Monitoring single-cell gene regulation under dynamically controllable conditions with integrated microfluidics and software

Scaling of stochastic growth and division dynamics: A comparative study of individual rod-shaped cells in the Mother Machine and SChemostat platforms

Microfluidic platforms enable long-term quantification of stochastic behaviors of individual bacterial cells under precisely controlled growth conditions. Yet, quantitative comparisons of physiological parameters and cell behaviors of different microorganisms in different experimental and device modalities is not available due to experiment-specific details affecting cell physiology. To rigorously assess the effects of mechanical confinement, we designed, engineered, and performed side-by-side experiments under otherwise identical conditions in the Mother Machine (with confinement) and the SChemostat (without confinement), using the latter as the ideal comparator. We established a protocol to cultivate a suitably engineered rod-shaped mutant of Caulobacter crescentus in the Mother Machine and benchmarked the differences in stochastic growth and division dynamics with respect to the SChemostat. While the single-cell growth rate distributions are remarkably similar, the mechanically confined cells in the Mother Machine experience a substantial increase in interdivision times. However, we find that the division ratio distribution precisely compensates for this increase, which in turn reflects identical emergent simplicities governing stochastic intergenerational homeostasis of cell sizes across device and experimental configurations, provided the cell sizes are appropriately mean-rescaled in each condition. Our results provide insights into the nature of the robustness of the bacterial growth and division machinery.

Yeast cell responses and survival during periodic osmotic stress are controlled by glucose availability

Natural environments of living organisms are often dynamic and multifactorial, with multiple parameters fluctuating over time. To better understand how cells respond to dynamically interacting factors, we quantified the effects of dual fluctuations of osmotic stress and glucose deprivation on yeast cells using microfluidics and time-lapse microscopy. Strikingly, we observed that cell proliferation, survival, and signaling depend on the phasing of the two periodic stresses. Cells divided faster, survived longer, and showed decreased transcriptional response when fluctuations of hyperosmotic stress and glucose deprivation occurred in phase than when the two stresses occurred alternatively. Therefore, glucose availability regulates yeast responses to dynamic osmotic stress, showcasing the key role of metabolic fluctuations in cellular responses to dynamic stress. We also found that mutants with impaired osmotic stress response were better adapted to alternating stresses than wild-type cells, showing that genetic mechanisms of adaptation to a persistent stress factor can be detrimental under dynamically interacting conditions.

Design and Model-Driven Analysis of Synthetic Circuits with the Staphylococcus aureus Dead-Cas9 (sadCas9) as a Programmable Transcriptional Regulator in Bacteria

Synthetic circuit design is crucial for engineering microbes that process environmental cues and provide biologically relevant outputs. To reliably scale-up circuit complexity, the availability of parts toolkits is central. Streptococcus pyogenes (sp)-derived CRISPR interference/dead-Cas9 (CRISPRi/spdCas9) is widely adopted for implementing programmable regulations in synthetic circuits, and alternative CRISPRi systems will further expand our toolkits of orthogonal components. Here, we showcase the potential of CRISPRi using the engineered dCas9 from Staphylococcus aureus (sadCas9), not previously used in bacterial circuits, that is attractive for its low size and high specificity. We designed a collection of ∼20 increasingly complex circuits and variants in Escherichia coli, including circuits with static function like one-/two-input logic gates (NOT, NAND), circuits with dynamic behavior like incoherent feedforward loops (iFFLs), and applied sadCas9 to fix a T7 polymerase-based cascade. Data demonstrated specific and efficient target repression (100-fold) and qualitatively successful functioning for all circuits. Other advantageous features included low sadCas9-borne cell load and orthogonality with spdCas9. However, different circuit variants showed quantitatively unexpected and previously unreported steady-state responses: the dynamic range, switch point, and slope of NOT/NAND gates changed for different output promoters, and a multiphasic behavior was observed in iFFLs, differing from the expected bell-shaped or sigmoidal curves. Model analysis explained the observed curves by complex interplays among components, due to reporter gene-borne cell load and regulator competition. Overall, CRISPRi/sadCas9 successfully expanded the available toolkit for bacterial engineering. Analysis of our circuit collection depicted the impact of generally neglected effects modulating the shape of component dose-response curves, to avoid drawing wrong conclusions on circuit functioning.

Microfluidic Giant Polymer Vesicles Equipped with Biopores for High-Throughput Screening of Bacteria

Understanding the mechanisms of antibiotic resistance is critical for the development of new therapeutics. Traditional methods for testing bacteria are often limited in their efficiency and reusability. Single bacterial cells can be studied at high throughput using double emulsions, although the lack of control over the oil shell permeability and limited access to the droplet interior present serious drawbacks. Here, a straightforward strategy for studying bacteria-encapsulating double emulsion-templated giant unilamellar vesicles (GUVs) is introduced. This microfluidic approach serves to simultaneously load bacteria inside synthetic GUVs and to permeabilize their membrane with the pore-forming peptide melittin. This enables antibiotic delivery or the influx of fresh medium into the GUV lumen for highly parallel cultivation and antimicrobial efficacy testing. Polymer-based GUVs proved to be efficient culture and analysis microvessels, as microfluidics allow easy selection and encapsulation of bacteria and rapid modification of culture conditions for antibiotic development. Further, a method for in situ profiling of biofilms within GUVs for high-throughput screening is demonstrated. Conceivably, synthetic GUVs equipped with biopores can serve as a foundation for the high-throughput screening of bacterial colony interactions during biofilm formation and for investigating the effect of antibiotics on biofilms.

Quantifying Dynamic Phenotypic Heterogeneity in Resistant Escherichia coli under Translation-Inhibiting Antibiotics

Understanding the phenotypic heterogeneity of antibiotic-resistant bacteria following treatment and the transitions between different phenotypes is crucial for developing effective infection control strategies. The study expands upon previous work by explicating chloramphenicol-induced phenotypic heterogeneities in growth rate, gene expression, and morphology of resistant Escherichia coli using time-lapse microscopy. Correlating the bacterial growth rate and cspC expression, four interchangeable phenotypic subpopulations across varying antibiotic concentrations are identified, surpassing the previously described growth rate bistability. Notably, bacterial cells exhibiting either fast or slow growth rates can concurrently harbor subpopulations characterized by high and low gene expression levels, respectively. To elucidate the mechanisms behind this enhanced heterogeneity, a concise gene expression network model is proposed and the biological significance of the four phenotypes is further explored. Additionally, by employing Hidden Markov Model fitting and integrating the non-equilibrium landscape and flux theory, the real-time data encompassing diverse bacterial traits are analyzed. This approach reveals dynamic changes and switching kinetics in different cell fates, facilitating the quantification of observable behaviors and the non-equilibrium dynamics and thermodynamics at play. The results highlight the multi-dimensional heterogeneous behaviors of antibiotic-resistant bacteria under antibiotic stress, providing new insights into the compromised antibiotic efficacy, microbial response, and associated evolution processes.

Tools and methods for high-throughput single-cell imaging with the mother machine

Despite much progress, image processing remains a significant bottleneck for high-throughput analysis of microscopy data. One popular platform for single-cell time-lapse imaging is the mother machine, which enables long-term tracking of microbial cells under precisely controlled growth conditions. While several mother machine image analysis pipelines have been developed in the past several years, adoption by a non-expert audience remains a challenge. To fill this gap, we implemented our own software, MM3, as a plugin for the multidimensional image viewer napari. napari-MM3 is a complete and modular image analysis pipeline for mother machine data, which takes advantage of the high-level interactivity of napari. Here, we give an overview of napari-MM3 and test it against several well-designed and widely-used image analysis pipelines, including BACMMAN and DeLTA. Researchers often analyze mother machine data with custom scripts using varied image analysis methods, but a quantitative comparison of the output of different pipelines has been lacking. To this end, we show that key single-cell physiological parameter correlations and distributions are robust to the choice of analysis method. However, we also find that small changes in thresholding parameters can systematically alter parameters extracted from single-cell imaging experiments. Moreover, we explicitly show that in deep learning based segmentation, "what you put is what you get" (WYPIWYG) - i.e., pixel-level variation in training data for cell segmentation can propagate to the model output and bias spatial and temporal measurements. Finally, while the primary purpose of this work is to introduce the image analysis software that we have developed over the last decade in our lab, we also provide information for those who want to implement mother-machine-based high-throughput imaging and analysis methods in their research.

iCLOTS: open-source, artificial intelligence-enabled software for analyses of blood cells in microfluidic and microscopy-based assays

While microscopy-based cellular assays, including microfluidics, have significantly advanced over the last several decades, there has not been concurrent development of widely-accessible techniques to analyze time-dependent microscopy data incorporating phenomena such as fluid flow and dynamic cell adhesion. As such, experimentalists typically rely on error-prone and time-consuming manual analysis, resulting in lost resolution and missed opportunities for innovative metrics. We present a user-adaptable toolkit packaged into the open-source, standalone Interactive Cellular assay Labeled Observation and Tracking Software (iCLOTS). We benchmark cell adhesion, single-cell tracking, velocity profile, and multiscale microfluidic-centric applications with blood samples, the prototypical biofluid specimen. Moreover, machine learning algorithms characterize previously imperceptible data groupings from numerical outputs. Free to download/use, iCLOTS addresses a need for a field stymied by a lack of analytical tools for innovative, physiologically-relevant assays of any design, democratizing use of well-validated algorithms for all end-user biomedical researchers who would benefit from advanced computational methods.

An image-guided microfluidic system for single-cell lineage tracking

Cell lineage tracking is a long-standing and unresolved problem in biology. Microfluidic technologies have the potential to address this problem, by virtue of their ability to manipulate and process single-cells in a rapid, controllable and efficient manner. Indeed, when coupled with traditional imaging approaches, microfluidic systems allow the experimentalist to follow single-cell divisions over time. Herein, we present a valve-based microfluidic system able to probe the decision-making processes of single-cells, by tracking their lineage over multiple generations. The system operates by trapping single-cells within growth chambers, allowing the trapped cells to grow and divide, isolating sister cells after a user-defined number of divisions and finally extracting them for downstream transcriptome analysis. The platform incorporates multiple cell manipulation operations, image processing-based automation for cell loading and growth monitoring, reagent addition and device washing. To demonstrate the efficacy of the microfluidic workflow, 6C2 (chicken erythroleukemia) and T2EC (primary chicken erythrocytic progenitors) cells are tracked inside the microfluidic device over two generations, with a cell viability rate in excess of 90%. Sister cells are successfully isolated after division and extracted within a 500 nL volume, which was demonstrated to be compatible with downstream single-cell RNA sequencing analysis.

High-Efficiency Single-Cell Containment Microdevices Based on Fluid Control

In this study, we developed a comb-shaped microfluidic device that can efficiently trap and culture a single cell (bacterium). Conventional culture devices have difficulty in trapping a single bacterium and often use a centrifuge to push the bacterium into the channel. The device developed in this study can store bacteria in almost all growth channels using the flowing fluid. In addition, chemical replacement can be performed in a few seconds, making this device suitable for culture experiments with resistant bacteria. The storage efficiency of microbeads that mimic bacteria was significantly improved from 0.2% to 84%. We used simulations to investigate the pressure loss in the growth channel. The pressure in the growth channel of the conventional device was more than 1400 PaG, whereas that of the new device was less than 400 PaG. Our microfluidic device was easily fabricated by a soft microelectromechanical systems method. The device was highly versatile and can be applied to various bacteria, such as Salmonella enterica serovar Typhimurium and Staphylococcus aureus.

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.

Navigating Environmental Transitions: the Role of Phenotypic Variation in Bacterial Responses

The ability of bacteria to respond to changes in their environment is critical to their survival, allowing them to withstand stress, form complex communities, and induce virulence responses during host infection. A remarkable feature of many of these bacterial responses is that they are often variable across individual cells, despite occurring in an isogenic population exposed to a homogeneous environmental change, a phenomenon known as phenotypic heterogeneity. Phenotypic heterogeneity can enable bet-hedging or division of labor strategies that allow bacteria to survive fluctuating conditions. Investigating the significance of phenotypic heterogeneity in environmental transitions requires dynamic, single-cell data. Technical advances in quantitative single-cell measurements, imaging, and microfluidics have led to a surge of publications on this topic. Here, we review recent discoveries on single-cell bacterial responses to environmental transitions of various origins and complexities, from simple diauxic shifts to community behaviors in biofilm formation to virulence regulation during infection. We describe how these studies firmly establish that this form of heterogeneity is prevalent and a conserved mechanism by which bacteria cope with fluctuating conditions. We end with an outline of current challenges and future directions for the field. While it remains challenging to predict how an individual bacterium will respond to a given environmental input, we anticipate that capturing the dynamics of the process will begin to resolve this and facilitate rational perturbation of environmental responses for therapeutic and bioengineering purposes.

Two-Layered Microfluidic Devices for High-Throughput Dynamic Analysis of Synthetic Gene Circuits in E. coli

Escherichia coli is a common chassis for synthetic gene circuit studies. In addition to the dose-response of synthetic gene circuits, the analysis of dynamic responses is also an important part of the future design of more complicated synthetic systems. Recently, microfluidic-based methods have been widely used for the analysis of gene expression dynamics. Here, we established a two-layered microfluidic platform for the systematic characterization of synthetic gene circuits (eight strains in eight different culture environments could be observed simultaneously with a 5 min time resolution). With this platform, both dose responses and dynamic responses with a high temporal resolution could be easily derived for further analysis. A controlled environment ensures the stability of the bacterial growth rate, excluding changes in gene expression dynamics caused by changes of the growth dilution rate. The precise environmental switch and automatic micrograph shooting ensured that there was nearly no time lag between the inducer addition and the data recording. We studied four four-node incoherent-feedforward-loop (IFFL) networks with different operators using this device. The experimental results showed that as the effect of inhibition increased, two of the IFFL networks generated pulselike dynamic gene expressions in the range of the inducer concentrations, which was different from the dynamics of the two other circuits with only a simple pattern of rising to the platform. Through fitting the dose-response curves and the dynamic response curves, corresponding parameters were derived and introduced to a simple model that could qualitatively explain the generation of pulse dynamics.

A knowledge integration strategy for the selection of a robust multi-stress biomarkers panel for Bacillus subtilis

One challenge in the engineering of biological systems is to be able to recognise the cellular stress states of bacterial hosts, as these stress states can lead to suboptimal growth and lower yields of target products. To enable the design of genetic circuits for reporting or mitigating the stress states, it is important to identify a relatively reduced set of gene biomarkers that can reliably indicate relevant cellular growth states in bacteria. Recent advances in high-throughput omics technologies have enhanced the identification of molecular biomarkers specific states in bacteria, motivating computational methods that can identify robust biomarkers for experimental characterisation and verification. Focused on identifying gene expression biomarkers to sense various stress states in Bacillus subtilis, this study aimed to design a knowledge integration strategy for the selection of a robust biomarker panel that generalises on external datasets and experiments. We developed a recommendation system that ranks the candidate biomarker panels based on complementary information from machine learning model, gene regulatory network and co-expression network. We identified a recommended biomarker panel showing high stress sensing power for a variety of conditions both in the dataset used for biomarker identification (mean f1-score achieved at 0.99), as well as in a range of independent datasets (mean f1-score achieved at 0.98). We discovered a significant correlation between stress sensing power and evaluation metrics such as the number of associated regulators in a B. subtilis gene regulatory network (GRN) and the number of associated modules in a B. subtilis co-expression network (CEN). GRNs and CENs provide information relevant to the diversity of biological processes encoded by biomarker genes. We demonstrate that quantitatively relating meaningful evaluation metrics with stress sensing power has the potential for recognising biomarkers that show better sensitivity and robustness to an extended set of stress conditions and enable a more reliable biomarker panel selection.

Growth condition-dependent differences in methylation imply transiently differentiated DNA methylation states in Escherichia coli

DNA methylation in bacteria frequently serves as a simple immune system, allowing recognition of DNA from foreign sources, such as phages or selfish genetic elements. However, DNA methylation also affects other cell phenotypes in a heritable manner (i.e. epigenetically). While there are several examples of methylation affecting transcription in an epigenetic manner in highly localized contexts, it is not well-established how frequently methylation serves a more general epigenetic function over larger genomic scales. To address this question, here we use Oxford Nanopore sequencing to profile DNA modification marks in three natural isolates of Escherichia coli. We first identify the DNA sequence motifs targeted by the methyltransferases in each strain. We then quantify the frequency of methylation at each of these motifs across the entire genome in different growth conditions. We find that motifs in specific regions of the genome consistently exhibit high or low levels of methylation. Furthermore, we show that there are replicable and consistent differences in methylated regions across different growth conditions. This suggests that during growth, E. coli transiently differentiate into distinct methylation states that depend on the growth state, raising the possibility that measuring DNA methylation alone can be used to infer bacterial growth states without additional information such as transcriptome or proteome data. These results show the utility of using Oxford Nanopore sequencing as an economic means to infer DNA methylation status. They also provide new insights into the dynamics of methylation during bacterial growth and provide evidence of differentiated cell states, a transient analog to what is observed in the differentiation of cell types in multicellular organisms.

Synthetic Micrographs of Bacteria (SyMBac) allows accurate segmentation of bacterial cells using deep neural networks

BACKGROUND

Deep-learning-based image segmentation models are required for accurate processing of high-throughput timelapse imaging data of bacterial cells. However, the performance of any such model strictly depends on the quality and quantity of training data, which is difficult to generate for bacterial cell images. Here, we present a novel method of bacterial image segmentation using machine learning models trained with Synthetic Micrographs of Bacteria (SyMBac).

RESULTS

We have developed SyMBac, a tool that allows for rapid, automatic creation of arbitrary amounts of training data, combining detailed models of cell growth, physical interactions, and microscope optics to create synthetic images which closely resemble real micrographs, and is capable of training accurate image segmentation models. The major advantages of our approach are as follows: (1) synthetic training data can be generated virtually instantly and on demand; (2) these synthetic images are accompanied by perfect ground truth positions of cells, meaning no data curation is required; (3) different biological conditions, imaging platforms, and imaging modalities can be rapidly simulated, meaning any change in one's experimental setup no longer requires the laborious process of manually generating new training data for each change. Deep-learning models trained with SyMBac data are capable of analysing data from various imaging platforms and are robust to drastic changes in cell size and morphology. Our benchmarking results demonstrate that models trained on SyMBac data generate more accurate cell identifications and precise cell masks than those trained on human-annotated data, because the model learns the true position of the cell irrespective of imaging artefacts. We illustrate the approach by analysing the growth and size regulation of bacterial cells during entry and exit from dormancy, which revealed novel insights about the physiological dynamics of cells under various growth conditions.

CONCLUSIONS

The SyMBac approach will help to adapt and improve the performance of deep-learning-based image segmentation models for accurate processing of high-throughput timelapse image data.

Microfluidics for long-term single-cell time-lapse microscopy: Advances and applications

Cells are inherently dynamic, whether they are responding to environmental conditions or simply at equilibrium, with biomolecules constantly being made and destroyed. Due to their small volumes, the chemical reactions inside cells are stochastic, such that genetically identical cells display heterogeneous behaviors and gene expression profiles. Studying these dynamic processes is challenging, but the development of microfluidic methods enabling the tracking of individual prokaryotic cells with microscopy over long time periods under controlled growth conditions has led to many discoveries. This review focuses on the recent developments of one such microfluidic device nicknamed the mother machine. We overview the original device design, experimental setup, and challenges associated with this platform. We then describe recent methods for analyzing experiments using automated image segmentation and tracking. We further discuss modifications to the experimental setup that allow for time-varying environmental control, replicating batch culture conditions, cell screening based on their dynamic behaviors, and to accommodate a variety of microbial species. Finally, this review highlights the discoveries enabled by this technology in diverse fields, such as cell-size control, genetic mutations, cellular aging, and synthetic biology.

Microfluidic single-cell scale-down bioreactors: A proof-of-concept for the growth of Corynebacterium glutamicum at oscillating pH values

In large-scale bioreactors, gradients in cultivation parameter such as oxygen, substrate and pH result in fluctuating cell environments. pH fluctuations were identified as a critical parameter for bioprocess performance. Traditionally, scale-down systems at the laboratory scale are used to analyze the effects of fluctuating pH values on strain and thus process performance. Here, we demonstrate the application of dynamic microfluidic single-cell cultivation (dMSCC) as a novel scale-down system for the characterization of Corynebacterium glutamicum growth using oscillating pH conditions as a model stress-factor. A detailed comparison between two-compartment reactor (two-CR) scale-down experiments and dMSCC was performed for one specific pH oscillation between reference pH 7 (~ 8 min) and disturbed pH 6 (~2 min). Similar reductions in growth rates were observed in both systems (dMSCC 21% and two-CR 27%) compared to undisturbed cultivation at pH 7. Afterwards, systematic experiments at symmetric and asymmetric pH oscillations between pH ranges of 4-6 and 8-11 and different intervals from 1 minute to 20 minutes, were performed to demonstrate the unique application range and throughput of the dMSCC system. Finally, the strength of the dMSCC application was demonstrated by mimicking fluctuating environmental conditions of a putative large-scale bioprocesse, which is difficult to conduct using two-CRs. This article is protected by copyright. All rights reserved.

Frequency spectra and the color of cellular noise

The invention of the Fourier integral in the 19th century laid the foundation for modern spectral analysis methods. This integral decomposes a temporal signal into its frequency components, providing deep insights into its generating process. While this idea has precipitated several scientific and technological advances, its impact has been fairly limited in cell biology, largely due to the difficulties in connecting the underlying noisy intracellular networks to the frequency content of observed single-cell trajectories. Here we develop a spectral theory and computational methodologies tailored specifically to the computation and analysis of frequency spectra of noisy intracellular networks. Specifically, we develop a method to compute the frequency spectrum for general nonlinear networks, and for linear networks we present a decomposition that expresses the frequency spectrum in terms of its sources. Several examples are presented to illustrate how our results provide frequency-based methods for the design and analysis of noisy intracellular networks.

The invention of the Fourier integral in the 19th century laid the foundation for modern spectral analysis methods. Here the authors develop frequency-based methods for analyzing the reaction mechanisms within living cells from distinctively noisy single-cell output trajectories and present forward engineering of synthetic oscillators and controllers.

Digital models in biotechnology: Towards multi-scale integration and implementation

Industrial biotechnology encompasses a large area of multi-scale and multi-disciplinary research activities. With the recent megatrend of digitalization sweeping across all industries, there is an increased focus in the biotechnology industry on developing, integrating and applying digital models to improve all aspects of industrial biotechnology. Given the rapid development of this field, we systematically classify the state-of-art modelling concepts applied at different scales in industrial biotechnology and critically discuss their current usage, advantages and limitations. Further, we critically analyzed current strategies to couple cell models with computational fluid dynamics to study the performance of industrial microorganisms in large-scale bioprocesses, which is of crucial importance for the bio-based production industries. One of the most challenging aspects in this context is gathering intracellular data under industrially relevant conditions. Towards comprehensive models, we discuss how different scale-down concepts combined with appropriate analytical tools can capture intracellular states of single cells. We finally illustrated how the efforts could be used to develop digitals models suitable for both cell factory design and process optimization at industrial scales in the future.

History-dependent physiological adaptation to lethal genetic modification under antibiotic exposure

Genetic modifications, such as gene deletion and mutations, could lead to significant changes in physiological states or even cell death. Bacterial cells can adapt to diverse external stresses, such as antibiotic exposure, but can they also adapt to detrimental genetic modification? To address this issue, we visualized the response of individual Escherichia coli cells to deletion of the antibiotic resistance gene under chloramphenicol (Cp) exposure, combining the light-inducible genetic recombination and microfluidic long-term single-cell tracking. We found that a significant fraction (∼40%) of resistance-gene-deleted cells demonstrated a gradual restoration of growth and stably proliferated under continuous Cp exposure without the resistance gene. Such physiological adaptation to genetic modification was not observed when the deletion was introduced in 10 hr or more advance before Cp exposure. Resistance gene deletion under Cp exposure disrupted the stoichiometric balance of ribosomal large and small subunit proteins (RplS and RpsB). However, the balance was gradually recovered in the cell lineages with restored growth. These results demonstrate that bacterial cells can adapt even to lethal genetic modifications by plastically gaining physiological resistance. However, the access to the resistance states is limited by the environmental histories and the timings of genetic modification.

Optimization and Fabrication of Multi-Level Microchannels for Long-Term Imaging of Bacterial Growth and Expansion

Bacteria are unicellular organisms whose length is usually around a few micrometers. Advances in microfabrication techniques have enabled the design and implementation of microdevices to confine and observe bacterial colony growth. Microstructures hosting the bacteria and microchannels for nutrient perfusion usually require separate microfabrication procedures due to different feature size requirements. This fact increases the complexity of device integration and assembly process. Furthermore, long-term imaging of bacterial dynamics over tens of hours requires stability in the microscope focusing mechanism to ensure less than one-micron drift in the focal axis. In this work, we design and fabricate an integrated multi-level, hydrodynamically-optimized microfluidic chip to study long-term Escherichia coli population dynamics in confined microchannels. Reliable long-term microscopy imaging and analysis has been limited by focus drifting and ghost effect, probably caused by the shear viscosity changes of aging microscopy immersion oil. By selecting a microscopy immersion oil with the most stable viscosity, we demonstrate successful captures of focally stable time-lapse bacterial images for ≥72 h. Our fabrication and imaging methodology should be applicable to other single-cell studies requiring long-term imaging.

Population genetics in microchannels

Many microbial populations proliferate in small channels. In such environments, reproducing cells organize in parallel lanes. Reproducing cells shift these lanes, potentially expelling other cells from the channel. In this paper, we combine theory and experiments to understand how these dynamics affects the diversity of a microbial population. We theoretically predict that genetic diversity is quickly lost along lanes of cells. Our experiments confirm that a population of proliferating Escherichia coli in a microchannel organizes into lanes of genetically identical cells within a few generations. Our findings elucidate the effect of lane formation on populations evolution, with potential applications ranging from microbial ecology in soil to dynamics of epithelial tissues in higher organisms.

Spatial constraints, such as rigid barriers, affect the dynamics of cell populations, potentially altering the course of natural evolution. In this paper, we investigate the population genetics of Escherichia coli proliferating in microchannels with open ends. Our analysis is based on a population model, in which reproducing cells shift entire lanes of cells toward the open ends of the channel. The model predicts that diversity is lost very rapidly within lanes but at a much slower pace among lanes. As a consequence, two mixed, neutral E. coli strains competing in a microchannel must organize into an ordered regular stripe pattern in the course of a few generations. These predictions are in quantitative agreement with our experiments. We also demonstrate that random mutations appearing in the middle of the channel are much more likely to reach fixation than those occurring elsewhere. Our results illustrate fundamental mechanisms of microbial evolution in spatially confined space.

Genome-wide gene expression noise in Escherichia coli is condition-dependent and determined by propagation of noise through the regulatory network

Although it is well appreciated that gene expression is inherently noisy and that transcriptional noise is encoded in a promoter’s sequence, little is known about the extent to which noise levels of individual promoters vary across growth conditions. Using flow cytometry, we here quantify transcriptional noise in Escherichia coli genome-wide across 8 growth conditions and find that noise levels systematically decrease with growth rate, with a condition-dependent lower bound on noise. Whereas constitutive promoters consistently exhibit low noise in all conditions, regulated promoters are both more noisy on average and more variable in noise across conditions. Moreover, individual promoters show highly distinct variation in noise across conditions. We show that a simple model of noise propagation from regulators to their targets can explain a significant fraction of the variation in relative noise levels and identifies TFs that most contribute to both condition-specific and condition-independent noise propagation. In addition, analysis of the genome-wide correlation structure of various gene properties shows that gene regulation, expression noise, and noise plasticity are all positively correlated genome-wide and vary independently of variations in absolute expression, codon bias, and evolutionary rate. Together, our results show that while absolute expression noise tends to decrease with growth rate, relative noise levels of genes are highly condition-dependent and determined by the propagation of noise through the gene regulatory network.

Genome-wide flow cytometry measurements reveal that gene expression noise in bacteria is highly condition-dependent; while absolute noise levels of all genes decrease with growth-rate, theoretical modeling shows that the relative noise levels of different genes are determined by the propagation of noise through the gene regulatory network (GRN). Thus GRN structure controls not only mean expression but also noise levels.

Distinguishing different modes of growth using single-cell data

Collection of high-throughput data has become prevalent in biology. Large datasets allow the use of statistical constructs such as binning and linear regression to quantify relationships between variables and hypothesize underlying biological mechanisms based on it. We discuss several such examples in relation to single-cell data and cellular growth. In particular, we show instances where what appears to be ordinary use of these statistical methods leads to incorrect conclusions such as growth being non-exponential as opposed to exponential and vice versa. We propose that the data analysis and its interpretation should be done in the context of a generative model, if possible. In this way, the statistical methods can be validated either analytically or against synthetic data generated via the use of the model, leading to a consistent method for inferring biological mechanisms from data. On applying the validated methods of data analysis to infer cellular growth on our experimental data, we find the growth of length in E. coli to be non-exponential. Our analysis shows that in the later stages of the cell cycle the growth rate is faster than exponential.

Single-Cell Technologies to Study Phenotypic Heterogeneity and Bacterial Persisters

Antibiotic persistence is a phenomenon in which rare cells of a clonal bacterial population can survive antibiotic doses that kill their kin, even though the entire population is genetically susceptible. With antibiotic treatment failure on the rise, there is growing interest in understanding the molecular mechanisms underlying bacterial phenotypic heterogeneity and antibiotic persistence. However, elucidating these rare cell states can be technically challenging. The advent of single-cell techniques has enabled us to observe and quantitatively investigate individual cells in complex, phenotypically heterogeneous populations. In this review, we will discuss current technologies for studying persister phenotypes, including fluorescent tags and biosensors used to elucidate cellular processes; advances in flow cytometry, mass spectrometry, Raman spectroscopy, and microfluidics that contribute high-throughput and high-content information; and next-generation sequencing for powerful insights into genetic and transcriptomic programs. We will further discuss existing knowledge gaps, cutting-edge technologies that can address them, and how advances in single-cell microbiology can potentially improve infectious disease treatment outcomes.

Are droplets really suitable for single-cell analysis? A case study on yeast in droplets

Single-cell analysis has become one of the main cornerstones of biotechnology, inspiring the advent of various microfluidic compartments for cell cultivation such as microwells, microtrappers, microcapillaries, and droplets. A fundamental assumption for using such microfluidic compartments is that unintended stress or harm to cells derived from the microenvironments is insignificant, which is a crucial condition for carrying out unbiased single-cell studies. Despite the significance of this assumption, simple viability or growth tests have overwhelmingly been the assay of choice for evaluating culture conditions while empirical studies on the sub-lethal effect on cellular functions have been insufficient in many cases. In this work, we assessed the effect of culturing cells in droplets on the cellular function using yeast morphology as an indicator. Quantitative morphological analysis using CalMorph, an image-analysis program, demonstrated that cells cultured in flasks, large droplets, and small droplets significantly differed morphologically. From these differences, we identified that the cell cycle was delayed in droplets during the G1 phase and during the process of bud growth likely due to the checkpoint mechanism and impaired mitochondrial function, respectively. Furthermore, comparing small and large droplets, cells cultured in large droplets were morphologically more similar to those cultured in a flask, highlighting the advantage of increasing the droplet size. These results highlight a potential source of bias in cell analysis using droplets and reinforce the significance of assessing culture conditions of microfluidic cultivation methods for specific study cases.

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.

Recent advances in single-cell analysis: Encapsulation materials, analysis methods and integrative platform for microfluidic technology

Traditional cell biology researches on cell populations by their origin, tissue, morphology, and secretions. Because of the heterogeneity of cells, research at the single-cell level can obtain more accurate and comprehensive information that reflects the physiological state and process of the cell, increasing the significance of single-cell analysis. The application of single-cell analysis is faced with the problem of contaminated or damaged cells caused by cell sample transportation. Reversible encapsulation of a single cell can protect cells from the external environment and open the encapsulation shell to release cells, thus preserving cell integrity and improving extraction efficiency of analytes. Meanwhile, microfluidic single cell analysis (MSCA) exhibits integration, miniaturization, and high throughput, which can considerably improve the efficiency of single-cell analysis. The researches on single-cell reversible encapsulation materials, single-cell analysis methods, and the MSCA integration platform are analyzed and summarized in this review. The problems of single-cell viability, network of single-cell signal, and simultaneous detection of multiple biotoxins in food based on single-cell are proposed for future research.

A microfluidic single-cell array for in situ laminar-flow-based comparative culturing of budding yeast cells

To facilitate in situ comparative culturing of budding yeast cells in a precisely controlled microenvironment, we developed a microfluidic single-cell array (MiSCA) with 96 traps (16 rows × 6 columns) for single-cell immobilization. Through optimization of the distances between neighboring traps and the applied flow rates by using a hydraulic equivalent circuit of the fluidic network, yeast cells were delivered to each column of the array by laminar focused flows and reliably captured at the traps by hydrodynamic forces with about 90% efficiency of cell immobilization. Immobilized cells in different columns within the same device can then be cultured in parallel while being exposed to different media and compounds delivered by laminar flows. For biological validation of the comparative cell-culturing device, we used budding yeast that can express yellow fluorescent protein upon the addition of β-estradiol in cell-culturing medium. Experimental results show successful induction of fluorescence in cells immobilized in desired columns that have been dosed with β-estradiol. The MiSCA system allows for performing sets of experiments and control experiments in parallel in the same device, or for executing comparative experiments under well-defined laminar-perfusion conditions with different media, as well as in situ monitoring of dynamic cellular responses upon different analytical compounds or reagents for single-cell analysis.

Challenges of analysing stochastic gene expression in bacteria using single-cell time-lapse experiments

Stochastic gene expression causes phenotypic heterogeneity in a population of genetically identical bacterial cells. Such non-genetic heterogeneity can have important consequences for the population fitness, and therefore cells implement regulation strategies to either suppress or exploit such heterogeneity to adapt to their circumstances. By employing time-lapse microscopy of single cells, the fluctuation dynamics of gene expression may be analysed, and their regulatory mechanisms thus deciphered. However, a careful consideration of the experimental design and data-analysis is needed to produce useful data for deriving meaningful insights from them. In the present paper, the individual steps and challenges involved in a time-lapse experiment are discussed, and a rigorous framework for designing, performing, and extracting single-cell gene expression dynamics data from such experiments is outlined.

Microfluidic Single-Cell Phenotyping of the Activity of Peptide-Based Antimicrobials

Antibiotic resistance is a major challenge for modern medicine, and there is a dire need to refresh the antibiotic development pipeline to treat infections that are resistant to currently available drugs. Peptide-based antimicrobials represent a promising source of novel anti-infectives, but their development is severely impeded due to the lack of suitable techniques to accurately quantify their antimicrobial efficacy. A major problem involves the heterogeneity of cellular phenotypes in response to these peptides, even within a clonal population of bacteria. There is thus a need to develop single-cell resolution assays to quantify drug efficacy for these novel therapeutics. We present here a detailed microfluidics-microscopy protocol for testing the efficacy of peptide-based antimicrobials on hundreds to thousands of individual bacteria in well-defined microenvironments. This enables the study of cell-to-cell differences in drug response within a clonal population. It is a highly versatile tool, which can be used to quantify drug efficacy, including the number of individual survivors at defined drug doses; it even enables the potential exploration of the molecular mechanisms of action of the drug, which are often unknown in the early stages of drug development. We present here protocols for working with Escherichia coli, but organisms of different geometric shapes and sizes may also be tested with suitable modifications of the microfluidic device.

ChipSeg: An Automatic Tool to Segment Bacterial and Mammalian Cells Cultured in Microfluidic Devices

Extracting quantitative measurements from time-lapse images is necessary in external feedback control applications, where segmentation results are used to inform control algorithms. We describe ChipSeg, a computational tool that segments bacterial and mammalian cells cultured in microfluidic devices and imaged by time-lapse microscopy, which can be used also in the context of external feedback control. The method is based on thresholding and uses the same core functions for both cell types. It allows us to segment individual cells in high cell density microfluidic devices, to quantify fluorescent protein expression over a time-lapse experiment, and to track individual mammalian cells. ChipSeg enables robust segmentation in external feedback control experiments and can be easily customized for other experimental settings and research aims.

Microfluidics for Single-Cell Study of Antibiotic Tolerance and Persistence Induced by Nutrient Limitation

Nutrient limitation is one of the most common triggers of antibiotic tolerance and persistence. Here, we present two microfluidic setups to study how spatial and temporal variation in nutrient availability lead to increased survival of bacteria to antibiotics. The first setup is designed to mimic the growth dynamics of bacteria in spatially structured populations (e.g., biofilms) and can be used to study how spatial gradients in nutrient availability, created by the collective metabolic activity of a population, increase antibiotic tolerance. The second setup captures the dynamics of feast-and-famine cycles that bacteria recurrently encounter in nature, and can be used to study how phenotypic heterogeneity in growth resumption after starvation increases survival of clonal bacterial populations. In both setups, the growth rates and metabolic activity of bacteria can be measured at the single-cell level. This is useful to build a mechanistic understanding of how spatiotemporal variation in nutrient availability triggers bacteria to enter phenotypic states that increase their tolerance to antibiotics.

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

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

Subpopulations of sensorless bacteria drive fitness in fluctuating environments

Populations of bacteria often undergo a lag in growth when switching conditions. Because growth lags can be large compared to typical doubling times, variations in growth lag are an important but often overlooked component of bacterial fitness in fluctuating environments. We here explore how growth lag variation is determined for the archetypical switch from glucose to lactose as a carbon source in Escherichia coli. First, we show that single-cell lags are bimodally distributed and controlled by a single-molecule trigger. That is, gene expression noise causes the population before the switch to divide into subpopulations with zero and nonzero lac operon expression. While "sensorless" cells with zero preexisting lac expression at the switch have long lags because they are unable to sense the lactose signal, any nonzero lac operon expression suffices to ensure a short lag. Second, we show that the growth lag at the population level depends crucially on the fraction of sensorless cells and that this fraction in turn depends sensitively on the growth condition before the switch. Consequently, even small changes in basal expression can significantly affect the fraction of sensorless cells, thereby population lags and fitness under switching conditions, and may thus be subject to significant natural selection. Indeed, we show that condition-dependent population lags vary across wild E. coli isolates. Since many sensory genes are naturally low expressed in conditions where their inducer is not present, bimodal responses due to subpopulations of sensorless cells may be a general mechanism inducing phenotypic heterogeneity and controlling population lags in switching environments. This mechanism also illustrates how gene expression noise can turn even a simple sensory gene circuit into a bet hedging module and underlines the profound role of gene expression noise in regulatory responses.

dMSCC: a microfluidic platform for microbial single-cell cultivation of Corynebacterium glutamicum under dynamic environmental medium conditions

In nature and in technical systems, microbial cells are often exposed to rapidly fluctuating environmental conditions. These conditions can vary in quality, e.g., the existence of a starvation zone, and quantity, e.g., the average residence time in this zone. For strain development and process design, cellular response to such fluctuations needs to be systematically analysed. However, the existing methods for physically imitating rapidly changing environmental conditions are limited in spatio-temporal resolution. Hence, we present a novel microfluidic system for cultivation of single cells and small cell clusters under dynamic environmental conditions (dynamic microfluidic single-cell cultivation (dMSCC)). This system enables the control of nutrient availability and composition between two media with second to minute resolution. We validate our technology using the industrially relevant model organism Corynebacterium glutamicum. The organism was exposed to different oscillation frequencies between nutrient excess (feasts) and scarcity (famine). The resulting changes in cellular physiology, such as the colony growth rate and cell morphology, were analysed and revealed significant differences in the growth rate and cell length between the different conditions. dMSCC also allows the application of defined but randomly changing nutrient conditions, which is important for reproducing more complex conditions from natural habitats and large-scale bioreactors. The presented system lays the foundation for the cultivation of cells under complex changing environmental conditions.

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.

Using fluorescence flow cytometry data for single-cell gene expression analysis in bacteria

Fluorescence flow cytometry is increasingly being used to quantify single-cell expression distributions in bacteria in high-throughput. However, there has been no systematic investigation into the best practices for quantitative analysis of such data, what systematic biases exist, and what accuracy and sensitivity can be obtained. We investigate these issues by measuring the same E. coli strains carrying fluorescent reporters using both flow cytometry and microscopic setups and systematically comparing the resulting single-cell expression distributions. Using these results, we develop methods for rigorous quantitative inference of single-cell expression distributions from fluorescence flow cytometry data. First, we present a Bayesian mixture model to separate debris from viable cells using all scattering signals. Second, we show that cytometry measurements of fluorescence are substantially affected by autofluorescence and shot noise, which can be mistaken for intrinsic noise in gene expression, and present methods to correct for these using calibration measurements. Finally, we show that because forward- and side-scatter signals scale non-linearly with cell size, and are also affected by a substantial shot noise component that cannot be easily calibrated unless independent measurements of cell size are available, it is not possible to accurately estimate the variability in the sizes of individual cells using flow cytometry measurements alone. To aid other researchers with quantitative analysis of flow cytometry expression data in bacteria, we distribute E-Flow, an open-source R package that implements our methods for filtering debris and for estimating true biological expression means and variances from the fluorescence signal. The package is available at https://github.com/vanNimwegenLab/E-Flow.

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.

New methodologies in ageing research

Ageing is arguably the most complex phenotype that occurs in humans. To understand and treat ageing as well as associated diseases, highly specialised technologies are emerging that reveal critical insight into the underlying mechanisms and provide new hope for previously untreated diseases. Herein, we describe the latest developments in cutting edge technologies applied across the field of ageing research. We cover emerging model organisms, high-throughput methodologies and machine-driven approaches. In all, this review will give you a glimpse of what will be pushing the field onwards and upwards.

Quantifying plasmid dynamics using single-cell microfluidics and image bioinformatics

Multicopy plasmids play an important role in bacterial ecology and evolution by accelerating the rate of adaptation and providing a platform for rapid gene amplification and evolutionary rescue. Despite the relevance of plasmids in bacterial evolutionary dynamics, evaluating the population-level consequences of randomly segregating and replicating plasmids in individual cells remains a challenging problem, both in theory and experimentally. In recent years, technological advances in fluorescence microscopy and microfluidics have allowed studying temporal changes in gene expression by quantifying the fluorescent intensity of individual cells under controlled environmental conditions. In this paper, we will describe the manufacture, experimental setup, and data analysis pipeline of different microfluidic systems that can be used to study plasmid dynamics, both in single-cells and in populations. To illustrate the benefits and limitations of microfluidics to study multicopy plasmid dynamics, we will use an experimental model system consisting on Escherichia coli K12 carrying non-conjugative, multicopy plasmids (19 copies per cell, in average) encoding different fluorescent markers and β-lactam resistance genes. First, we will use an image-based flow cytometer to estimate changes in the allele distribution of a heterogeneous population under different selection regimes. Then we will use a mothermachine microfluidic device to obtain time-series of fluorescent intensity of individual cells to argue that plasmid segregation and replication dynamics are inherently stochastic processes. Finally, using a microchemostat, we track thousands of cells in time to reconstruct bacterial lineages and evaluate the allele frequency distributions that emerge in response to a range of selective pressures.

A review on microfluidics manipulation of the extracellular chemical microenvironment and its emerging application to cell analysis

Spatiotemporal manipulation of extracellular chemical environments with simultaneous monitoring of cellular responses plays an essential role in exploring fundamental biological processes and expands our understanding of underlying mechanisms. Despite the rapid progress and promising successes in manipulation strategies, many challenges remain due to the small size of cells and the rapid diffusion of chemical molecules. Fortunately, emerging microfluidic technology has become a powerful approach for precisely controlling the extracellular chemical microenvironment, which benefits from its integration capacity, automation, and high-throughput capability, as well as its high resolution down to submicron. Here, we summarize recent advances in microfluidics manipulation of the extracellular chemical microenvironment, including the following aspects: i) Spatial manipulation of chemical microenvironments realized by convection flow-, diffusion-, and droplet-based microfluidics, and surface chemical modification; ii) Temporal manipulation of chemical microenvironments enabled by flow switching/shifting, moving/flowing cells across laminar flows, integrated microvalves/pumps, and droplet manipulation; iii) Spatiotemporal manipulation of chemical microenvironments implemented by a coupling strategy and open-space microfluidics; and iv) High-throughput manipulation of chemical microenvironments. Finally, we briefly present typical applications of the above-mentioned technical advances in cell-based analyses including cell migration, cell signaling, cell differentiation, multicellular analysis, and drug screening. We further discuss the future improvement of microfluidics manipulation of extracellular chemical microenvironments to fulfill the needs of biological and biomedical research and applications.

pH-Controlled Coacervate-Membrane Interactions within Liposomes

Membraneless organelles formed by liquid-liquid phase separation are dynamic structures that are employed by cells to spatiotemporally regulate their interior. Indeed, complex coacervation-based phase separation is involved in a multitude of biological tasks ranging from photosynthesis to cell division to chromatin organization, and more. Here, we use an on-chip microfluidic method to control and study the formation of membraneless organelles within liposomes, using pH as the main control parameter. We show that a transmembrane proton flux that is created by a stepwise change in the external pH can readily bring about the coacervation of encapsulated components in a controlled manner. We employ this strategy to induce and study electrostatic as well as hydrophobic interactions between the coacervate and the lipid membrane. Electrostatic interactions using charged lipids efficiently recruit coacervates to the membrane and restrict their movement along the inner leaflet. Hydrophobic interactions via cholesterol-tagged RNA molecules provide even stronger interactions, causing coacervates to wet the membrane and affect the local lipid-membrane structure, reminiscent of coacervate-membrane interactions in cells. The presented technique of pH-triggered coacervation within cell-sized liposomes may find applications in synthetic cells and in studying biologically relevant phase separation reactions in a bottom-up manner.

Dynamic Environmental Control in Microfluidic Single-Cell Cultivations: From Concepts to Applications

Microfluidic single-cell cultivation (MSCC) is an emerging field within fundamental as well as applied biology. During the last years, most MSCCs were performed at constant environmental conditions. Recently, MSCC at oscillating and dynamic environmental conditions has started to gain significant interest in the research community for the investigation of cellular behavior. Herein, an overview of this topic is given and microfluidic concepts that enable oscillating and dynamic control of environmental conditions with a focus on medium conditions are discussed, and their application in single-cell research for the cultivation of both mammalian and microbial cell systems is demonstrated. Furthermore, perspectives for performing MSCC at complex dynamic environmental profiles of single parameters and multiparameters (e.g., pH and O₂ ) in amplitude and time are discussed. The technical progress in this field provides completely new experimental approaches and lays the foundation for systematic analysis of cellular metabolism at fluctuating environments.

Simulation and practice of particle inertial focusing in 3D-printed serpentine microfluidic chips via commercial 3D-printers

Inertial focusing of particles in serpentine microfluidic chips has been studied over the past decade. Here, a study to investigate the particle inertial focusing in 3D-printed serpentine microfluidic chips was conducted by simulation and practice. A test model was designed and printed using four commercial 3D-printers. Commercial inkjet 3D-printers have shown the best printing channel resolution of up to 0.1 mm. The force analysis of particle inertial focusing in 3D-printed microfluidic chips with large cross-sectional channels was discussed. Important parameters such as the channel curvature and flow velocity were studied by simulation. The optimal channel curvature and flow velocity are 5.9 mm and 480 μL min^-1 (Re: 29.8 and De: 4.49) in the 3D-printed microfluidic chips with 0.2 mm × 0.4 mm cross-sectional channels. Under these optimal conditions, particles were well focused in the middle of the channel. Furthermore, two kinds of cancer cells were focused in these 3D-printed serpentine microfluidic chips under the optimal conditions. We envision that this improved study would provide helpful insights into simulating particle inertial focusing in 3D-printed microfluidic chips and promoting 3D-printed microfluidic chips to commercial production.