Microbial individuality: how single-cell heterogeneity enables population level strategies

Feedback between stochastic gene networks and population dynamics enables cellular decision-making

Phenotypic selection occurs when genetically identical cells are subject to different reproductive abilities due to cellular noise. Such noise arises from fluctuations in reactions synthesizing proteins and plays a crucial role in how cells make decisions and respond to stress or drugs. We propose a general stochastic agent-based model for growing populations capturing the feedback between gene expression and cell division dynamics. We devise a finite state projection approach to analyze gene expression and division distributions and infer selection from single-cell data in mother machines and lineage trees. We use the theory to quantify selection in multi-stable gene expression networks and elucidate that the trade-off between phenotypic switching and selection enables robust decision-making essential for synthetic circuits and developmental lineage decisions. Using live-cell data, we demonstrate that combining theory and inference provides quantitative insights into bet-hedging-like response to DNA damage and adaptation during antibiotic exposure in Escherichia coli.

Bet-hedging and variability in plant development: seed germination and beyond

When future conditions are unpredictable, bet-hedging strategies can be advantageous. This can involve isogenic individuals producing different phenotypes, under the same environmental conditions. Ecological studies provide evidence that variability in seed germination time has been selected for as a bet-hedging strategy. We demonstrate how variability in germination time found in Arabidopsis could function as a bet-hedging strategy in the face of unpredictable lethal stresses. Despite a body of knowledge on how the degree of seed dormancy versus germination is controlled, relatively little is known about how differences between isogenic seeds in a batch are generated. We review proposed mechanisms for generating variability in germination time and the current limitations and new possibilities for testing the model predictions. We then look beyond germination to the role of variability in seedling and adult plant growth and review new technologies for quantification of noisy gene expression dynamics. We discuss evidence for phenotypic variability in plant traits beyond germination being under genetic control and propose that variability in stress response gene expression could function as a bet-hedging strategy. We discuss open questions about how noisy gene expression could lead to between-plant heterogeneity in gene expression and phenotypes. This article is part of a discussion meeting issue 'Causes and consequences of stochastic processes in development and disease'.

Dynamical model of antibiotic responses linking expression of resistance genes to metabolism explains emergence of heterogeneity during drug exposures

Antibiotic responses in bacteria are highly dynamic and heterogeneous, with sudden exposure of bacterial colonies to high drug doses resulting in the coexistence of recovered and arrested cells. The dynamics of the response is determined by regulatory circuits controlling the expression of resistance genes, which are in turn modulated by the drug's action on cell growth and metabolism. Despite advances in understanding gene regulation at the molecular level, we still lack a framework to describe how feedback mechanisms resulting from the interdependence between expression of resistance and cell metabolism can amplify naturally occurring noise and create heterogeneity at the population level. To understand how this interplay affects cell survival upon exposure, we constructed a mathematical model of the dynamics of antibiotic responses that links metabolism and regulation of gene expression, based on the tetracycline resistancetetoperon inE. coli. We use this model to interpret measurements of growth and expression of resistance in microfluidic experiments, both in single cells and in biofilms. We also implemented a stochastic model of the drug response, to show that exposure to high drug levels results in large variations of recovery times and heterogeneity at the population level. We show that stochasticity is important to determine how nutrient quality affects cell survival during exposure to high drug concentrations. A quantitative description of how microbes respond to antibiotics in dynamical environments is crucial to understand population-level behaviors such as biofilms and pathogenesis.

Portable dielectrophoresis for biology: ADEPT facilitates cell trapping, separation, and interactions

Dielectrophoresis is a powerful and well-established technique that allows label-free, non-invasive manipulation of cells and particles by leveraging their electrical properties. The practical implementation of the associated electronics and user interface in a biology laboratory, however, requires an engineering background, thus hindering the broader adoption of the technique. In order to address these challenges and to bridge the gap between biologists and the engineering skills required for the implementation of DEP platforms, we report here a custom-built, compact, universal electronic platform termed ADEPT (adaptable dielectrophoresis embedded platform tool) for use with a simple microfluidic chip containing six microelectrodes. The versatility of the open-source platform is ensured by a custom-developed graphical user interface that permits simple reconfiguration of the control signals to address a wide-range of specific applications: (i) precision positioning of the single bacterium/cell/particle in the micrometer range; (ii) viability-based separation by achieving a 94% efficiency in separating live and dead yeast; (iii) phenotype-based separation by achieving a 96% efficiency in separating yeast and Bacillus subtilis; (iv) cell-cell interactions by steering a phagocytosis process where a granulocyte engulfs E. coli RGB-S bacterium. Together, the set of experiments and the platform form a complete basis for a wide range of possible applications addressing various biological questions exploiting the plug-and-play design and the intuitive GUI of ADEPT.

Gene expression tradeoffs determine bacterial survival and adaptation to antibiotic stress

To optimize their fitness, cells face the crucial task of efficiently responding to various stresses. This necessitates striking a balance between conserving resources for survival and allocating resources for growth and division. The fundamental principles governing these tradeoffs is an outstanding challenge in the physics of living systems. In this study, we introduce a coarse-grained theoretical framework for bacterial physiology that establishes a connection between the physiological state of cells and their survival outcomes in dynamic environments, particularly in the context of antibiotic exposure. Predicting bacterial survival responses to varying antibiotic doses proves challenging due to the profound influence of the physiological state on critical parameters, such as the Minimum Inhibitory Concentration (MIC) and killing rates, even within an isogenic cell population. Our proposed theoretical model bridges the gap by linking extracellular antibiotic concentration and nutrient quality to intracellular damage accumulation and gene expression. This framework allows us to predict and explain the control of cellular growth rate, death rate, MIC and survival fraction in a wide range of time-varying environments. Surprisingly, our model reveals that cell death is rarely due to antibiotic levels being above the maximum physiological limit, but instead survival is limited by the inability to alter gene expression sufficiently quickly to transition to a less susceptible physiological state. Moreover, bacteria tend to overexpress stress response genes at the expense of reduced growth, conferring greater protection against further antibiotic exposure. This strategy is in contrast to those employed in different nutrient environments, in which bacteria allocate resources to maximize growth rate. This highlights an important tradeoff between the cellular capacity for growth and the ability to survive antibiotic exposure.

Variation in growth rates between cultures hinders the cultivation of ammonia-oxidizing bacteria

Ammonia-oxidizing bacteria, key players in the nitrogen cycle, have been the focus of extensive research. Numerous novel species have been isolated and their growth dynamics were studied. Despite these efforts, controlling their growth to obtain diverse physiological findings remains a challenge. These bacteria often fail to grow, even under optimal conditions. This unpredictable growth pattern could be viewed as a survival strategy. Understanding this heterogeneous behavior could enhance our ability to culture these bacteria. In this study, the variation in the growth rate was quantified for the ammonia-oxidizing bacterium Nitrosomonas mobilis Ms1. Our findings revealed significant growth rate variation under low inoculum conditions. Interestingly, higher cell densities resulted in more stable cultures. A comparative analysis of three Nitrosomonas species showed a correlation between growth rate variation and culture failure. The greater the variation in growth rate, the higher the likelihood of culture failure.

Cellular and population strategies underpinning neurotoxin production and sporulation in Clostridium botulinum type E cultures

IMPORTANCE

Toxin production and sporulation are key determinants of pathogenesis in Clostridia. Toxins cause the clinical manifestation of clostridial diseases, including diarrhea and colitis, tissue damage, and systemic effects on the nervous system. Spores ensure long-term survival and persistence in the environment, act as infectious agents, and initiate the host tissue colonization leading to infection. Understanding the interplay between toxin production and sporulation and their coordination in bacterial cells and cultures provides novel intervention points for controlling the public health and food safety risks caused by clostridial diseases. We demonstrate environmentally driven cellular heterogeneity in botulinum neurotoxin and spore production in Clostridium botulinum type E populations and discuss the biological rationale of toxin and spore production in the pathogenicity and ecology of C. botulinum. The results invite to reassess the epidemiology of botulism and may have important implications in the risk assessment and risk management strategies in food processing and human and animal health.

Dynamical model of antibiotic responses linking expression of resistance to metabolism explains emergence of heterogeneity during drug exposures

Antibiotic responses in bacteria are highly dynamic and heterogeneous, with sudden exposure of bacterial colonies to high drug doses resulting in the coexistence of recovered and arrested cells. The dynamics of the response is determined by regulatory circuits controlling the expression of resistance genes, which are in turn modulated by the drug's action on cell growth and metabolism. Despite advances in understanding gene regulation at the molecular level, we still lack a framework to describe how feedback mechanisms resulting from the interdependence between expression of resistance and cell metabolism can amplify naturally occurring noise and create heterogeneity at the population level. To understand how this interplay affects cell survival upon exposure, we constructed a mathematical model of the dynamics of antibiotic responses that links metabolism and regulation of gene expression, based on the tetracycline resistance tet operon in E. coli. We use this model to interpret measurements of growth and expression of resistance in microfluidic experiments, both in single cells and in biofilms. We also implemented a stochastic model of the drug response, to show that exposure to high drug levels results in large variations of recovery times and heterogeneity at the population level. We show that stochasticity is important to determine how nutrient quality affects cell survival during exposure to high drug concentrations. A quantitative description of how microbes respond to antibiotics in dynamical environments is crucial to understand population-level behaviors such as biofilms and pathogenesis.

Droplet-based high-throughput single microbe RNA sequencing by smRandom-seq

Bacteria colonize almost all parts of the human body and can differ significantly. However, the population level transcriptomics measurements can only describe the average bacteria population behaviors, ignoring the heterogeneity among bacteria. Here, we report a droplet-based high-throughput single-microbe RNA-seq assay (smRandom-seq), using random primers for in situ cDNA generation, droplets for single-microbe barcoding, and CRISPR-based rRNA depletion for mRNA enrichment. smRandom-seq showed a high species specificity (99%), a minor doublet rate (1.6%), a reduced rRNA percentage (32%), and a sensitive gene detection (a median of ~1000 genes per single E. coli). Furthermore, smRandom-seq successfully captured transcriptome changes of thousands of individual E. coli and discovered a few antibiotic resistant subpopulations displaying distinct gene expression patterns of SOS response and metabolic pathways in E. coli population upon antibiotic stress. smRandom-seq provides a high-throughput single-microbe transcriptome profiling tool that will facilitate future discoveries in microbial resistance, persistence, microbe-host interaction, and microbiome research.

RespectM revealed metabolic heterogeneity powers deep learning for reshaping the DBTL cycle

Synthetic biology, relying on Design-Build-Test-Learn (DBTL) cycle, aims to solve medicine, manufacturing, and agriculture problems. However, the DBTL cycle's Learn (L) step lacks predictive power for the behavior of biological systems, resulting from the incompatibility between sparse testing data and chaotic metabolic networks. Herein, we develop a method, "RespectM," based on mass spectrometry imaging, which is able to detect metabolites at a rate of 500 cells per hour with high efficiency. In this study, 4,321 single cell level metabolomics data were acquired, representing metabolic heterogeneity. An optimizable deep neural network was applied to learn from metabolic heterogeneity and a "heterogeneity-powered learning (HPL)" based model was trained as well. By testing the HPL based model, we suggest minimal operations to achieve high triglyceride production for engineering. The HPL strategy could revolutionize rational design and reshape the DBTL cycle.

Dynamic proteome trade-offs regulate bacterial cell size and growth in fluctuating nutrient environments

Bacteria dynamically regulate cell size and growth to thrive in changing environments. While previous studies have characterized bacterial growth physiology at steady-state, a quantitative understanding of bacterial physiology in time-varying environments is lacking. Here we develop a quantitative theory connecting bacterial growth and division rates to proteome allocation in time-varying nutrient environments. In such environments, cell size and growth are regulated by trade-offs between prioritization of biomass accumulation or division, resulting in decoupling of single-cell growth rate from population growth rate. Specifically, bacteria transiently prioritize biomass accumulation over production of division machinery during nutrient upshifts, while prioritizing division over growth during downshifts. When subjected to pulsatile nutrient concentration, we find that bacteria exhibit a transient memory of previous metabolic states due to the slow dynamics of proteome reallocation. This allows for faster adaptation to previously seen environments and results in division control which is dependent on the time-profile of fluctuations.

Experimental kS estimation: A comparison of methods for Corynebacterium glutamicum from lab to microfluidic scale

Knowledge about the specific affinity of whole cells toward a substrate, commonly referred to as k_S , is a crucial parameter for characterizing growth within bioreactors. State-of-the-art methodologies measure either uptake or consumption rates at different initial substrate concentrations. Alternatively, cell dry weight or respiratory data like online oxygen and carbon dioxide transfer rates can be used to estimate k_S . In this work, a recently developed substrate-limited microfluidic single-cell cultivation (sl-MSCC) method is applied for the estimation of k_S values under defined environmental conditions. This method is benchmarked with two alternative microtiter plate methods, namely high-frequency biomass measurement (HFB) and substrate-limited respiratory activity monitoring (sl-RA). As a model system, the substrate affinity k_S of Corynebacterium glutamicum ATCC 13032 regarding glucose was investigated assuming a Monod-type growth response. A k_S of <70.7 mg/L (with 95% probability) with HFB, 8.55 ± 1.38 mg/L with sl-RA, and 2.66 ± 0.99 mg/L with sl-MSCC was obtained. Whereas HFB and sl-RA are suitable for a fast initial k_S estimation, sl-MSCC allows an affinity estimation by determining t_D at concentrations less or equal to the k_S value. Thus, sl-MSCC lays the foundation for strain-specific k_S estimations under defined environmental conditions with additional insights into cell-to-cell heterogeneity.

[Study on biofilm formation and heterogeneity in Clostridium perfringens]

Many bacteria form biofilms and survive in the actual environment. Biofilms are not only a major form of bacteria but are also involved in tolerance to environmental stresses and antibiotics, suggesting the association with bacterial pathogenesis. Cells within biofilms display phenotypic heterogeneity; thus, even bacteria, unicellular organisms, can functionally differentiate and show multicellular behavior. Therefore, it is necessary to understand bacteria as a population to control their survival and pathogenesis in the actual environment. Previously, we found that Clostridium perfringens, an anaerobic pathogenic bacterium, form different structures in different temperatures and phenotypic heterogeneity on biofilm matrix gene expression within the biofilm. In this article, I summarize the results of our research on biofilms and their heterogeneity, the mechanisms of post-transcriptional gene expression regulation of virulence genes, and bacteria-host interactions mediated by extracellular membrane vesicles.

Decoupling of respiration rates and abundance in marine prokaryoplankton

The ocean-atmosphere exchange of CO2 largely depends on the balance between marine microbial photosynthesis and respiration. Despite vast taxonomic and metabolic diversity among marine planktonic bacteria and archaea (prokaryoplankton)1-3, their respiration usually is measured in bulk and treated as a 'black box' in global biogeochemical models4; this limits the mechanistic understanding of the global carbon cycle. Here, using a technology for integrated phenotype analyses and genomic sequencing of individual microbial cells, we show that cell-specific respiration rates differ by more than 1,000× among prokaryoplankton genera. The majority of respiration was found to be performed by minority members of prokaryoplankton (including the Roseobacter cluster), whereas cells of the most prevalent lineages (including Pelagibacter and SAR86) had extremely low respiration rates. The decoupling of respiration rates from abundance among lineages, elevated counts of proteorhodopsin transcripts in Pelagibacter and SAR86 cells and elevated respiration of SAR86 at night indicate that proteorhodopsin-based phototrophy3,5-7 probably constitutes an important source of energy to prokaryoplankton and may increase growth efficiency. These findings suggest that the dependence of prokaryoplankton on respiration and remineralization of phytoplankton-derived organic carbon into CO2 for its energy demands and growth may be lower than commonly assumed and variable among lineages.

Patterns of interdivision time correlations reveal hidden cell cycle factors

The time taken for cells to complete a round of cell division is a stochastic process controlled, in part, by intracellular factors. These factors can be inherited across cellular generations which gives rise to, often non-intuitive, correlation patterns in cell cycle timing between cells of different family relationships on lineage trees. Here, we formulate a framework of hidden inherited factors affecting the cell cycle that unifies known cell cycle control models and reveals three distinct interdivision time correlation patterns: aperiodic, alternator, and oscillator. We use Bayesian inference with single-cell datasets of cell division in bacteria, mammalian and cancer cells, to identify the inheritance motifs that underlie these datasets. From our inference, we find that interdivision time correlation patterns do not identify a single cell cycle model but generally admit a broad posterior distribution of possible mechanisms. Despite this unidentifiability, we observe that the inferred patterns reveal interpretable inheritance dynamics and hidden rhythmicity of cell cycle factors. This reveals that cell cycle factors are commonly driven by circadian rhythms, but their period may differ in cancer. Our quantitative analysis thus reveals that correlation patterns are an emergent phenomenon that impact cell proliferation and these patterns may be altered in disease.

High-throughput determination of dry mass of single bacterial cells by ultrathin membrane resonators

How bacteria are able to maintain their size remains an open question. Techniques that can measure the biomass (dry mass) of single cells with high precision and high-throughput are demanded to elucidate this question. Here, we present a technological approach that combines the transport, guiding and focusing of individual bacteria from solution to the surface of an ultrathin silicon nitride membrane resonator in vacuum. The resonance frequencies of the membrane undergo abrupt variations at the instants where single cells land on the membrane surface. The resonator design displays a quasi-symmetric rectangular shape with an extraordinary capture area of 0.14 mm², while maintaining a high mass resolution of 0.7 fg (1 fg = 10⁻¹⁵g) to precisely resolve the dry mass of single cells. The small rectangularity of the membrane provides unprecedented frequency density of vibration modes that enables to retrieve the mass of individual cells with high accuracy by specially developed inverse problem theory. We apply this approach for profiling the dry mass distribution in Staphylococcus epidermidis and Escherichia coli cells. The technique allows the determination of the dry mass of single bacterial cells with an accuracy of about 1% at an unparalleled throughput of 20 cells/min. Finally, we revisit Koch & Schaechter model developed during 60 s to assess the intrinsic sources of stochasticity that originate cell size heterogeneity in steady-state populations. The results reveal the importance of mass resolution to correctly describe these mechanisms.

A technological approach combines transport, guiding and focusing of individual bacteria from solution to ultrathin membrane resonators for dry mass determination of single cells with accuracy within 1% and throughput of 20 cells/min.

Stochastic activation of a family of TetR type transcriptional regulators controls phenotypic heterogeneity in Acinetobacter baumannii

Phenotypic heterogeneity is an important mechanism for regulating bacterial virulence, where a single regulatory switch is typically activated to generate virulent and avirulent subpopulations. The opportunistic pathogen Acinetobacter baumannii can transition at high frequency between virulent opaque (VIR-O) and avirulent translucent subpopulations, distinguished by cells that form opaque or translucent colonies. We demonstrate that expression of 11 TetR-type transcriptional regulators (TTTRs) can drive cells from the VIR-O opaque subpopulation to cells that form translucent colonies. Remarkably, in a subpopulation of VIR-O cells, four of these TTTRs were stochastically activated in different combinations to drive cells to the translucent state. The resulting translucent subvariants exhibited unique phenotypic differences and the majority were avirulent. Due to their functional redundancy, a quadruple mutant with all four of these TTTRs inactivated was required to observe a loss of switching from the VIR-O state. Further, we demonstrate a small RNA, SrvS, acts as a "rheostat," where the levels of SrvS expression influences both the VIR-O to translucent switching frequency, and which TTTR is activated when VIR-O cells switch. In summary, this work has revealed a new paradigm for phenotypic switching in bacteria, where an unprecedented number of related transcriptional regulators are activated in different combinations to control virulence and generate unique translucent subvariants with distinct phenotypic properties.

scATACpipe: A nextflow pipeline for comprehensive and reproducible analyses of single cell ATAC-seq data

Single cell ATAC-seq (scATAC-seq) has become the most widely used method for profiling open chromatin landscape of heterogeneous cell populations at a single-cell resolution. Although numerous software tools and pipelines have been developed, an easy-to-use, scalable, reproducible, and comprehensive pipeline for scATAC-seq data analyses is still lacking. To fill this gap, we developed scATACpipe, a Nextflow pipeline, for performing comprehensive analyses of scATAC-seq data including extensive quality assessment, preprocessing, dimension reduction, clustering, peak calling, differential accessibility inference, integration with scRNA-seq data, transcription factor activity and footprinting analysis, co-accessibility inference, and cell trajectory prediction. scATACpipe enables users to perform the end-to-end analysis of scATAC-seq data with three sub-workflow options for preprocessing that leverage 10x Genomics Cell Ranger ATAC software, the ultra-fast Chromap procedures, and a set of custom scripts implementing current best practices for scATAC-seq data preprocessing. The pipeline extends the R package ArchR for downstream analysis with added support to any eukaryotic species with an annotated reference genome. Importantly, scATACpipe generates an all-in-one HTML report for the entire analysis and outputs cluster-specific BAM, BED, and BigWig files for visualization in a genome browser. scATACpipe eliminates the need for users to chain different tools together and facilitates reproducible and comprehensive analyses of scATAC-seq data from raw reads to various biological insights with minimal changes of configuration settings for different computing environments or species. By applying it to public datasets, we illustrated the utility, flexibility, versatility, and reliability of our pipeline, and demonstrated that our scATACpipe outperforms other workflows.

Photophysiological response of Symbiodiniaceae single cells to temperature stress

Photosynthetic dinoflagellates in the family Symbiodiniaceae engage in symbiosis with scleractinian corals. As coral ‘bleaching’ is partly governed by the thermal sensitivity of different Symbiodiniaceae lineages, numerous studies have investigated their temperature sensitivity. However, the systematic identification of single-cells with increased temperature resistance among these dinoflagellates has remained inaccessible, mostly due to a lack of technologies operating at the microscale. Here, we employed a unique combination of microfluidics, miniaturized temperature control, and chlorophyll fluorometry to characterize the single-cell heterogeneity among five representative species within the Symbiodiniaceae family under temperature stress. We monitored single-cell maximum quantum yields (F_v/F_m) of photosystem (PS) II under increasing temperature stress (22‒39 °C, + 1 °C every 15 min), and detected a significant F_v/F_m reduction at lineage-specific temperatures ranging from 28 °C to 34 °C alongside a 40- to 180- fold increase in intraspecific heterogeneity under elevated temperatures (>31 °C). We discovered that the initial F_v/F_m of a cell could predict the same cell’s ability to perform PSII photochemistry under moderate temperature stress (<32 °C), suggesting its use as a proxy for measuring the thermal sensitivity among Symbiodiniaceae. In combination, our study highlights the heterogeneous thermal sensitivity among photosynthetic Symbiodiniaceae and adds critical resolution to our understanding of temperature-induced coral bleaching.

Emergent expression of fitness-conferring genes by phenotypic selection

Genotypic and phenotypic adaptation is the consequence of ongoing natural selection in populations and is key to predicting and preventing drug resistance. Whereas classic antibiotic persistence is all-or-nothing, here we demonstrate that an antibiotic resistance gene displays linear dose-responsive selection for increased expression in proportion to rising antibiotic concentration in growing Escherichia coli populations. Furthermore, we report the potentially wide-spread nature of this form of emergent gene expression (EGE) by instantaneous phenotypic selection process under bactericidal and bacteriostatic antibiotic treatment, as well as an amino acid synthesis pathway enzyme under a range of auxotrophic conditions. We propose an analogy to Ohm's law in electricity (V = IR), where selection pressure acts similarly to voltage (V), gene expression to current (I), and resistance (R) to cellular machinery constraints and costs. Lastly, mathematical modeling using agent-based models of stochastic gene expression in growing populations and Bayesian model selection reveal that the EGE mechanism requires variability in gene expression within an isogenic population, and a cellular "memory" from positive feedbacks between growth and expression of any fitness-conferring gene. Finally, we discuss the connection of the observed phenomenon to a previously described general fluctuation-response relationship in biology.

Pervasive sequence-level variation in the transcriptome of Plasmodium falciparum

Single-nucleotide variations (SNVs) in RNA, arising from co- and post-transcriptional phenomena including transcription errors and RNA-editing, are well studied in a range of organisms. In the malaria parasite Plasmodium falciparum, stage-specific and non-specific gene-expression variations accompany the parasite's array of developmental and morphological phenotypes over the course of its complex life cycle. However, the extent, rate and effect of sequence-level variation in the parasite's transcriptome are unknown. Here, we report the presence of pervasive, non-specific SNVs in the P. falciparum transcriptome. SNV rates for a gene were correlated to gene length (r[Formula: see text]0.65-0.7) but not to the AT-content of that gene. Global SNV rates for the P. falciparum lines we used, and for publicly available P. vivax and P. falciparum clinical isolate datasets, were of the order of 10-3 per base, ∼10× higher than rates we calculated for bacterial datasets. These variations may reflect an intrinsic transcriptional error rate in the parasite, and RNA editing may be responsible for a subset of them. This seemingly characteristic property of the parasite may have implications for clinical outcomes and the basic biology and evolution of P. falciparum and parasite biology more broadly. We anticipate that our study will prompt further investigations into the exact sources, consequences and possible adaptive roles of these SNVs.

Visualization of mRNA Expression in Pseudomonas aeruginosa Aggregates Reveals Spatial Patterns of Fermentative and Denitrifying Metabolism

Gaining insight into the behavior of bacteria at the single-cell level is important given that heterogeneous microenvironments strongly influence microbial physiology. The hybridization chain reaction (HCR) is a technique that provides in situ molecular signal amplification, enabling simultaneous mapping of multiple target RNAs at small spatial scales. To refine this method for biofilm applications, we designed and validated new probes to visualize the expression of key catabolic genes in Pseudomonas aeruginosa aggregates. In addition to using existing probes for the dissimilatory nitrate reductase (narG), we developed probes for a terminal oxidase (ccoN1), nitrite reductase (nirS), nitrous oxide reductase (nosZ), and acetate kinase (ackA). These probes can be used to determine gene expression levels across heterogeneous populations such as biofilms. Using these probes, we quantified gene expression across oxygen gradients in aggregate populations grown using the agar block biofilm assay (ABBA). We observed distinct patterns of catabolic gene expression, with upregulation occurring in particular ABBA regions both within individual aggregates and over the aggregate population. Aerobic respiration (ccoN1) showed peak expression under oxic conditions, whereas fermentation (ackA) showed peak expression in the anoxic cores of high metabolic activity aggregates near the air-agar interface. Denitrification genes narG, nirS, and nosZ showed peak expression in hypoxic and anoxic regions, although nirS expression remained at peak levels deeper into anoxic environments than other denitrification genes. These results reveal that the microenvironment correlates with catabolic gene expression in aggregates, and they demonstrate the utility of HCR in unveiling cellular activities at the microscale level in heterogeneous populations. IMPORTANCE To understand bacteria in diverse contexts, we must understand the variations in behaviors and metabolisms they express spatiotemporally. Populations of bacteria are known to be heterogeneous, but the ways this variation manifests can be challenging to characterize due to technical limitations. By focusing on energy conservation, we demonstrate that HCR v3.0 can visualize nuances in gene expression, allowing us to understand how metabolism in Pseudomonas aeruginosa biofilms responds to microenvironmental variation at high spatial resolution. We validated probes for four catabolic genes, including a constitutively expressed oxidase, acetate kinase, nitrite reductase, and nitrous oxide reductase. We showed that the genes for different modes of metabolism are expressed in overlapping but distinct subpopulations according to oxygen concentrations in a predictable fashion. The spatial transcriptomic technique described here has the potential to be used to map microbial activities across diverse environments.

Metabolic Engineering Strategies for Improved Lipid Production and Cellular Physiological Responses in Yeast Saccharomyces cerevisiae

Microbial lipids have been a hot topic in the field of metabolic engineering and synthetic biology due to their increased market and important applications in biofuels, oleochemicals, cosmetics, etc. This review first compares the popular hosts for lipid production and explains the four modules for lipid synthesis in yeast, including the fatty acid biosynthesis module, lipid accumulation module, lipid sequestration module, and fatty acid modification module. This is followed by a summary of metabolic engineering strategies that could be used for enhancing each module for lipid production. In addition, the efforts being invested in improving the production of value-added fatty acids in engineered yeast, such as cyclopropane fatty acid, ricinoleic acid, gamma linoleic acid, EPA, and DHA, are included. A discussion is further made on the potential relationships between lipid pathway engineering and consequential changes in cellular physiological properties, such as cell membrane integrity, intracellular reactive oxygen species level, and mitochondrial membrane potential. Finally, with the rapid development of synthetic biology tools, such as CRISPR genome editing tools and machine learning models, this review proposes some future trends that could be employed to engineer yeast with enhanced intracellular lipid production while not compromising much of its cellular health.

Viral tag and grow: a scalable approach to capture and characterize infectious virus-host pairs

Viral metagenomics (viromics) has reshaped our understanding of DNA viral diversity, ecology, and evolution across Earth's ecosystems. However, viromics now needs approaches to link newly discovered viruses to their host cells and characterize them at scale. This study adapts one such method, sequencing-enabled viral tagging (VT), to establish "Viral Tag and Grow" (VT + Grow) to rapidly capture and characterize viruses that infect a cultivated target bacterium, Pseudoalteromonas. First, baseline cytometric and microscopy data improved understanding of how infection conditions and host physiology impact populations in VT flow cytograms. Next, we extensively evaluated "and grow" capability to assess where VT signals reflect adsorption alone or wholly successful infections that lead to lysis. Third, we applied VT + Grow to a clonal virus stock, which, coupled to traditional plaque assays, revealed significant variability in burst size-findings that hint at a viral "individuality" parallel to the microbial phenotypic heterogeneity literature. Finally, we established a live protocol for public comment and improvement via protocols.io to maximally empower the research community. Together these efforts provide a robust foundation for VT researchers, and establish VT + Grow as a promising scalable technology to capture and characterize viruses from mixed community source samples that infect cultivable bacteria.

How to Perform a Microfluidic Cultivation Experiment—A Guideline to Success

As a result of the steadily ongoing development of microfluidic cultivation (MC) devices, a plethora of setups is used in biological laboratories for the cultivation and analysis of different organisms. Because of their biocompatibility and ease of fabrication, polydimethylsiloxane (PDMS)-glass-based devices are most prominent. Especially the successful and reproducible cultivation of cells in microfluidic systems, ranging from bacteria over algae and fungi to mammalians, is a fundamental step for further quantitative biological analysis. In combination with live-cell imaging, MC devices allow the cultivation of small cell clusters (or even single cells) under defined environmental conditions and with high spatio-temporal resolution. Yet, most setups in use are custom made and only few standardised setups are available, making trouble-free application and inter-laboratory transfer tricky. Therefore, we provide a guideline to overcome the most frequently occurring challenges during a MC experiment to allow untrained users to learn the application of continuous-flow-based MC devices. By giving a concise overview of the respective workflow, we give the reader a general understanding of the whole procedure and its most common pitfalls. Additionally, we complement the listing of challenges with solutions to overcome these hurdles. On selected case studies, covering successful and reproducible growth of cells in MC devices, we demonstrate detailed solutions to solve occurring challenges as a blueprint for further troubleshooting. Since developer and end-user of MC devices are often different persons, we believe that our guideline will help to enhance a broader applicability of MC in the field of life science and eventually promote the ongoing advancement of MC.

Droplet-based microfluidics platform for antifungal analysis against filamentous fungi

Fungicides are extensively used in agriculture to control fungal pathogens which are responsible for significant economic impact on plant yield and quality. The conventional antifungal screening techniques, such as water agar and 96-well plates, are based on laborious protocols and bulk analysis, restricting the analysis at the single spore level and are time consuming. In this study, we present a droplet-based microfluidic platform that enables antifungal analysis of single spores of filamentous fungus Alternaria alternata. A droplet-based viability assay was developed, allowing the germination and hyphal growth of single A. alternata spores within droplets. The viability was demonstrated over a period of 24 h and the antifungal screening was achieved using Kunshi/Tezuma as antifungal agent. The efficacy results of the droplet-based antifungal analysis were compared and validated with the results obtained from conventional protocols. The percentage inhibitions assessed by the droplet-based platform were equivalent with those obtained by the other two methods, and the Pearson correlation analysis showed high correlation between the three assays. Taken together, this droplet-based microfluidic platform provides a wide range of potential applications for the analysis of fungicide resistance development as well as combinatorial screening of other antimicrobial agents and even antagonistic fungi.

Genome structural variation in Escherichia coli O157:H7

The human zoonotic pathogen Escherichia coli O157:H7 is defined by its extensive prophage repertoire including those that encode Shiga toxin, the factor responsible for inducing life-threatening pathology in humans. As well as introducing genes that can contribute to the virulence of a strain, prophage can enable the generation of large-chromosomal rearrangements (LCRs) by homologous recombination. This work examines the types and frequencies of LCRs across the major lineages of the O157:H7 serotype. We demonstrate that LCRs are a major source of genomic variation across all lineages of E. coli O157:H7 and by using both optical mapping and Oxford Nanopore long-read sequencing prove that LCRs are generated in laboratory cultures started from a single colony and that these variants can be recovered from colonized cattle. LCRs are biased towards the terminus region of the genome and are bounded by specific prophages that share large regions of sequence homology associated with the recombinational activity. RNA transcriptional profiling and phenotyping of specific structural variants indicated that important virulence phenotypes such as Shiga-toxin production, type-3 secretion and motility can be affected by LCRs. In summary, E. coli O157:H7 has acquired multiple prophage regions over time that act to continually produce structural variants of the genome. These findings raise important questions about the significance of this prophage-mediated genome contingency to enhance adaptability between environments.

Single-Cell RNA Sequencing Reveals Cellular Heterogeneity and Stage Transition under Temperature Stress in Synchronized Plasmodium falciparum Cells

The malaria parasite has a complex life cycle exhibiting phenotypic and morphogenic variations in two different hosts by existing in heterogeneous developmental states. To investigate this cellular heterogeneity of the parasite within the human host, we performed single-cell RNA sequencing of synchronized Plasmodium cells under control and temperature treatment conditions. Using the Malaria Cell Atlas (https://www.sanger.ac.uk/science/tools/mca) as a guide, we identified 9 subtypes of the parasite distributed across known intraerythrocytic stages. Interestingly, temperature treatment results in the upregulation of the AP2-G gene, the master regulator of sexual development in a small subpopulation of the parasites. Moreover, we identified a heterogeneous stress-responsive subpopulation (clusters 5, 6, and 7 [∼10% of the total population]) that exhibits upregulation of stress response pathways under normal growth conditions. We also developed an online exploratory tool that will provide new insights into gene function under normal and temperature stress conditions. Thus, our study reveals important insights into cell-to-cell heterogeneity in the parasite population under temperature treatment that will be instrumental toward a mechanistic understanding of cellular adaptation and population dynamics in Plasmodium falciparum. IMPORTANCE The malaria parasite has a complex life cycle exhibiting phenotypic variations in two different hosts accompanied by cell-to-cell variability that is important for stress tolerance, immune evasion, and drug resistance. To investigate cellular heterogeneity determined by gene expression, we performed single-cell RNA sequencing (scRNA-seq) of about 12,000 synchronized Plasmodium cells under physiologically relevant normal (37°C) and temperature stress (40°C) conditions phenocopying the cyclic bouts of fever experienced during malarial infection. In this study, we found that parasites exhibit transcriptional heterogeneity in an otherwise morphologically synchronized culture. Also, a subset of parasites is continually committed to gametocytogenesis and stress-responsive pathways. These observations have important implications for understanding the mechanisms of drug resistance generation and vaccine development against the malaria parasite.

Causes and consequences of pattern diversification in a spatially self-organizing microbial community

Surface-attached microbial communities constitute a vast amount of life on our planet. They contribute to all major biogeochemical cycles, provide essential services to our society and environment, and have important effects on human health and disease. They typically consist of different interacting genotypes that arrange themselves non-randomly across space (referred to hereafter as spatial self-organization). While spatial self-organization is important for the functioning, ecology, and evolution of these communities, the underlying determinants of spatial self-organization remain unclear. Here, we performed a combination of experiments, statistical modeling, and mathematical simulations with a synthetic cross-feeding microbial community consisting of two isogenic strains. We found that two different patterns of spatial self-organization emerged at the same length and time scales, thus demonstrating pattern diversification. This pattern diversification was not caused by initial environmental heterogeneity or by genetic heterogeneity within populations. Instead, it was caused by nongenetic heterogeneity within populations, and we provide evidence that the source of this nongenetic heterogeneity is local differences in the initial spatial positionings of individuals. We further demonstrate that the different patterns exhibit different community-level properties; namely, they have different expansion speeds. Together, our results demonstrate that pattern diversification can emerge in the absence of initial environmental heterogeneity or genetic heterogeneity within populations and can affect community-level properties, thus providing novel insights into the causes and consequences of microbial spatial self-organization.

Tunable phenotypic variability through an autoregulatory alternative sigma factor circuit

Genetically identical individuals in bacterial populations can display significant phenotypic variability. This variability can be functional, for example by allowing a fraction of stress prepared cells to survive an otherwise lethal stress. The optimal fraction of stress prepared cells depends on environmental conditions. However, how bacterial populations modulate their level of phenotypic variability remains unclear. Here we show that the alternative sigma factor σV circuit in Bacillus subtilis generates functional phenotypic variability that can be tuned by stress level, environmental history and genetic perturbations. Using single-cell time-lapse microscopy and microfluidics, we find the fraction of cells that immediately activate σV under lysozyme stress depends on stress level and on a transcriptional memory of previous stress. Iteration between model and experiment reveals that this tunability can be explained by the autoregulatory feedback structure of the sigV operon. As predicted by the model, genetic perturbations to the operon also modulate the response variability. The conserved sigma-anti-sigma autoregulation motif is thus a simple mechanism for bacterial populations to modulate their heterogeneity based on their environment.

Non-Genetic Diversity in Chemosensing and Chemotactic Behavior

Non-genetic phenotypic diversity plays a significant role in the chemotactic behavior of bacteria, influencing how populations sense and respond to chemical stimuli. First, we review the molecular mechanisms that generate phenotypic diversity in bacterial chemotaxis. Next, we discuss the functional consequences of phenotypic diversity for the chemosensing and chemotactic performance of single cells and populations. Finally, we discuss mechanisms that modulate the amount of phenotypic diversity in chemosensory parameters in response to changes in the environment.

Determination of elemental distribution and evaluation of elemental concentration in single Saccharomyces cerevisiae cells using single cell-inductively coupled plasma mass spectrometry

Single-cell analysis using inductively coupled plasma mass spectrometry (SC-ICP-MS) is a method to obtain qualitative and quantitative information of the elemental content and distribution of single cells. Six intrinsic target elements were analyzed in yeast cells at different cell growth phases cultured in medium with different phosphorus concentrations (0, 7, 14 mM) to study its effect on cell growth and composition. SC-ICP-MS results were compared with those obtained by the acid digestion and the average ratio was 0.81. The limits of detection of this method were 0.08, 2.54, 12.5, 0.02, 0.02, and 0.08 fg cell-1 for Mg, P, K, Mn, Cu, and Zn, respectively. During the exponential growth phase, the cells exhibited higher elemental contents, wider distribution for most elements, and larger cell size in comparison to the stationary growth phase. Phosphorus-free conditions reduced the average P content in single cells of stationary growth phase from 650 to 80 fg. Phosphorus deficiency led to decreasing intracellular concentrations not only of P but also of K and Cu, and to increasing Zn concentration after 48 h. Mg maintained its concentration at ∼0.11 fg µm-3 and did not change significantly under the three investigated conditions after 48 h. Accordingly, Mg content was successfully used to estimate the intracellular concentration of other intrinsic elements in single yeast cells. SC-ICP-MS is suited to determine target elements in single yeast cells, and allows the study of heterogeneity of cell composition and effects of stressors on the elemental content, distribution, and concentrations of intrinsic elements.

An ABA-GA bistable switch can account for natural variation in the variability of Arabidopsis seed germination time

Genetically identical plants growing in the same conditions can display heterogeneous phenotypes. Here we use Arabidopsis seed germination time as a model system to examine phenotypic variability and its underlying mechanisms. We show extensive variation in seed germination time variability between Arabidopsis accessions and use a multiparent recombinant inbred population to identify two genetic loci involved in this trait. Both loci include genes implicated in modulating abscisic acid (ABA) sensitivity. Mutually antagonistic regulation between ABA, which represses germination, and gibberellic acid (GA), which promotes germination, underlies the decision to germinate and can act as a bistable switch. A simple stochastic model of the ABA-GA network shows that modulating ABA sensitivity can generate the range of germination time distributions we observe experimentally. We validate the model by testing its predictions on the effects of exogenous hormone addition. Our work provides a foundation for understanding the mechanism and functional role of phenotypic variability in germination time.

Yeast cell fate control by temporal redundancy modulation of transcription factor paralogs

Recent single-cell studies have revealed that yeast stress response involves transcription factors that are activated in pulses. However, it remains unclear whether and how these dynamic transcription factors temporally interact to regulate stress survival. Here we show that budding yeast cells can exploit the temporal relationship between paralogous general stress regulators, Msn2 and Msn4, during stress response. We find that individual pulses of Msn2 and Msn4 are largely redundant, and cells can enhance the expression of their shared targets by increasing their temporal divergence. Thus, functional redundancy between these two paralogs is modulated in a dynamic manner to confer fitness advantages for yeast cells, which might feed back to promote the preservation of their redundancy. This evolutionary implication is supported by evidence from Msn2/Msn4 orthologs and analyses of other transcription factor paralogs. Together, we show a cell fate control mechanism through temporal redundancy modulation in yeast, which may represent an evolutionarily important strategy for maintaining functional redundancy between gene duplicates.

Massively parallel gene expression variation measurement of a synonymous codon library

BACKGROUND

Cell-to-cell variation in gene expression strongly affects population behavior and is key to multiple biological processes. While codon usage is known to affect ensemble gene expression, how codon usage influences variation in gene expression between single cells is not well understood.

RESULTS

Here, we used a Sort-seq based massively parallel strategy to quantify gene expression variation from a green fluorescent protein (GFP) library containing synonymous codons in Escherichia coli. We found that sequences containing codons with higher tRNA Adaptation Index (TAI) scores, and higher codon adaptation index (CAI) scores, have higher GFP variance. This trend is not observed for codons with high Normalized Translation Efficiency Index (nTE) scores nor from the free energy of folding of the mRNA secondary structure. GFP noise, or squared coefficient of variance (CV2), scales with mean protein abundance for low-abundant proteins but does not change at high mean protein abundance.

CONCLUSIONS

Our results suggest that the main source of noise for high-abundance proteins is likely not originating at translation elongation. Additionally, the drastic change in mean protein abundance with small changes in protein noise seen from our library implies that codon optimization can be performed without concerning gene expression noise for biotechnology applications.

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.

Shaving and breaking bacterial chains with a viscous flow

Some food and ferment manufacturing steps such as spray-drying result in the application of viscous stresses to bacteria. This study explores how a viscous flow impacts both bacterial adhesion functionality and bacterial cell organization using a combined experimental and modeling approach. As a model organism we study Lactobacillus rhamnosus GG (LGG) "wild type" (WT), known to feature strong adhesive affinities towards beta-lactoglobulin thanks to pili produced by the bacteria on cell surfaces, along with three cell-surface mutant strains. Applying repeated flows with high shear-rates reduces bacterial adhesive abilities up to 20% for LGG WT. Bacterial chains are also broken by this process, into 2-cell chains at low industrial shear rates, and into single cells at very high shear rates. To rationalize the experimental observations we study numerically and analytically the Stokes equations describing viscous fluid flow around a chain of elastically connected spheroidal cell bodies. In this model setting we examine qualitatively the relationship between surface traction (force per unit area), a proxy for pili removal rate, and bacterial chain length (number of cells). Longer chains result in higher maximal surface tractions, particularly at the chain extremities, while inner cells enjoy a small protection from surface tractions due to hydrodynamic interactions with their neighbors. Chain rupture therefore may act as a mechanism to preserve surface adhesive functionality in bacteria.

Genomic plasticity of pathogenic Escherichia coli mediates d-serine tolerance via multiple adaptive mechanisms

Pathogens ensure infection of favored sites in the body by responding to chemical signals. One chemical abundant in urine, the amino acid d-Ser, is toxic to EHEC and reduces expression of the machinery used for host cell attachment, making the bladder an unfavorable environment. We observed that under d-Ser stress, EHEC acquires genetic changes that lead to blocking d-Ser uptake into the cell or activating a silent enzyme for degrading d-Ser. This prevents growth inhibition and, critically, inhibits the repression of attachment machinery normally caused by d-Ser. These findings highlight the importance of pathogen evolution in determining how host molecules regulate colonization. These interactions underpin a process known as niche restriction that is important for pathogen success within the host.

The molecular environment of the host can have profound effects on the behavior of resident bacterial species. We recently established how the sensing and response of enterohemorrhagic Escherichia coli (EHEC) to d-serine (d-Ser) resulted in down-regulation of type 3 secretion system-dependent colonization, thereby avoiding unfavorable environments abundant in this toxic metabolite. However, this model ignores a key determinant of the success of bacterial pathogens, adaptive evolution. In this study, we have explored the adaptation of EHEC to d-Ser and its consequences for pathogenesis. We rapidly isolated multiple, independent, EHEC mutants whose growth was no longer compromised in the presence of d-Ser. Through a combination of whole-genome sequencing and transcriptomics, we showed that tolerance could be attributed to disruption of one of two d-Ser transporters and/or activation of a previously nonfunctional d-Ser deaminase. While the implication of cytoplasmic transport in d-Ser toxicity was unsurprising, disruption of a single transporter, CycA, was sufficient to completely overcome the repression of type 3 secretion system activity normally associated with exposure to d-Ser. Despite the fact that this reveals a mechanism by which evolution could drive a pathogen to colonize new niches, interrogation of sequenced E. coli O157:H7 genomes showed a high level of CycA conservation, highlighting a strong selective pressure for functionality. Collectively, these data show that CycA is a critically important conduit for d-Ser uptake that is central to the niche restriction of EHEC.

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.

Dynamic metabolic resource allocation based on the maximum entropy principle

Organisms have evolved a variety of mechanisms to cope with the unpredictability of environmental conditions, and yet mainstream models of metabolic regulation are typically based on strict optimality principles that do not account for uncertainty. This paper introduces a dynamic metabolic modelling framework that is a synthesis of recent ideas on resource allocation and the powerful optimal control formulation of Ramkrishna and colleagues. In particular, their work is extended based on the hypothesis that cellular resources are allocated among elementary flux modes according to the principle of maximum entropy. These concepts both generalise and unify prior approaches to dynamic metabolic modelling by establishing a smooth interpolation between dynamic flux balance analysis and dynamic metabolic models without regulation. The resulting theory is successful in describing 'bet-hedging' strategies employed by cell populations dealing with uncertainty in a fluctuating environment, including heterogenous resource investment, accumulation of reserves in growth-limiting conditions, and the observed behaviour of yeast growing in batch and continuous cultures. The maximum entropy principle is also shown to yield an optimal control law consistent with partitioning resources between elementary flux mode families, which has important practical implications for model reduction, selection, and simulation.

Plasmid expression level heterogeneity monitoring via heterologous eGFP production at the single-cell level in Cupriavidus necator

A methodology for plasmid expression level monitoring of eGFP expression suitable for dynamic processes was assessed during fermentation. This technique was based on the expression of a fluorescent biosensor (eGFP) encoded on a recombinant plasmid coupled to single-cell analysis. Fluorescence intensity at single-cell level was measured by flow cytometry. We demonstrated that promoter evaluation based on single-cell analysis versus classic global analysis brings valuable insights. Single-cell analysis pointed out the fact that intrinsic fluorescence increased with the strength of the promoter up to a threshold. Beyond that, cell permeability increases to excrete the fluorescent protein in the medium. The metabolic load due to the increase in the eGFP production in the case of strong constitutive promoters leads to slower growth kinetics compared with plasmid-free cells. With the strain Cupriavidus necator Re2133, growth rate losses were measured from 3% with the weak constitutive promoter P_lac to 56% with the strong constitutive promoter P_j5. Through this work, it seems crucial to find a compromise between the fluorescence intensity in single cells and the metabolic load; in our conditions, the best compromise found was the weak promoter P_lac. The plasmid expression level monitoring method was tested in the presence of a heterogeneous population induced by plasmid-curing methods. For all the identified subpopulations, the plasmid expression level heterogeneity was significantly detected at the level of fluorescence intensity in single cells. After cell sorting, growth rate and cultivability were assessed for each subpopulation. In conclusion, this eGFP biosensor makes it possible to follow the variations in the level of plasmid expression under conditions of population heterogeneity.Key Points•Development of a plasmid expression level monitoring method at the single-cell level by flow cytometry.•Promoter evaluation by single-cell analysis: cell heterogeneity and strain robustness.•Reporter system optimization for efficient subpopulation detection in pure cultures.

Optofluidic detection setup for multi-parametric analysis of microbiological samples in droplets

High-throughput microbiological experimentation using droplet microfluidics is limited due to the complexity and restricted versatility of the available detection techniques. Current detection setups are bulky, complicated, expensive, and require tedious optical alignment procedures while still mostly limited to fluorescence. In this work, we demonstrate an optofluidic detection setup for multi-parametric analyses of droplet samples by easily integrating micro-lenses and embedding optical fibers for guiding light in and out of the microfluidic chip. The optofluidic setup was validated for detection of absorbance, fluorescence, and scattered light. The developed platform was used for simultaneous detection of multiple parameters in different microbiological applications like cell density determination, growth kinetics, and antibiotic inhibition assays. Combining the high-throughput potential of droplet microfluidics with the ease, flexibility, and simplicity of optical fibers results in a powerful platform for microbiological experiments.

Development and characterization of Escherichia coli triple reporter strains for investigation of population heterogeneity in bioprocesses

Background

Today there is an increasing demand for high yielding robust and cost efficient biotechnological production processes. Although cells in these processes originate from isogenic cultures, heterogeneity induced by intrinsic and extrinsic influences is omnipresent. To increase understanding of this mechanistically poorly understood phenomenon, advanced tools that provide insights into single cell physiology are needed.

Results

Two Escherichia coli triple reporter strains have been designed based on the industrially relevant production host E. coli BL21(DE3) and a modified version thereof, E. coli T7E2. The strains carry three different fluorescence proteins chromosomally integrated. Single cell growth is followed with EmeraldGFP (EmGFP)-expression together with the ribosomal promoter rrnB. General stress response of single cells is monitored by expression of sigma factor rpoS with mStrawberry, whereas expression of the nar-operon together with TagRFP657 gives information about oxygen limitation of single cells. First, the strains were characterized in batch operated stirred-tank bioreactors in comparison to wildtype E. coli BL21(DE3). Afterwards, applicability of the triple reporter strains for investigation of population heterogeneity in bioprocesses was demonstrated in continuous processes in stirred-tank bioreactors at different growth rates and in response to glucose and oxygen perturbation simulating gradients on industrial scale. Population and single cell level physiology was monitored evaluating general physiology and flow cytometry analysis of fluorescence distributions of the triple reporter strains. Although both triple reporter strains reflected physiological changes that were expected based on the expression characteristics of the marker proteins, the triple reporter strain based on E. coli T7E2 showed higher sensitivity in response to environmental changes. For both strains, noise in gene expression was observed during transition from phases of non-growth to growth. Apparently, under some process conditions, e.g. the stationary phase in batch cultures, the fluorescence response of EmGFP and mStrawberry is preserved, whereas TagRFP657 showed a distinct response.

Conclusions

Single cell growth, general stress response and oxygen limitation of single cells could be followed using the two triple reporter strains developed in this study. They represent valuable tools to study population heterogeneity in bioprocesses significantly increasing the level of information compared to the use of single reporter strains.

Trade-offs between gene expression, growth and phenotypic diversity in microbial populations

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Limitations in molecular resources for gene expression influence bacterial physiology.

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Bacteria optimise trade-offs between resource allocation and growth.

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Resource allocation plays a role in the emergence of phenotypic heterogeneity.

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Trade-offs between bet-hedging and growth can be harnessed in biotechnology.

Bacterial cells have a limited number of resources that can be allocated for gene expression. The intracellular competition for these resources has an impact on the cell physiology. Bacteria have evolved mechanisms to optimize resource allocation in a variety of scenarios, showing a trade-off between the resources used to maximise growth (e.g. ribosome synthesis) and the rest of cellular functions. Limitations in gene expression also play a role in generating phenotypic diversity, which is advantageous in fluctuating environments, at the expenses of decreasing growth rates. Our current understanding of these trade-offs can be exploited for biotechnological applications benefiting from the selective manipulation of the allocation of resources.

Leveraging transcriptional dynamics to improve BRAF inhibitor responses in melanoma

BACKGROUND

Melanoma is a heterogeneous tumour, but the impact of this heterogeneity upon therapeutic response is not well understood.

METHODS

Single cell mRNA analysis was used to define the transcriptional heterogeneity of melanoma and its dynamic response to BRAF inhibitor therapy and treatment holidays. Discrete transcriptional states were defined in cell lines and melanoma patient specimens that predicted initial sensitivity to BRAF inhibition and the potential for effective re-challenge following resistance. A mathematical model was developed to maintain competition between the drug-sensitive and resistant states, which was validated in vivo.

FINDINGS

Our analyses showed melanoma cell lines and patient specimens to be composed of >3 transcriptionally distinct states. The cell state composition was dynamically regulated in response to BRAF inhibitor therapy and drug holidays. Transcriptional state composition predicted for therapy response. The differences in fitness between the different transcriptional states were leveraged to develop a mathematical model that optimized therapy schedules to retain the drug sensitive population. In vivo validation demonstrated that the personalized adaptive dosing schedules outperformed continuous or fixed intermittent BRAF inhibitor schedules.

INTERPRETATION

Our study provides the first evidence that transcriptional heterogeneity at the single cell level predicts for initial BRAF inhibitor sensitivity. We further demonstrate that manipulating transcriptional heterogeneity through personalized adaptive therapy schedules can delay the time to resistance.

FUNDING

This work was funded by the National Institutes of Health. The funder played no role in assembly of the manuscript.