Microbial heterogeneity affects bioprocess robustness: Dynamic single-cell analysis contributes to understanding of microbial populations

Giving the cells what they need when they need it: Biosensor-based feeding control

"Giving the cells exactly what they need, when they need it" is the core idea behind the proposed bioprocess control strategy: operating bioprocess based on the physiological behavior of the microbial population rather than exclusive monitoring of environmental parameters. We are envisioning to achieve this through the use of genetically encoded biosensors combined with online flow cytometry (FCM) to obtain a time-dependent "physiological fingerprint" of the population. We developed a biosensor based on the glnA promoter (glnAp) and applied it for monitoring the nitrogen-related nutritional state of Escherichia coli. The functionality of the biosensor was demonstrated through multiple cultivation runs performed at various scales-from microplate to 20 L bioreactor. We also developed a fully automated bioreactor-FCM interface for on-line monitoring of the microbial population. Finally, we validated the proposed strategy by performing a fed-batch experiment where the biosensor signal is used as the actuator for a nitrogen feeding feedback control. This new generation of process control, -based on the specific needs of the cells, -opens the possibility of improving process development on a short timescale and therewith, the robustness and performance of fermentation processes.

Quantifying microbial robustness in dynamic environments using microfluidic single-cell cultivation

BACKGROUND

Microorganisms must respond to changes in their environment. Analysing the robustness of functions (i.e. performance stability) to such dynamic perturbations is of great interest in both laboratory and industrial settings. Recently, a quantification method capable of assessing the robustness of various functions, such as specific growth rate or product yield, across different conditions, time frames, and populations has been developed for microorganisms grown in a 96-well plate. In micro-titer-plates, environmental change is slow and undefined. Dynamic microfluidic single-cell cultivation (dMSCC) enables the precise maintenance and manipulation of microenvironments, while tracking single cells over time using live-cell imaging. Here, we combined dMSCC and a robustness quantification method to a pipeline for assessing performance stability to changes occurring within seconds or minutes.

RESULTS

Saccharomyces cerevisiae CEN.PK113-7D, harbouring a biosensor for intracellular ATP levels, was exposed to glucose feast-starvation cycles, with each condition lasting from 1.5 to 48 min over a 20 h period. A semi-automated image and data analysis pipeline was developed and applied to assess the performance and robustness of various functions at population, subpopulation, and single-cell resolution. We observed a decrease in specific growth rate but an increase in intracellular ATP levels with longer oscillation intervals. Cells subjected to 48 min oscillations exhibited the highest average ATP content, but the lowest stability over time and the highest heterogeneity within the population.

CONCLUSION

The proposed pipeline enabled the investigation of function stability in dynamic environments, both over time and within populations. The strategy allows for parallelisation and automation, and is easily adaptable to new organisms, biosensors, cultivation conditions, and oscillation frequencies. Insights on the microbial response to changing environments will guide strain development and bioprocess optimisation.

LTP2 hypomorphs show genotype-by-environment interaction in early seedling traits in Arabidopsis thaliana

Isogenic individuals can display seemingly stochastic phenotypic differences, limiting the accuracy of genotype-to-phenotype predictions. The extent of this phenotypic variation depends in part on genetic background, raising questions about the genes involved in controlling stochastic phenotypic variation. Focusing on early seedling traits in Arabidopsis thaliana, we found that hypomorphs of the cuticle-related gene LIPID TRANSFER PROTEIN 2 (LTP2) greatly increased variation in seedling phenotypes, including hypocotyl length, gravitropism and cuticle permeability. Many ltp2 hypocotyls were significantly shorter than wild-type hypocotyls while others resembled the wild-type. Differences in epidermal properties and gene expression between ltp2 seedlings with long and short hypocotyls suggest a loss of cuticle integrity as the primary determinant of the observed phenotypic variation. We identified environmental conditions that reveal or mask the increased variation in ltp2 hypomorphs and found that increased expression of its closest paralog LTP1 is necessary for ltp2 phenotypes. Our results illustrate how decreased expression of a single gene can generate starkly increased phenotypic variation in isogenic individuals in response to an environmental challenge.

Monitoring Phenotype Heterogeneity at the Single-Cell Level within Bacillus Populations Producing Poly-3-hydroxybutyrate by Label-Free Super-resolution Infrared Imaging

Phenotypic heterogeneity is commonly found among bacterial cells within microbial populations due to intrinsic factors as well as equipping the organisms to respond to external perturbations. The emergence of phenotypic heterogeneity in bacterial populations, particularly in the context of using these bacteria as microbial cell factories, is a major concern for industrial bioprocessing applications. This is due to the potential impact on overall productivity by allowing the growth of subpopulations consisting of inefficient producer cells. Monitoring the spread of phenotypes across bacterial cells within the same population at the single-cell level is key to the development of robust, high-yield bioprocesses. Here, we discuss the novel development of optical photothermal infrared (O-PTIR) spectroscopy to probe phenotypic heterogeneity within Bacillus strains by monitoring the production of the bioplastic poly-3-hydroxybutyrate (PHB) at the single-cell level. Measurements obtained on single-point and in imaging mode show significant variability in the PHB content within bacterial cells, ranging from whether or not a cell produces PHB to variations in the intragranular biochemistry of PHB within bacterial cells. Our results show the ability of O-PTIR spectroscopy to probe PHB production at the single-cell level in a rapid, label-free, and semiquantitative manner. These findings highlight the potential of O-PTIR spectroscopy in single-cell microbial metabolomics as a whole-organism fingerprinting tool that can be used to monitor the dynamic of bacterial populations as well as for understanding their mechanisms for dealing with environmental stress, which is crucial for metabolic engineering research.

Impact of mixing insufficiencies on L-phenylalanine production with an Escherichia coli reporter strain in a novel two-compartment bioreactor

BACKGROUND

The omnipresence of population heterogeneity in industrial bioprocesses originates from prevailing dynamic bioprocess conditions, which promote differences in the expression of cellular characteristics. Despite the awareness, the concrete consequences of this phenomenon remain poorly understood.

RESULTS

Therefore, for the first time, a L-phenylalanine overproducing Escherichia coli quadruple reporter strain was established for monitoring of general stress response, growth behavior, oxygen limitation and product formation of single cells based on mTagBFP2, mEmerald, CyOFP1, and mCardinal2 expression measured by flow cytometry. This strain was applied for the fed-batch production of L-phenylalanine from glycerol and ammonia in a stirred-tank bioreactor at homogeneous conditions compared to the same process in a novel two-compartment bioreactor. This two-compartment bioreactor consists of a stirred-tank bioreactor with an initial volume of 0.9 L (homogeneous zone) with a coiled flow inverter with a fixed working volume of 0.45 L as a bypass (limitation zone) operated at a mean hydraulic residence time of 102 s. The product formation was similar in both bioreactor setups with maximum L-phenylalanine concentrations of 21.1 ± 0.6 g L-1 demonstrating the consistency of this study's microbial L-phenylalanine production. However, cell growth was vulnerable to repetitive exposure to the dynamically changing conditions in the two-compartment bioreactor with maximum biomass yields reduced by 21%. The functionality of reporter molecules was approved in the stirred-tank bioreactor cultivation, in which expressed fluorescence levels of all four markers were in accordance with respective process state variables. Additional evaluation of the distributions on single-cell level revealed the presence of population heterogeneity in both bioprocesses. Especially for the marker of the general stress response and the product formation, the corresponding histograms were characterized by bimodal shapes and broad distributions. These phenomena were pronounced particularly at the beginning and the end of the fed-batch process.

CONCLUSIONS

The here shown findings confirm multiple reporter strains to be a noninvasive tool for monitoring cellular characteristics and identifying potential subpopulations in bioprocesses. In combination with experiments in scale-down setups, these can be utilized for a better physiological understanding of bioprocesses and support future scale-up procedures.

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.

Influence of Varying Pre-Culture Conditions on the Level of Population Heterogeneity in Batch Cultures with an Escherichia coli Triple Reporter Strain

When targeting robust, high-yielding bioprocesses, phenomena such as population heterogeneity have to be considered. Therefore, the influence of the conditions which the cells experience prior to the main culture should also be evaluated. Here, the influence of a pre-culture medium (complex vs. minimal medium), optical density for inoculation of the main culture (0.005, 0.02 and 0.0125) and harvest time points of the pre-culture in exponential growth phase (early, mid and late) on the level of population heterogeneity in batch cultures of the Escherichia coli triple reporter strain G7_BL21(DE3) in stirred-tank bioreactors was studied. This strain allows monitoring the growth (rrnB-EmGFP), general stress response (rpoS-mStrawberry) and oxygen limitation (nar-TagRFP657) of single cells through the expression of fluorescent proteins. Data from batch cultivations with varying pre-culture conditions were analysed with principal component analysis. According to fluorescence data, the pre-culture medium had the largest impact on population heterogeneities during the bioprocess. While a minimal medium as a pre-culture medium elevated the differences in cellular growth behaviour in the subsequent batch process, a complex medium increased the general stress response and led to a higher population heterogeneity. The latter was promoted by an early harvest of the cells with low inoculation density. Seemingly, nar-operon expression acted independently of the pre-culture conditions.

Mimicked Mixing-Induced Heterogeneities of Industrial Bioreactors Stimulate Long-Lasting Adaption Programs in Ethanol-Producing Yeasts

Commercial-scale bioreactors create an unnatural environment for microbes from an evolutionary point of view. Mixing insufficiencies expose individual cells to fluctuating nutrient concentrations on a second-to-minute scale while transcriptional and translational capacities limit the microbial adaptation time from minutes to hours. This mismatch carries the risk of inadequate adaptation effects, especially considering that nutrients are available at optimal concentrations on average. Consequently, industrial bioprocesses that strive to maintain microbes in a phenotypic sweet spot, during lab-scale development, might suffer performance losses when said adaptive misconfigurations arise during scale-up. Here, we investigated the influence of fluctuating glucose availability on the gene-expression profile in the industrial yeast Ethanol Red™. The stimulus-response experiment introduced 2 min glucose depletion phases to cells growing under glucose limitation in a chemostat. Even though Ethanol Red™ displayed robust growth and productivity, a single 2 min depletion of glucose transiently triggered the environmental stress response. Furthermore, a new growth phenotype with an increased ribosome portfolio emerged after complete adaptation to recurring glucose shortages. The results of this study serve a twofold purpose. First, it highlights the necessity to consider the large-scale environment already at the experimental development stage, even when process-related stressors are moderate. Second, it allowed the deduction of strain engineering guidelines to optimize the genetic background of large-scale production hosts.

PHB production from food waste hydrolysates by Halomonas bluephagenesis Harboring PHB operon linked with an essential gene

Food wastes can be hydrolyzed into soluble microbial substrates, contributing to sustainability. Halomonas spp.-based Next Generation Industrial Biotechnology (NGIB) allows open, unsterile fermentation, eliminating the need for sterilization to avoid the Maillard reaction that negatively affects cell growth. This is especially important for food waste hydrolysates, which have a high nutrient content but are unstable due to batch, sources, or storage conditions. These make them unsuitable for polyhydroxyalkanoate (PHA) production, which usually requires limitation on either nitrogen, phosphorous, or sulfur. In this study, H. bluephagenesis was constructed by overexpressing the PHA synthesis operon phaCAB_Cn (cloned from Cupriavidus necator) controlled by the essential gene ompW (encoding outer membrane protein W) promoter and the constitutive porin promoter that are continuously expressed at high levels throughout the cell growth process, allowing poly(3-hydroxybutyrate) (PHB) production to proceed in nutrient-rich (also nitrogen-rich) food waste hydrolysates of various sources. The recombinant H. bluephagenesis termed WZY278 generated 22 g L^-1 cell dry weight (CDW) containing 80 wt% PHB when cultured in food waste hydrolysates in shake flasks, and it was grown to 70 g L^-1 CDW containing 80 wt% PHB in a 7-L bioreactor via fed-batch cultivation. Thus, unsterilizable food waste hydrolysates can become nutrient-rich substrates for PHB production by H. bluephagenesis able to be grown contamination-free under open conditions.

Microfluidic single-cell scale-down systems: introduction, application, and future challenges

Performance losses during the scaling-up of bioprocesses from the laboratory to the production scale are common obstacles caused by the formation of concentration gradients in bioreactors. To overcome these obstacles, so-called scale-down bioreactors are used to analyze selected large-scale conditions and are one of the most important predictive tools for the successful transfer of bioprocesses from the lab to the industrial scale. In this regard, cellular behavior is usually measured as an averaged value, neglecting possible cell-to-cell heterogeneity within the culture. In contrast, microfluidic single-cell cultivation (MSCC) systems offer the possibility of understanding cellular processes on a single-cell level. To date, most MSCC systems have a limited choice of cultivation parameters that are not representative of bioprocess-relevant environmental conditions. Herein, we critically review recent advances in MSCC that allow the cultivation and analysis of cells under dynamic (bioprocess-relevant) environmental conditions. Finally, we discuss what technological advances and efforts are needed to bridge the gap between current MSCC systems and the use of these systems as single-cell scale-down devices.

Comparison of plasmid stabilization systems during heterologous isopropanol production in fed-batch bioreactor

Strain robustness during production of recombinant molecules is of major interest to ensure bioprocess profitability. The heterogeneity of populations has been shown in the literature as a source of instability in bioprocesses. Thus, the heterogeneity of the population was studied by evaluating the robustness of the strains (stability of plasmid expression, cultivability, membrane integrity and macroscopic cell behavior) during well-controlled fedbatch cultures. On the context of microbial production of chemical molecules, isopropanol (IPA) has been produced by recombinant strains of Cupriavidus necator. Plasmid stability was monitored by the plate count method to assess the impact of isopropanol production on plasmid stability, depending on implanted plasmid stabilization systems for strain engineering designs. With the reference strain Re2133/pEG7c, an isopropanol titer of 15.1 g·L^-1 could be achieved. When the isopropanol concentration has reached about 8 g. L^-1, cell permeability increased (up to 25 %) and plasmid stability decreased significantly (up to 1.5 decimal reduction rate) resulting in decreased isopropanol production rates. Bioprocess robustness under isopropanol producing conditions was then investigated with two plasmid construction strategies (1) Post Segregational Killing hok/sok (in Re2133/pEG20) and (2) expression of GroESL chaperon proteins (in Re2133/pEG23). Plasmid stability for strain Re2133/pEG20 (PSK hok/sok) appears to be improved up to 11 g. L^-1 of IPA compared to the reference strain (8 g. L^-1 IPA). Nevertheless, cell permeability followed the same dynamic as the reference strain with a drastic increase around 8 g. L^-1 IPA. On the contrary, the Re2133/pEG23 strain made it possible to minimize the cell permeability (with a constant value at 5 % IP permeability) and to increase the growth capacities in response to increased isopropanol concentrations but plasmid stability was the weakest. The metabolic burden, linked to either the overexpression of GroESL chaperones or the PSK hok/sok system, seems to be deleterious for the overall isopropanol production compared to the reference strain (RE2133/pEG7c) even if we have shown that the overexpression chaperones GroESL improve membrane integrity and PSK system hok/sok improve plasmid stability as long as isopropanol concentration does not exceed 11 g L^- 1.

Diverse mechanisms of bioproduction heterogeneity in fermentation and their control strategies

Microbial bioproduction often faces challenges related to populational heterogeneity, where cells exhibit varying biosynthesis capabilities. Bioproduction heterogeneity can stem from genetic and non-genetic factors, resulting in decreased titer, yield, stability, and reproducibility. Consequently, understanding and controlling bioproduction heterogeneity are crucial for enhancing the economic competitiveness of large-scale biomanufacturing. In this review, we provide a comprehensive overview of current understandings of the various mechanisms underlying bioproduction heterogeneity. Additionally, we examine common strategies for controlling bioproduction heterogeneity based on these mechanisms. By implementing more robust measures to mitigate heterogeneity, we anticipate substantial enhancements in the scalability and stability of bioproduction processes.

ONE-SENTENCE SUMMARY

This review summarizes current understandings of different mechanisms of bioproduction heterogeneity and common control strategies based on these mechanisms.

Microbial lifelines in bioprocesses: From concept to application

Bioprocesses are scaled up for the production of large product quantities. With larger fermenter volumes, mixing becomes increasingly inefficient and environmental gradients get more prominent than in smaller scales. Environmental gradients have an impact on the microorganism's metabolism, which makes the prediction of large-scale performance difficult and can lead to scale-up failure. A promising approach for improved understanding and estimation of dynamics of microbial populations in large-scale bioprocesses is the analysis of microbial lifelines. The lifeline of a microbe in a bioprocess is the experience of environmental gradients from a cell's perspective, which can be described as a time series of position, environment and intracellular condition. Currently, lifelines are predominantly determined using models with computational fluid dynamics, but new technical developments in flow-following sensor particles and microfluidic single-cell cultivation open the door to a more interdisciplinary concept. We critically review the current concepts and challenges in lifeline determination and application of lifeline analysis, as well as strategies for the integration of these techniques into bioprocess development. Lifelines can contribute to a successful scale-up by guiding scale-down experiments and identifying strain engineering targets or bioreactor optimisations.

Performing in spite of starvation: How Saccharomyces cerevisiae maintains robust growth when facing famine zones in industrial bioreactors

In fed-batch operated industrial bioreactors, glucose-limited feeding is commonly applied for optimal control of cell growth and product formation. Still, microbial cells such as yeasts and bacteria are frequently exposed to glucose starvation conditions in poorly mixed zones or far away from the feedstock inlet point. Despite its commonness, studies mimicking related stimuli are still underrepresented in scale-up/scale-down considerations. This may surprise as the transition from glucose limitation to starvation has the potential to provoke regulatory responses with negative consequences for production performance. In order to shed more light, we performed gene-expression analysis of Saccharomyces cerevisiae grown in intermittently fed chemostat cultures to study the effect of limitation-starvation transitions. The resulting glucose concentration gradient was representative for the commercial scale and compelled cells to tolerate about 76 s with sub-optimal substrate supply. Special attention was paid to the adaptation status of the population by discriminating between first time and repeated entry into the starvation regime. Unprepared cells reacted with a transiently reduced growth rate governed by the general stress response. Yeasts adapted to the dynamic environment by increasing internal growth capacities at the cost of rising maintenance demands by 2.7%. Evidence was found that multiple protein kinase A (PKA) and Snf1-mediated regulatory circuits were initiated and ramped down still keeping the cells in an adapted trade-off between growth optimization and down-regulation of stress response. From this finding, primary engineering guidelines are deduced to optimize both the production host's genetic background and the design of scale-down experiments.

microbeSEG: A deep learning software tool with OMERO data management for efficient and accurate cell segmentation

In biotechnology, cell growth is one of the most important properties for the characterization and optimization of microbial cultures. Novel live-cell imaging methods are leading to an ever better understanding of cell cultures and their development. The key to analyzing acquired data is accurate and automated cell segmentation at the single-cell level. Therefore, we present microbeSEG, a user-friendly Python-based cell segmentation tool with a graphical user interface and OMERO data management. microbeSEG utilizes a state-of-the-art deep learning-based segmentation method and can be used for instance segmentation of a wide range of cell morphologies and imaging techniques, e.g., phase contrast or fluorescence microscopy. The main focus of microbeSEG is a comprehensible, easy, efficient, and complete workflow from the creation of training data to the final application of the trained segmentation model. We demonstrate that accurate cell segmentation results can be obtained within 45 minutes of user time. Utilizing public segmentation datasets or pre-labeling further accelerates the microbeSEG workflow. This opens the door for accurate and efficient data analysis of microbial cultures.

Controlling microbial co-culture based on substrate pulsing can lead to stability through differential fitness advantages

Microbial consortia are an exciting alternative for increasing the performances of bioprocesses for the production of complex metabolic products. However, the functional properties of microbial communities remain challenging to control, considering the complex interaction mechanisms occurring between co-cultured microbial species. Indeed, microbial communities are highly dynamic and can adapt to changing environmental conditions through complex mechanisms, such as phenotypic diversification. We focused on stabilizing a co-culture of Saccharomyces cerevisiae and Escherichia coli in continuous cultures. Our preliminary data pointed out that transient diauxic shifts could lead to stable co-culture by providing periodic fitness advantages to the yeast. Based on a computational toolbox called MONCKS (for MONod-type Co-culture Kinetic Simulation), we were able to predict the dynamics of diauxic shift for both species based on a cybernetic approach. This toolbox was further used to predict the frequency of diauxic shift to be applied to reach co-culture stability. These simulations were successfully reproduced experimentally in continuous bioreactors with glucose pulsing. Finally, based on a bet-hedging reporter, we observed that the yeast population exhibited an increased phenotypic diversification process in co-culture compared with mono-culture, suggesting that this mechanism could be the basis of the metabolic fitness of the yeast.

Being able to manipulate the dynamics of microbial co-cultures is a technical challenge that need to be addressed in order to get a deeper insight about how microbial communities are evolving in their ecological context, as well as for exploiting the potential offered by such communities in an applied context e.g., for setting up more robust bioprocesses relying on the use of several microbial species. In this study, we used continuous cultures of bacteria (E. coli) and yeast (S. cerevisiae) in order to demonstrate that a simple nutrient pulsing strategy can be used for adjusting the composition of the community with time. As expected, during growth on glucose, E. coli quickly outcompeted S. cerevisiae. However, when glucose is pulsed into the culture, increased metabolic fitness of the yeast was observed upon reconsumption of the main side metabolites i.e., acetate and ethanol, leading to a robust oscillating growth profile for both species. The optimal pulsing frequency was predicted based on a cybernetic version of a Monod growth model taking into account the main metabolic routes involved in the process. Considering the limited number of metabolic details needed, this cybernetic approach could be generalized to other communities.

Control of phenotypic diversification based on serial cultivations on different carbon sources leads to improved bacterial xylanase production

Thermobacillus xylanilyticus is a thermophilic and hemicellulolytic bacterium of interest for the production of thermostable hemicellulases. Enzymes' production by this bacterium is challenging, because the proliferation of a cheating subpopulation of cells during exponential growth impairs the production of xylanase after serial cultivations. Accordingly, a strategy of successive cultivations with cells transfers in stationary phase and the use of wheat bran and wheat straw as carbon sources were tested. The ratio between subpopulations and their corresponding metabolic activities were studied by flow cytometry and the resulting hemicellulases production (xylanase, acetyl esterase and β-xylosidase) followed. During serial cultivations, the results pointed out an increase of the enzymatic activities. On xylan, compared to the first cultivation, the xylanase activity increases by 7.15-fold after only four cultivations. On the other hand, the debranching activities were increased by 5.88-fold and 57.2-fold on wheat straw and by 2.77-fold and 3.34-fold on wheat bran for β-xylosidase and acetyl esterase, respectively. The different enzymatic activities then stabilized, reached a plateau and further decreased. Study of the stability and reversibility of the enzyme production revealed cell-to-cell heterogeneities in metabolic activities which could be linked to the reversibility of enzymatic activity changes. Thus, the strategy of successive transfers during the stationary phase of growth, combined with the use of complex lignocellulosic substrates as carbon sources, is an efficient strategy to optimize the hemicellulases production by T. xylanilyticus, by preventing the selection of cheaters.

Single-Cell Analysis of CHO Cells Reveals Clonal Heterogeneity in Hyperosmolality-Induced Stress Response

Hyperosmolality can occur during industrial fed-batch cultivation processes of Chinese hamster ovary (CHO) cells as highly concentrated feed and base solutions are added to replenish nutrients and regulate pH values. Some effects of hyperosmolality, such as increased cell size and growth inhibition, have been elucidated by previous research, but the impact of hyperosmolality and the specific effects of the added osmotic-active reagents have rarely been disentangled. In this study, CHO cells were exposed to four osmotic conditions between 300 mOsm/kg (physiologic condition) and 530 mOsm/kg (extreme hyperosmolality) caused by the addition of either high-glucose-supplemented industrial feed or mannitol as an osmotic control. We present novel single-cell cultivation data revealing heterogeneity in mass gain and cell division in response to these treatments. Exposure to extreme mannitol-induced hyperosmolality and to high-glucose-oversupplemented feed causes cell cycle termination, mtDNA damage, and mitochondrial membrane depolarization, which hints at the onset of premature stress-induced senescence. Thus, this study shows that both mannitol-induced hyperosmolality (530 mOsm/kg) and glucose overfeeding induce severe negative effects on cell growth and mitochondrial activity; therefore, they need to be considered during process development for commercial production.

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.

New insights into phenotypic heterogeneity for the distinct lipid accumulation of Schizochytrium sp. H016

BACKGROUND

Schizochytrium sp. is a marine heterotrophic protist and an important sustainable resource for high value-added docosahexaenoic acid in the future. The production of different phenotypes during the continuous subculture of Schizochytrium sp. results in a serious reduction in lipid yield and complicates the used of this strain in scientific research and industrial production. Hence, obtaining an improved understanding of the phenotypic differences and molecular mechanisms underlying the cell-to-cell heterogeneity of Schizochytrium sp. is necessary.

RESULTS

After continuous culture passage, Schizochytrium sp. H016 differentiated into two subpopulations with different morphologies and showed decreased capacity for lipid production. The presence of cell subpopulations with degraded lipid droplets led to a substantial decrease in overall lipid yield. Here, a rapid screening strategy based on fluorescence-activated cell sorting was proposed to classify and isolate subpopulations quickly in accordance with their lipid-producing capability. The final biomass and lipid yield of the subpopulation with high cell lipid content (i.e., H016-H) were 38.83 and 17.22 g/L, respectively, which were 2.07- and 5.38-fold higher than those of the subpopulation with low lipid content (i.e., H016-L), respectively. Subsequently, time‑resolved transcriptome analysis was performed to elucidate the mechanism of phenotypic heterogeneity in different subpopulations. Results showed that the expression of genes related to the cell cycle and lipid degradation was significantly upregulated in H016-L, whereas the metabolic pathways related to fatty acid synthesis and glyceride accumulation were remarkably upregulated in H016-H.

CONCLUSION

This study innovatively used flow cytometry combined with transcriptome technology to provide new insights into the phenotypic heterogeneity of different cell subpopulations of Schizochytrium sp. Furthermore, these results lay a strong foundation for guiding the breeding of oleaginous microorganisms with high lipid contents.

Monitoring Intracellular Metabolite Dynamics in Saccharomyces cerevisiae during Industrially Relevant Famine Stimuli

Carbon limitation is a common feeding strategy in bioprocesses to enable an efficient microbiological conversion of a substrate to a product. However, industrial settings inherently promote mixing insufficiencies, creating zones of famine conditions. Cells frequently traveling through such regions repeatedly experience substrate shortages and respond individually but often with a deteriorated production performance. A priori knowledge of the expected strain performance would enable targeted strain, process, and bioreactor engineering for minimizing performance loss. Today, computational fluid dynamics (CFD) coupled to data-driven kinetic models are a promising route for the in silico investigation of the impact of the dynamic environment in the large-scale bioreactor on microbial performance. However, profound wet-lab datasets are needed to cover relevant perturbations on realistic time scales. As a pioneering study, we quantified intracellular metabolome dynamics of Saccharomyces cerevisiae following an industrially relevant famine perturbation. Stimulus-response experiments were operated as chemostats with an intermittent feed and high-frequency sampling. Our results reveal that even mild glucose gradients in the range of 100 µmol·L−1 impose significant perturbations in adapted and non-adapted yeast cells, altering energy and redox homeostasis. Apparently, yeast sacrifices catabolic reduction charges for the sake of anabolic persistence under acute carbon starvation conditions. After repeated exposure to famine conditions, adapted cells show 2.7% increased maintenance demands.

Advances in automated real-time flow cytometry for monitoring of bioreactor processes

Flow cytometry and its technological possibilities have greatly advanced in the past decade as analysis tool for single cell properties and population distributions of different cell types in bioreactors. Along the way, some solutions for automated real-time flow cytometry (ART-FCM) were developed for monitoring of bioreactor processes without operator interference over extended periods with variable sampling frequency. However, there is still great potential for ART-FCM to evolve and possibly become a standard application in bioprocess monitoring and process control. This review first addresses different components of an ART-FCM, including the sampling device, the sample-processing unit, the unit for sample delivery to the flow cytometer and the settings for measurement of pre-processed samples. Also, available algorithms are presented for automated data analysis of multi-parameter fluorescence datasets derived from ART-FCM experiments. Furthermore, challenges are discussed for integration of fluorescence-activated cell sorting into an ART-FCM setup for isolation and separation of interesting subpopulations that can be further characterized by for instance omics-methods. As the application of ART-FCM is especially of interest for bioreactor process monitoring, including investigation of population heterogeneity and automated process control, a summary of already existing setups for these purposes is given. Additionally, the general future potential of ART-FCM is addressed.

High-Throughput Time-Lapse Fluorescence Microscopy Screening for Heterogeneously Expressed Genes in Bacillus subtilis

Elucidating phenotypic heterogeneity in clonal bacterial populations is important for both the fundamental understanding of bacterial behavior and the synthetic engineering of bacteria in biotechnology. In this study, we present and validate a high-throughput and high-resolution time-lapse fluorescence microscopy-based strategy to easily and systematically screen for heterogeneously expressed genes in the Bacillus subtilis model bacterium. This screen allows detection of expression patterns at high spatial and temporal resolution, which often escape detection by other approaches, and can readily be extrapolated to other bacteria. A proof-of-concept screening in B. subtilis revealed both recognized and yet unrecognized heterogeneously expressed genes, thereby validating the approach.

IMPORTANCE Differential gene expression among isogenic siblings often leads to phenotypic heterogeneity and the emergence of complex social behavior and functional capacities within clonal bacterial populations. Despite the importance of such features for both the fundamental understanding and synthetic engineering of bacterial behavior, approaches to systematically map such population heterogeneity are scarce. In this context, we have elaborated a new time-lapse fluorescence microscopy-based strategy to easily and systematically screen for such heterogeneously expressed genes in bacteria with high resolution and throughput. A proof-of-concept screening in the Bacillus subtilis model bacterium revealed both recognized and yet unrecognized heterogeneously expressed genes, thereby validating our approach.

Robustness: linking strain design to viable bioprocesses

Microbial cell factories are becoming increasingly popular for the sustainable production of various chemicals. Metabolic engineering has led to the design of advanced cell factories; however, their long-term yield, titer, and productivity falter when scaled up and subjected to industrial conditions. This limitation arises from a lack of robustness - the ability to maintain a constant phenotype despite the perturbations of such processes. This review describes predictable and stochastic industrial perturbations as well as state-of-the-art technologies to counter process variability. Moreover, we distinguish robustness from tolerance and discuss the potential of single-cell studies for improving system robustness. Finally, we highlight ways of achieving consistent and comparable quantification of robustness that can guide the selection of strains for industrial bioprocesses.

Study of plasmid-based expression level heterogeneity under plasmid-curing like conditions in Cupriavidus necator

Plasmid expression level heterogeneity in Cupriavidus necator was studied in response to stringent culture conditions, supposed to enhance plasmid instability, through plasmid curing strategies. Two plasmid curing strategies were compared based on their efficiency at generating heterogeneity in batch: rifampicin addition and temperature increase. A temperature increase from 30° to 37 °C was the most efficient plasmid curing strategy. To generate a heterogeneous population in terms of plasmid expression levels, successive batches at supra-optimal culture temperature (i.e. 37 °C) were initially conducted. Three distinct fluorescent subpopulations P₀ (not fluorescent), P₁ (low fluorescence intensity, median = 1 10³) and P₂ (high fluorescence intensity, median = 6 10³) were obtained. From there, the chemostat culture was implemented to study the long-term stress response under well-controlled environment at defined dilution rates. For dilution rates comprised between 0.05 and 0.10 h^-1, the subpopulation P₂ (62% vs 90%) was favored compared to P₁ cells (54% vs 1%), especially when growth rate increased. Our biosensor was efficient at discriminating subpopulation presenting different expression levels under stringent culture conditions. Plus, we showed that controlling growth kinetics had a stabilizing impact on plasmid expression levels, even under heterogeneous expression conditions.

OUP accepted manuscript

Optimization of metabolism to maximize production of bio-based chemicals must consistently balance cellular resources for biocatalyst growth and desired compound synthesis. This mini-review discusses synthetic biology strategies for dynamically controlling expression of genes to enable dual-phase fermentations in which growth and production are separated into dedicated phases. Emphasis is placed on practical examples which can be reliably scaled to commercial production with the current state of technology. Recent case studies are presented, and recommendations are provided for environmental signals and genetic control circuits.

Microbial technologies for biotherapeutics production: Key tools for advanced biopharmaceutical process development and control

Current trends in the biopharmaceutical market such as the diversification of therapies as well as the increasing time-to-market pressure will trigger the rethinking of bioprocess development and production approaches. Thereby, the importance of development time and manufacturing costs will increase, especially for microbial production. In the present review, we investigate three technological approaches which, to our opinion, will play a key role in the future of biopharmaceutical production. The first cornerstone of process development is the generation and effective utilization of platform knowledge. Building processes on well understood microbial and technological platforms allows to accelerate early-stage bioprocess development and to better condense this knowledge into multi-purpose technologies and applicable mathematical models. Second, the application of verified scale down systems and in silico models for process design and characterization will reduce the required number of large scale batches before dossier submission. Third, the broader availability of mathematical process models and the improvement of process analytical technologies will increase the applicability and acceptance of advanced control and process automation in the manufacturing scale. This will reduce process failure rates and subsequently cost of goods. Along these three aspects we give an overview of recently developed key tools and their potential integration into bioprocess development strategies.

Recombinant Protein Production with Escherichia coli in Glucose and Glycerol Limited Chemostats

Recently, there has been a resurgence of interest in continuous bioprocessing as a cost-optimised production strategy, driven by a rising global requirement for recombinant proteins used as biological drugs. This strategy could provide several benefits over traditional batch processing, including smaller bioreactors, smaller facilities, and overall reduced plant footprints and investment costs. Continuous processes may also offer improved product quality and minimise heterogeneity, both in the culture and in the product. In this paper, a model protein, green fluorescent protein (GFP) mut3*, was used to test the recombinant protein expression in an Escherichia coli strain with industrial relevance grown in chemostat. An important factor in enabling stable productivity in continuous cultures is the carbon source. We have studied the viability and heterogeneity of the chemostat cultures using a chemically defined medium based on glucose or glycerol as the single carbon source. As a by-product of biodiesel production, glycerol is expected to become a sustainable alternative substrate to glucose. We have found that although glycerol gives a higher cell density, it also generates higher heterogeneity in the culture and a less stable recombinant protein production. We suggest that manipulating the balance between different subpopulations to increase the proportion of productive cells may be a possible solution for making glycerol a successful alternative to glucose.

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.

Dynamic control in metabolic engineering: Theories, tools, and applications

Metabolic engineering has allowed the production of a diverse number of valuable chemicals using microbial organisms. Many biological challenges for improving bio-production exist which limit performance and slow the commercialization of metabolically engineered systems. Dynamic metabolic engineering is a rapidly developing field that seeks to address these challenges through the design of genetically encoded metabolic control systems which allow cells to autonomously adjust their flux in response to their external and internal metabolic state. This review first discusses theoretical works which provide mechanistic insights and design choices for dynamic control systems including two-stage, continuous, and population behavior control strategies. Next, we summarize molecular mechanisms for various sensors and actuators which enable dynamic metabolic control in microbial systems. Finally, important applications of dynamic control to the production of several metabolite products are highlighted, including fatty acids, aromatics, and terpene compounds. Altogether, this review provides a comprehensive overview of the progress, advances, and prospects in the design of dynamic control systems for improved titer, rate, and yield metrics in metabolic engineering.

Potential of Integrating Model-Based Design of Experiments Approaches and Process Analytical Technologies for Bioprocess Scale-Down

Typically, bioprocesses on an industrial scale are dynamic systems with a certain degree of variability, system inhomogeneities, and even population heterogeneities. Therefore, the scaling of such processes from laboratory to industrial scale and vice versa is not a trivial task. Traditional scale-down methodologies consider several technical parameters, so that systems on the laboratory scale tend to qualitatively reflect large-scale effects, but not the dynamic situation in an industrial bioreactor over the entire process, from the perspective of a cell. Supported by the enormous increase in computing power, the latest scientific focus is on the application of dynamic models, in combination with computational fluid dynamics to quantitatively describe cell behavior. These models allow the description of possible cellular lifelines which in turn can be used to derive a regime analysis for scale-down experiments. However, the approaches described so far, which were for a very few process examples, are very labor- and time-intensive and cannot be validated easily. In parallel, alternatives have been developed based on the description of the industrial process with hybrid process models, which describe a process mechanistically as far as possible in order to determine the essential process parameters with their respective variances. On-line analytical methods allow the characterization of population heterogeneity directly in the process. This detailed information from the industrial process can be used in laboratory screening systems to select relevant conditions in which the cell and process related parameters reflect the situation in the industrial scale. In our opinion, these technologies, which are available in research for modeling biological systems, in combination with process analytical techniques are so far developed that they can be implemented in industrial routines for faster development of new processes and optimization of existing ones.

Single-Cell Mass Spectroscopic Analysis for Quantifying Active Metabolic Pathway Heterogeneity in a Bacterial Population on an Electrode

Microbial electrochemical catalysis based on respiratory reactions coupled with extracellular electron transport (EET), which is critical for bioenergy applications, strongly depends on the biocompatibility of the electrode material. However, the comparison of materials for such physiological responses has been difficult because of the lack of a quantitative assay for characterizing cellular metabolism at the electrode surface. Here, we developed a single-cell analysis method specific for the cells attached to the electrode to quantify active metabolic pathway heterogeneity as an index of physiological cell/electrode interaction, which generally increases with metabolic robustness in the microbial population. Nanoscale secondary ion mass spectrometry followed by microbial current production with model EET-capable bacteria, Shewanella oneidensis MR-1 and its mutant strains lacking carbon assimilation pathways, showed that different active metabolic pathways resulted in nearly identical ¹³C/¹⁵N assimilation ratios for individual cells in the presence of isotopically labeled nutrients, demonstrating a correlation between the ¹³C/¹⁵N ratio and the active metabolic pathway. Compared to the nonelectrode conditions, the heterogeneity of the assimilated ¹³C/¹⁵N ratio was highly enhanced on the electrode surface, suggesting that the metabolic robustness of the microbial population increased through the electrochemical interaction with the electrode. The present methodology enables us to quantitatively compare and screen electrode materials that increase the robustness of microbial electrocatalysis.

Development and application of a cultivation platform for mammalian suspension cell lines with single-cell resolution

In bioproduction processes, cellular heterogeneity can cause unpredictable process outcomes or even provoke process failure. Still, cellular heterogeneity is not examined systematically in bioprocess research and development. One reason for this shortcoming is the applied average bulk analyses, which are not able to detect cell-to-cell differences. In this study, we present a microfluidic tool for mammalian single-cell cultivation (MaSC) of suspension cells. The design of our platform allows cultivation in highly controllable environments. As a model system, Chinese hamster ovary cells (CHO-K1) were cultivated over 150 h. Growth behavior was analyzed on a single-cell level and resulted in growth rates between 0.85 and 1.16 day^-1 . At the same time, heterogeneous growth and division behavior, for example, unequal division time, as well as rare cellular events like polynucleation or reversed mitosis were observed, which would have remained undetected in a standard population analysis based on average measurements. Therefore, MaSC will open the door for systematic single-cell analysis of mammalian suspension cells. Possible fields of application represent basic research topics like cell-to-cell heterogeneity, clonal stability, pharmaceutical drug screening, and stem cell research, as well as bioprocess related topics such as media development and novel scale-down approaches.

Microfluidics for Biotechnology: Bridging Gaps to Foster Microfluidic Applications

Microfluidics and novel lab-on-a-chip applications have the potential to boost biotechnological research in ways that are not possible using traditional methods. Although microfluidic tools were increasingly used for different applications within biotechnology in recent years, a systematic and routine use in academic and industrial labs is still not established. For many years, absent innovative, ground-breaking and “out-of-the-box” applications have been made responsible for the missing drive to integrate microfluidic technologies into fundamental and applied biotechnological research. In this review, we highlight microfluidics’ offers and compare them to the most important demands of the biotechnologists. Furthermore, a detailed analysis in the state-of-the-art use of microfluidics within biotechnology was conducted exemplarily for four emerging biotechnological fields that can substantially benefit from the application of microfluidic systems, namely the phenotypic screening of cells, the analysis of microbial population heterogeneity, organ-on-a-chip approaches and the characterisation of synthetic co-cultures. The analysis resulted in a discussion of potential “gaps” that can be responsible for the rare integration of microfluidics into biotechnological studies. Our analysis revealed six major gaps, concerning the lack of interdisciplinary communication, mutual knowledge and motivation, methodological compatibility, technological readiness and missing commercialisation, which need to be bridged in the future. We conclude that connecting microfluidics and biotechnology is not an impossible challenge and made seven suggestions to bridge the gaps between those disciplines. This lays the foundation for routine integration of microfluidic systems into biotechnology research procedures.

Automated Conditional Screening of Multiple Escherichia coli Strains in Parallel Adaptive Fed-Batch Cultivations

In bioprocess development, the host and the genetic construct for a new biomanufacturing process are selected in the early developmental stages. This decision, made at the screening scale with very limited information about the performance in larger reactors, has a major influence on the efficiency of the final process. To overcome this, scale-down approaches during screenings that show the real cell factory performance at industrial-like conditions are essential. We present a fully automated robotic facility with 24 parallel mini-bioreactors that is operated by a model-based adaptive input design framework for the characterization of clone libraries under scale-down conditions. The cultivation operation strategies are computed and continuously refined based on a macro-kinetic growth model that is continuously re-fitted to the available experimental data. The added value of the approach is demonstrated with 24 parallel fed-batch cultivations in a mini-bioreactor system with eight different Escherichia coli strains in triplicate. The 24 fed-batch cultivations were run under the desired conditions, generating sufficient information to define the fastest-growing strain in an environment with oscillating glucose concentrations similar to industrial-scale bioreactors.

Monitoring Parallel Robotic Cultivations with Online Multivariate Analysis

In conditional microbial screening, a limited number of candidate strains are tested at different conditions searching for the optimal operation strategy in production (e.g., temperature and pH shifts, media composition as well as feeding and induction strategies). To achieve this, cultivation volumes of >10 mL and advanced control schemes are required to allow appropriate sampling and analyses. Operations become even more complex when the analytical methods are integrated into the robot facility. Among other multivariate data analysis methods, principal component analysis (PCA) techniques have especially gained popularity in high throughput screening. However, an important issue specific to high throughput bioprocess development is the lack of so-called golden batches that could be used as a basis for multivariate analysis. In this study, we establish and present a program to monitor dynamic parallel cultivations in a high throughput facility. PCA was used for process monitoring and automated fault detection of 24 parallel running experiments using recombinant E. coli cells expressing three different fluorescence proteins as the model organism. This approach allowed for capturing events like stirrer failures and blockage of the aeration system and provided a good signal to noise ratio. The developed application can be easily integrated in existing data- and device-infrastructures, allowing automated and remote monitoring of parallel bioreactor systems.

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.

Towards a widespread adoption of metabolic modeling tools in biopharmaceutical industry: a process systems biology engineering perspective

In biotechnology, the emergence of high-throughput technologies challenges the interpretation of large datasets. One way to identify meaningful outcomes impacting process and product attributes from large datasets is using systems biology tools such as metabolic models. However, these tools are still not fully exploited for this purpose in industrial context due to gaps in our knowledge and technical limitations. In this paper, key aspects restraining the routine implementation of these tools are highlighted in three research fields: monitoring, network science and hybrid modeling. Advances in these fields could expand the current state of systems biology applications in biopharmaceutical industry to address existing challenges in bioprocess development and improvement.

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.

Coculturing Bacteria Leads to Reduced Phenotypic Heterogeneities

Isogenic bacterial populations are known to exhibit phenotypic heterogeneity at the single-cell level. Because of difficulties in assessing the phenotypic heterogeneity of a single taxon in a mixed community, the importance of this deeper level of organization remains relatively unknown for natural communities. In this study, we have used membrane-based microcosms that allow the probing of the phenotypic heterogeneity of a single taxon while interacting with a synthetic or natural community. Individual taxa were studied under axenic conditions, as members of a coculture with physical separation, and as a mixed culture. Phenotypic heterogeneity was assessed through both flow cytometry and Raman spectroscopy. Using this setup, we investigated the effect of microbial interactions on the individual phenotypic heterogeneities of two interacting drinking water isolates. Through flow cytometry we have demonstrated that interactions between these bacteria lead to a reduction of their individual phenotypic diversities and that this adjustment is conditional on the bacterial taxon. Single-cell Raman spectroscopy confirmed a taxon-dependent phenotypic shift due to the interaction. In conclusion, our data suggest that bacterial interactions may be a general driver of phenotypic heterogeneity in mixed microbial populations.IMPORTANCE Laboratory studies have shown the impact of phenotypic heterogeneity on the survival and functionality of isogenic populations. Because phenotypic heterogeneity plays an important role in pathogenicity and virulence, antibiotic resistance, biotechnological applications, and ecosystem properties, it is crucial to understand its influencing factors. An unanswered question is whether bacteria in mixed communities influence the phenotypic heterogeneity of their community partners. We found that coculturing bacteria leads to a reduction in their individual phenotypic heterogeneities, which led us to the hypothesis that the individual phenotypic diversity of a taxon is dependent on the community composition.