Taking control over microbial populations

Biotechnol. J. 2017, 12

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Graphical Abstract (will be published online)

Review

Taking control over microbial populations: Current approaches for exploiting biological noise in bioprocesses Frank Delvigne, Jonathan Baert, Hosni Sassi, Patrick Fickers, Alexander Grünberger and Christian Dusny DOI 10.1002/biot.201600549

Biological noise has multiple sources and induces cell to cell variation in phenotypic efficiency (i.e., cell productivity, robustness…). Biological noise can now be captured through mechanistic models. These models can be used for increasing the efficiency of the single cell toolbox aiming at controlling bioprocess on the basis of population heterogeneity.

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Biotechnology Journal

Biotechnol. J. 2017, 12, 1600549

DOI 10.1002/biot.201600549


Review

Taking control over microbial populations: Current approaches for exploiting biological noise in bioprocesses Frank Delvigne1, Jonathan Baert1, Hosni Sassi1, Patrick Fickers1, Alexander Grünberger2,3 and Christian Dusny4 1University

of Liège, TERRA research center, Gembloux Agro-Bio Tech, Microbial Processes and Interactions (MiPI lab), Gembloux, Belgium 2Forschungszentrum Jülich GmbH, IBG-1: Biotechnology, Jülich, Germany 3Multiscale Bioengineering, Bielefeld University, 33615 Bielefeld, Germany 4Department Solar Materials, Helmholtz Centre for Environmental Research (UFZ), Leipzig, Germany

Phenotypic plasticity of microbial cells has attracted much attention and several research efforts have been dedicated to the description of methods aiming at characterizing phenotypic heterogeneity and its impact on microbial populations. However, different approaches have also been suggested in order to take benefit from noise in a bioprocess perspective, e.g. by increasing the robustness or productivity of a microbial population. This review is dedicated to outline these controlling methods. A common issue, that has still to be addressed, is the experimental identification and the mathematical expression of noise. Indeed, the effective interfacing of microbial physiology with external parameters that can be used for controlling physiology depends on the acquisition of reliable signals. Latest technologies, like single cell microfluidics and advanced flow cytometric approaches, enable linking physiology, noise, heterogeneity in productive microbes with environmental cues and hence allow correctly mapping and predicting biological behavior via mathematical representations. However, like in the field of electronics, signals are perpetually subjected to noise. If appropriately interpreted, this noise can give an additional insight into the behavior of the individual cells within a microbial population of interest. This review focuses on recent progress made at describing, treating and exploiting biological noise in the context of microbial populations used in various bioprocess applications.

Received Revised Accepted Accepted article online

26 JAN 2017 10 APR 2017 12 APR 2017 n

Keywords: Flow cytometry · Microbial stress · Microfluidics · Phenotypic heterogeneity · Single cell

1 Controlling microbial phenotypic heterogeneity: state-of-the-art Controlling cellular physiology in real-time, by triggering for example the synthesis of recombinant proteins or metabolites using environmental parameters, has long been considered as a biotechnological dream. However, advances made in synthetic biology allowed the creation of synthetic metabolic pathways that dynamically adapt to changes in cell physiology and also environmental factors [1–3]. In parallel, several technical advances rendered interfacing microbial cells to physical devices possible, by Correspondence: Frank Delvigne E-mail: [email protected] Abbreviations: , ;

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engineering of synthetic gene networks and coupling these to the cells native natural regulatory circuits [4]. However, it is important to highlight that both the physical (comprising mechanical, electrical and electronical parts) and the biological components to be interfaced are inherently noisy [5]. Over the past decades, our understanding of biological noise and its impact on microbial population heterogeneity has tremendously increased [6–9]. It can be stated that, at the actual stage of development, it is feasible to control the degree of microbial population heterogeneity for various applications, starting from the selection of high performers during bioprocesses to the selection of improved variants of production strains [10]. Also the choice of the regulatory system to be implemented has been shown to have an impact on intrapopulation heterogeneity [11]. At this stage, it is important to point out that several terms can be used for

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describing similar mechanisms. In a general way, it is considered that noise, or stochasticity (i.e. the cause) in intracellular biochemical reactions drives cell-to-cell heterogeneity (i.e. the consequence) [12]. Similar terms used for describing the consequences of noise are population heterogeneity or plasticity or diversification. However, if these examples shall be generalized to more case studies, additional efforts must be made at the level of characterizing and controlling the intensity of heterogeneity, as well as at the level of the development of new parts and devices that allow the on-site identification and exploitation of population heterogeneity. This need for a better characterization of microbial heterogeneity is not new and has been briefly evoked in a special issue of this journal dedicated to a previous ESBES symposium [13]. Additionally, there is a growing attention of the scientific community towards a better exploration of microbial individuality and biological noise since it has been demonstrated that noise confers functionality to the microbial population [14–16] notably through collectivism strategies [17, 18]. This review aims at highlighting existing technical solutions for the effective exploitation of biological noise for driving the production of chemical and biological compounds by microbial populations.

1.1 Molecular mechanisms of biological noise and predicting cellular collective behavior in response to extracellular perturbations One of the key challenges that has to be addressed for further improving the robustness of microbial bioprocesses is the impact of extracellular perturbations on population physiology. Indeed, when cultures are operated at large-scale, bioreactor inhomogeneities lead to the exposure of the microbial cells to a plethora of environmental perturbations, such as pH, nutrients, dissolved oxygen [19]. Existing studies that aimed at investigating the response of microbial cells to process-related environmental perturbations have been conducted at different levels: computational fluid dynamics for characterizing concentration gradients perceived by cells [20], design of scale-down reactors for reproducing large-scale mixing conditions at lab-scale [21, 22], investigating the transcriptional and translational response of cells in front of perturbations [23] and, ultimately, implementing metabolic engineering strategies for enhancing the robustness of microbial cells upon perturbations [24, 25]. More recently, efforts have been made for characterizing the effect of such perturbations at the single cell level [26]. The difficulty in such approaches relies on the fact that biomolecular networks are inherently noisy and potentially lead to different outcomes when considering individual cells belonging to the same population, such phenomena being often referred as microbial population heterogeneity.

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Basically, two important components leading to noise can be considered, i.e. extrinsic components that are related to global cellular and environmental factors such as variations in growth rate, polymerase activity, ribosome abundance in individual cells or extracellular cues and intrinsic components related to transcription, translation and regulation of specific genes [12, 27, 28]. It is important to consider a common basis for defining these components in both biological and engineering science. As a result, several molecular mechanisms can be advanced as drivers for biological noise (Fig. 1A). At first, biological noise depends mostly on the overall architecture of the gene regulatory network [29] but also on the promoter activity itself [7, 8, 30]. Promoter activity is the consequence of stochastic interactions between regulators and operators. Therefore, any modification of the promoter architecture, such as varying the number or strength of operators, may strongly affect the level of cellto-cell variability and thus the biological noise [31]. Gene expression results from interactions between operators with positive or negative regulators (i.e. activators and repressors, respectively). For weak promoters, a faster repressor dissociation rate occurs, leading to smaller fluctuations in mRNA transcript numbers [32]. In this condition, most cells express mRNA close to the population average and thus show a low level of biological noise. By contrast, strong operators release repressors at low rates, resulting in low fluctuations of the promoter state of activity. With this repression pattern, two extreme situations could be found. Similar situations can occur for highly induced promoters that switch off very rarely [7]. Regulatory mechanisms are more complex in the case of promoters with multi-operator sites. For promoters that feature two repressor binding sites, three scenarios must be considered in regard to the ability of repressors to bind to operators: (i) repressors bind independently, (ii) repressors bind cooperatively, (iii) a single repressor binds to the operators simultaneously by creating DNA secondary structure (i.e. loops). Sanchez et al. have shown that cooperative repression led to a higher level of noise compared to independent repression [31]. The simultaneous binding of the repressors on the two operators is a rare event. However, when it occurs, the two repressors stabilize each other, leading to a long-lasting repression event and thus a higher level of biological noise. In contrast to repression, induction of gene expression occurs when a regulatory protein (i.e. inducer) binds to the promoter from which they increase the rate of transcription. Similar to repressors, stochastic association and dissociation of activators cause fluctuations in the level of transcription rate. Strong inducers bind more tightly to the operators than weak ones, leading to lower dissociation rates and thus lower levels of biological noise. Gene expression is often an interplay between the action of repressors and inducers on the operator. Theoretical predictions have demonstrated (for a model considering a



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Figure 1. (A) Scheme showing the different components involved in noise generation at the level of a microbial cell. Extracellular perturbations account for the extrinsic component of noise. Intracellular processes exhibit an intrinsic component of noise (i.e. mainly due to the natural stochasticity of the biochemical reactions), but also for the extrinsic component of noise. Indeed, unevent content of biochemical species due notably to the impact of cell division, is also included in the extrinsic component of noise. Items in red in the figure represent biochemical species or molecular mechanisms which have been recognized as playing an important role at the level of both the intrinsic and extrinsic components of noise. (B) Fluorescent biosensors are widely used for investigated population heterogeneity generated by biological noise. However, genetically encoded biosensors are subjected to the same noise generating mechanisms as the natural genetic elements depicted in (A). Flu denotes the average fluorescence of a cell population.

simple induction-repression situation with two operators) that at low expression level, induction events create considerably more noise than repression [32]. Therefore, induction of gene expression by increasing the concentration of an activator leads to an increase in biological noise and a moderate expression level than reducing the repressor concentration. At high expression levels, the noise generated by inducers and repressors are of similar magnitude. In this case, regulation of gene expression can be achieved by modulating activators or repressors independently. In case of dual or multiple activation architectures, binding of inducers on operators could be independent or cooperative (i.e. the binding of the first regulatory protein facilitates the binding of the following ones). Most of the time, inducers interact with each other via protein-protein or protein-DNA interactions. This stabilizes the active state of the promoter and thus increases biological noise. Therefore, the presence of multiple operators increases the level of cell-to-cell variability and this effect is amplified in the case of cooperative binding of regulatory proteins on operators. It can be hence concluded that the strength of the operator is a major determinant of phenotypic noise. Strong operators are likely to cause more cell-to-cell variability in comparison to weak ones. These aspects should be considered when it comes to controlling biological noise for improving bioprocess robustness or efficiency. Besides initiation of transcription, other parameters such mRNA stability must be also considered for understanding and controlling biological noise. Indeed, the rate


of mRNA degradation can significantly affect cell-to-cell variability [33]. Fast mRNA degradation processes increase noise as compared to phenomenon that tend to stabilize mRNA (i.e. utilization of specific mRNA binding protein that protect them from degradation) and thus reduce noise. In eukaryotic yeast, chromatin regulation has been also shown to be involved in expression variability [34–36]. The addition of nucleosome-disfavoring sequences to yeast promoters allowed a reduction in the variability of gene expression. For instance, insertion of poly(dA:dT) into the promoter region disfavored nucleosome formation [37]. This simple measure renders the operator more accessible to regulator molecules, and in turn increases promoter dynamics and decreases expression variability. Dadiani et al. demonstrated this effect for a set of 22 promoters with different lengths of poly(dA:dT) parts located upstream of operator sites [34]. Another parameter that has to be considered for controlling biological noise is the structure of ribosome binding sites. Lower transcription levels result in an increased noise at the single cell level due to stochasticity in gene expression [38]. Quantitative studies have found that translational strengths are highly dependent on the sequences adjacent to ribosome binding sites (RBSs) [39, 40]. Therefore, translation initiation rate could be tuned by using ribosome binding site (RBS) spacer regions. The spacer regions consist of simple sequence repeats (SSR) between the Shine–Dalgarno sequence and the initiation codon of target gene. This has been experimentally demonstrated by Egbert and Klavins in E. coli by creating a

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GFP library containing (AT)6 to (AT)12 SSR [41]. Indeed, they found a relationship between the length of the SSR and the strength of the resulting RBS. Therefore metabolic engineering and synthetic biology approaches for controlling the biological noise must consider not only the promoter architecture of the gene to be expressed, but also the intracellular half-life of the corresponding mRNA and the architecture of the corresponding RBS. Since biological noise is resulting from a cascade of mechanisms (including molecular species abundance itself). It is thus actually difficult to setup rationale metabolic engineering strategies for controlling noise. Highthroughput studies are thus generally required in order to make the link between genetic structures and noise level. Indeed, beside the affinity for RBS, translation is also stochastically affected by mRNA secondary structure that affects binding of the ribosome and also the probability of unbinding or frameshifting [42]. Stochasticity in translation is also affected by the amount of molecular species involved in the reactions, such as the ribosome density or the amount of available tRNA [43]. For example, the ribosome density in E. coli cells correlates with the specific growth rate and varies between 6.8 × 103 ribosomes cell-1 at µ = 0.6 h-1 up to 72 × 103 ribosomes cell-1 at µ = 2.5 h-1. A correlation of the same magnitude can be found for the growth rate-dependent abundance of total tRNA copies per E. coli cell [44]. These aspects are particularly important in bioprocesses for the synthesis of recombinant proteins by various hosts. Population heterogeneity has been often investigated using genetically-encoded fluorescent biosensors [45]. The basic principle of biosensors relies on the synthesis of a fluorescent compound triggered by one of the mechanisms shown in Fig. 1A (promoter induction [46], riboswitches based on mRNA secondary structure [47], transcription factors [48], …). An ideal biosensor has to be specific and its response must be proportional to amplitude and frequency of the environmental stimulus or the intracellular process that has to be investigated. Although fluorescent biosensors typically scale with the magnitude of the stimulus or process, a real-world biosensor has to be synthesized upon onset or degraded upon alleviation of a specific stimulus via the biosynthetic machinery. The time exposure for these processes results in a temporal phase shift of the sensors output signal. This sensor-specific responsiveness of the biosensor has to be considered when using its signal for real-time control of population heterogeneity. However, developments in protein engineering of fluorescent biosensors realized to come closer to the ideal via fast-folding fluorescent proteins or the addition of protein tags for accelerated degradation [49, 50]. Besides scalable biosensors, synthetic biology also allows to engineer sensor circuits that release a digital output upon activation. Such biological logic gates can be used for recording order, timing and duration of input stimuli on the basis of specific single cell responses [51].

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With approaches like this, a heterogeneous response of the population can be translated into a signal for proper control of the population properties. Here, mathematical representations, such as stochastic models, allow for the acquisition of unique physiological features, e.g. information about the architecture of the transcriptional regulatory network [52].

1.2 The single cell toolbox In this section, we will review the toolbox of single cell analysis and the components that can be used for analyzing microbial population heterogeneity. Single cell technologies comprise experimental, but also in silico approaches that can be used for characterizing cell-to-cell differences in a microbial population. Since most of these components have been extensively reviewed in other papers [26, 53, 54], this section focuses on the different categories of parts and devices for analyzing cellular physiology and inherent noise in real-time. Combining single cell analysis tools with technological components for controlling population heterogeneity can be summarized with the term single cell loop. A typical device in this context comprises the following parts (Fig. 2): genetic parts (i.e. biosensors), (micro-) cultivation device, detection device (i.e. for the acquisition of the signal released from the biological system), data treatment and interpretation and, finally, an actuator allowing feedback control according to the biological signal. Genetic parts include tunable genetic devices (i.e. such as light switchable proteins that will be described in the next section) and biosensors, the latter being critical since it gives direct access to the physiological properties of the cells. However, these fields of research are too broad to be reviewed in detail here, but excellent review papers can be found in the literature [55–57]. Besides genetically-encoded biosensors, exogenous dyes can be used for deciphering microbial phenotypic heterogeneity. However, staining protocols are typically restricted to global physiological processes (i.e. membrane permeabilization, intracellular esterase activity) and potentially lead to artifacts [58]. However, after proper control experiments and an appropriate calibration procedure, some of these dyes enable the effective analysis of important physiological parameters such as the electron transport chain activity at a single cell resolution [59], or the determination of the extent of cell membrane permeabilization induced by nutrient limiting conditions in a bioreactor cultivation [22, 60]. Cultivation devices comprise either classical bioreactors or devices that have been especially designed for being compatible with single cell characterization, such as microfluidics single cell bioreactors [61] or microdroplet reactors [62]. Microfluidic approaches are typically favorable for exploring and quantifying cell heterogeneity, as they allow to stringently control the extracellular environment and hence enable to directly link the observed



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datory. In the case of flow cytometry data, measuring dynamics of the population suffers from the lack of knowledge at the level of the specific cell trajectories and physicochemical gradients within the cultivation volume. This is due to the fact that population heterogeneity is generally captured as a temporal physiological snapshot of the population [68]. In the case of microscopy imaging, environmental factors can be controlled, but image analysis can be tedious and prone to error when generating highthroughput physiological data [69, 70]. Given the fact that computational image analysis tools are now very effective, even for the investigation of the intracellular components of microbial cells [71], the experimental throughput of these methods is limited by extensive computational efforts. These critical issues imply new data processing strategies, which will be addressed in depth in Section 2. In order to control population heterogeneity, feedback loops are indispensable. Feedback loops include interfaced actuators and signals and enable the effective exploitation of biological noise for controlling cell physiology.

1.3 From single cell toolbox to single cell loop: using single cell data to control microbial populations

Figure 2. (A) Presentation of the single cell loop, or the actual devices and approaches that can be combined for analyzing/exploiting microbial population heterogeneity. (B) Example of the effective implementation of the single cell loop concept: use of on-line flow cytometry for controlling cell population h eterogeneity in continuous bioreactor.

cellular state with the properties of the extracellular environment [63]. Detection devices for single cell parameters are numerous, but the most interesting ones are those that can be used on-line or in real-time in order to assess population dynamics. Microscopy can be easily used in time-lapse mode, but its use is limited to cultivation devices that are compatible with optical measurements. Flow cytometry can also be used on-line, and several approaches have been designed to directly interface analytics and cultivation devices [64–66]. A perpetual challenge with these dynamic single cell analysis technologies remains at the level of the data treatment procedures in order to fine-tune cultivation parameters in response to the physiological state of the population [57, 67]. As can be seen, identifying and describing population heterogeneity or biological noise on the basis of single cell data is not a straightforward task. Proper adaptation of analytical and controlling technologies to the biological system investigated or the sensing technology involved is man-


Based on the single cell loop concept depicted in Fig. 2, it is possible to design fully automated devices for controlling microbial population heterogeneity for different applications, i.e. exploitation of noise for isolating clones with improved phenotypes [72, 73], guiding adaptive laboratory evolution (ALE) [74–77], and controlling bioprocesses based on the analysis of biological noise [66, 78–82]]. All these applications have been summarized in Table 1. During the past few years, several approaches have been developed based on different components of the single cell loop (Fig. 2). Table 1 summarizes the most important contributions in that field with regards to components used in single cell loops. An important example is the implementation of optogenetic control strategies, i.e. a biological technique based on the use of light for triggering intracellular reactions [82]. Typically, this technique relies on light sensitive proteins [83], light inducible two-component systems [84] or photo-caged inducers [85, 86]. In this context, a fully automated platform, comprising several modules of a single cell loop, has been designed for controlling protein production in E. coli [82]. In this study, recombinant protein GFP production in E. coli was controlled based on a light-switchable two-component system. Activation by green light led to the autophosphorylation of a sensor histidine kinase, which binds in its phosphorylated state to a specific promoter, which in turn triggers the production of GFP. The system can be reset by red light. By triggering successive pulses of red and green light, the extent of GFP production could be maintained at distinct levels in a given range of expression. The experimental system comprised a transparent bioreactor chamber that

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was interfaced with an automated on-line flow cytometry. Based on flow cytometry analyses, mean levels of intracellular GFP were estimated and the results were fed into a red/green light feedback control loop. By using a similar strategy, controlling the production of heterologous protein in Saccharomyces cerevisiae has been achieved based on optogenetic feedback control strategy [87]. In this study, the genetic device was based on a fusion of the Arabidopsis thaliana cryptochrome 2 and its interaction partner to a DNA-binding and a DNA activation domain, respectively. These two proteins are able to dimerize upon activation by blue light exposure, connecting the DNA-binding and the DNA-activation domain and driving the expression of the target protein. The experimental apparatus comprised a bioreactor chamber connected to a microfluidic device where cells could be imaged by fluorescence microscopy. Protein concentrations were measured based on a protein-YFP fusion and protein production was feedback controlled via blue light illumination. One key feature of the two abovementioned experimental devices is their ability to deliver single cell data via flow cytometry and microscopy imaging respectively. However, in the discussed studies, only mean signals were exploited in the feedback control loops. Both approaches have the potential to identify and also control the degree of intrapopulation heterogeneity in terms of gene expression based on single cell data via feedback control strategies. These studies demonstrate how population heterogeneity or statistical inference related to biological noise can be exploited for productive purposes. Besides optogenetic approaches, gene expression patterns can also be controlled in nearly real-time by the addition of inducing of repressing compounds. An excellent example constitutes the production of recombinant protein driven by the GAL1 promoter in S. cerevisiae. Here, a feedback loop strategy was applied that controlled the addition of inducing or repressing sugars [80, 88]. Both studies represent impressive examples of how feedback control strategies can be exploited for increasing the efficiency of heterologous protein production processes in yeast. Such strategies bear the potential to balance the level of protein biosynthesis in yeast and hence to decrease detrimental effects of the metabolic burden in bioprocesses. More generally, the above-described strategies can be extended to other biosensor/actuator combinations for improving bioprocesses on the basis of cell population heterogeneity (Table 1). Another impressive example is the control of E. coli populations for the production of serine based on a quorum sensing (QS) regulatory circuit [89]. On a similar basis (i.e. the use of a QS circuit), it was also possible to control noise levels of E. coli populations growing in biofilms [90]. This has been achieved by engineering the cells on the basis of a bistable switch, leading to a true digital response of the cells in front of environmental perturba-

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tions (Fig. 1). Such studies pave the way for a better understanding of the interactions in multicellular environments, but also give new insight on how to use single species biofilms as a basis for the design of continuous and robust bioprocesses [91]. A nice example at this level is the variation in the biocatalytic performances of Pseudomonas putida that is subjected to phenotypic variations [11, 92], and not to variation in the plasmid copy number of these engineered cells [93]. In this specific case, it has been shown that culturing these cells in biofilm reactors leads to improved robustness [94], probably due to alterations in the population structure. Taken altogether, these examples show that it is feasible to exploit noise for improving microbial bioprocesses (see Table 1 for a list of previous applications). This idea is not new, and has notably been expressed more than a decade ago, but merely on a theoretical basis [95]. However, the effective exploitation of noise is now feasible, given the advances made at the technical level, but also at the level of mechanistically understanding the drivers of phenotypic diversification. These findings have led to the design of robust metabolically engineered cells and the formulation of stochastic models that can be further used to control noise (Section 2).

1.4 Genetic engineering for controlling of microbial population heterogeneity Actually, a lot of efforts have to be given to the design of synthetic biological parts with fully characterized properties. In the sense of synthetic biology, new biological entities like gene circuits, catalytic proteins or even whole cells will be used in a modular fashion to synthesize new tools with predictable properties. A proper trait of microorganism is their ability to gather information form their surrounding environment (i.e. medium composition and culture conditions) and to adjust their metabolism accordingly by modifying their pattern of gene expression. Much of this regulation occurs at the initiation step of gene transcription by interactions of regulatory proteins (i.e. regulators) and specific promoter regions (i.e. operators) that induce or repress gene expression. This process has been demonstrated to be inherently stochastic, since the pathway leading from gene transcription to protein synthesis (i.e. translation) exhibits several random components, principally due to the low amount of the reacting species in the intracellular volume [12, 95]. In nature, stochastic mechanisms play a pivotal role in the cellular metabolism and its adaptation to environmental fluctuation (i.e. availability of nutrient sources) or the development of microbial persistence mechanisms in specific environmental niches [15, 18]. However, this variability could impair bioprocesses control and efficiency in term of yield, since producer and non-producer cell phenotypes can coexist [45]. A main reason for variability is biological noise, which comprises extrinsic and intrinsic compo-


Single cell loop

Application Adaptive evolution experiment (ALE)

Fed-bacth control

ALE

Drinking water treatment

Fed-batch control and production of recombinant proteins Induction of recombinant protein from the GAL1 expression system in yeast Control of growth rate in perfectly defined environment

Organism

S. cerevisiae

CHO cells

E. Coli

Mixed microbial species


P. pastoris E. coli S. cerevisiae

S. cerevisiae

P. pastoris H. polymorpha C. Glutamicum

Envirostat : microbial cells are immobilized in a microfluidic growth chamber by nDEP. Continuous feeding allows for the precise control of the micro-environment of the cells. Feedback control could be potentially used for improving growth conditions

Cultivation in microfluidic chamber. Cell population is characterized by imaging. Single cell data are used for feeding the culture either with glucose or galactose for controlling the level of induction of GAL1

On-line flow cytometry connected to fed-batch stirred bioreactor. Feedback control loop can be used for controlling nutrient and inducer feeding

On-line flow cytometry connected to disinfection facilities. Could potentially beneficiate from a feedback control loop in order to adjust disinfectant dosage

Morbidostat : continuous mini-bioreactor connected to optical density measurement. Antibiotic feeding is automatically adjusted based on cell density

Fed-batch stirred bioreactor with automated flow cytometry. Flow cytometry data are used for adjusting feeding of the bioreactor

Cytostat : continuous stirred bioreactor with automated flow cytometry. Flow cytometry data are used for adjusting dilution rate for facilitating the isolation of evolved strains

Devices

Fiore et al. [2015]

Dusny et al. [2012] Cell size, division frequency

Broger et al. [2011]

Hammes et al. [2012] Arnoldini et al. [2013]

Toprak et al. [2012]

Sitton et al. [2008]

Kacmar et al. [2006] Gilbert et al. [2009]

References

GFP fluorescence linked with GAL1 activity

Absolute cell count, FSC and GFP fluorescence

Absolute count and fluorescence intensity due to viability labelling

None. However, the morbidostat concept could potentially be significantly improved by the integration of single cell measurement since optical density is strongly affected by cell size and shape and can lead to strong biases

Viable cell count based on FSC/SSC signals

Absolute cell count

Single cell data

Table 1. summary of previous works involving single cell technologies for various microbiological applications. For each case study, the state of the single cell loop has been specified (Green symbol means a complete single cell loop with feedback control based on single cell data. Red symbol means an incomplete single cell loop with some of the elements displayed in Fig. 2A missing).

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Single cell loop

Table 1. continued

Application Evaluation of fitness of microbial population under fluctuating environmental conditions

ALE with automated isolation of evolved microbes ALE

Precise control of cell growth and gene expression

Organism

E. coli

E. coli

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E. coli S. cerevisiae

E. coli

Automated flow cytometry connected to a minibioreactor chamber. Gene expression is detected on the basis of GFP synthesis. Feedback control loop is achieved by illuminating the cultivation chamber for the activation of a light-inducible two-component system. The activation of the twocomponent system induces phosphorylation of CcaR which furhter binds to a promoter triggering the synthesis of GFP

VERT : several strains of the same species are tagged with three different fluorescent proteins. By using automated flow cytometry, the different subpopulations can be easily tracked and evolution can be visualized in real time when one of the sub-population increases due to the acquisition of evolved trait conferring a higher fitness

A specific continuous cultivation microchambers is used for investigating growth properties at a single cell resolution. Evolved strains are recovered by using optical tweezers

Chemoflux : a specific microfluidics chamber allowing the continuous culture of microlonies can be controlled for rapidly swithcing between carbon sources (i.e., glucose or lactose) and inducer (IPTG). The set-up allows for investigating memory effect at the level of the Lac operon

Devices

Probst et al. [2013]

Reyes et al.[2012] Almario et al. [2013]

Milias-Argeitis et al. [2016]

GFP, YFP and DsRed

GFP synthesis at the single cell level under the control of the cpcG2 promoter

Lambert and Kussell [2014]

Growth rate is measured by measuring the progression of the microcolonies in the microcultivation chambers

Cell size and division rate

References

Single cell data

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nents [12]. The extrinsic part of the biological noise is related to a cell-to-cell variability in terms of metabolic state, copy number of regulatory proteins, RNA polymerases, ribosomes, etc. Intrinsic noise is related to the stochasticity in the occurrence of biochemical reactions leading to the anabolism or catabolism of macromolecules (RNA, proteins) including gene activation or repression. Several experimental evidences suggest that intrinsic noise is the dominant source of cell-to-cell variability for intracellular mRNA concentration and thus for gene expression. This phenomenon has been described in bacteria, yeast and even in mammalian cells [5, 96, 97]. However, recent studies also demonstrated that the regulatory background of the microbial host can be a decisive factor for the development of heterogeneity in gene expression. Lindmeyer et al. could show that gene promoter elements in Pseudomonas sp., which were derived from the host itself or closely related strains from the same genus, are prone to inherent clonal variability in recombinant gene expression patterns [11]. The variability in gene expression resulted in varying biocatalytic activities of individual isogenic cultures. However, these effects could be minimized by employing orthogonal promoter systems that are unaffected by the hosts native regulatory background. This study demonstrates that matching host and expression system might be an appropriate measure for improving the efficiency of a microbial bioprocess via genetic control of population heterogeneity. Another important aspect of regulation is the quantitative link between the activity of genetic circuits and extracellular abundances of inducer and repressor molecules. In the sense of synthetic biology, a quantitative understanding of regulatory elements such as promoters is indispensable for a rational biological redesign and hence controlling heterogeneity and biological noise. Obtaining such knowledge with populations-based approaches can be difficult, as heterogeneity remains often masked behind the population average. Furthermore, a quantitative relation between extracellular conditions and gene expression can be biased by mixing effects and physicochemical gradients that result from the metabolic activity of the population [98]. Microfluidic technologies enable the analysis of gene expression dynamics in single microbes under stringently controlled environmental conditions. By uncoupling cells from population effects, mechanisms of promoter regulation can be studied in a quantitative manner. A recent study by Dusny et al. demonstrated the importance of microfluidics for studying regulatory phenomena in single cells by investigating the regulation of a methanol-specific promoter system (pMOX) by glucose-induced carbon catabolite repression in yeast [99]. In contrast to the previous assumption that pMOX is repressed at extracellular glucose concentrations of about 1 g L-1, it could be shown that pMOX in isolated cells was subjected to carbon catabolite repression at extracellular glucose concentrations



as low as 5 × 10-4 g L-1. These results demonstrate how constraining extracellular parameters can be exploited for understanding genetic circuits in single cells in a quantitative manner. Such knowledge could be valuable to accurately represent biological behavior in mathematical models.

2 Mathematical characterization of microbial population heterogeneity as prerequisite for the effective implementation of control strategies Single cell data led to new findings and enables new applications in the field of microbiology [15, 26]. However, the way in which single cell data are interpreted is not straightforward and there is an urgent need for a common mathematical formalism. More specifically, novel mathematical approaches are needed for interpreting and/or quantifying the degree of heterogeneity of a microbial population [100]. Finally, special attention has to be paid to the formal description of dynamics in terms of microbial population heterogeneity.

2.1 Approaches for quantifying microbial population heterogeneity Dynamics of microbial population heterogeneity can be followed either by using flow cytometry (FC) or dedicated microfluidic cultivation devices where cells can be tracked via time-lapse imaging [68]. Flow cytometry enables high-throughput characterization of cell suspensions, a feature being reinforced by the implementation of on-line platforms. This implementation is quite direct for dilute suspension of cells, and on-line FC have been successfully applied for monitoring microbial abundance and viability in drinking water facilities [79, 101]. Specific interfaces have also been developed for monitoring high cell densities by adding supplementary dilution modules [65, 102]. Actually, a lowcost and multiplexed version of such an interface is available for simultaneously monitoring several (mini)-bioreactors [64]. Based on that, we have discussed in Section 1.2 how FC measurements can be used to perform feedback control on microbial populations based on optogenetic control [82]. However, besides technical challenges, the use of automated or on-line FC for process control is quite rare due to the lack of an efficient and standardized mathematical formalism for expressing cell population heterogeneity. Indeed, it is quite easy to extract mean, median and standard deviations from FC data, but what happens when cell population distributions are bimodal or multimodal and what is the actual biological relevance of this heterogeneity? There are three basic approaches for expressing cell population heterogeneity from FC data (Fig. 3). The first

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Figure 3. Snapshots of different alternatives for monitoring microbial population heterogeneity in function of time by on-line flow cytometry. (A) Use of the mean-to-median ration. (B) Use of cytometry fingerprint. (C) Use of a scaling-law.

one (Fig. 3A) is based on the comparison of the mean and the median of cell parameter distributions. Indeed, mean and median are not necessarily following the same trends when cell populations shift from unimodal distributions towards bi- or multimodal distributions [64, 67]. A second approach (Fig. 3B) is based on a fingerprinting approach. In this case, each FC distribution is converted to a fingerprint, comprising given amounts of cells per bins or gate [103]. Such bins or gates can be automatically generated by using appropriate software, such as FlowFP, limiting

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deviations linked with manual operations [104]. This approach has been mainly used for describing mixed communities [105–107], but also bears potential for the treatment of dynamic FC data. However, the two abovementioned methods are not taking the biological meaning of the heterogeneity in cell property distributions into account. On the basis of genetically-encoded fluorescent biosensors it has been shown that noise in (i.e. the ratio between standard deviation and mean of the protein distribution) in protein expression scales with protein abun-



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dance. This scaling-law has been validated for E. coli [108] and S. cerevisiae [109] on the basis of genome-scale GFP clone libraries. On-line flow cytometry data can be directly integrated into this scaling-law, and it has been shown that GFP biosensors controlled by natural promotor systems follow the trends dictated by the scaling-law [110] (Fig. 3C). However, for non-natural, synthetic promoters, or for engineered GFP, this trend is not followed. These finding suggest that the noise-to-phenotype link is determined by many more parameters than just promotor strength (see Fig. 1 for a description of known molecular drivers for biological noise). This fact encourages the use of more sophisticated data treatment approaches, notably based on stochastic models in order to gain deeper insight into the mechanistic basis of biological noise and its impact on cell population heterogeneity. A different approach for the acquisition of single cell data as a function of time relies on the use of dedicated microfluidics cultivation tools. Numerous designs of microfluidic bioreactors can be found in the literature that allow the isolation, cultivation and analysis of single cells or microcolonies under manipulable environmental conditions. However, two specific designs will be described more precisely in this section due to their potential for the cultivation of cells in defined micro-environments. The first device has been developed by the team of Grünberger and Kohlheyer [111]. Computational fluid dynamics has been used in order to confirm that the microcolonies generated from a single cell in this device can be cultivated in a perfectly controlled environment [112]. However, even in constant environmental conditions, strong cell-to-cell variations have been observed. The second device has been developed by the team of Dusny and Schmid [113]. The Envirostat microfluidic bioreactor enables the isolation and cultivation of a single cell in a continuous medium flow. Cell trapping is implemented by contactless negative dielectrophesis. With this device, intimate connections of specific growth rates of individual microbial cells and environmental parameters could be obtained. Although this technological approach is limited in analytical throughput, it is capable of delivering highly relevant single cell data by maximizing the degrees of analytical freedom. The central advantage of microfluidic devices for single cell analysis is the differentiation between extrinsic and intrinsic sources of biological noise by environmental control.

2.2 Linking single cell experiments and stochastic simulation: towards a better understanding of biological noise and its effects We have shown in the previous section that the mathematical tools for the appropriate treatment of dynamic single cell data are not straightforward. A suitable approach to treat single cell data can be found at the level



of modeling and simulation tools. Indeed, it is well known that biological complexity, among which biological noise and the resulting population heterogeneity is an important component, can be captured through an interplay between modeling and experimentation [114]. This is notably a foundational principle of synthetic biology, since synthetic genetic parts and devices can be easily modeled and experimentally characterized. In this context, single cell behavior can be captured through stochastic models [33, 115]. The probabilistic nature of biochemical reactions can be expressed through a chemical master equation, compiling the different probability densities that a given intracellular reaction occurs at a given time interval. However, this equation cannot be solved analytically and requires the use of numerical simulations. Several options exist at this level, but the most widely used is the Gillespie algorithm allowing direct simulation of the chemical master equation [116]. Since this approach requires intensive computational efforts, alternatives are now being investigated. The simplest one being the use of the Langevin equation, consisting in adding a random variable into the deterministic differential equation expressing the mass balance of an intracellular species (typically a protein) in order to take cell-to-cell variability into account [95]. However, the use of the Langevin equation does not lead to the estimation of the probability densities. That is why many efforts have been provided in order to reduce the computational effort required for running simulations on the basis of the Gillespie algorithm, e.g. by using the tau-leap approach [117] or the finite-state projection approach [118]. The interest in using advanced stochastic models relies in their capacity to be used as effective tools in order to find the signal behind the noise [9, 52, 119]. Indeed, proper model formulation helps in finding the mechanistic basis of gene regulation by integrating the natural stochasticity in gene expression, which, in turn can be used to drive feedback controller for controlling population heterogeneity during cultivation (Fig. 3). At the fundamental level, this approach has notably been used for proving the importance of phenotypic diversification of Pseudomonas putida in soil for xylene degradation. The use of a combination of experimental approach (flow cytometry) and a stochastic model provided new insights in the genetic regulation of the TOL network components and their stochastic induction by aromatic compounds. In a second study, the same approach has been used for a better understanding of the carbon central metabolism in Bacillus subtilis [8]. Anew, combing experimental (based on time-lapse microscopy in this case) and stochastic modeling approaches led to a better understanding of the switch between glycolysis and gluconeogenesis in this organism. This combination of experimental/modeling approaches could be virtually applied to all the basic concepts of microbiology for revisiting central biological dogmata. This

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has been done notably for re-interpreting diauxic shift [120–122], quorum sensing [123] or persistence [124]. On the applied side, it has also been demonstrated that single cell technologies can be helpful for controlling population heterogeneity in the context of bioprocesses [45, 57]. One key issue at this level is the calibration of the single cell data. Indeed, most of the experimental data are expressed in arbitrary units (i.e. relative fluorescence unit RFU), which makes it difficult to directly compare it with model results, which are typically expressed in physical units (i.e. number of proteins/mRNA per cell). However, expressing the results on a physical basis would indeed help researchers in comparing data acquired with different equipment (i.e. comparison of different flow cytometers, or comparison between flow cytometry and microscopic imaging), which is one important prerequisite for advances in synthetic biology [125]. Calibration beads, as well as specific calibration workflows are available both for flow cytometry [126] and time-lapse microscopy [127]. By using these dedicated calibration workflows, it is possible to convert fluorescence units into molecules of equivalent fluorophore (MEF), which can then be extended to specific tags, such as GFP, if standards are available. Additionally, proper quantification of intracellular species gives access to databases, such as the bionumbers database [128], allowing the description of the main cellular processes on a mathematical basis. For example, protein abundance for E. coli is approximately 3.106 proteins/cell [129], among which some very abundant proteins (e.g. the murein lipoprotein Lpp) can be found at more than 105 copies/cell. Quantification and visualization of protein investment at the single cell level can be achieved through the use of a computational tool called Proteomap [130]. Such data are very important in order to assess the representativeness of the stochastic models presented above. In a bioprocess perspective, the specific abundance of protein per cell could be used as a proxy for estimating the metabolic burden associated with heterologous protein production.

3 Label free technologies We have shown that one of the bottlenecks of single cell technologies for controlling population heterogeneity is related to the use of optical signals for the phenotypic characterization of cells, requiring genetic modifications of the host. However, such genetic modifications are not possible for all biotechnological applications due to various reasons such as safety regulations and cost-intensive inactivation of the genetically modified biomass. Therefore label-free approaches are necessary for further expanding the applicability of single cell technologies for bioprocess development. A feasible label-free approach for characterizing single cells is sequencing their DNA and RNA. Via amplification, even low-abundance RNA

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species can be amplified and quantified via next generation sequencing approaches. However, besides the fact that stunning preliminary results have been obtained such as the characterization of the metabolic flexibility of population of the giant bacteria Thiomargarita [131], single cell sequencing is still challenging due to the relatively low abundance of intracellular analyte species (except for large bacteria such as Thiomargarita, which remains an exception to the general rule), and its susceptibility to bias arising from exogenous nucleotide contaminations. A promising approach for sequencing of single microbes constitutes the microfluidic segregation of individual cells into liquid compartments. Single cell sequencing of microbes can be efficiently performed in dedicated droplet microfluidic tools, as the many basic sample processing steps, such as addition of liquids, droplet splitting and spotting, can be performed with high precision. However, up to now droplet technologies have been mainly used for discovering new enzymatic functions from environmental samples by metagenomic approaches [132, 133]. This demonstrates that we are still afar from using droplet technologies for the high-throughput transcriptomic characterization of clonal populations that consist of hundreds of thousands of individual microbes (which is the common sampling range that can be actually investigated by the available single cell toolbox described in Section 1). Actually, two label-free single cell technologies are evaluated for microbial systems, i.e. confocal Raman microspectroscopy (CRM) and secondary ion mass spectrometry (SIMS). These methods are of outstanding interest considering the fact that they are label-free and can be applied at a high spatial resolution. CRM can be used to evaluate cellular composition (i.e. mainly DNA, lipids and protein composition) at the single cell level on the basis of their Raman spectra [134]. This technique has been successfully used to make the distinction between planktonic and biofilm cells for several environmental isolates, including Pseudomonas species [135]. High resolution imaging with secondary ion mass spectrometry (SIMS) allows to perform isotope probing at a single cell level, leading to the determination of metabolic activity of specific microbial cells in mixed cultures [136].

4 Concluding remarks Single cell technologies hold great potential for a better understanding of population dynamics, in both engineered technical environments and natural ecosystems. Technologies and related equipment are actually available for controlling the degree of population heterogeneity. However, such technologies require several toolboxes (Fig. 1) and the proof-of-principle has been demonstrated only for specific case studies. In order to expand these approaches to a broader set of applications, efforts have to be made at three



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levels. First, at the level of biosensing strategies, reliable signals from single cells are required for describing the physiological state of the population. Synthetic biology has recently expanded the set of biosensors available by designing artificial biological parts with known behavior. Second, effective actuators are needed for controlling the state of the population according to the signal delivered by the biosensor. To date, only light has been demonstrated as an effective actuator, but the range of actuators has to be expanded. For example, simple standardized actuators (i.e. valves, pumps, …) that are installed on bioreactors by default, could be used to control the degree of heterogeneity via advanced feeding strategies. Third, population heterogeneity needs standardization in terms of nomenclature and mathematical representation. Furthermore, more efficient data treatment procedures have to be developed. Sophisticated approaches such as fingerprinting strategies and advanced stochastic simulation procedures are available, but need to be adapted to questions of bioprocess engineering and control strategies in this context. Most of the technical drawbacks linked with actual microbial single cell technologies have to be attributed to the use of optical signals that require staining prior to analysis or genetic transformation of the strain. Label free technologies present a promising alternative in this respect but need further development in order to be effectively applied for studying phenotypic heterogeneity in microbial populations. The examples discussed in this review already give hints on the enormous potential of single cell-based control strategies for bioprocessing. Phenotypic intrapopulational heterogeneity due to noise is a fact and its role for increasing the productive output of a population or its robstuness against detrimental environmental effects is currently underrated. With responsive biosensors and suitable actuators, fine-tuning of regulatory circuits that govern gene expression patterns in microbial populations might enable controlling cellular heterogeneity in near real time and on demand. Conceivable scenarios include triggering and maintaining customized heterogeneity patterns for different production processes or phases of a bioprocess. Moreover, single cell-based control strategies might be applied to compensate for scale effects that occur upon transferring processes from lab to production scale. We are convinced that the current technical and also conceptual challenges that are associated with single cell technologies for controlling intrapopulation heterogeneity will be overcome in future and finally lead to a more efficient application of microbial cells as natural catalysts in technical environments.


Figure 4. Practical informations extracted from the analysis of microbial population dynamics and associated biological noise. Proper treatment of single cell data allows to get usefull insigths about underlying mechanistic basis of gene expression and regulation. These informations can in turn be used for process control.

FD and HS are supported by a research grant from the Wagralim-Biowin cluster of excellence in Wallonia, Belgium (Single Cells project n° 7273). JB is supported by a PhD grant provided by the Belgian Fund for Scientific Research (FRS-FNRS, PDR n° T.0250.13). AG is supported by a postdoctoral grant provided by the Helmholtz Association (PD-311). The authors declare no conflict of interest.


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Frank Delvigne is actually professor in microbial biotechnology at the University of Liège (Belgium) and director of the FoodIsLife platform (TERRA research center). This platform comprises several tools dedicated to specific experiments in applied microbiology, including on-line flow cytometers and scale-up/down pilot-scale facilities (with bioreactors ranging from 20 mL to 2000 L). His current research topics are focused on the analysis and control of microbial populations in different contexts, i.e. impact of environmental perturbations on cellular dynamics, single species biofilm and microbial interactions.

Christian Dusny received his Ph.D. degree from the Department of Biochemical and Chemical Engineering, TU Dortmund University. He is currently heading the laboratory of microbial single cell analysis at the Department Solar Materials, Helmholtz Centre for Environmental Research UFZ, Leipzig. His current research is focusing on the analysis of microbial biocatalysts with microfluidic single cell technologies; in particular, he is interested in quantifying mass and energy flow in single microbial photocatalysts.

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