The role of physiological heterogeneity in microbial population behavior

Microfluidic characterization of single-cell biophysical properties and the applications in cancer diagnosis

Single-cell biophysical properties play a crucial role in regulating cellular physiological states and functions, demonstrating significant potential in the fields of life sciences and clinical diagnostics. Therefore, over the last few decades, researchers have developed various detection tools to explore the relationship between the biophysical changes of biological cells and human diseases. With the rapid advancement of modern microfabrication technology, microfluidic devices have quickly emerged as a promising platform for single-cell analysis offering advantages including high-throughput, exceptional precision, and ease of manipulation. Consequently, this paper provides an overview of the recent advances in microfluidic analysis and detection systems for single-cell biophysical properties and their applications in the field of cancer. The working principles and latest research progress of single-cell biophysical property detection are first analyzed, highlighting the significance of electrical and mechanical properties. The development of data acquisition and processing methods for real-time, high-throughput, and practical applications are then discussed. Furthermore, the differences in biophysical properties between tumor and normal cells are outlined, illustrating the potential for utilizing single-cell biophysical properties for tumor cell identification, classification, and drug response assessment. Lastly, we summarize the limitations of existing microfluidic analysis and detection systems in single-cell biophysical properties, while also pointing out the prospects and future directions of their applications in cancer diagnosis and treatment.

A secondary model for the effect of pH on the variability in growth fitness of Listeria innocua strains

Variability in microbial growth is a keystone of modern Quantitative Microbiological Risk Assessment (QMRA). However, there are still significant knowledge gaps on how to model variability, with the most common assumption being that variability is constant. This is implemented by an error term (with constant variance) added on top of the secondary growth model (for the square root of the growth rate). However, this may go against microbial ecology principles, where differences in growth fitness among bacterial strains would be more prominent in the vicinity of the growth limits than at optimal growth conditions. This study coins the term "secondary models for variability", evaluating whether they should be considered in QMRA instead of the constant strain variability hypothesis. For this, 21 strains of Listeria innocua were used as case study, estimating their growth rate by the two-fold dilution method at pH between 5 and 10. Estimates of between-strain variability and experimental uncertainty were obtained for each pH using mixed-effects models, showing the lowest variability at optimal growth conditions, increasing towards the growth limits. Nonetheless, the experimental uncertainty also increased towards the extremes, evidencing the need to analyze both sources of variance independently. A secondary model was thus proposed, relating strain variability and pH conditions. Although the modelling approach certainly has some limitations that would need further experimental validation, it is an important step towards improving the description of variability in QMRA, being the first model of this type in the field.

A modular microfluidic platform to study how fluid shear stress alters estrogen receptor phenotype in ER+ breast cancer cells

Metastatic breast cancer leads to poor prognoses and worse outcomes in patients due to its invasive behavior and poor response to therapy. It is still unclear what biophysical and biochemical factors drive this more aggressive phenotype in metastatic cancer; however recent studies have suggested that exposure to fluid shear stress in the vasculature could cause this. In this study a modular microfluidic platform capable of mimicking the magnitude of fluid shear stress (FSS) found in human vasculature was designed and fabricated. This device provides a platform to evaluate the effects of FSS on MCF-7 cell line, an estrogen receptor positive (ER+) breast cancer cell line, during circulation in the vessels. Elucidation of the effects of FSS on MCF-7 cells was carried out utilizing two approaches: single cell analysis and bulk analysis. For single cell analysis, cells were trapped in a microarray after exiting the serpentine channel and followed by immunostaining on the device (on-chip). Bulk analysis was performed after cells were collected in a microtube at the outlet of the microfluidic serpentine channel for western blotting (off-chip). It was found that cells exposed to an FSS magnitude of 10 dyn/cm2 with a residence time of 60 s enhanced expression of the proliferation marker Ki67 in the MCF-7 cell line at a single cell level. To understand possible mechanisms for enhanced Ki67 expression, on-chip and off-chip analyses were performed for pro-growth and survival pathways ERK, AKT, and JAK/STAT. Results demonstrated that after shearing the cells phosphorylation of p-AKT, p-mTOR, and p-STAT3 were observed. However, there was no change in p-ERK1/2. AKT is a mediator of ER rapid signaling, analysis of phosphorylated ERα was carried out and no significant differences between sheared and non-sheared populations were observed. Taken together these results demonstrate that FSS can increase phosphorylation of proteins associated with a more aggressive phenotype in circulating cancer cells. These findings provide additional information that may help inform why cancer cells located at metastatic sites are usually more aggressive than primary breast cancer cells.

Density physics-informed neural networks reveal sources of cell heterogeneity in signal transduction

The transduction time between signal initiation and final response provides valuable information on the underlying signaling pathway, including its speed and precision. Furthermore, multi-modality in a transduction-time distribution indicates that the response is regulated by multiple pathways with different transduction speeds. Here, we developed a method called density physics-informed neural networks (Density-PINNs) to infer the transduction-time distribution from measurable final stress response time traces. We applied Density-PINNs to single-cell gene expression data from sixteen promoters regulated by unknown pathways in response to antibiotic stresses. We found that promoters with slower signaling initiation and transduction exhibit larger cell-to-cell heterogeneity in response intensity. However, this heterogeneity was greatly reduced when the response was regulated by slow and fast pathways together. This suggests a strategy for identifying effective signaling pathways for consistent cellular responses to disease treatments. Density-PINNs can also be applied to understand other time delay systems, including infectious diseases.

Insights into the molecular basis of c-di-GMP signalling in Pseudomonas aeruginosa

The opportunistic human pathogen Pseudomonas aeruginosa can cause severe infections in immunocompromized people or cystic fibrosis (CF) patients. Because of its remarkable ability to invade the host and withstand the bacteriocidal effect of most conventional antibiotics, the infection caused by P. aeruginosa has become a major concern for human health. The switch from acute to chronic infection is governed by the second messenger bis-(3'-5')-cyclic dimeric guanosine mono-phosphate (c-di-GMP) in P. aeruginosa, and c-di-GMP is now recognized to regulate many important biological processes in pathogenesis. The c-di-GMP signalling mechanisms in P. aeruginosa have been studied extensively in the past decade, revealing complicated c-di-GMP metabolism and signalling network. In this review, the underlying mechanisms of this signalling network will be discussed, mainly focussing on how environmental cues regulate c-di-GMP signalling, protein-protein interaction mediated functional regulation, heterogeneity of c-di-GMP and cross talk between c-di-GMP signalling and other signalling systems. Understanding the molecular mechanism underlying the complex c-di-GMP signalling network would be beneficial for developing therapeutic approaches and antibacterial agents to combat the threat from P. aeruginosa.

Single-cell patterning and characterisation of antibiotic persistent bacteria using bio-sCAPA

In microbiology, accessing single-cell information within large populations is pivotal. Here we introduce bio-sCAPA, a technique for patterning bacterial cells in defined geometric arrangements and monitoring their growth in various nutrient environments. We demonstrate bio-sCAPA with a study of subpopulations of antibiotic-tolerant bacteria, known as persister cells, which can survive exposure to high doses of antibiotics despite lacking any genetic resistance to the drug. Persister cells are associated with chronic and relapsing infections, yet are difficult to study due in part to a lack of scalable, single-cell characterisation methods. As >105 cells can be patterned on each template, and multiple templates can be patterned in parallel, bio-sCAPA allows for very rare population phenotypes to be monitored with single-cell precision across various environmental conditions. Using bio-sCAPA, we analysed the phenotypic characteristics of single Staphylococcus aureus cells tolerant to flucloxacillin and rifampicin killing. We find that antibiotic-tolerant S. aureus cells do not display significant heterogeneity in growth rate and are instead characterised by prolonged lag-time phenotypes alone.

Optical-based microbubble for on-demand droplet release from static droplet array (SDA) for dispensing one droplet into one tube

Static droplet array (SDA) is a pivotal tool for high-capacity screening assays, yet extraction and collection the target droplets that contain unique analytes or cells from the SDA remains one major technical bottleneck that limits its broader application. Here we present an optical-based on-demand droplet release (OODR) system by incorporating a 1064 nm laser-responsive indium tin oxide (ITO) layer into a chamber array-based droplet microfluidic chip. By focusing the 1064 nm laser onto the ITO layer, microbubbles can be created via local heating to selectively push-out the droplets from the chamber. Then the released droplet is readily exported in a one-droplet-one-tube (ODOT) manner by the inherent capillary force into pipette tip. Releasing of the droplets containing fluorescein sodium demonstrated ∼100% successful rate (9 out of 6400 droplets were successfully released) and low residual (only ∼5% of the droplet volume remains in the chamber). White or fluorescence image-based releasing of single-cell-droplets directly after cell loading or multi-cells-droplets derived from on-chip single-cell cultivation for both E. coli and yeast cells further demonstrated the wide applicability of OODR. The present system is user-friendly and has the potential to be applied in various high-throughput screening assays, including single molecule/cell analysis, drug screening, and phenotype-based cell sorting.

bglG Regulates the Heterogeneity Driven by the Acid Tolerance Response in Lacticaseibacillus paracasei L9

The acid tolerance of lactic acid bacteria is crucial for their fermentation and probiotic functions. Acid adaption significantly enhances the acid tolerance of strains, and the phenotypic heterogeneity driven by the acid tolerance response (ATR) contributes to this process by providing a selective advantage in harsh environments. The mechanism of heterogeneity under the ATR is not yet clear, but individual gene expression differences are recognized as the cause. In this study, we observed four heterogeneous subpopulations (viable, injured, dead, and unstained) of Lacticaseibacillus paracasei L9 (L9) induced by acid adaption (pH 5.0, 40 min) using flow cytometry. The viable subpopulation represented a significantly superior acid tolerance to the injured subpopulation or total population. Different subpopulations were sorted and transcriptomic analysis was performed. Five genes were found to be upregulated in the viable subpopulation and downregulated in the injured subpopulation, and bglG (LPL9_RS14735) was identified as having a key role in this process. Using salicin (glucoside)-inducing gene expression and gene insertion mutagenesis, we verified that bglG regulated the heterogeneity of the acid stress response and that the relevant mechanisms might be related to activating hsp20. This study provides new evidence for the mechanism of the ATR and may contribute to the theoretical basis of improving the acid tolerance of Lacticaseibacillus paracasei L9.

Anomalous diffusion of E. coli under microfluidic confinement and chemical gradient

We report a two-layer microfluidic device to study the combined effect of confinement and chemical gradient on the motility of wild-type E. coli. We track individual E. coli in 50 μm and 10 μm wide microchannels, with a channel height of 2 μm, to generate quasi-2D conditions. We find that contrary to expectations, bacterial trajectories are superdiffusive even in the absence of a chemical (glucose) gradient. The superdiffusive behaviour becomes more pronounced upon introducing a chemical gradient or strengthening the lateral confinement. Run length distributions for weak lateral confinement in the absence of chemical gradients follow an exponential distribution. Both confinement and chemoattraction induce deviations from this behaviour, with the run length distributions approaching a power-law form under these conditions. Both confinement and chemoattraction suppress large-angle tumbles as well. Our results suggest that wild-type E. coli modulates both its runs and tumbles in a similar manner under physical confinement and chemical gradient. Our findings have implications for understanding how bacteria modulate their motility behaviour in natural habitats.

An Isotope-Labeled Single-Cell Raman Spectroscopy Approach for Tracking the Physiological Evolution Trajectory of Bacteria toward Antibiotic Resistance

Understanding evolution of antibiotic resistance is vital for containing its global spread. Yet our ability to in situ track highly heterogeneous and dynamic evolution is very limited. Here, we present a new single-cell approach integrating D₂ O-labeled Raman spectroscopy, advanced multivariate analysis, and genotypic profiling to in situ track physiological evolution trajectory toward resistance. Physiological diversification of individual cells from isogenic population with cyclic ampicillin treatment is captured. Advanced multivariate analysis of spectral changes classifies all individual cells into four subsets of sensitive, intrinsic tolerant, evolved tolerant and resistant. Remarkably, their dynamic shifts with evolution are depicted and spectral markers of each state are identified. Genotypic analysis validates the phenotypic shift and provides insights into the underlying genetic basis. The new platform advances rapid phenotyping resistance evolution and guides evolution control.

Exploring the fundamental limit of antimicrobial susceptibility by near-single-cell electrical impedance spectroscopy

Many novel susceptibility tests are being developed to tackle the worldwide problem of antimicrobial resistance (AMR). The key driver behind these developments, that is the need to reduce the response time, requires an understanding of which bacterial characteristic needs to be monitored to provide a rapid and ideally universal signature of susceptibility. Many characteristics have already been studied, most notably bacterial growth, metabolism and motility. Here, we consider electrical impedance to directly access bacterial metabolism, which can be considered a fundamental indicator of bacterial viability. By studying the electrical response of individual bacteria to an antibiotic challenge, we detect antimicrobial action close to its biological limit. Specifically, we find that it takes 30-60 min to register significant changes in impedance for clinical concentrations of antibiotics, in line with other rapid indicators. Our findings suggest that 60 min is the fundamental lower limit of response time for a realistic susceptibility test at clinically relevant antibiotic concentrations.

High-Resolution Bacterial Cytological Profiling Reveals Intrapopulation Morphological Variations upon Antibiotic Exposure

Phenotypic heterogeneity is crucial to bacterial survival and could provide insights into the mechanism of action (MOA) of antibiotics, especially those with polypharmacological actions. Although phenotypic changes among individual cells could be detected by existing profiling methods, due to the data complexity, only population average data were commonly used, thereby overlooking the heterogeneity. In this study, we developed a high-resolution bacterial cytological profiling method that can capture morphological variations of bacteria upon antibiotic treatment. With an unprecedented single-cell resolution, this method classifies morphological changes of individual cells into known MOAs with an overall accuracy above 90%. We next showed that combinations of two antibiotics induce altered cell morphologies that are either unique or similar to that of an antibiotic in the combinations. With these combinatorial profiles, this method successfully revealed multiple cytological changes caused by a natural product-derived compound that, by itself, is inactive against Acinetobacter baumannii but synergistically exerts its multiple antibacterial activities in the presence of colistin. The findings have paved the way for future single-cell profiling in bacteria and have highlighted previously underappreciated intrapopulation variations caused by antibiotic perturbation.

Mid-Infrared Photothermal-Fluorescence In Situ Hybridization for Functional Analysis and Genetic Identification of Single Cells

Simultaneous identification and metabolic analysis of microbes with single-cell resolution and high throughput are necessary to answer the question of "who eats what, when, and where" in complex microbial communities. Here, we present a mid-infrared photothermal-fluorescence in situ hybridization (MIP-FISH) platform that enables direct bridging of genotype and phenotype. Through multiple improvements of MIP imaging, the sensitive detection of isotopically labeled compounds incorporated into proteins of individual bacterial cells became possible, while simultaneous detection of FISH labeling with rRNA-targeted probes enabled the identification of the analyzed cells. In proof-of-concept experiments, we showed that the clear spectral red shift in the protein amide I region due to incorporation of ¹³C atoms originating from ¹³C-labeled glucose can be exploited by MIP-FISH to discriminate and identify ¹³C-labeled bacterial cells within a complex human gut microbiome sample. The presented methods open new opportunities for single-cell structure-function analyses for microbiology.

Host plant physiological transformation and microbial population heterogeneity as important determinants of the Soft Rot Pectobacteriaceae-plant interactions

Pectobacterium and Dickeya species belonging to the Soft Rot Pectobacteriaceae (SRP) are one of the most devastating phytopathogens. They degrade plant tissues by producing an arsenal of plant cell wall degrading enzymes. However, SRP-plant interactions are not restricted to the production of these "brute force" weapons. Additionally, these bacteria apply stealth behavior related to (1) manipulation of the host plant via induction of susceptible responses and (2) formation of heterogeneous populations with functionally specialized cells. Our review aims to summarize current knowledge on SRP-induced plant susceptible responses and on the heterogeneity of SRP populations. The review shows that SRP are capable of adjusting the host's hormonal balance, inducing host-mediated plant cell wall modification, promoting iron assimilation by the host, stimulating the accumulation of reactive oxygen species and host cell death, and activating the synthesis of secondary metabolites that are ineffective in limiting disease progression. By this means, SRP facilitate host plant susceptibility. During host colonization, SRP populations produce various functionally specialized cells adapted for enhanced virulence, increased resistance, motility, vegetative growth, or colonization of the vascular system. This enables SRP to perform self-contradictory tasks, which benefits a population's overall fitness in various environments, including host plants. Such stealthy tactical actions facilitate plant-SRP interactions and disease progression.

Bifidobacterium longum subsp. infantis utilizes human milk urea to recycle nitrogen within the infant gut microbiome

Human milk guides the structure and function of microbial commensal communities that colonize the nursing infant gut. Indigestible molecules dissolved in human milk establish a microbiome often dominated by bifidobacteria capable of utilizing these substrates. Interestingly, urea accounts for ~15% of total human milk nitrogen, representing a potential reservoir for microbiota that may be salvaged for critical metabolic operations during lactation and neonatal development. Accordingly, B. infantis strains are competent for urea nitrogen utilization, constituting a previously hypothetical phenotype in commensal bacteria hosted by humans. Urease gene expression, downstream nitrogen metabolic pathways, and enzymatic activity are induced during urea utilization to yield elevated ammonia concentrations. Moreover, biosynthetic networks relevant to infant nutrition and development are transcriptionally responsive to urea utilization including branched chain and other essential amino acids. Importantly, isotopically labeled urea nitrogen is broadly distributed throughout the expressed B. infantis proteome. This incisively demonstrates that the previously inaccessible urea nitrogen is incorporated into microbial products available for infant host utilization. In aggregate, B. infantis possesses the requisite phenotypic foundation to participate in human milk urea nitrogen recycling within its infant host and thus may be a key contributor to nitrogen homeostasis early in life.

Decoupling of respiration rates and abundance in marine prokaryoplankton

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

Exposure to stressors and antimicrobials induces cell-autonomous ultrastructural heterogeneity of an intracellular bacterial pathogen

Despite their clonality, intracellular bacterial pathogens commonly show remarkable physiological heterogeneity during infection of host cells. Physiological heterogeneity results in distinct ultrastructural morphotypes, but the correlation between bacterial physiological state and ultrastructural appearance remains to be established. In this study, we showed that individual cells of Salmonella enterica serovar Typhimurium are heterogeneous in their ultrastructure. Two morphotypes based on the criterion of cytoplasmic density were discriminated after growth under standard culture conditions, as well as during intracellular lifestyle in mammalian host cells. We identified environmental conditions which affect cytoplasmic densities. Using compounds generating oxygen radicals and defined mutant strains, we were able to link the occurrence of an electron-dense ultrastructural morphotype to exposure to oxidative stress and other stressors. Furthermore, by combining ultrastructural analyses of Salmonella during infection and fluorescence reporter analyses for cell viability, we provided evidence that two characterized ultrastructural morphotypes with electron-lucent or electron-dense cytoplasm represent viable cells. Moreover, the presence of electron-dense types is stress related and can be experimentally induced only when amino acids are available in the medium. Our study proposes ultrastructural morphotypes as marker for physiological states of individual intracellular pathogens providing a new marker for single cell analyses.

Fast bacterial growth reduces antibiotic accumulation and efficacy

Phenotypic variations between individual microbial cells play a key role in the resistance of microbial pathogens to pharmacotherapies. Nevertheless, little is known about cell individuality in antibiotic accumulation. Here, we hypothesise that phenotypic diversification can be driven by fundamental cell-to-cell differences in drug transport rates. To test this hypothesis, we employed microfluidics-based single-cell microscopy, libraries of fluorescent antibiotic probes and mathematical modelling. This approach allowed us to rapidly identify phenotypic variants that avoid antibiotic accumulation within populations of Escherichia coli, Pseudomonas aeruginosa, Burkholderia cenocepacia, and Staphylococcus aureus. Crucially, we found that fast growing phenotypic variants avoid macrolide accumulation and survive treatment without genetic mutations. These findings are in contrast with the current consensus that cellular dormancy and slow metabolism underlie bacterial survival to antibiotics. Our results also show that fast growing variants display significantly higher expression of ribosomal promoters before drug treatment compared to slow growing variants. Drug-free active ribosomes facilitate essential cellular processes in these fast-growing variants, including efflux that can reduce macrolide accumulation. We used this new knowledge to eradicate variants that displayed low antibiotic accumulation through the chemical manipulation of their outer membrane inspiring new avenues to overcome current antibiotic treatment failures.

Microfluidic technology for multiple single-cell capture

Microfluidic devices are widely used in single-cell capture and for pairing single cells or groups of cells for cell-cell interaction analysis; these advances have improved drug screening and cell signal transduction analysis. The complex in vivo environment involves interactions between two cells and among multiple cells of the same or different phenotypes. This study reviewed the core principles and performance of several microfluidic multiple- and single-cell capture methods, namely, the microwell, valve, trap, and droplet methods. The advantages and disadvantages of the methods were compared, and suggestions regarding their application to multiple-cell capture were provided. The results may serve as a reference for research on microfluidic multiple single-cell coculture technology.

Stochastic nucleoid segregation dynamics as a source of the phenotypic variability in E. coli

Segregation of the replicating chromosome from a single to two nucleoid bodies is one of the major processes in growing bacterial cells. The segregation dynamics is tuned by intricate interactions with other cellular processes such as growth and division, ensuring flexibility in a changing environment. We hypothesize that the internal stochasticity of the segregation process may be the source of cell-to-cell phenotypic variability, in addition to the well-established gene expression noise and uneven partitioning of low copy number components. We compare dividing cell lineages with filamentous cells, where the lack of the diffusion barriers is expected to reduce the impact of other factors on the variability of nucleoid segregation dynamics. The nucleoid segregation was monitored using time-lapse microscopy in live E. coli cells grown in linear grooves. The main characteristics of the segregation process, namely, the synchrony of partitioning, rates of separation, and final positions, as well as the variability of these characteristics, were determined for dividing and filamentous lineages growing under the same conditions. Indeed, the gene expression noise was considerably homogenized along filaments as determined from the distribution of CFP and YFP stochastically expressed from the chromosome. We find that 1) the synchrony of nucleoid partitioning is progressively decreasing during consecutive cell cycles, but to a significantly lesser degree in filamentous than in dividing cells; 2) the mean partitioning rate of nucleoids is essentially the same in dividing and filamentous cells, displaying a substantial variability in both; and 3) nucleoids segregate to the same distances in dividing and filamentous cells. Variability in distances is increasing during successive cell cycles, but to a much lesser extent in filamentous cells. Our findings indicate that the variability of the chromosome segregation dynamics is reduced upon removal of boundaries between nucleoids, whereas the remaining variability is essentially inherent to the nucleoid itself.

A graph neural network model to estimate cell-wise metabolic flux using single-cell RNA-seq data

The metabolic heterogeneity and metabolic interplay between cells are known as significant contributors to disease treatment resistance. However, with the lack of a mature high-throughput single-cell metabolomics technology, we are yet to establish systematic understanding of the intra-tissue metabolic heterogeneity and cooperative mechanisms. To mitigate this knowledge gap, we developed a novel computational method, namely, single-cell flux estimation analysis (scFEA), to infer the cell-wise fluxome from single-cell RNA-sequencing (scRNA-seq) data. scFEA is empowered by a systematically reconstructed human metabolic map as a factor graph, a novel probabilistic model to leverage the flux balance constraints on scRNA-seq data, and a novel graph neural network-based optimization solver. The intricate information cascade from transcriptome to metabolome was captured using multilayer neural networks to capitulate the nonlinear dependency between enzymatic gene expressions and reaction rates. We experimentally validated scFEA by generating an scRNA-seq data set with matched metabolomics data on cells of perturbed oxygen and genetic conditions. Application of scFEA on this data set showed the consistency between predicted flux and the observed variation of metabolite abundance in the matched metabolomics data. We also applied scFEA on five publicly available scRNA-seq and spatial transcriptomics data sets and identified context- and cell group-specific metabolic variations. The cell-wise fluxome predicted by scFEA empowers a series of downstream analyses including identification of metabolic modules or cell groups that share common metabolic variations, sensitivity evaluation of enzymes with regards to their impact on the whole metabolic flux, and inference of cell-tissue and cell-cell metabolic communications.

Inducible Population Quality Control of Engineered Bacillus subtilis for Improved N-Acetylneuraminic Acid Biosynthesis

Biosynthesis by microorganisms using renewable feedstocks is an important approach for realizing sustainable chemical manufacturing. However, cell-to-cell variation in biosynthesis capability during fermentation restricts the robustness and efficiency of bioproduction, hampering the industrialization of biosynthesis. Herein, we developed an inducible population quality control system (iPopQC) for dynamically modulating the producing and nonproducing subpopulations of engineered Bacillus subtilis, which was constructed via inducible promoter- and metabolite-responsive biosensor-based genetic circuit for regulating essential genes. Moreover, iPopQC achieved a 1.97-fold increase in N-acetylneuraminic acid (NeuAc) titer by enriching producing cell subpopulation during cultivation, representing 52% higher than that of previous PopQC. Strains with double-output iPopQC cocoupling the expression of double essential genes with NeuAc production improved production robustness further, retaining NeuAc production throughout 96 h of fermentation, upon which the strains cocoupling one essential gene expression with NeuAc production abolished the production ability.

Metabolic history and metabolic fitness as drivers of anabolic heterogeneity in isogenic microbial populations

Microbial populations often display different degrees of heterogeneity in their substrate assimilation, that is, anabolic heterogeneity. It has been shown that nutrient limitations are a relevant trigger for this behaviour. Here we explore the dynamics of anabolic heterogeneity under nutrient replete conditions. We applied time-resolved stable isotope probing and nanoscale secondary ion mass spectrometry to quantify substrate assimilation by individual cells of Pseudomonas putida, P. stutzeri and Thauera aromatica. Acetate and benzoate at different concentrations were used as substrates. Anabolic heterogeneity was quantified by the cumulative differentiation tendency index. We observed two major, opposing trends of anabolic heterogeneity over time. Most often, microbial populations started as highly heterogeneous, with heterogeneity decreasing by various degrees over time. The second, less frequently observed trend, saw microbial populations starting at low or very low heterogeneity, and remaining largely stable over time. We explain these trends as an interplay of metabolic history (e.g. former growth substrate or other nutrient limitations) and metabolic fitness (i.e. the fine-tuning of metabolic pathways to process a defined growth substrate). Our results offer a new viewpoint on the intra-population functional diversification often encountered in the environment, and suggests that some microbial populations may be intrinsically heterogeneous.

Quorum Sensing in Fungal Species

Quorum sensing (QS) is one of the most studied cell-cell communication mechanisms in fungi. Research in the last 20 years has explored various fungal QS systems that are involved in a wide range of biological processes, especially eukaryote- or fungus-specific behaviors, mirroring the significant contribution of QS regulation to fungal biology and evolution. Based on recent progress, we summarize in this review fungal QS regulation, with an emphasis on its functional role in behaviors unique to fungi or eukaryotes. We suggest that using fungi as genetically amenable eukaryotic model systems to address why and how QS regulation is integrated into eukaryotic reproductive strategies and molecular or cellular processes could be an important direction for QS research. Expected final online publication date for the Annual Review of Microbiology, Volume 75 is October 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

A Possible Flow Cytometry-Based Viability and Vitality Assessment Protocol for Pathogenic Vibrio cholerae O1 and O139 Postexposure to Simulated Gastric Fluid

During the intake of contaminated water, for diarrheal disease to occur, Vibrio cholerae must survive through the bactericidal digestive secretion of gastric fluid during passage through the stomach. Determining the viability of these bacteria is challenging, with the standard cultivation methods for viability being time-consuming and unable to culture cells that may still function accordingly. This study assessed the use of enzyme action and membrane integrity as alternatives for determining vitality and viability, respectively, in gastric acid-stressed pathogenic Vibrio cholerae O1 and O139, using fluorescent probes thiazole orange (TO) for viability based on membrane integrity, carboxyfluorescein diacetate (CFDA) with acetoxymethyl ester (AM) for vitality based on metabolic activity, and propidium iodide (PI) for cell death/damage due to loss of membrane integrity, with flow cytometry. Simulated gastric fluid-treated bacterial cells were labelled with blends of TO+PI and CFDA-AM+PI, and these stained cells were separated into heterologous populations based on their fluorescence signal. The gastric acid exposed cells presented with high green fluorescence signals after staining with the metabolic probe CFDA-AM, which indicated intact (live) cells due to being metabolically active, whereas when the same cells were stained with the DNA probe (TO), these appeared to be in a “stressed state” due to loss of membrane integrity. Damaged cells (dead cells) showed high red fluorescence levels after staining with PI probe. The use of flow cytometry with fluorescent probes is a favorable method for evaluating the vitality and viability of bacteria when cells are labelled with a combination of CFDA-AM+PI.

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

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

Microsecond fingerprint stimulated Raman spectroscopic imaging by ultrafast tuning and spatial-spectral learning

Label-free vibrational imaging by stimulated Raman scattering (SRS) provides unprecedented insight into real-time chemical distributions. Specifically, SRS in the fingerprint region (400-1800 cm-1) can resolve multiple chemicals in a complex bio-environment. However, due to the intrinsic weak Raman cross-sections and the lack of ultrafast spectral acquisition schemes with high spectral fidelity, SRS in the fingerprint region is not viable for studying living cells or large-scale tissue samples. Here, we report a fingerprint spectroscopic SRS platform that acquires a distortion-free SRS spectrum at 10 cm-1 spectral resolution within 20 µs using a polygon scanner. Meanwhile, we significantly improve the signal-to-noise ratio by employing a spatial-spectral residual learning network, reaching a level comparable to that with 100 times integration. Collectively, our system enables high-speed vibrational spectroscopic imaging of multiple biomolecules in samples ranging from a single live microbe to a tissue slice.

Measurement of Protein Mobility in Listeria monocytogenes Reveals a Unique Tolerance to Osmotic Stress and Temperature Dependence of Diffusion

Protein mobility in the cytoplasm is essential for cellular functions, and slow diffusion may limit the rates of biochemical reactions in the living cell. Here, we determined the apparent lateral diffusion coefficient (D_L) of GFP in Listeria monocytogenes as a function of osmotic stress, temperature, and media composition. We find that D_L is much less affected by hyperosmotic stress in L. monocytogenes than under similar conditions in Lactococcus lactis and Escherichia coli. We find a temperature optimum for protein diffusion in L. monocytogenes at 30°C, which deviates from predicted trends from the generalized Stokes-Einstein equation under dilute conditions and suggests that the structure of the cytoplasm and macromolecular crowding vary as a function of temperature. The turgor pressure of L. monocytogenes is comparable to other Gram-positive bacteria like Bacillus subtilis and L. lactis but higher in a knockout strain lacking the stress-inducible sigma factor SigB. We discuss these findings in the context of how L. monocytogenes survives during environmental transmission and interaction with the human host.

Fluorescent macrolide probes - synthesis and use in evaluation of bacterial resistance

The emerging crisis of antibiotic resistance requires a multi-pronged approach in order to avert the onset of a post-antibiotic age. Studies of antibiotic uptake and localisation in live cells may inform the design of improved drugs and help develop a better understanding of bacterial resistance and persistence. To facilitate this research, we have synthesised fluorescent derivatives of the macrolide antibiotic erythromycin. These analogues exhibit a similar spectrum of antibiotic activity to the parent drug and are capable of labelling both Gram-positive and -negative bacteria for microscopy. The probes localise intracellularly, with uptake in Gram-negative bacteria dependent on the level of efflux pump activity. A plate-based assay established to quantify bacterial labelling and localisation demonstrated that the probes were taken up by both susceptible and resistant bacteria. Significant intra-strain and -species differences were observed in these preliminary studies. In order to examine uptake in real-time, the probe was used in single-cell microfluidic microscopy, revealing previously unseen heterogeneity of uptake in populations of susceptible bacteria. These studies illustrate the potential of fluorescent macrolide probes to characterise and explore drug uptake and efflux in bacteria.

Macrolide fluorescent probes illuminate the interactions between antibiotics and bacteria, providing new insight into mechanisms of resistance.

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.

Capturing and Understanding the Dynamics and Heterogeneity of Gene Expression in the Living Cell

The regulation of gene expression is a fundamental process enabling cells to respond to internal and external stimuli or to execute developmental programs. Changes in gene expression are highly dynamic and depend on many intrinsic and extrinsic factors. In this review, we highlight the dynamic nature of transient gene expression changes to better understand cell physiology and development in general. We will start by comparing recent in vivo procedures to capture gene expression in real time. Intrinsic factors modulating gene expression dynamics will then be discussed, focusing on chromatin modifications. Furthermore, we will dissect how cell physiology or age impacts on dynamic gene regulation and especially discuss molecular insights into acquired transcriptional memory. Finally, this review will give an update on the mechanisms of heterogeneous gene expression among genetically identical individual cells. We will mainly focus on state-of-the-art developments in the yeast model but also cover higher eukaryotic systems.

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.

A brief guide for the measurement and interpretation of microbial functional diversity

The importance of functional diversity for the functioning and behaviour of microbial communities is clear, yet the widespread incorporation of functional diversity measurements into environmental microbiology study designs remains surprisingly limited. This may, at least to some extent, be a consequence of the unique conceptual and methodological challenges to measuring functional diversity in microbial communities. To facilitate the increased incorporation of functional diversity measurements into environmental microbiology study designs, we review here the process and some key caveats for measuring functional diversity and provide specific examples. We highlight three main decision points and provide guidance to making these decisions based on the underlying mechanisms for how functional diversity relates to an ecosystem process or property of interest. We discuss the selection of an appropriate type of functional trait, selection of the specificity at which functional diversity will be measured, and selection of an appropriate metric for estimating functional diversity from quantitative measures of those traits. We further discuss decisions regarding the use of one- or multi-dimensional measures of functional diversity and how advances in the field of trait-based community ecology could be applied or adapted to address questions in environmental microbiology.

Agent-Based Models Predict Emergent Behavior of Heterogeneous Cell Populations in Dynamic Microenvironments

Computational models are most impactful when they explain and characterize biological phenomena that are non-intuitive, unexpected, or difficult to study experimentally. Countless equation-based models have been built for these purposes, but we have yet to realize the extent to which rules-based models offer an intuitive framework that encourages computational and experimental collaboration. We develop ARCADE, a multi-scale agent-based model to interrogate emergent behavior of heterogeneous cell agents within dynamic microenvironments and demonstrate how complexity of intracellular metabolism and signaling modules impacts emergent dynamics. We perform in silico case studies on context, competition, and heterogeneity to demonstrate the utility of our model for gaining computational and experimental insight. Notably, there exist (i) differences in emergent behavior between colony and tissue contexts, (ii) linear, non-linear, and multimodal consequences of parameter variation on competition in simulated co-cultures, and (iii) variable impact of cell and population heterogeneity on emergent outcomes. Our extensible framework is easily modified to explore numerous biological systems, from tumor microenvironments to microbiomes.

Large-scale label-free single-cell analysis of paramylon in Euglena gracilis by high-throughput broadband Raman flow cytometry

Microalga-based biomaterial production has attracted attention as a new source of drugs, foods, and biofuels. For enhancing the production efficiency, it is essential to understand its differences between heterogeneous microalgal subpopulations. However, existing techniques are not adequate to address the need due to the lack of single-cell resolution or the inability to perform large-scale analysis and detect small molecules. Here we demonstrated large-scale single-cell analysis of Euglena gracilis (a unicellular microalgal species that produces paramylon as a potential drug for HIV and colon cancer) with our recently developed high-throughput broadband Raman flow cytometer at a throughput of >1,000 cells/s. Specifically, we characterized the intracellular content of paramylon from single-cell Raman spectra of 10,000 E. gracilis cells cultured under five different conditions and found that paramylon contents in E. gracilis cells cultured in an identical condition is given by a log-normal distribution, which is a good model for describing the number of chemicals in a reaction network. The capability of characterizing distribution functions in a label-free manner is an important basis for isolating specific cell populations for synthetic biology via directed evolution based on the intracellular content of metabolites.

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.

Next-generation physiology approaches to study microbiome function at single cell level

The function of cells in their native habitat often cannot be reliably predicted from genomic data or from physiology studies of isolates. Traditional experimental approaches to study the function of taxonomically and metabolically diverse microbiomes are limited by their destructive nature, low spatial resolution or low throughput. Recently developed technologies can offer new insights into cellular function in natural and human-made systems and how microorganisms interact with and shape the environments that they inhabit. In this Review, we provide an overview of these next-generation physiology approaches and discuss how the non-destructive analysis of cellular phenotypes, in combination with the separation of the target cells for downstream analyses, provide powerful new, complementary ways to study microbiome function. We anticipate that the widespread application of next-generation physiology approaches will transform the field of microbial ecology and dramatically improve our understanding of how microorganisms function in their native environment.

Exploiting nonionic surfactants to enhance fatty alcohol production in Rhodosporidium toruloides

Fatty alcohols (FOHs) are important feedstocks in the chemical industry to produce detergents, cosmetics, and lubricants. Microbial production of FOHs has become an attractive alternative to production in plants and animals due to growing energy demands and environmental concerns. However, inhibition of cell growth caused by intracellular FOH accumulation is one major issue that limits FOH titers in microbial hosts. In addition, identification of FOH‐specific exporters remains a challenge and previous studies towards this end are limited. To alleviate the toxicity issue, we exploited nonionic surfactants to promote the export of FOHs in Rhodosporidium toruloides, an oleaginous yeast that is considered an attractive next‐generation host for the production of fatty acid‐derived chemicals. Our results showed FOH export efficiency was dramatically improved and the growth inhibition was alleviated in the presence of small amounts of tergitol and other surfactants. As a result, FOH titers increase by 4.3‐fold at bench scale to 352.6 mg/L. With further process optimization in a 2‐L bioreactor, the titer was further increased to 1.6 g/L. The method we show here can potentially be applied to other microbial hosts and may facilitate the commercialization of microbial FOH production.

A Gene Cluster That Encodes Histone Deacetylase Inhibitors Contributes to Bacterial Persistence and Antibiotic Tolerance in Burkholderia thailandensis

The discovery of antibiotics such as penicillin and streptomycin marked a historic milestone in the 1940s and heralded a new era of antimicrobial therapy as the modern standard for medical treatment. Yet, even in those early days of discovery, it was noted that a small subset of cells (∼1 in 10⁵) survived antibiotic treatment and continued to persist, leading to recurrence of chronic infection. These persisters are phenotypic variants that have modified their physiology to survive environmental stress. In this study, we have performed three transcriptomic screens to identify persistence genes that are common between three different stressor conditions. In particular, we identified genes that function in the synthesis of secondary metabolites, small molecules, and complex lipids, which are likely required to maintain the persistence state. Targeting universal persistence genes can lead to the development of clinically relevant antipersistence therapeutics for infectious disease management.

Persister cells are genetically identical variants in a bacterial population that have phenotypically modified their physiology to survive environmental stress. In bacterial pathogens, persisters are able to survive antibiotic treatment and reinfect patients in a frustrating cycle of chronic infection. To better define core persistence mechanisms for therapeutics development, we performed transcriptomics analyses of Burkholderia thailandensis populations enriched for persisters via three methods: flow sorting for low proton motive force, meropenem treatment, and culture aging. Although the three persister-enriched populations generally displayed divergent gene expression profiles that reflect the multimechanistic nature of stress adaptations, there were several common gene pathways activated in two or all three populations. These include polyketide and nonribosomal peptide synthesis, Clp proteases, mobile elements, enzymes involved in lipid metabolism, and ATP-binding cassette (ABC) transporter systems. In particular, identification of genes that encode polyketide synthases (PKSs) and fatty acid catabolism factors indicates that generation of secondary metabolites, natural products, and complex lipids could be part of the metabolic program that governs the persistence state. We also found that loss-of-function mutations in the PKS-encoding gene locus BTH_I2366, which plays a role in biosynthesis of histone deacetylase (HDAC) inhibitors, resulted in increased sensitivity to antibiotics targeting DNA replication. Furthermore, treatment of multiple bacterial pathogens with a fatty acid synthesis inhibitor, CP-640186, potentiated the efficacy of meropenem against the persister populations. Altogether, our results suggest that bacterial persisters may exhibit an outwardly dormant physiology but maintain active metabolic processes that are required to maintain persistence.

IMPORTANCE The discovery of antibiotics such as penicillin and streptomycin marked a historic milestone in the 1940s and heralded a new era of antimicrobial therapy as the modern standard for medical treatment. Yet, even in those early days of discovery, it was noted that a small subset of cells (∼1 in 10⁵) survived antibiotic treatment and continued to persist, leading to recurrence of chronic infection. These persisters are phenotypic variants that have modified their physiology to survive environmental stress. In this study, we have performed three transcriptomic screens to identify persistence genes that are common between three different stressor conditions. In particular, we identified genes that function in the synthesis of secondary metabolites, small molecules, and complex lipids, which are likely required to maintain the persistence state. Targeting universal persistence genes can lead to the development of clinically relevant antipersistence therapeutics for infectious disease management.

Interaction variability shapes succession of synthetic microbial ecosystems

Cellular interactions are a major driver for the assembly and functioning of microbial communities. Their strengths are shown to be highly variable in nature; however, it is unclear how such variations regulate community behaviors. Here we construct synthetic Lactococcus lactis consortia and mathematical models to elucidate the role of interaction variability in ecosystem succession and to further determine if casting variability into modeling empowers bottom-up predictions. For a consortium of bacteriocin-mediated cooperation and competition, we find increasing the variations of cooperation, from either altered labor partition or random sampling, drives the community into distinct structures. When the cooperation and competition are additionally modulated by pH, ecosystem succession becomes jointly controlled by the variations of both interactions and yields more diversified dynamics. Mathematical models incorporating variability successfully capture all of these experimental observations. Our study demonstrates interaction variability as a key regulator of community dynamics, providing insights into bottom-up predictions of microbial ecosystems.

Cellular interactions are a major driver of microbial communities and shown highly variable in strength. Here the authors construct synthetic consortia and mathematical models to elucidate the role of interaction variability in driving ecosystem succession.

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.

Revealing the Metabolic Activity of Persisters in Mycobacteria by Single-Cell D2O Raman Imaging Spectroscopy

The metabolic activity of bacterial cells largely differentiates even within a clonal population. Such metabolic divergence among cells is thought to play an important role for phenotypic adaptation to ever-changing environmental conditions, such as antibiotic persistence. It has long been thought that persisters are in a state called dormancy, in which cells are metabolically inactive and do not grow. However, recent studies suggest that some types of persisters are not necessarily dormant, triggering a debate about the mechanisms of persisters. Here, we combined single-cell Raman imaging spectroscopy and D₂O labeling to analyze metabolic activities of bacterial persister cells. Metabolically active cells uptake deuterium through metabolic processes and give distinct C-D Raman bands, which are direct indicators of metabolic activity. Using this imaging method, we characterized the metabolic activity of Mycobacterium smegmatis, a fast-growing model for Mycobacterium tuberculosis. We found that persister cells of M. smegmatis show certain metabolic activity and active cell growth in the presence of the antibiotic rifampicin. Interestingly, persistence is not correlated with growth rate prior to antibiotic exposure. These results show that dormancy is not responsible for the persistence of M. smegmatis cells against rifampicin, suggesting that the mechanism of persistence largely varies depending on the type of antibiotics and bacteria. Our results successfully demonstrate the potential of our perfusion-based single-cell D₂O Raman imaging system for the analysis of the metabolic activity and growth of bacterial persister cells.

Single cell ecology

Cells are the building blocks of life, from single-celled microbes through to multi-cellular organisms. To understand a multitude of biological processes we need to understand how cells behave, how they interact with each other and how they respond to their environment. The use of new methodologies is changing the way we study cells allowing us to study them on minute scales and in unprecedented detail. These same methods are allowing researchers to begin to sample the vast diversity of microbes that dominate natural environments. The aim of this special issue is to bring together research and perspectives on the application of new approaches to understand the biological properties of cells, including how they interact with other biological entities. This article is part of a discussion meeting issue 'Single cell ecology'.

Microbial coexistence through chemical-mediated interactions

Many microbial functions happen within communities of interacting species. Explaining how species with disparate growth rates can coexist is important for applications such as manipulating host-associated microbiota or engineering industrial communities. Here, we ask how microbes interacting through their chemical environment can achieve coexistence in a continuous growth setup (similar to an industrial bioreactor or gut microbiota) where external resources are being supplied. We formulate and experimentally constrain a model in which mediators of interactions (e.g. metabolites or waste-products) are explicitly incorporated. Our model highlights facilitation and self-restraint as interactions that contribute to coexistence, consistent with our intuition. When interactions are strong, we observe that coexistence is determined primarily by the topology of facilitation and inhibition influences not their strengths. Importantly, we show that consumption or degradation of chemical mediators moderates interaction strengths and promotes coexistence. Our results offer insights into how to build or restructure microbial communities of interest.

Quantitative macromolecular patterns in phytoplankton communities resolved at the taxonomical level by single-cell Synchrotron FTIR-spectroscopy

BACKGROUND

Technical limitations regarding bulk analysis of phytoplankton biomass limit our comprehension of carbon fluxes in natural populations and, therefore, of carbon, nutrients and energy cycling in aquatic ecosystems. In this study, we took advantage of Synchrotron FTIR micro-spectroscopy and the partial least square regression (PLSr) algorithm to simultaneously quantify the protein, lipid and carbohydrate content at the single-cell level in a mock phytoplankton community (composed by a cyanobacterium, a green-alga and a diatom) grown at two temperatures (15 °C and 25 °C).

RESULTS

The PLSr models generated to quantify cell macromolecules presented high quality fit (R² ≥ 0.90) and low error of prediction (RMSEP 2-6% of dry weight). The regression coefficients revealed that the prediction of each macromolecule was not exclusively dependent on spectral features corresponding to that compound, but rather on all major macromolecular pools, reflecting adjustments in the overall cell carbon balance. The single-cell analysis, studied by means of Kernel density estimators, showed that the modes of density distribution of macromolecules were different at 15 °C and 25 °C. However, a substantial proportion of cells was biochemically identical at the two temperatures because of population heterogeneity.

CONCLUSIONS

The spectroscopic approach presented in this study allows the quantification of macromolecules in single phytoplankton cells. This method showed that population heterogeneity most likely ensures a backup of non-acclimated cells that may rapidly exploit new favourable niches. This finding may have important consequences for the ecology of phytoplankton populations and shows that the "average cell" concept might substantially limit our comprehension of population dynamics and biogeochemical cycles in aquatic ecosystems.

Hydrodynamic shuttling for deterministic high-efficiency multiple single-cell capture in a microfluidic chip

Studies on cellular heterogeneity have emerged as a powerful approach for developing new strategies to treat diseases including cancer. However, it is difficult to set up an in vitro co-culture experiment to study the interaction of individual live cells. In this paper, we report a hydrodynamic shuttling chip (HSC) which can deterministically capture single cells into microfluidic chambers to set up multiple single-cell co-culture experiments in which individual live cells can be microscopically observed. Using this chip device, we demonstrated a triple single-cell culture of oral squamous cell carcinoma and lymphatic endothelial cells to observe the effect of cell-cell interaction on the cell motility. Triple, single-cell pairing efficiency by our HSC device was eightfold higher than that of the probabilistic method. Using this HSC device, we were able to perform triple-culture experiments to show the cell type-dependent motility of oral squamous cell carcinoma and lymphatic endothelial cells, which was not observed in co-culture experiments.