Analyzing Microbial Population Heterogeneity—Expanding the Toolbox of Microfluidic Single-Cell Cultivations

Microfluidic approaches in microbial ecology

Microbial life is at the heart of many diverse environments and regulates most natural processes, from the functioning of animal organs to the cycling of global carbon. Yet, the study of microbial ecology is often limited by challenges in visualizing microbial processes and replicating the environmental conditions under which they unfold. Microfluidics operates at the characteristic scale at which microorganisms live and perform their functions, thus allowing for the observation and quantification of behaviors such as growth, motility, and responses to external cues, often with greater detail than classical techniques. By enabling a high degree of control in space and time of environmental conditions such as nutrient gradients, pH levels, and fluid flow patterns, microfluidics further provides the opportunity to study microbial processes in conditions that mimic the natural settings harboring microbial life. In this review, we describe how recent applications of microfluidic systems to microbial ecology have enriched our understanding of microbial life and microbial communities. We highlight discoveries enabled by microfluidic approaches ranging from single-cell behaviors to the functioning of multi-cellular communities, and we indicate potential future opportunities to use microfluidics to further advance our understanding of microbial processes and their implications.

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.

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.

Validated In Silico Population Model of Escherichia coli

Flux balance analysis (FBA) and ordinary differential equation models have been instrumental in depicting the metabolic functioning of a cell. Nevertheless, they demonstrate a population's average behavior (summation of individuals), thereby portraying homogeneity. However, living organisms such as Escherichia coli contain more biochemical reactions than engaging metabolites, making them an underdetermined and degenerate system. This results in a heterogeneous population with varying metabolic patterns. We have formulated a population systems biology model that predicts this degeneracy by emulating a diverse metabolic makeup with unique biochemical signatures. The model mimics the universally accepted experimental view that a subpopulation of bacteria, even under normal growth conditions, renders a unique biochemical state, leading to the synthesis of metabolites and persister progenitors of antibiotic resistance and biofilms. We validate the platform's predictions by producing commercially important heterologous (isobutanol) and homologous (shikimate) metabolites. The predicted fluxes are tested in vitro resulting in 32- and 42-fold increased product of isobutanol and shikimate, respectively. Moreover, we authenticate the platform by mimicking a bacterial population in the presence of glyphosate, a metabolic pathway inhibitor. Here, we observe a fraction of subsisting persisters despite inhibition, thus affirming the signature of a heterogeneous populace. The platform has multiple uses based on the disposition of the user.

Microfluidics as an Emerging Platform for Exploring Soil Environmental Processes: A Critical Review

Investigating environmental processes, especially those occurring in soils, calls for innovative and multidisciplinary technologies that can provide insights at the microscale. The heterogeneity, opacity, and dynamics make the soil a "black box" where interactions and processes are elusive. Recently, microfluidics has emerged as a powerful research platform and experimental tool which can create artificial soil micromodels, enabling exploring soil processes on a chip. Micro/nanofabricated microfluidic devices can mimic some of the key features of soil with highly controlled physical and chemical microenvironments at the scale of pores, aggregates, and microbes. The combination of various techniques makes microfluidics an integrated approach for observation, reaction, analysis, and characterization. In this review, we systematically summarize the emerging applications of microfluidic soil platforms, from investigating soil interfacial processes and soil microbial processes to soil analysis and high-throughput screening. We highlight how innovative microfluidic devices are used to provide new insights into soil processes, mechanisms, and effects at the microscale, which contribute to an integrated interrogation of the soil systems across different scales. Critical discussions of the practical limitations of microfluidic soil platforms and perspectives of future research directions are summarized. We envisage that microfluidics will represent the technological advances toward microscopic, controllable, and in situ soil research.

Simulating single-cell metabolism using a stochastic flux-balance analysis algorithm

Stochasticity from gene expression in single cells is known to drive metabolic heterogeneity at the level of cellular populations, which is understood to have important consequences for issues such as microbial drug tolerance and treatment of human diseases like cancer. Despite considerable advancements in profiling the genomes, transcriptomes, and proteomes of single cells, it remains difficult to experimentally characterize their metabolism at the genome scale. Computational methods could bridge this gap toward a systems understanding of single-cell biology. To address this challenge, we developed stochastic simulation algorithm with flux-balance analysis embedded (SSA-FBA), a computational framework for simulating the stochastic dynamics of the metabolism of individual cells using genome-scale metabolic models with experimental estimates of gene expression and enzymatic reaction rate parameters. SSA-FBA extends the constraint-based modeling formalism of metabolic network modeling to the single-cell regime, enabling simulation when experimentation is intractable. We also developed an efficient implementation of SSA-FBA that leverages the topology of embedded flux-balance analysis models to significantly reduce the computational cost of simulation. As a preliminary case study, we built a reduced single-cell model of Mycoplasma pneumoniae and used SSA-FBA to illustrate the role of stochasticity on the dynamics of metabolism at the single-cell level.

Yeast cell segmentation in microstructured environments with deep learning

Cell segmentation is a major bottleneck in extracting quantitative single-cell information from microscopy data. The challenge is exasperated in the setting of microstructured environments. While deep learning approaches have proven useful for general cell segmentation tasks, previously available segmentation tools for the yeast-microstructure setting rely on traditional machine learning approaches. Here we present convolutional neural networks trained for multiclass segmenting of individual yeast cells and discerning these from cell-similar microstructures. An U-Net based semantic segmentaiton approach, as well as a direct instance segmentation approach with a Mask R-CNN are demonstrated. We give an overview of the datasets recorded for training, validating and testing the networks, as well as a typical use-case. We showcase the methods' contribution to segmenting yeast in microstructured environments with a typical systems or synthetic biology application. The models achieve robust segmentation results, outperforming the previous state-of-the-art in both accuracy and speed. The combination of fast and accurate segmentation is not only beneficial for a posteriori data processing, it also makes online monitoring of thousands of trapped cells or closed-loop optimal experimental design feasible from an image processing perspective. Code is and data samples are available at https://git.rwth-aachen.de/bcs/projects/tp/multiclass-yeast-seg.

Next-Generation Antimicrobial Resistance Surveillance System Based on the Internet-of-Things and Microfluidic Technique

Antimicrobial resistance (AMR) of foodborne pathogens is a global crisis in public health and economic growth. A real-time surveillance system is key to track the emergence of AMR bacteria and provides a comprehensive AMR trend from farm to fork. However, current AMR surveillance systems, which integrate results from multiple laboratories using the conventional broth microdilution method, are labor-intensive and time-consuming. To address these challenges, we present the internet of things (IoT), including colorimetric-based microfluidic sensors, a custom-built portable incubator, and machine learning algorithms, to monitor AMR trends in real time. As a top priority microbe that poses risks to human health, Campylobacter was selected as a bacterial model to demonstrate and validate the IoT-assisted AMR surveillance. Image classification with convolution neural network ResNet50 on the colorimetric sensors achieved an accuracy of 99.5% in classifying bacterial growth/inhibition patterns. The IoT was used to carry out a small-scale survey study, identifying eight Campylobacter isolates out of 35 chicken samples. A 96% agreement on Campylobacter AMR profiles was achieved between the results from the IoT and the conventional broth microdilution method. The data collected from the intelligent sensors were transmitted from local computers to a cloud server, facilitating real-time data collection and integration. A web browser was developed to demonstrate the spatial and temporal AMR trends to end-users. This rapid, cost-effective, and portable approach is able to monitor, assess, and mitigate the burden of bacterial AMR in the agri-food chain.

Advances and opportunities in image analysis of bacterial cells and communities

The cellular morphology and sub-cellular spatial structure critically influence the function of microbial cells. Similarly, the spatial arrangement of genotypes and phenotypes in microbial communities has important consequences for cooperation, competition, and community functions. Fluorescence microscopy techniques are widely used to measure spatial structure inside living cells and communities, which often results in large numbers of images that are difficult or impossible to analyze manually. The rapidly evolving progress in computational image analysis has recently enabled the quantification of a large number of properties of single cells and communities, based on traditional analysis techniques and convolutional neural networks. Here, we provide a brief introduction to core concepts of automated image processing, recent software tools and how to validate image analysis results. We also discuss recent advances in image analysis of microbial cells and communities, and how these advances open up opportunities for quantitative studies of spatiotemporal processes in microbiology, based on image cytometry and adaptive microscope control.

High concentrations of dissolved biogenic methane associated with cyanobacterial blooms in East African lake surface water

The contribution of oxic methane production to greenhouse gas emissions from lakes is globally relevant, yet uncertainties remain about the levels up to which methanogenesis can counterbalance methanotrophy by leading to CH₄ oversaturation in productive surface waters. Here, we explored the biogeochemical and microbial community variation patterns in a meromictic soda lake, in the East African Rift Valley (Kenya), showing an extraordinarily high concentration of methane in oxic waters (up to 156 µmol L⁻¹). Vertical profiles of dissolved gases and their isotopic signature indicated a biogenic origin of CH₄. A bloom of Oxyphotobacteria co-occurred with abundant hydrogenotrophic and acetoclastic methanogens, mostly found within suspended aggregates promoting the interactions between Bacteria, Cyanobacteria, and Archaea. Moreover, aggregate sedimentation appeared critical in connecting the lake compartments through biomass and organic matter transfer. Our findings provide insights into understanding how hydrogeochemical features of a meromictic soda lake, the origin of carbon sources, and the microbial community profiles, could promote methane oversaturation and production up to exceptionally high rates.

Fazi et al. report on an extraordinarily high biogenic methane concentration detected in the surface water of Lake Sonachi, Kenya. Using gas chromatography and microbiome profiling, they determine that these high concentrations are associated with cyanobacterial blooms and help provide insight to methanogenesis in meromictic soda lakes.

Spatiotemporal dynamics of growth and death within spherical bacterial colonies

Bacterial growth within colonies and biofilms is heterogeneous. Local reduction of growth rates has been associated with tolerance against various antibiotics. However, spatial gradients of growth rates are poorly characterized in three-dimensional bacterial colonies. Here, we report two spatially resolved methods for measuring growth rates in bacterial colonies. As bacteria grow and divide, they generate a velocity field that is directly related to the growth rates. We derive profiles of growth rates from the velocity field and show that they are consistent with the profiles obtained by single-cell-counting. Using these methods, we reveal that even small colonies initiated with a few thousand cells of the human pathogen Neisseria gonorrhoeae develop a steep gradient of growth rates within two generations. Furthermore, we show that stringent response decelerates growth inhibition at the colony center. Based on our results, we suggest that aggregation-related growth inhibition can protect gonococci from external stresses even at early biofilm stages.

Bacterial phenotypic heterogeneity in DNA repair and mutagenesis

Genetically identical cells frequently exhibit striking heterogeneity in various phenotypic traits such as their morphology, growth rate, or gene expression. Such non-genetic diversity can help clonal bacterial populations overcome transient environmental challenges without compromising genome stability, while genetic change is required for long-term heritable adaptation. At the heart of the balance between genome stability and plasticity are the DNA repair pathways that shield DNA from lesions and reverse errors arising from the imperfect DNA replication machinery. In principle, phenotypic heterogeneity in the expression and activity of DNA repair pathways can modulate mutation rates in single cells and thus be a source of heritable genetic diversity, effectively reversing the genotype-to-phenotype dogma. Long-standing evidence for mutation rate heterogeneity comes from genetics experiments on cell populations, which are now complemented by direct measurements on individual living cells. These measurements are increasingly performed using fluorescence microscopy with a temporal and spatial resolution that enables localising, tracking, and counting proteins with single-molecule sensitivity. In this review, we discuss which molecular processes lead to phenotypic heterogeneity in DNA repair and consider the potential consequences on genome stability and dynamics in bacteria. We further inspect these concepts in the context of DNA damage and mutation induced by antibiotics.

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.

DNA recovery from Droplet Digital™ PCR emulsions using liquid nitrogen

Droplet microfluidics is a technology that enables the production and manipulation of small volumes. In biosciences, the most popular application of this technology is Droplet Digital™ PCR (ddPCR™), where parallel nanoliter-scale PCR assays are used to provide a high sensitivity and specificity for DNA detection. However, the recovery of PCR products for downstream applications such as sequencing can be challenging due to the droplets' stability. Here we compared five methods for disrupting the droplets to recover DNA. We found that rapid freezing in liquid nitrogen results in a clear phase separation and recovery of up to 70% of the DNA content. Liquid nitrogen freezing can thus offer a simple and environmentally friendly protocol for recovering DNA from ddPCR.

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

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

Challenges and approaches in assessing the interplay between microorganisms and their physical micro-environments

Spatial structure over scales ranging from nanometres to centimetres (and beyond) varies markedly in diverse habitats and the industry-relevant settings that support microbial activity. Developing an understanding of the interplay between a structured environment and the associated microbial processes and ecology is fundamental, but challenging. Several novel approaches have recently been developed and implemented to help address key questions for the field: from the use of imaging tools such as X-ray Computed Tomography to explore microbial growth in soils, to the fabrication of scratched materials to examine microbial-surface interactions, to the design of microfluidic devices to track microbial biofilm formation and the metabolic processes therein. This review discusses new approaches and challenges for incorporating structured elements into the study of microbial processes across different scales. We highlight how such methods can be pivotal for furthering our understanding of microbial interactions with their environments.

Microfluidic platforms for the dynamic characterisation of synthetic circuitry

Generating novel functionality from well characterised synthetic parts and modules lies at the heart of synthetic biology. Ideally, circuitry is rationally designed in silico with quantitatively predictive models to predetermined design specifications. Synthetic circuits are intrinsically stochastic, often dynamically modulated and set in a dynamic fluctuating environment within a living cell. To build more complex circuits and to gain insight into context effects, intrinsic noise and transient performance, characterisation techniques that resolve both heterogeneity and dynamics are required. Here we review recent advances in both in vitro and in vivo microfluidic technologies that are suitable for the characterisation of synthetic circuitry, modules and parts.

Microfluidic Single-Cell Analytics

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

Comparison of Escherichia coli surface attachment methods for single-cell microscopy

For in vivo, single-cell imaging bacterial cells are commonly immobilised via physical confinement or surface attachment. Different surface attachment methods have been used both for atomic force and optical microscopy (including super resolution), and some have been reported to affect bacterial physiology. However, a systematic comparison of the effects these attachment methods have on the bacterial physiology is lacking. Here we present such a comparison for bacterium Escherichia coli, and assess the growth rate, size and intracellular pH of cells growing attached to different, commonly used, surfaces. We demonstrate that E. coli grow at the same rate, length and internal pH on all the tested surfaces when in the same growth medium. The result suggests that tested attachment methods can be used interchangeably when studying E. coli physiology.

The Rocky Road From Fed-Batch to Continuous Processing With E. coli

Escherichia coli still serves as a beloved workhorse for the production of many biopharmaceuticals as it fulfills essential criteria, such as having fast doubling times, exhibiting a low risk of contamination, and being easy to upscale. Most industrial processes in E. coli are carried out in fed-batch mode. However, recent trends show that the biotech industry is moving toward time-independent processing, trying to improve the space-time yield, and especially targeting constant quality attributes. In the 1950s, the term "chemostat" was introduced for the first time by Novick and Szilard, who followed up on the previous work performed by Monod. Chemostat processing resulted in a major hype 10 years after its official introduction. However, enthusiasm decreased as experiments suffered from genetic instabilities and physiology issues. Major improvements in strain engineering and the usage of tunable promotor systems facilitated chemostat processes. In addition, critical process parameters have been identified, and the effects they have on diverse quality attributes are understood in much more depth, thereby easing process control. By pooling the knowledge gained throughout the recent years, new applications, such as parallelization, cascade processing, and population controls, are applied nowadays. However, to control the highly heterogeneous cultivation broth to achieve stable productivity throughout long-term cultivations is still tricky. Within this review, we discuss the current state of E. coli fed-batch process understanding and its tech transfer potential within continuous processing. Furthermore, the achievements in the continuous upstream applications of E. coli and the continuous downstream processing of intracellular proteins will be discussed.

Microfluidic cultivation and analysis tools for interaction studies of microbial co-cultures

Microbial consortia are fascinating yet barely understood biological systems with an elusive intrinsic complexity. Studying microbial consortia and the interactions of their members is of major importance for the understanding, engineering and control of synthetic and natural microbial consortia. Microfluidic cultivation and analysis devices are versatile tools for the study of microbial interactions at the single-cell level. While there is a vast amount of literature on microfluidics for the investigation of monocultures only few studies on co-cultures have been conducted in this context. Here we give an overview of different microfluidic single-cell cultivation tools for the analysis of microbial consortia with a focus on their physiology, growth dynamics and cellular interactions. Finally, central challenges and perspectives for the future application of microfluidic tools for microbial consortia investigations will be given.