Cell-to-cell variation and specialization in sugar metabolism in clonal bacterial populations

The intersection of host in vivo metabolism and immune responses to infection with kinetoplastid and apicomplexan parasites

SUMMARYProtozoan parasite infection dramatically alters host metabolism, driven by immunological demand and parasite manipulation strategies. Immunometabolic checkpoints are often exploited by kinetoplastid and protozoan parasites to establish chronic infection, which can significantly impair host metabolic homeostasis. The recent growth of tools to analyze metabolism is expanding our understanding of these questions. Here, we review and contrast host metabolic alterations that occur in vivo during infection with Leishmania, trypanosomes, Toxoplasma, Plasmodium, and Cryptosporidium. Although genetically divergent, there are commonalities among these pathogens in terms of metabolic needs, induction of the type I immune responses required for clearance, and the potential for sustained host metabolic dysbiosis. Comparing these pathogens provides an opportunity to explore how transmission strategy, nutritional demand, and host cell and tissue tropism drive similarities and unique aspects in host response and infection outcome and to design new strategies to treat disease.

The interplay between metabolic stochasticity and cAMP-CRP regulation in single E. coli cells

The inherent stochasticity of metabolism raises a critical question for understanding homeostasis: are cellular processes regulated in response to internal fluctuations? Here, we show that, in E. coli cells under constant external conditions, catabolic enzyme expression continuously responds to metabolic fluctuations. The underlying regulatory feedback is enabled by the cyclic AMP (cAMP) and cAMP receptor protein (CRP) system, which controls catabolic enzyme expression based on metabolite concentrations. Using single-cell microscopy, genetic constructs in which this feedback is disabled, and mathematical modeling, we show how fluctuations circulate through the metabolic and genetic network at sub-cell-cycle timescales. Modeling identifies four noise propagation modes, including one specific to CRP regulation. Together, these modes correctly predict noise circulation at perturbed cAMP levels. The cAMP-CRP system may thus have evolved to control internal metabolic fluctuations in addition to external growth conditions. We conjecture that second messengers may more broadly function to achieve cellular homeostasis.

Extensive cellular multi-tasking within Bacillus subtilis biofilms

Bacillus subtilis is a soil-dwelling bacterium that can form biofilms, or communities of cells surrounded by a self-produced extracellular matrix. In biofilms, genetically identical cells often exhibit heterogeneous transcriptional phenotypes, so that subpopulations of cells carry out essential yet costly cellular processes that allow the entire population to thrive. Surprisingly, the extent of phenotypic heterogeneity and the relationships between subpopulations of cells within biofilms of even in well-studied bacterial systems like B. subtilis remains largely unknown. To determine relationships between these subpopulations of cells, we created 182 strains containing pairwise combinations of fluorescent transcriptional reporters for the expression state of 14 different genes associated with potential cellular subpopulations. We determined the spatial organization of the expression of these genes within biofilms using confocal microscopy, which revealed that many reporters localized to distinct areas of the biofilm, some of which were co-localized. We used flow cytometry to quantify reporter co-expression, which revealed that many cells "multi-task," simultaneously expressing two reporters. These data indicate that prior models describing B. subtilis cells as differentiating into specific cell types, each with a specific task or function, were oversimplified. Only a few subpopulations of cells, including surfactin and plipastatin producers, as well as sporulating and competent cells, appear to have distinct roles based on the set of genes examined here. These data will provide us with a framework with which to further study and make predictions about the roles of diverse cellular phenotypes in B. subtilis biofilms. IMPORTANCE Many microbes differentiate, expressing diverse phenotypes to ensure their survival in various environments. However, studies on phenotypic differentiation have typically examined only a few phenotypes at one time, thus limiting our knowledge about the extent of differentiation and phenotypic overlap in the population. We investigated the spatial organization and gene expression relationships for genes important in B. subtilis biofilms. In doing so, we mapped spatial gene expression patterns and expanded the number of cell populations described in the B. subtilis literature. It is likely that other bacteria also display complex differentiation patterns within their biofilms. Studying the extent of cellular differentiation in other microbes may be important when designing therapies for disease-causing bacteria, where studying only a single phenotype may be masking underlying phenotypic differentiation relevant to infection outcomes.

Effective bet-hedging through growth rate dependent stability

Microbes in the wild face highly variable and unpredictable environments and are naturally selected for their average growth rate across environments. Apart from using sensory regulatory systems to adapt in a targeted manner to changing environments, microbes employ bet-hedging strategies where cells in an isogenic population switch stochastically between alternative phenotypes. Yet, bet-hedging suffers from a fundamental trade-off: Increasing the phenotype-switching rate increases the rate at which maladapted cells explore alternative phenotypes but also increases the rate at which cells switch out of a well-adapted state. Consequently, it is currently believed that bet-hedging strategies are effective only when the number of possible phenotypes is limited and when environments last for sufficiently many generations. However, recent experimental results show that gene expression noise generally decreases with growth rate, suggesting that phenotype-switching rates may systematically decrease with growth rate. Such growth rate dependent stability (GRDS) causes cells to be more explorative when maladapted and more phenotypically stable when well-adapted, and we show that GRDS can almost completely overcome the trade-off that limits bet-hedging, allowing for effective adaptation even when environments are diverse and change rapidly. We further show that even a small decrease in switching rates of faster-growing phenotypes can substantially increase long-term fitness of bet-hedging strategies. Together, our results suggest that stochastic strategies may play an even bigger role for microbial adaptation than hitherto appreciated.

Public good-driven release of heterogeneous resources leads to genotypic diversification of an isogenic yeast population

Understanding the basis of biological diversity remains a central problem in evolutionary biology. Using microbial systems, adaptive diversification has been studied in (a) spatially heterogeneous environments, (b) temporally segregated resources, and (c) resource specialization in a homogeneous environment. However, it is not well understood how adaptive diversification can take place in a homogeneous environment containing a single resource. Starting from an isogenic population of yeast Saccharomyces cerevisiae, we report rapid adaptive diversification, when propagated in an environment containing melibiose as the carbon source. The diversification is driven due to a public good enzyme α-galactosidase, which hydrolyzes melibiose into glucose and galactose. The diversification is driven by mutations at a single locus, in the GAL3 gene in the S. cerevisiae GAL/MEL regulon. We show that metabolic co-operation involving public resources could be an important mode of generating biological diversity. Our study demonstrates sympatric diversification of yeast starting from an isogenic population and provides detailed mechanistic insights into the factors and conditions responsible for generating and maintaining the population diversity.

Single-cell Raman spectroscopy identifies Escherichia coli persisters and reveals their enhanced metabolic activities

Microbial persisters are the featured tiny sub-population of microorganisms that are highly tolerant to multiple antimicrobials. Currently, studies on persisters remain a considerable challenge owing to technical limitations. Here, we explored the application of single-cell Raman spectroscopy (SCRS) in the investigation of persisters. Escherichia coli (ATCC 25922) cells were treated with a lethal dosage of ampicillin (100 μg/mL, 32 × MIC, 4 h) for the formation of persisters. The biochemical characters of E. coli and its persisters were assessed by SCRS, and their metabolic activities were labeled and measured with D₂O-based single-cell Raman spectroscopy (D₂O-Ramanometry). Notable differences in the intensity of Raman bands related to major cellular components and metabolites were observed between E. coli and its ampicillin-treated persisters. Based on their distinct Raman spectra, E. coli and its persister cells were classified into different projective zones through the principal component analysis and t-distributed stochastic neighbor embedding. According to the D₂O absorption rate, E. coli persisters exhibited higher metabolic activities than those of untreated E. coli. Importantly, after the termination of ampicillin exposure, these persister cells showed a temporal pattern of D₂O intake that was distinct from non-persister cells. To our knowledge, this is the first report on identifying E. coli persisters and assessing their metabolic activities through the integrated SCRS and D₂O-Ramanometry approach. These novel findings enhance our understanding of the phenotypes and functionalities of microbial persister cells. Further investigations could be extended to other pathogens by disclosing microbial pathogenicity mechanisms for developing novel therapeutic strategies and approaches.

Single-cell stable isotope probing in microbial ecology

Environmental and host-associated microbiomes are typically diverse assemblages of organisms performing myriad activities and engaging in a network of interactions that play out in spatially structured contexts. As the sum of these activities and interactions give rise to overall microbiome function, with important consequences for environmental processes and human health, elucidating specific microbial activities within complex communities is a pressing challenge. Single-cell stable isotope probing (SC-SIP) encompasses multiple techniques that typically utilize Raman microspectroscopy or nanoscale secondary ion mass spectrometry (NanoSIMS) to enable spatially resolved tracking of isotope tracers in cells, cellular components, and metabolites. SC-SIP techniques are uniquely suited for illuminating single-cell activities in microbial communities and for testing hypotheses about cellular functions generated for example from meta-omics datasets. Here, we illustrate the insights enabled by SC-SIP techniques by reviewing selected applications in microbiology and offer a perspective on their potential for future research.

Exploiting Information and Control Theory for Directing Gene Expression in Cell Populations

Microbial populations can adapt to adverse environmental conditions either by appropriately sensing and responding to the changes in their surroundings or by stochastically switching to an alternative phenotypic state. Recent data point out that these two strategies can be exhibited by the same cellular system, depending on the amplitude/frequency of the environmental perturbations and on the architecture of the genetic circuits involved in the adaptation process. Accordingly, several mitigation strategies have been designed for the effective control of microbial populations in different contexts, ranging from biomedicine to bioprocess engineering. Technically, such control strategies have been made possible by the advances made at the level of computational and synthetic biology combined with control theory. However, these control strategies have been applied mostly to synthetic gene circuits, impairing the applicability of the approach to natural circuits. In this review, we argue that it is possible to expand these control strategies to any cellular system and gene circuits based on a metric derived from this information theory, i.e., mutual information (MI). Indeed, based on this metric, it should be possible to characterize the natural frequency of any gene circuits and use it for controlling gene circuits within a population of cells.

Nutrient and salt depletion synergistically boosts glucose metabolism in individual Escherichia coli cells

The interaction between a cell and its environment shapes fundamental intracellular processes such as cellular metabolism. In most cases growth rate is treated as a proximal metric for understanding the cellular metabolic status. However, changes in growth rate might not reflect metabolic variations in individuals responding to environmental fluctuations. Here we use single-cell microfluidics-microscopy combined with transcriptomics, proteomics and mathematical modelling to quantify the accumulation of glucose within Escherichia coli cells. In contrast to the current consensus, we reveal that environmental conditions which are comparatively unfavourable for growth, where both nutrients and salinity are depleted, increase glucose accumulation rates in individual bacteria and population subsets. We find that these changes in metabolic function are underpinned by variations at the translational and posttranslational level but not at the transcriptional level and are not dictated by changes in cell size. The metabolic response-characteristics identified greatly advance our fundamental understanding of the interactions between bacteria and their environment and have important ramifications when investigating cellular processes where salinity plays an important role.

Multiple timescales in bacterial growth homeostasis

In balanced exponential growth, bacteria maintain many properties statistically stable for a long time: cell size, cell cycle time, and more. As these are strongly coupled variables, it is not a-priori obvious which are directly regulated and which are stabilized through interactions. Here, we address this problem by separating timescales in bacterial single-cell dynamics. Disentangling homeostatic set points from fluctuations around them reveals that some variables, such as growth-rate, cell size and cycle time, are "sloppy" with highly volatile set points. Quantifying the relative contribution of environmental and internal sources, we find that sloppiness is primarily driven by the environment. Other variables such as fold-change define "stiff" combinations of coupled variables with robust set points. These results are manifested geometrically as a control manifold in the space of variables: set points span a wide range of values within the manifold, whereas out-of-manifold deviations are constrained. Our work offers a generalizable data-driven approach for identifying control variables in a multidimensional system.

NanoSIP: NanoSIMS Applications for Microbial Biology

High-resolution imaging with secondary ion mass spectrometry (nanoSIMS) has become a standard method in systems biology and environmental biogeochemistry and is broadly used to decipher ecophysiological traits of environmental microorganisms, metabolic processes in plant and animal tissues, and cross-kingdom symbioses. When combined with stable isotope-labeling-an approach we refer to as nanoSIP-nanoSIMS imaging offers a distinctive means to quantify net assimilation rates and stoichiometry of individual cell-sized particles in both low- and high-complexity environments. While the majority of nanoSIP studies in environmental and microbial biology have focused on nitrogen and carbon metabolism (using ¹⁵N and ¹³C tracers), multiple advances have pushed the capabilities of this approach in the past decade. The development of a high-brightness oxygen ion source has enabled high-resolution metal analyses that are easier to perform, allowing quantification of metal distribution in cells and environmental particles. New preparation methods, tools for automated data extraction from large data sets, and analytical approaches that push the limits of sensitivity and spatial resolution have allowed for more robust characterization of populations ranging from marine archaea to fungi and viruses. NanoSIMS studies continue to be enhanced by correlation with orthogonal imaging and 'omics approaches; when linked to molecular visualization methods, such as in situ hybridization and antibody labeling, these techniques enable in situ function to be linked to microbial identity and gene expression. Here we present an updated description of the primary materials, methods, and calculations used for nanoSIP, with an emphasis on recent advances in nanoSIMS applications, key methodological steps, and potential pitfalls.

Noise suppression in stochastic genetic circuits using PID controllers

Inside individual cells, protein population counts are subject to molecular noise due to low copy numbers and the inherent probabilistic nature of biochemical processes. We investigate the effectiveness of proportional, integral and derivative (PID) based feedback controllers to suppress protein count fluctuations originating from two noise sources: bursty expression of the protein, and external disturbance in protein synthesis. Designs of biochemical reactions that function as PID controllers are discussed, with particular focus on individual controllers separately, and the corresponding closed-loop system is analyzed for stochastic controller realizations. Our results show that proportional controllers are effective in buffering protein copy number fluctuations from both noise sources, but this noise suppression comes at the cost of reduced static sensitivity of the output to the input signal. In contrast, integral feedback has no effect on the protein noise level from stochastic expression, but significantly minimizes the impact of external disturbances, particularly when the disturbance comes at low frequencies. Counter-intuitively, integral feedback is found to amplify external disturbances at intermediate frequencies. Next, we discuss the design of a coupled feedforward-feedback biochemical circuit that approximately functions as a derivate controller. Analysis using both analytical methods and Monte Carlo simulations reveals that this derivative controller effectively buffers output fluctuations from bursty stochastic expression, while maintaining the static input-output sensitivity of the open-loop system. In summary, this study provides a systematic stochastic analysis of biochemical controllers, and paves the way for their synthetic design and implementation to minimize deleterious fluctuations in gene product levels.

In the noisy cellular environment, biochemical species such as genes, RNAs and proteins that often occur at low molecular counts, are subject to considerable stochastic fluctuations in copy numbers over time. How cellular biochemical processes function reliably in the face of such randomness is an intriguing fundamental problem. Increasing evidence suggests that random fluctuations (noise) in protein copy numbers play important functional roles, such as driving genetically identical cells to different cell fates. Moreover, many disease states have been attributed to elevated noise levels in specific proteins. Here we systematically investigate design of biochemical systems that function as proportional, integral and derivative-based feedback controllers to suppress protein count fluctuations arising from bursty expression of the protein and external disturbance in protein synthesis. Our results show that different controllers are effective in buffering different noise components, and identify ranges of feedback gain for minimizing deleterious fluctuations in protein levels.

Reducing phenotypic instabilities of a microbial population during continuous cultivation based on cell switching dynamics

Predicting the fate of individual cells among a microbial population (i.e., growth and gene expression) remains a challenge, especially when this population is exposed to very dynamic environmental conditions, such as those encountered during continuous cultivation. Indeed, the dynamic nature of a continuous cultivation process implies the potential diversification of the microbial population resulting in genotypic and phenotypic heterogeneity. The present work focused on the induction of the arabinose operon in Escherichia coli as a model system to study this diversification process in continuous cultivations. As a preliminary step, the green fluorescent protein (GFP) level triggered by an arabinose-inducible ParaBAD promoter was tracked by flow cytometry in chemostat cultivations with glucose-arabinose co-feeding. For a wide range of glucose-arabinose co-feeding concentrations in the chemostats, the simultaneous occurrence of GFP positive and negative subpopulation was observed. In the second set of experiments, continuous cultivation was performed by adding glucose continuously and arabinose based on the capability of individual cells to switch from low GFP to high GFP expression states, performed with a technology setup called segregostat. In the segregostat cultivation mode, on-line flow cytometry analysis was used for adjusting the arabinose/glucose transitions based on the phenotypic switching profiles of the microbial population. This strategy allowed finding an appropriate arabinose pulsing frequency, leading to prolonged maintenance of the induction level with a limited increase in the phenotypic diversity for more than 60 generations. The results suggest that the steady forcing of individual cells into a given phenotypic trajectory may not be the best strategy for controlling cell populations. Instead, allowing individual cells to switch periodically around a predefined threshold seems to be a more robust strategy leading to oscillations, but within a predictable cell population behavior range.

Development of a rapid method for assessing the efficacy of antibacterial photocatalytic coatings

Visible-light activated photocatalytic coatings may represent an attractive antimicrobial solution in domains such as food, beverage, pharmaceutical, biomedical and wastewater remediation. However, testing methods to determine the antibacterial effects of photocatalytic coatings are limited and require specialist expertise. This paper describes the development of a method that enables rapid screening of coatings for photocatalytic-antibacterial activity. Relying on the ability of viable microorganisms to reduce the dye resazurin from a blue to a pink colour, the method relates the time taken to detect this colour change with number of viable microorganisms. The antibacterial activity of two photocatalytic materials (bismuth oxide and titanium dioxide) were screened against two pathogenic organisms (Escherichia coli and Klebsiella pneumoniae) that represent potential target microorganisms using traditional testing and enumeration techniques (BS ISO 27447:2009) and the novel rapid method. Bismuth oxide showed excellent antibacterial activity under ambient visible light against E. coli, but was less effective against K. pneumoniae. The rapid method showed excellent agreement with existing tests in terms of number of viable cells recovered. Due to advantages such as low cost, high throughput, and less reliance on microbiological expertise, this method is recommended for researchers seeking an inexpensive first-stage screen for putative photocatalytic-antibacterial coatings.

Availability of the Molecular Switch XylR Controls Phenotypic Heterogeneity and Lag Duration during Escherichia coli Adaptation from Glucose to Xylose

The glucose-xylose metabolic transition is of growing interest as a model to explore cellular adaption since these molecules are the main substrates resulting from the deconstruction of lignocellulosic biomass. Here, we investigated the role of the XylR transcription factor in the length of the lag phases when the bacterium Escherichia coli needs to adapt from glucose- to xylose-based growth. First, a variety of lag times were observed when different strains of E. coli were switched from glucose to xylose. These lag times were shown to be controlled by XylR availability in the cells with no further effect on the growth rate on xylose. XylR titration provoked long lag times demonstrated to result from phenotypic heterogeneity during the switch from glucose to xylose, with a subpopulation unable to resume exponential growth, whereas the other subpopulation grew exponentially on xylose. A stochastic model was then constructed based on the assumption that XylR availability influences the probability of individual cells to switch to xylose growth. The model was used to understand how XylR behaves as a molecular switch determining the bistability set-up. This work shows that the length of lag phases in E. coli is controllable and reinforces the role of stochastic mechanism in cellular adaptation, paving the way for new strategies for the better use of sustainable carbon sources in bioeconomy.

Computation of Single-Cell Metabolite Distributions Using Mixture Models

Metabolic heterogeneity is widely recognized as the next challenge in our understanding of non-genetic variation. A growing body of evidence suggests that metabolic heterogeneity may result from the inherent stochasticity of intracellular events. However, metabolism has been traditionally viewed as a purely deterministic process, on the basis that highly abundant metabolites tend to filter out stochastic phenomena. Here we bridge this gap with a general method for prediction of metabolite distributions across single cells. By exploiting the separation of time scales between enzyme expression and enzyme kinetics, our method produces estimates for metabolite distributions without the lengthy stochastic simulations that would be typically required for large metabolic models. The metabolite distributions take the form of Gaussian mixture models that are directly computable from single-cell expression data and standard deterministic models for metabolic pathways. The proposed mixture models provide a systematic method to predict the impact of biochemical parameters on metabolite distributions. Our method lays the groundwork for identifying the molecular processes that shape metabolic heterogeneity and its functional implications in disease.

Single-Cell Technologies to Understand the Mechanisms of Cellular Adaptation in Chemostats

There is a growing interest in continuous manufacturing within the bioprocessing community. In this context, the chemostat process is an important unit operation. The current application of chemostat processes in industry is limited although many high yielding processes are reported in literature. In order to reach the full potential of the chemostat in continuous manufacture, the output should be constant. However, adaptation is often observed resulting in changed productivities over time. The observed adaptation can be coupled to the selective pressure of the nutrient-limited environment in the chemostat. We argue that population heterogeneity should be taken into account when studying adaptation in the chemostat. We propose to investigate adaptation at the single-cell level and discuss the potential of different single-cell technologies, which could be used to increase the understanding of the phenomena. Currently, none of the discussed single-cell technologies fulfill all our criteria but in combination they may reveal important information, which can be used to understand and potentially control the adaptation.

Microbial metabolic noise

From the time a cell was first placed under the microscope, it became apparent that identifying two clonal cells that "look" identical is extremely challenging. Since then, cell-to-cell differences in shape, size, and protein content have been carefully examined, informing us of the ultimate limits that hinder two cells from occupying an identical phenotypic state. Here, we present recent experimental and computational evidence that similar limits emerge also in cellular metabolism. These limits pertain to stochastic metabolic dynamics and, thus, cell-to-cell metabolic variability, including the resulting adapting benefits. We review these phenomena with a focus on microbial metabolism and conclude with a brief outlook on the potential relationship between metabolic noise and adaptive evolution. This article is categorized under: Metabolic Diseases > Computational Models Metabolic Diseases > Biomedical Engineering.

In Vivo Feedback Control of an Antithetic Molecular-Titration Motif in Escherichia coli Using Microfluidics

We study both in silico and in vivo the real-time feedback control of a molecular titration motif that has been earmarked as a fundamental component of antithetic and multicellular feedback control schemes in E. coli. We show that an external feedback control strategy can successfully regulate the average fluorescence output of a bacterial cell population to a desired constant level in real-time. We also provide in silico evidence that the same strategy can be used to track a time-varying reference signal where the set-point is switched to a different value halfway through the experiment. We use the experimental data to refine and parametrize an in silico model of the motif that can be used as an error computation module in future embedded or multicellular control experiments.

Functional roles of microbial cell-to-cell heterogeneity and emerging technologies for analysis and control

Clonal cell populations often display significant cell-to-cell phenotypic heterogeneity, even when maintained under constant external conditions. This variability can result from the inherently stochastic nature of transcription and translation processes, which leads to varying numbers of transcripts and proteins per cell. Here, we showcase studies that reveal links between stochastic cellular events and biological functions in isogenic microbial populations. Then, we highlight emerging tools from engineering, computation, and synthetic and molecular biology that enable precise measurement, control, and analysis of gene expression noise in microorganisms. The capabilities offered by this sophisticated toolbox will shape future directions in the field and generate insight into the behavior of living systems at the single-cell level.

Bacterial metabolic heterogeneity: origins and applications in engineering and infectious disease

Bacteria within an isoclonal population display significant heterogeneity in metabolism, even under tightly controlled environmental conditions. Metabolic heterogeneity enables influential functions not possible or measurable at the ensemble scale. Several molecular and cellular mechanisms are likely to give rise to metabolic heterogeneity including molecular noise in metabolic enzyme expression, positive feedback loops, and asymmetric partitioning of cellular components during cell division. Dissection of the mechanistic origins of metabolic heterogeneity has been enabled by recent developments in single-cell analytical tools. Finally, we provide a discussion of recent studies examining the importance of metabolic heterogeneity in applied settings such as infectious disease and metabolic engineering.

Metabolic Heterogeneity and Cross-Feeding in Bacterial Multicellular Systems

Cells in assemblages differentiate and perform distinct roles. Though many pathways of differentiation are understood at the molecular level in multicellular eukaryotes, the elucidation of similar processes in bacterial assemblages is recent and ongoing. Here, we discuss examples of bacterial differentiation, focusing on cases in which distinct metabolisms coexist and those that exhibit cross-feeding, with one subpopulation producing substrates that are metabolized by a second subpopulation. We describe several studies of single-species systems, then segue to studies of multispecies metabolic heterogeneity and cross-feeding in the clinical setting. Many of the studies described exemplify the application of new techniques and modeling approaches that provide insights into metabolic interactions relevant for bacterial growth outside the laboratory.

Direct Observation of the Dynamics of Single-Cell Metabolic Activity during Microbial Diauxic Growth

Population-level analyses are rapidly becoming inadequate to answer many of biomedical science and microbial ecology's most pressing questions. The role of microbial populations within ecosystems and the evolutionary selective pressure on individuals depend fundamentally on the metabolic activity of single cells. Yet, many existing single-cell technologies provide only indirect evidence of metabolic specialization because they rely on correlations between transcription and phenotype established at the level of the population to infer activity. In this study, we take a top-down approach using isotope labels and secondary ion mass spectrometry to track the uptake of carbon and nitrogen atoms from different sources into biomass and directly observe dynamic changes in anabolic specialization at the level of single cells. We investigate the classic microbiological phenomenon of diauxic growth at the single-cell level in the model methylotroph Methylobacterium extorquens In nature, this organism inhabits the phyllosphere, where it experiences diurnal changes in the available carbon substrates, necessitating an overhaul of central carbon metabolism. We show that the population exhibits a unimodal response to the changing availability of viable substrates, a conclusion that supports the canonical model but has thus far been supported by only indirect evidence. We anticipate that the ability to monitor the dynamics of anabolism in individual cells directly will have important applications across the fields of ecology, medicine, and biogeochemistry, especially where regulation downstream of transcription has the potential to manifest as heterogeneity that would be undetectable with other existing single-cell approaches.IMPORTANCE Understanding how genetic information is realized as the behavior of individual cells is a long-term goal of biology but represents a significant technological challenge. In clonal microbial populations, variation in gene regulation is often interpreted as metabolic heterogeneity. This follows the central dogma of biology, in which information flows from DNA to RNA to protein and ultimately manifests as activity. At present, DNA and RNA can be characterized in single cells, but the abundance and activity of proteins cannot. Inferences about metabolic activity usually therefore rely on the assumption that transcription reflects activity. By tracking the atoms from which they build their biomass, we make direct observations of growth rate and substrate specialization in individual cells throughout a period of growth in a changing environment. This approach allows the flow of information from DNA to be constrained from the distal end of the regulatory cascade and will become an essential tool in the rapidly advancing field of single-cell metabolism.

Microfluidic Single-Cell Analytics

What is the impact of cellular heterogeneity on process performance? How do individual cells contribute to averaged process productivity? Single-cell analysis is a key technology for answering such key questions of biotechnology, beyond bulky measurements with populations. The analysis of cellular individuality, its origins, and the dependency of process performance on cellular heterogeneity has tremendous potential for optimizing biotechnological processes in terms of metabolic, reaction, and process engineering. Microfluidics offer unmatched environmental control of the cellular environment and allow massively parallelized cultivation of single cells. However, the analytical accessibility to a cell's physiology is of crucial importance for obtaining the desired information on the single-cell production phenotype. Highly sensitive analytics are required to detect and quantify the minute amounts of target analytes and small physiological changes in a single cell. For their application to biotechnological questions, single-cell analytics must evolve toward the measurement of kinetics and specific rates of the smallest catalytic unit, the single cell. In this chapter, we focus on an introduction to the latest single-cell analytics and their application for obtaining physiological parameters in a biotechnological context from single cells. We present and discuss recent advancements in single-cell analytics that enable the analysis of cell-specific growth, uptake, and production kinetics, as well as the gene expression and regulatory mechanisms at a single-cell level.

Quantitation and Comparison of Phenotypic Heterogeneity Among Single Cells of Monoclonal Microbial Populations

Phenotypic heterogeneity within microbial populations arises even when the cells are exposed to putatively constant and homogeneous conditions. The outcome of this phenomenon can affect the whole function of the population, resulting in, for example, new "adapted" metabolic strategies and impacting its fitness at given environmental conditions. Accounting for phenotypic heterogeneity becomes thus necessary, due to its relevance in medical and applied microbiology as well as in environmental processes. Still, a comprehensive evaluation of this phenomenon requires a common and unique method of quantitation, which allows for the comparison between different studies carried out with different approaches. Consequently, in this study, two widely applicable indices for quantitation of heterogeneity were developed. The heterogeneity coefficient (HC) is valid when the population follows unimodal activity, while the differentiation tendency index (DTI) accounts for heterogeneity implying outbreak of subpopulations and multimodal activity. We demonstrated the applicability of HC and DTI for heterogeneity quantitation on stable isotope probing with nanoscale secondary ion mass spectrometry (SIP-nanoSIMS), flow cytometry, and optical microscopy datasets. The HC was found to provide a more accurate and precise measure of heterogeneity, being at the same time consistent with the coefficient of variation (CV) applied so far. The DTI is able to describe the differentiation in single-cell activity within monoclonal populations resolving subpopulations with low cell abundance, individual cells with similar phenotypic features (e.g., isotopic content close to natural abundance, as detected with nanoSIMS). The developed quantitation approach allows for a better understanding on the impact and the implications of phenotypic heterogeneity in environmental, medical and applied microbiology, microbial ecology, cell biology, and biotechnology.

Coupled metabolic-hydrodynamic modeling enabling rational scale-up of industrial bioprocesses

Metabolomics aims to address what and how regulatory mechanisms are coordinated to achieve flux optimality, different metabolic objectives as well as appropriate adaptations to dynamic nutrient availability. Recent decades have witnessed that the integration of metabolomics and fluxomics within the goal of synthetic biology has arrived at generating the desired bioproducts with improved bioconversion efficiency. Absolute metabolite quantification by isotope dilution mass spectrometry represents a functional readout of cellular biochemistry and contributes to the establishment of metabolic (structured) models required in systems metabolic engineering. In industrial practices, population heterogeneity arising from fluctuating nutrient availability frequently leads to performance losses, that is reduced commercial metrics (titer, rate, and yield). Hence, the development of more stable producers and more predictable bioprocesses can benefit from a quantitative understanding of spatial and temporal cell-to-cell heterogeneity within industrial bioprocesses. Quantitative metabolomics analysis and metabolic modeling applied in computational fluid dynamics (CFD)-assisted scale-down simulators that mimic industrial heterogeneity such as fluctuations in nutrients, dissolved gases, and other stresses can procure informative clues for coping with issues during bioprocessing scale-up. In previous studies, only limited insights into the hydrodynamic conditions inside the industrial-scale bioreactor have been obtained, which makes case-by-case scale-up far from straightforward. Tracking the flow paths of cells circulating in large-scale bioreactors is a highly valuable tool for evaluating cellular performance in production tanks. The "lifelines" or "trajectories" of cells in industrial-scale bioreactors can be captured using Euler-Lagrange CFD simulation. This novel methodology can be further coupled with metabolic (structured) models to provide not only a statistical analysis of cell lifelines triggered by the environmental fluctuations but also a global assessment of the metabolic response to heterogeneity inside an industrial bioreactor. For the future, the industrial design should be dependent on the computational framework, and this integration work will allow bioprocess scale-up to the industrial scale with an end in mind.

The ecology and evolution of microbial metabolic strategies

Free-living microbes are generally capable of growing on multiple different nutrients. Some of those nutrients are used simultaneously, while others are used sequentially. The pattern of nutrient preferences and co-utilization defines the metabolic strategy of a microorganism. Metabolic strategies can substantially affect ecological interactions between species, but their evolution and distribution across the tree of life remain poorly characterized. We discuss how the confluence of better computational models of genotype-phenotype maps and high-throughput experimental tools can help us fill gaps in our knowledge and incorporate metabolic strategies into quantitative predictive models of microbial consortia.

Quantitative Flow Cytometry to Understand Population Heterogeneity in Response to Changes in Substrate Availability in Escherichia coli and Saccharomyces cerevisiae Chemostats

Microbial cells in bioprocesses are usually described with averaged parameters. But in fact, single cells within populations vary greatly in characteristics such as stress resistance, especially in response to carbon source gradients. Our aim was to introduce tools to quantify population heterogeneity in bioprocesses using a combination of reporter strains, flow cytometry, and easily comprehensible parameters. We calculated mean, mode, peak width, and coefficient of variance to describe distribution characteristics and temporal shifts in fluorescence intensity. The skewness and the slope of cumulative distribution function plots illustrated differences in distribution shape. These parameters are person-independent and precise. We demonstrated this by quantifying growth-related population heterogeneity of Saccharomyces cerevisiae and Escherichia coli reporter strains in steady-state of aerobic glucose-limited chemostat cultures at different dilution rates and in response to glucose pulses. Generally, slow-growing cells showed stronger responses to glucose excess than fast-growing cells. Cell robustness, measured as membrane integrity after exposure to freeze-thaw treatment, of fast-growing cells was strongly affected in subpopulations of low membrane robustness. Glucose pulses protected subpopulations of fast-growing but not slower-growing yeast cells against membrane damage. Our parameters could successfully describe population heterogeneity, thereby revealing physiological characteristics that might have been overlooked during traditional averaged analysis.

Stochastic modelling reveals mechanisms of metabolic heterogeneity

Phenotypic variation is a hallmark of cellular physiology. Metabolic heterogeneity, in particular, underpins single-cell phenomena such as microbial drug tolerance and growth variability. Much research has focussed on transcriptomic and proteomic heterogeneity, yet it remains unclear if such variation permeates to the metabolic state of a cell. Here we propose a stochastic model to show that complex forms of metabolic heterogeneity emerge from fluctuations in enzyme expression and catalysis. The analysis predicts clonal populations to split into two or more metabolically distinct subpopulations. We reveal mechanisms not seen in deterministic models, in which enzymes with unimodal expression distributions lead to metabolites with a bimodal or multimodal distribution across the population. Based on published data, the results suggest that metabolite heterogeneity may be more pervasive than previously thought. Our work casts light on links between gene expression and metabolism, and provides a theory to probe the sources of metabolite heterogeneity.

The effect of acetate on population heterogeneity in different cellular characteristics of Escherichia coli in aerobic batch cultures

Acetate as the major by-product in industrial-scale bioprocesses with Escherichia coli is found to decrease process efficiency as well as to be toxic to cells, which has several effects like a significant induction of cellular stress responses. However, the underlying phenomena are poorly explored. Therefore, we studied time-resolved population heterogeneity of the E. coli growth reporter strain MG1655/pGS20PrrnBGFPAAV expressing destabilized green fluorescent protein during batch growth on acetate and glucose as sole carbon sources. Additionally, we applied five fluorescent stains targeting different cellular properties (viability as well as metabolic and respiratory activity). Quantitative analysis of flow cytometry data verified that bacterial populations in the bioreactor are more heterogeneous in growth as well as stronger metabolically challenged during growth on acetate as sole carbon source, compared to growth on glucose or acetate after diauxic shift. Interestingly, with acetate as sole carbon source, significant subpopulations were found with some cells that seem to be more robust than the rest of the population. In conclusion, following batch cultures population heterogeneity was evident in all measured parameters. Our approach enabled a deeper study of heterogeneity during growth on the favored substrate glucose as well as on the toxic by-product acetate. Using a combination of activity fluorescent dyes proved to be an accurate and fast alternative as well as a supplement to the use of a reporter strain. However, the choice of combination of stains should be well considered depending on which population traits to aim for.

Single-cell imaging of phosphorus uptake shows that key harmful algae rely on different phosphorus sources for growth

Single-cell measurements of biochemical processes have advanced our understanding of cellular physiology in individual microbes and microbial populations. Due to methodological limitations, little is known about single-cell phosphorus (P) uptake and its importance for microbial growth within mixed field populations. Here, we developed a nanometer-scale secondary ion mass spectrometry (nanoSIMS)-based approach to quantify single-cell P uptake in combination with cellular CO₂ and N₂ fixation. Applying this approach during a harmful algal bloom (HAB), we found that the toxin-producer Nodularia almost exclusively used phosphate for growth at very low phosphate concentrations in the Baltic Sea. In contrast, the non-toxic Aphanizomenon acquired only 15% of its cellular P-demand from phosphate and ~85% from organic P. When phosphate concentrations were raised, Nodularia thrived indicating that this toxin-producer directly benefits from phosphate inputs. The phosphate availability in the Baltic Sea is projected to rise and therefore might foster more frequent and intense Nodularia blooms with a concomitant rise in the overall toxicity of HABs in the Baltic Sea. With a projected increase in HABs worldwide, the capability to use organic P may be a critical factor that not only determines the microbial community structure, but the overall harmfulness and associated costs of algal blooms.

Calculation of Single Cell Assimilation Rates From SIP-NanoSIMS-Derived Isotope Ratios: A Comprehensive Approach

The nanoSIMS-based chemical microscopy has been introduced in biology over a decade ago. The spatial distribution of elements and isotopes analyzed by nanoSIMS can be used to reconstruct images of biological samples with a resolution down to tens of nanometers, and can be also interpreted quantitatively. Currently, a unified approach for calculation of single cell assimilation rates from nanoSIMS-derived changes in isotope ratios is missing. Here we present a comprehensive concept of assimilation rate calculation with a rigorous mathematical model based on quantitative evaluation of nanoSIMS-derived isotope ratios. We provide a detailed description of data acquisition and treatment, including the selection and accumulation of nanoSIMS scans, defining regions of interest and extraction of isotope ratios. Next, we present alternative methods to determine the cellular volume and the density of the element under scrutiny. Finally, to compensate for alterations of original isotopic ratios, our model considers corrections for sample preparation methods (e.g., air dry, chemical fixation, permeabilization, hybridization), and when known, for the stable isotope fractionation associated with utilization of defined growth substrates. As proof of concept we implemented this protocol to quantify the assimilation of ¹³C-labeled glucose by single cells of Pseudomonas putida. In addition, we provide a calculation template where all protocol-derived formulas are directly available to facilitate routine assimilation rate calculations by nanoSIMS users.

Rocking Aspergillus: morphology-controlled cultivation of Aspergillus niger in a wave-mixed bioreactor for the production of secondary metabolites

Background

Filamentous fungi including Aspergillus niger are cell factories for the production of organic acids, proteins and bioactive compounds. Traditionally, stirred-tank reactors (STRs) are used to cultivate them under highly reproducible conditions ensuring optimum oxygen uptake and high growth rates. However, agitation via mechanical stirring causes high shear forces, thus affecting fungal physiology and macromorphologies. Two-dimensional rocking-motion wave-mixed bioreactor cultivations could offer a viable alternative to fungal cultivations in STRs, as comparable gas mass transfer is generally achievable while deploying lower friction and shear forces. The aim of this study was thus to investigate for the first time the consequences of wave-mixed cultivations on the growth, macromorphology and product formation of A. niger.

Results

We investigated the impact of hydrodynamic conditions on A. niger cultivated at a 5 L scale in a disposable two-dimensional rocking motion bioreactor (CELL-tainer^®) and a BioFlo STR (New Brunswick^®), respectively. Two different A. niger strains were analysed, which produce heterologously the commercial drug enniatin B. Both strains expressed the esyn1 gene that encodes a non-ribosomal peptide synthetase ESYN under control of the inducible Tet-on system, but differed in their dependence on feeding with the precursors d-2-hydroxyvaleric acid and l-valine. Cultivations of A. niger in the CELL-tainer resulted in the formation of large pellets, which were heterogeneous in size (diameter 300–800 μm) and not observed during STR cultivations. When talcum microparticles were added, it was possible to obtain a reduced pellet size and to control pellet heterogeneity (diameter 50–150 μm). No foam formation was observed under wave-mixed cultivation conditions, which made the addition of antifoam agents needless. Overall, enniatin B titres of about 1.5–2.3 g L⁻¹ were achieved in the CELL-tainer^® system, which is about 30–50% of the titres achieved under STR conditions.

Conclusions

This is the first report studying the potential use of single-use wave-mixed reactor systems for the cultivation of A. niger. Although final enniatin yields are not competitive yet with titres achieved under STR conditions, wave-mixed cultivations open up new avenues for the cultivation of shear-sensitive mutant strains as well as high cell-density cultivations.