suction of target cell were also demonstrated by the TPA probe in the semi-closed microchip. It is considered that the TPA probe can contribute to the manipulation of single cell.

Analysis of the lag phase to exponential growth transition by incorporating inoculum characteristics

During the last decade, individual-based modelling (IbM) has proven to be a valuable tool for modelling and studying microbial dynamics. As each individual is considered as an independent entity with its own characteristics, IbM enables the study of microbial dynamics and the inherent variability and heterogeneity. IbM simulations and (single-cell) experimental research form the basis to unravel individual cell characteristics underlying population dynamics. In this study, the IbM framework MICRODIMS, i.e., MICRObial Dynamics Individual-based Model/Simulator, is used to investigate the system dynamics (with respect to the model and the system modelled). First, the impact of the time resolution on the simulation accuracy is discussed. Second, the effect of the inoculum state and size on emerging individual dynamics, such as individual mass, individual age and individual generation time distribution dynamics, is studied. The distributions of individual characteristics are more informative during the lag phase and the transition to the exponential growth phase than during the exponential phase. The first generation time distributions are strongly influenced by the inoculum state. All inocula with a pronounced heterogeneity, except the inocula starting from a uniform distribution, exhibit commonly observed microbial behaviour, like a more spread first generation time distribution compared to following generations and a fast stabilisation of biomass and age distributions.

Tightly regulated and heritable division control in single bacterial cells

The robust surface adherence property of the aquatic bacterium Caulobacter crescentus permits visualization of single cells in a linear microfluidic culture chamber over an extended number of generations. The division rate of Caulobacter in this continuous-flow culture environment is substantially faster than in other culture apparati and is independent of flow velocity. Analysis of the growth and division of single isogenic cells reveals that the cell cycle control network of this bacterium generates an oscillatory output with a coefficient of variation lower than that of all other bacterial species measured to date. DivJ, a regulator of polar cell development, is necessary for maintaining low variance in interdivision timing, as transposon disruption of divJ significantly increases the coefficient of variation of both interdivision time and the rate of cell elongation. Moreover, interdivision time and cell division arrest are significantly correlated between mother and daughter cells, providing evidence for epigenetic inheritance of cell division behavior in Caulobacter. The single-cell growth/division results reported here suggest that future predictive models of Caulobacter cell cycle regulation should include parameters describing the variance and inheritance properties of this system.

On-chip pH measurement using functionalized gel-microbeads positioned by optical tweezers

This paper demonstrates local pH measurement in a microchip using a pH-sensing gel-microbead. To achieve this, the gel-microbead made of a hydrophilic photo-crosslinkable resin was functionalized with the pH indicator bromothymol blue (BTB). The primary constituent of this photo-crosslinkable resin is poly(ethylene glycol). Gel-microbeads impregnated with BTB were obtained by stirring the mixture solution, which was composed of the resin, BTB, and an electrolyte solution. The gel-microbead is polymerized by UV illumination. The polymerized gel-microbead can be manipulated by optical tweezers and made to adhere to a glass surface. The local pH was measured from the color of the gel-microbead impregnated with BTB by calibrated color information in the YCrCb color space. We succeeded in measuring the local pH value using the pH-sensing gel-microbead by manipulating and positioning it at the desired point in the microchip.

Heritable stochastic switching revealed by single-cell genealogy

The partitioning and subsequent inheritance of cellular factors like proteins and RNAs is a ubiquitous feature of cell division. However, direct quantitative measures of how such nongenetic inheritance affects subsequent changes in gene expression have been lacking. We tracked families of the yeast Saccharomyces cerevisiae as they switch between two semi-stable epigenetic states. We found that long after two cells have divided, they continued to switch in a synchronized manner, whereas individual cells have exponentially distributed switching times. By comparing these results to a Poisson process, we show that the time evolution of an epigenetic state depends initially on inherited factors, with stochastic processes requiring several generations to decorrelate closely related cells. Finally, a simple stochastic model demonstrates that a single fluctuating regulatory protein that is synthesized in large bursts can explain the bulk of our results.

When cells divide, not only DNA but an entire pattern of gene expression can be passed from mother to daughter cell. Once cell division is complete, random processes cause this pattern to change, with closely related cells growing less similar over time. We measured inheritance of a dynamic gene-expression state in single yeast cells. We used an engineered network where individual cells switch between two semi-stable states (ON and OFF), even in a constant environment. Several generations after cells have physically separated, many pairs of closely related cells switch in near synchrony. We quantified this effect by measuring how likely a mother cell is to have switched given that the daughter cell has already switched. This yields a conditional probability distribution that is very different from the exponential one found in the entire population of switching cells. We measured the extent to which this correlation between switching cells persists by comparing our results with a model Poisson process. Together, these findings demonstrate the inheritance of a dynamic gene expression state whose post-division changes include both random factors arising from noise as well as correlated factors that originate in two related cells' shared history. Finally, we constructed a model that demonstrates that our major findings can be explained by burst-like fluctuations in the levels of a single regulatory protein.

When cells divide, each daughter cell inherits a share of the contents of the mother. If the contents include a regulatory system with a feedback loop, sister cells switch states in synchrony.

Cell-to-cell heterogeneity in growth rate and gene expression in Methylobacterium extorquens AM1

Cell-to-cell heterogeneity in gene expression and growth parameters was assessed in the facultative methylotroph Methylobacterium extorquens AM1. A transcriptional fusion between a well-characterized methylotrophy promoter (P(mxaF)) and gfp(uv) (encoding a variant of green fluorescent protein [GFPuv]) was used to assess single-cell gene expression. Using a flowthrough culture system and laser scanning microscopy, data on fluorescence and cell size were obtained over time through several growth cycles for cells grown on succinate or methanol. Cells were grown continuously with no discernible lag between divisions, and high cell-to-cell variability was observed for cell size at division (2.5-fold range), division time, and growth rate. When individual cells were followed over multiple division cycles, no direct correlation was observed between the growth rate before a division and the subsequent growth rate or between the cell size at division and the subsequent growth rate. The cell-to-cell variability for GFPuv fluorescence from the P(mxaF) promoter was less, with a range on the order of 1.5-fold. Fluorescence and growth rate were also followed during a carbon shift experiment, in which cells growing on succinate were shifted to methanol. Variability of the response was observed, and the growth rate at the time of the shift from succinate to methanol was a predictor of the response. Higher growth rates at the time of the substrate shift resulted in greater decreases in growth rates immediately after the shift, but full induction of P(mxaF)-gfp(uv) was achieved faster. These results demonstrate that in M. extorquens, physiological heterogeneity at the single-cell level plays an important role in determining the population response to the metabolic shift examined.

Microbial phenotypic heterogeneity and antibiotic tolerance

Phenotypic heterogeneity, defined as metastable variation in cellular parameters generated by epigenetic mechanisms, is crucial for the persistence of bacterial populations under fluctuating selective pressures. Diversity ensures that some individuals will survive a potentially lethal stress, such as an antibiotic, that would otherwise obliterate the entire population. The refractoriness of bacterial infections to antibiotic therapy has been ascribed to antibiotic-tolerant variants known as 'persisters'. The persisters are not drug-resistant mutants and it is unclear why they survive antibiotic pressure that kills their genetically identical siblings. Recent conceptual and technological advances are beginning to yield some surprising new insights into the mechanistic basis of this clinically important manifestation of phenotypic heterogeneity.

Quantitative evaluation of cell-to-cell communication effects in cell group class using on-chip individual-cell-based cultivation system

Cell-to-cell communication is considered to underlie the coordinated behavior and the multicellularity of cell group class, which cannot be explained only by the knowledge of lower class of life system from molecule to individual cell, because they are determined by at least two different ways: diffusible chemical signals and their direct physical contacts. We show in this paper a new method of individual-cell-based cell observation that can estimate the role of cell-to-cell communication, diffusible chemical signals, and physical contacts as separated properties, by applying an on-chip individual-cell-based cultivation system. The exchange of stationary phase medium on isolated individual Escherichia coli from exponential phase medium and the control of physical contacts indicated that the cell-to-cell direct contact did not affect the growth rate; only the communication through diffusible signals affects the growth rates as Hill's equation manner.