Single-cell microbiology: tools, technologies, and applications

A single-microbe living bioelectronic sensor for intracellular amperometric analysis

Subcellularly amperometric analysis in situ is crucial for understanding intracellular redox biochemistry and subcellular heterogeneity. Unfortunately, the ultra-small size and complex microenvironment inside the cell pose a great challenge to achieve this goal. To address the challenge, a minimized living microbial sensor has been fabricated in this work for amperometric analysis. Here, by fabricating the dimidiate microelectrode as the working electrode, while fitting a living electroactive bacterium (EAB) as the transducer, outward extracellular electron transfer (EET) of the sensory EAB is correlated with the concentration of lactic acid, which is electrochemically recorded and thus displays an electrical signal output for detection. In specific, the S. oneidensis modified dimidiate microelectrode (S.O.@GNE-NPE) acts as an integrated electroanalytical device to generate the electrical signal in situ. The established microcircuit provides unprecedented precision and sensitivity, contributing to subcellular amperometric measurement. The microbial sensor shows a linear response in the concentration range of 0-60 mM, with a limit of detection (LOD) at 0.3 mM. The microsensor also demonstrates good selectivity against interferences. Additionally, intracellular analysis of lactic acid provides direct evidence of enhanced lactic metabolism in cancer cells as a result of "Warburg Effect". This work shows an example of nano-, bio- and electric technologies that have been integrated on the EAB-modified dimidiate microelectrode, and achieves intracellular biosensing application through such integration. It may give a new strategy on the combination of micro/nanotechnologies with sensory EAB for the necessary development of bioelectronic devices.

Beyond two dimensions: Exploring 3D dielectrophoresis for microparticle control using carbon electrodes

This study explores the frontiers of microparticle manipulation by introducing an actuator platform for the three-dimensional positioning of microparticles using dielectrophoresis (DEP), a technique known for its selectivity and ease of integration with microtechnology. Leveraging advancements in carbon-based devices due to their biocompatibility and electrochemical stability, our work extends the application of DEP from two-dimensional constraints to precise 3D positioning within microvolumes, employing a photolithography-based fabrication process known as Carbon-MEMS technology (C-MEMS). We present the design, finite element simulation, fabrication, and testing of this platform, which utilizes a unique combination of planar and 3D carbon microelectrodes individually addressable on a transparent substrate. This setup enables the application of DEP forces, allowing for high-throughput manipulation of multiple microparticles simultaneously, as well as displacement of individual microparticles in any desired direction. Demonstrated with spherical 1μm and 10μm diameter polystyrene microparticles, this platform features straightforward fabrication and is suitable for batch industrial production. The study concludes with a discussion of the platform's advantages and limitations, marking a significant step toward a valuable tool for studying complex biological systems.

Space, the final frontier: The spatial component of phytoplankton-bacterial interactions

Microscale interactions between marine phytoplankton and bacteria shape the microenvironment of individual cells, impacting their physiology and ultimately influencing global-scale biogeochemical processes like carbon and nutrient cycling. In dilute environments such as the ocean water column, metabolic exchange between microorganisms likely requires close proximity between partners. However, the biological strategies to achieve this physical proximity remain an understudied aspect of phytoplankton-bacterial associations. Understanding the mechanisms by which these microorganisms establish and sustain spatial relationships and the extent to which spatial proximity is necessary for interactions to occur, is critical to learning how spatial associations influence the ecology of phytoplankton and bacterial communities. Here, we provide an overview of current knowledge on the role of space in shaping interactions among ocean microorganisms, encompassing behavioural and metabolic evidence. We propose that characterising phytoplankton-bacterial interactions from a spatial perspective can contribute to a mechanistic understanding of the establishment and maintenance of these associations and, consequently, an enhanced ability to predict the impact of microscale processes on ecosystem-wide phenomena.

A two-dimensional calibration for resolving nano-positioner pedestal micro-deformation crosstalk

This paper proposes a two-dimensional calibration method for resolving the nano-positioner pedestal micro-deformation crosstalk errors during the auction process. This refinement aims to enhance the nano-positioner's accuracy and reduce coupling crosstalk errors. The reaction force exerted by the piezoelectric actuator can cause micro-deformation in the nano-positioner sensor pedestal, causing substantial errors in uncalibrated closed-loop control. This phenomenon has been previously overlooked. According to the two-degree of freedom parallel-symmetric decoupled nano-positioner deformation characteristics, a two-dimensional calibration matrix is proposed to suppress the crosstalk between two axes caused by the micro-deformation of the sensor pedestal. Experimental results show that the calibrated closed-loop system reduces X-axis and Y-axis coupling crosstalk errors to 1/67th and 1/18th of the uncalibrated closed-loop system, respectively, concurrently enhancing the positioning accuracy to 11.8 and 17 times that of the uncalibrated closed-loop system. The calibrated closed-loop feedback control method proposed in this paper proves effective for all compact nano-positioners with pedestal deformation issues. This research provides a crucial reference for the design of nano-positioners, offering insights into enhancing their accuracy and mitigating crosstalk issues.

Immunological and molecular diagnostic techniques in fish health: present and future prospectus

Fish health management is critical to aquaculture and fisheries as it directly affects sustainability and productivity. Fish disease diagnosis has taken a massive stride because of advances in immunological and molecular diagnostic tools which provide a sensitive, quick, and accurate means of identifying diseases. This review presents an overview of the main molecular and immunological diagnostic methods for determining the health of fish. The immunological techniques help to diagnose different fish diseases by detecting specific antigens and antibodies. The application of immunological techniques to vaccine development is also examined in this review. The genetic identification of pathogens is made possible by molecular diagnostic techniques that enable the precise identification of bacterial, viral, and parasitic organisms in addition to evaluating host reactions and genetic variation associated with resistance to disease. The combination of molecular and immunological methods has resulted in the creation of novel techniques for thorough evaluation of fish health. These developments improve treatment measures, pathogen identification and provide new information about the variables affecting fish health, such as genetic predispositions and environmental stresses. In the framework of sustainable fish farming and fisheries management, this paper focuses on the importance of these diagnostic techniques that play a crucial role in protecting fish populations and the aquatic habitats. This review also examines the present and potential future directions in immunological and molecular diagnostic techniques in fish health.

Single-cell investigative genetics: Single-cell data produces genotype distributions concentrated at the true genotype across all mixture complexities

In the absence of a suspect the forensic aim is investigative, and the focus is one of discerning what genotypes best explain the evidence. In traditional systems, the list of candidate genotypes may become vast if the sample contains DNA from many donors or the information from a minor contributor is swamped by that of major contributors, leading to lower evidential value for a true donor's contribution and, as a result, possibly overlooked or inefficient investigative leads. Recent developments in single-cell analysis offer a way forward, by producing data capable of discriminating genotypes. This is accomplished by first clustering single-cell data by similarity without reference to a known genotype. With good clustering it is reasonable to assume that the scEPGs in a cluster are of a single contributor. With that assumption we determine the probability of a cluster's content given each possible genotype at each locus, which is then used to determine the posterior probability mass distribution for all genotypes by application of Bayes' rule. A decision criterion is then applied such that the sum of the ranked probabilities of all genotypes falling in the set is at least 1-α. This is the credible genotype set and is used to inform database search criteria. Within this work we demonstrate the salience of single-cell analysis by performance testing a set of 630 previously constructed admixtures containing up to 5 donors of balanced and unbalanced contributions. We use scEPGs that were generated by isolating single cells, employing a direct-to-PCR extraction treatment, amplifying STRs that are compliant with existing national databases and applying post-PCR treatments that elicit a detection limit of one DNA copy. We determined that, for these test data, 99.3% of the true genotypes are included in the 99.8% credible set, regardless of the number of donors that comprised the mixture. We also determined that the most probable genotype was the true genotype for 97% of the loci when the number of cells in a cluster was at least two. Since efficient investigative leads will be borne by posterior mass distributions that are narrow and concentrated at the true genotype, we report that, for this test set, 47,900 (86%) loci returned only one credible genotype and of these 47,551 (99%) were the true genotype. When determining the LR for true contributors, 91% of the clusters rendered LR>1018, showing the potential of single-cell data to positively affect investigative reporting.

Recent Developments and Applications of Microbial Electrochemical Biosensors

This chapter provides a comprehensive overview of microbial electrochemical biosensors, which are a unique class of biosensors that utilize the metabolic activity of microorganisms to convert chemical signals into electrical signals. The principles and mechanisms of these biosensors are discussed, including the different types of microorganisms that can be used. The various applications of microbial electrochemical biosensors in fields such as environmental monitoring, medical diagnostics, and food safety are also explored. The chapter concludes with a discussion of future research directions and potential advancements in the field of microbial electrochemical biosensors.

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

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

Microfluidic based single cell or droplet manipulation: Methods and applications

The isolation of single cell or droplet is first and crucial step to single-cell analysis, which is important for cancer research and diagnostic methods. This review provides an overview of technologies that are currently used or in development to realize the isolation. Microfluidic based manipulation is an emerging technology with the distinct advantages of miniaturization and low cost. Therefore, recent developments in microfluidic isolated methods have attracted extensive attention. We introduced herein five strategies based on microfluid: trap, microfluidic discrete manipulation, bioprinter, capillary and inertial force. For every technology, their basic principles and features were discussed firstly. Then some modified approaches and applications were listed as the extension. Finally, we compared the advantages and drawbacks of these methods, and analyzed the trend of the manipulation based on microfluidics.

Microbiome single cell atlases generated with a commercial instrument

Single cell sequencing is useful for resolving complex systems into their composite cell types and computationally mining them for unique features that are masked in pooled sequencing. However, while commercial instruments have made single cell analysis widespread for mammalian cells, analogous tools for microbes are limited. Here, we present EASi-seq (Easily Accessible Single microbe sequencing). By adapting the single cell workflow of the commercial Mission Bio Tapestri instrument, this method allows for efficient sequencing of individual microbes' genomes. EASi-seq allows thousands of microbes to be sequenced per run and, as we show, can generate detailed atlases of human and environmental microbiomes. The ability to capture large shotgun genome datasets from thousands of single microbes provides new opportunities in discovering and analyzing species subpopulations. To facilitate this, we develop a companion bioinformatic pipeline that clusters microbes by similarity, improving whole genome assembly, strain identification, taxonomic classification, and gene annotation. In addition, we demonstrate integration of metagenomic contigs with the EASi-seq datasets to reduce capture bias and increase coverage. Overall, EASi-seq enables high quality single cell genomic data for microbiome samples using an accessible workflow that can be run on a commercially available platform.

Single-Cell Proteomics with Spatial Attributes: Tools and Techniques

Now-a-days, the single-cell proteomics (SCP) concept is attracting interest, especially in clinical research, because it can identify the proteomic signature specific to diseased cells. This information is very essential when dealing with the progression of certain diseases, such as cancer, diabetes, Alzheimer's, etc. One of the major drawbacks of conventional destructive proteomics is that it gives an average idea about the protein expression profile in the disease condition. During the extraction of the protein from a biopsy or blood sample, proteins may come from both diseased cells and adjacent normal cells or any other cells from the disease environment. Again, SCP along with spatial attributes is utilized to learn about the heterogeneous function of a single protein. Before performing SCP, it is necessary to isolate single cells. This can be done by various techniques, including fluorescence-activated cell sorting (FACS), magnetic-activated cell sorting (MACS), laser capture microdissection (LCM), microfluidics, manual cell picking/micromanipulation, etc. Among the different approaches for proteomics, mass spectrometry-based proteomics tools are widely used for their high resolution as well as sensitivity. This Review mainly focuses on the mass spectrometry-based approaches for the study of single-cell proteomics.

Single-Cell Isolation Microfluidic Chip Based on Thermal Bubble Micropump Technology

The isolation of single cells is essential for the development of single cell analysis methods, such as single-cell sequencing, monoclonal antibodies, and drug development. Traditional single-cell isolation techniques include flow cytometry (FACS), laser capture microdissection (LCM), micromanipulation, etc., but their operations are complex and have low throughput. Here, we present a microfluidic chip that can isolate individual cells from cell suspension and release them onto a well plate. It uses thermal bubble micropump technology to drive the fluid flow, and single-cell isolation is achieved by matching the flow resistance of the flow channel. Therefore, injection pumps and peristaltic pumps are not required for cell loading. Because of its small size, we can integrate hundreds of single-cell functional modules, which makes high-throughput single-cell isolation possible. For polystyrene beads, the capture rate of the single bead is close to 100%. Finally, the method has been applied to cells, and the capture rate of the single cell is also about 75%. This is a promising method for single-cell isolation.

Analysis of Salmonella lineage-specific traits upon cell sorting

Microbial cell individuality is receiving increasing interest in the scientific community. Individual cells within clonal populations exhibit noticeable phenotypic heterogeneity. The advent of fluorescent protein technology and advances in single-cell analysis has revealed phenotypic cell variant in bacterial populations. This heterogeneity is evident in a wide range of phenotypes, for example, individual cells display variable degrees of gene expression and survival under selective conditions and stresses, and can exhibit differing propensities to host interactions. Last few years, numerous cell sorting approaches have been employed for resolving the properties of bacterial subpopulations. This review provides an overview of applications of cell sorting to analyze Salmonella lineage-specific traits, including bacterial evolution studies, gene expression analysis, response to diverse cellular stresses and characterization of diverse bacterial phenotypic variants.

Estimating and Assessing Differential Equation Models with Time-Course Data

Ordinary differential equation (ODE) models are widely used to describe chemical or biological processes. This Article considers the estimation and assessment of such models on the basis of time-course data. Due to experimental limitations, time-course data are often noisy, and some components of the system may not be observed. Furthermore, the computational demands of numerical integration have hindered the widespread adoption of time-course analysis using ODEs. To address these challenges, we explore the efficacy of the recently developed MAGI (MAnifold-constrained Gaussian process Inference) method for ODE inference. First, via a range of examples we show that MAGI is capable of inferring the parameters and system trajectories, including unobserved components, with appropriate uncertainty quantification. Second, we illustrate how MAGI can be used to assess and select different ODE models with time-course data based on MAGI's efficient computation of model predictions. Overall, we believe MAGI is a useful method for the analysis of time-course data in the context of ODE models, which bypasses the need for any numerical integration.

In Search of Proximate Triggers of Anthrax Outbreaks in Wildlife: A Hypothetical Individual-Based Model of Plasmid Transfer within Bacillus Communities

Bacillus anthracis, the causative agent of anthrax in humans, livestock, and wildlife, exists in a community with hundreds of other species of bacteria in the environment. Work on the genetics of these communities has shown that B. anthracis shares a high percentage of chromosomal genes with both B. thuringiensis and B. cereus, and that phenotypic differences among these bacteria can result from extra-chromosomal DNA in the form of plasmids. We developed a simple hypothetical individual-based model to simulate the likelihood of detecting plasmids with genes encoding anthrax toxins within bacterial communities composed of B. anthracis, B. thuringiensis, and B. cereus, and the surrounding matrix of extra-cellular polymeric substances. Simulation results suggest the horizontal transfer of plasmids with genes encoding anthrax toxins among Bacillus species persisting outside the host could function as a proximate factor triggering anthrax outbreaks.

Methods for assessment of the tumour microenvironment and immune interactions in non-small cell lung cancer. A narrative review

Non-small cell lung cancer (NSCLC) is one of the leading causes of cancer death worldwide. Immunotherapy with immune checkpoint inhibitors (ICI) has significantly improved outcomes in some patients, however 80-85% of patients receiving immunotherapy develop primary resistance, manifesting as a lack of response to therapy. Of those that do have an initial response, disease progression may occur due to acquired resistance. The make-up of the tumour microenvironment (TME) and the interaction between tumour infiltrating immune cells and cancer cells can have a large impact on the response to immunotherapy. Robust assessment of the TME with accurate and reproducible methods is vital to understanding mechanisms of immunotherapy resistance. In this paper we will review the evidence of several methodologies to assess the TME, including multiplex immunohistochemistry, imaging mass cytometry, flow cytometry, mass cytometry and RNA sequencing.

Petri-plate, bacteria, and laser optical scattering sensor

Classical microbiology has paved the path forward for the development of modern biotechnology and microbial biosensing platforms. Microbial culturing and isolation using the Petri plate revolutionized the field of microbiology. In 1887, Julius Richard Petri invented possibly the most important tool in microbiology, the Petri plate, which continues to have a profound impact not only on reliably isolating, identifying, and studying microorganisms but also manipulating a microbe to study gene expression, virulence properties, antibiotic resistance, and production of drugs, enzymes, and foods. Before the recent advances in gene sequencing, microbial identification for diagnosis relied upon the hierarchal testing of a pure culture isolate. Direct detection and identification of isolated bacterial colonies on a Petri plate with a sensing device has the potential for revolutionizing further development in microbiology including gene sequencing, pathogenicity study, antibiotic susceptibility testing , and for characterizing industrially beneficial traits. An optical scattering sensor designated BARDOT (bacterial rapid detection using optical scattering technology) that uses a red-diode laser, developed at the beginning of the 21^st century at Purdue University, some 220 years after the Petri-plate discovery can identify and study bacteria directly on the plate as a diagnostic tool akin to Raman scattering and hyperspectral imaging systems for application in clinical and food microbiology laboratories.

Identifying Bacterial Lineages in Salmonella by Flow Cytometry

Advances in technologies that permit high-resolution analysis of events in single cells have revealed that phenotypic heterogeneity is a widespread phenomenon in bacteria. Flow cytometry has the potential to describe the distribution of cellular properties within a population of bacterial cells and has yielded invaluable information about the ability of isogenic cells to diversify into phenotypic subpopulations. This review will discuss several single-cell approaches that have recently been applied to define phenotypic heterogeneity in populations of Salmonella enterica.

Glioma Stem Cells: Novel Data Obtained by Single-Cell Sequencing

Glioma is the most common type of primary CNS tumor, composed of cells that resemble normal glial cells. Recent genetic studies have provided insight into the inter-tumoral heterogeneity of gliomas, resulting in the updated 2021 WHO classification of gliomas. Thorough understanding of inter-tumoral heterogeneity has already improved the prognosis and treatment outcomes of some types of gliomas. Currently, the challenge for researchers is to study the intratumoral cell heterogeneity of newly defined glioma subtypes. Cancer stem cells (CSCs) present in gliomas and many other tumors are an example of intratumoral heterogeneity of great importance. In this review, we discuss the modern concept of glioma stem cells and recent single-cell sequencing-driven progress in the research of intratumoral glioma cell heterogeneity. The particular emphasis was placed on the recently revealed variations of the cell composition of the subtypes of the adult-type diffuse gliomas, including astrocytoma, oligodendroglioma and glioblastoma. The novel data explain the inconsistencies in earlier glioma stem cell research and also provide insight into the development of more effective targeted therapy and the cell-based immunotherapy of gliomas. Separate sections are devoted to the description of single-cell sequencing approach and its role in the development of cell-based immunotherapies for glioma.

Advances in single-cell sequencing technology in the field of hepatocellular carcinoma

Tumors are a class of diseases characterized by altered genetic information and uncontrolled growth. Sequencing technology provide researchers with a better way to explore specific tumor pathogenesis. In recent years, single-cell sequencing technology has shone in tumor research, especially in the study of liver cancer, revealing phenomena that were unexplored by previous studies. Single-cell sequencing (SCS) is a technique for sequencing the cellular genome, transcriptome, epigenome, proteomics, or metabolomics after dissociation of tissues into single cells. Compared with traditional bulk sequencing, single-cell sequencing can dissect human tumors at single-cell resolution, finely delineate different cell types, and reveal the heterogeneity of tumor cells. In view of the diverse pathological types and complex pathogenesis of hepatocellular carcinoma (HCC), the study of the heterogeneity among tumor cells can help improve its clinical diagnosis, treatment and prognostic judgment. On this basis, SCS has revolutionized our understanding of tumor heterogeneity, tumor immune microenvironment, and clonal evolution of tumor cells. This review summarizes the basic process and development of single-cell sequencing technology and its increasing role in the field of hepatocellular carcinoma.

Single-Cell RNA Sequencing in Lung Cancer: Revealing Phenotype Shaping of Stromal Cells in the Microenvironment

The lung tumor microenvironment, which is composed of heterogeneous cell populations, plays an important role in the progression of lung cancer and is closely related to therapeutic efficacy. Increasing evidence has shown that stromal components play a key role in regulating tumor invasion, metastasis and drug resistance. Therefore, a better understanding of stromal components in the tumor microenvironment is helpful for the diagnosis and treatment of lung cancer. Rapid advances in technology have brought our understanding of disease into the genetic era, and single-cell RNA sequencing has enabled us to describe gene expression profiles with unprecedented resolution, enabling quantitative analysis of gene expression at the single-cell level to reveal the correlations among heterogeneity, signaling pathways, drug resistance and microenvironment molding in lung cancer, which is important for the treatment of this disease. In this paper, several common single-cell RNA sequencing methods and their advantages and disadvantages are briefly introduced to provide a reference for selection of suitable methods. Furthermore, we review the latest progress of single-cell RNA sequencing in the study of stromal cells in the lung tumor microenvironment.

Rules of Engagement: A Guide to Developing Agent-Based Models

Agent-based models (ABM), also called individual-based models, first appeared several decades ago with the promise of nearly real-time simulations of active, autonomous individuals such as animals or objects. The goal of ABMs is to represent a population of individuals (agents) interacting with one another and their environment. Because of their flexible framework, ABMs have been widely applied to study systems in engineering, economics, ecology, and biology. This chapter is intended to guide the users in the development of an agent-based model by discussing conceptual issues, implementation, and pitfalls of ABMs from first principles. As a case study, we consider an ABM of the multi-scale dynamics of cellular interactions in a microbial community. We develop a lattice-free agent-based model of individual cells whose actions of growth, movement, and division are influenced by both their individual processes (cell cycle) and their contact with other cells (adhesion and contact inhibition).

Illuminate the hidden: in vivo mapping of microscale pH in the mycosphere using a novel whole-cell biosensor

The pH of an environment is both a driver and the result of diversity and functioning of microbial habitats such as the area affected by fungal hyphae (mycosphere). Here we used a novel pH-sensitive bioreporter, Synechocystis sp. PCC6803_peripHlu, and ratiometric fluorescence microscopy, to spatially and temporally resolve the mycosphere pH at the micrometre scale. Hyphae of the basidiomycete Coprionopsis cinerea were allowed to overgrow immobilised and homogeneously embedded pH bioreporters in an agarose microcosm. Signals of >700 individual cells in an area of 0.4 × 0.8 mm were observed over time and used to create highly resolved (3 × 3 µm) pH maps using geostatistical approaches. C. cinerea changed the pH of the agarose from 6.9 to ca. 5.0 after 48 h with hyphal tips modifying pH in their vicinity up to 1.8 mm. pH mapping revealed distinct microscale spatial variability and temporally stable gradients between pH 4.4 and 5.8 over distances of ≈20 µm. This is the first in vivo mapping of a mycosphere pH landscape at the microscale. It underpins the previously hypothesised establishment of pH gradients serving to create spatially distinct mycosphere reaction zones.

Eukaryotic Cell Capture by Amplified Magnetic in situ Hybridization Using Yeast as a Model

A non-destructive approach based on magnetic in situ hybridization (MISH) and hybridization chain reaction (HCR) for the specific capture of eukaryotic cells has been developed. As a prerequisite, a HCR-MISH procedure initially used for tracking bacterial cells was here adapted for the first time to target eukaryotic cells using a universal eukaryotic probe, Euk-516R. Following labeling with superparamagnetic nanoparticles, cells from the model eukaryotic microorganism Saccharomyces cerevisiae were hybridized and isolated on a micro-magnet array. In addition, the eukaryotic cells were successfully targeted in an artificial mixture comprising bacterial cells, thus providing evidence that HCR-MISH is a promising technology to use for specific microeukaryote capture in complex microbial communities allowing their further morphological characterization. This new study opens great opportunities in ecological sciences, thus allowing the detection of specific cells in more complex cellular mixtures in the near future.

Analytics and visualization tools to characterize single-cell stochasticity using bacterial single-cell movie cytometry data

Background

Time-lapse microscopy live-cell imaging is essential for studying the evolution of bacterial communities at single-cell resolution. It allows capturing detailed information about the morphology, gene expression, and spatial characteristics of individual cells at every time instance of the imaging experiment. The image analysis of bacterial "single-cell movies" (videos) generates big data in the form of multidimensional time series of measured bacterial attributes. If properly analyzed, these datasets can help us decipher the bacterial communities' growth dynamics and identify the sources and potential functional role of intra- and inter-subpopulation heterogeneity. Recent research has highlighted the importance of investigating the role of biological "noise" in gene regulation, cell growth, cell division, etc. Single-cell analytics of complex single-cell movie datasets, capturing the interaction of multiple micro-colonies with thousands of cells, can shed light on essential phenomena for human health, such as the competition of pathogens and benign microbiome cells, the emergence of dormant cells (“persisters”), the formation of biofilms under different stress conditions, etc. However, highly accurate and automated bacterial bioimage analysis and single-cell analytics methods remain elusive, even though they are required before we can routinely exploit the plethora of data that single-cell movies generate.

Results

We present visualization and single-cell analytics using R (ViSCAR), a set of methods and corresponding functions, to visually explore and correlate single-cell attributes generated from the image processing of complex bacterial single-cell movies. They can be used to model and visualize the spatiotemporal evolution of attributes at different levels of the microbial community organization (i.e., cell population, colony, generation, etc.), to discover possible epigenetic information transfer across cell generations, infer mathematical and statistical models describing various stochastic phenomena (e.g., cell growth, cell division), and even identify and auto-correct errors introduced unavoidably during the bioimage analysis of a dense movie with thousands of overcrowded cells in the microscope's field of view.

Conclusions

ViSCAR empowers researchers to capture and characterize the stochasticity, uncover the mechanisms leading to cellular phenotypes of interest, and decipher a large heterogeneous microbial communities' dynamic behavior. ViSCAR source code is available from GitLab at https://gitlab.com/ManolakosLab/viscar.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04409-9.

Single-Cell Analysis of Mycobacteria Using Microfluidics and Time-Lapse Microscopy

Studies on cell-to-cell phenotypic variation in microbial populations, with individuals sharing the same genetic background, provide insights not only on bacterial behavior but also on the adaptive spectrum of the population. Phenotypic variation is an innate property of microbial populations, and this can be further amplified under stressful conditions, providing a fitness advantage. Furthermore, phenotypic variation may also precede a latter step of genetic-based diversification, resulting in the transmission of the most beneficial phenotype to the progeny. While population-wide studies provide a measure of the collective average behavior, single-cell studies, which have expanded over the last decade, delve into the behavior of smaller subpopulations that would otherwise remain concealed. In this chapter, we describe approaches to carry out spatiotemporal analysis of individual mycobacterial cells using time-lapse microscopy. Our method encompasses the fabrication of a microfluidic device; the assembly of a microfluidic system suitable for long-term imaging of mycobacteria; and the quantitative analysis of single-cell behavior under varying growth conditions. Phenotypic variation is conceivably associated to the resilience and endurance of mycobacterial cells. Therefore, shedding light on the dynamics of this phenomenon, on the transience or stability of the given phenotype, on its molecular bases and its functional consequences, offers new scope for intervention.

Application of single cell genomics to focal epilepsies: A call to action

Focal epilepsies are the largest epilepsy subtype and associated with significant morbidity. Somatic variation is a newly recognized genetic mechanism underlying a subset of focal epilepsies, but little is known about the processes through which somatic mosaicism causes seizures, the cell types carrying the pathogenic variants, or their developmental origin. Meanwhile, the inception of single cell biology has completely revolutionized the study of neurological diseases and has the potential to answer some of these key questions. Focusing on single cell genomics, transcriptomics, and epigenomics in focal epilepsy research, circumvents the averaging artifact associated with studying bulk brain tissue and offers the kind of granularity that is needed for investigating the consequences of somatic mosaicism. Here we have provided a brief overview of some of the most developed single cell techniques and the major considerations around applying them to focal epilepsy research.

Single-cell RNA sequencing in cancer: Applications, advances, and emerging challenges

Cancer has become one of the greatest threats to human health, and new technologies are urgently needed to further clarify the mechanisms of cancer so that better detection and treatment strategies can be developed. At present, extensive genomic analysis and testing of clinical specimens shape the insights into carcinoma. Nevertheless, carcinoma of humans is a complex ecosystem of cells, including carcinoma cells and immunity-related and stroma-related subsets, with accurate characteristics obscured by extensive genome-related approaches. A growing body of research shows that sequencing of single-cell RNA (scRNA-seq) is emerging to be an effective way for dissecting human tumor tissue at single-cell resolution, presenting one prominent way for explaining carcinoma biology. This review summarizes the research progress of scRNA-seq in the field of tumors, focusing on the application of scRNA-seq in tumor circulating cells, tumor stem cells, tumor drug resistance, the tumor microenvironment, and so on, which provides a new perspective for tumor research.

Best practices on the differential expression analysis of multi-species RNA-seq

Advances in transcriptome sequencing allow for simultaneous interrogation of differentially expressed genes from multiple species originating from a single RNA sample, termed dual or multi-species transcriptomics. Compared to single-species differential expression analysis, the design of multi-species differential expression experiments must account for the relative abundances of each organism of interest within the sample, often requiring enrichment methods and yielding differences in total read counts across samples. The analysis of multi-species transcriptomics datasets requires modifications to the alignment, quantification, and downstream analysis steps compared to the single-species analysis pipelines. We describe best practices for multi-species transcriptomics and differential gene expression.

Single-cell fluidic force microscopy reveals stress-dependent molecular interactions in yeast mating

Sexual agglutinins of the budding yeast Saccharomyces cerevisiae are proteins mediating cell aggregation during mating. Complementary agglutinins expressed by cells of opposite mating types "a" and "α" bind together to promote agglutination and facilitate fusion of haploid cells. By means of an innovative single-cell manipulation assay combining fluidic force microscopy with force spectroscopy, we unravel the strength of single specific bonds between a- and α-agglutinins (~100 pN) which require pheromone induction. Prolonged cell-cell contact strongly increases adhesion between mating cells, likely resulting from an increased expression of agglutinins. In addition, we highlight the critical role of disulfide bonds of the a-agglutinin and of histidine residue H₂₇₃ of α-agglutinin. Most interestingly, we find that mechanical tension enhances the interaction strength, pointing to a model where physical stress induces conformational changes in the agglutinins, from a weak-binding folded state, to a strong-binding extended state. Our single-cell technology shows promises for understanding and controlling the complex mechanism of yeast sexuality.

Dual functional luminescent nanoprobes for monitoring oxygen and chloride concentration changes in cells

A dual functional nanoprobe Pd-Q+@PDMS was proposed to simultaneously monitor Cl- and O2, leading to the determination of an average Cl- concentration of 85.7 ± 5.5 mM in lysosomes of HeLa cells. Mimicking ischemic conditions, the cells exhibited a luminescence change corresponding to a decreasing subcellular Cl- concentration.

NMR microsystem for label-free characterization of 3D nanoliter microtissues

Performing chemical analysis at the nanoliter (nL) scale is of paramount importance for medicine, drug development, toxicology, and research. Despite the numerous methodologies available, a tool for obtaining chemical information non-invasively is still missing at this scale. Observer effects, sample destruction and complex preparatory procedures remain a necessary compromise. Among non-invasive spectroscopic techniques, one able to provide holistic and highly resolved chemical information in-vivo is nuclear magnetic resonance (NMR). For its renowned informative power and ability to foster discoveries and life-saving applications, efficient NMR at microscopic scales is highly sought after, but so far technical limitations could not match the stringent necessities of microbiology, such as biocompatible handling, ease of use, and high throughput. Here we introduce a novel microsystem, which combines CMOS technology with 3D microfabrication, enabling nL NMR as a platform tool for non-invasive spectroscopy of organoids, 3D cell cultures, and early stage embryos. In this study we show its application to microlivers models simulating non-alcoholic fatty liver disease, demonstrating detection of lipid metabolism dynamics in a time frame of 14 days based on 117 measurements of single 3D human liver microtissues.

Comparative study on the inactivation of MS2 and M13 bacteriophages using energetic femtosecond lasers

Femtosecond (fs) laser irradiation techniques are emerging tools for inactivating viruses that do not involve ionizing radiation. In this work, the inactivation of two bacteriophages representing protective capsids with different geometric constraints, that is, the near-spherical MS2 (with a diameter of 27 nm) and the filamentous M13 (with a length of 880 nm) is compared using energetic visible and near-infrared fs laser pulses with various energies, pulse durations, and exposure times. Intriguingly, the results show that inactivation using 400 nm lasers is substantially more efficient for MS2 compared to M13. In contrast, using 800 nm lasers, M13 was slightly more efficiently inactivated. For both viruses, the genome was exposed to a harmful environment upon fs-laser irradiation. However, in addition to the protection of the genome, the metastable capsids differ in many properties required for stepwise cell entry that may explain their dissimilar behavior after (partial) disassembly. For MS2, the dominant mechanism of fs-laser inactivation was the aggregation of the viral capsid proteins, whereas aggregation did not affect M13 inactivation, suggesting that the dominant mechanism of M13 inactivation was related to breaking of secondary protein links.

Single-Cell Sequencing in Human Genital Infections

Human genital infections are one of the most concerning issues worldwide and can be categorized into sexually transmitted, urinary tract and vaginal infections. These infections, if left untreated, can disseminate to the other parts of the body and cause more complicated illnesses such as pelvic inflammatory disease, urethritis, and anogenital cancers. The effective treatment against these infections is further complicated by the emergence of antimicrobial resistance in the genital infection causing pathogens. Furthermore, the development and applications of single-cell sequencing technologies have open new possibilities to study the drug resistant clones, cell to cell variations, the discovery of acquired drug resistance mutations, transcriptional diversity of a pathogen across different infection stages, to identify rare cell types and investigate different cellular states of genital infection causing pathogens, and to develop novel therapeutical strategies. In this chapter, I will provide a complete review of the applications of single-cell sequencing in human genital infections before discussing their limitations and challenges.

FTIR micro-spectroscopy using synchrotron-based and thermal source-based radiation for probing live bacteria

Fourier transform infrared (FTIR) spectroscopy has proven to be a non-invasive tool to analyse cells without the hurdle of employing exogenous dyes or probes. Nevertheless, the study of single live bacteria in their aqueous environment has long remained a big challenge, due to the strong infrared absorption of water and the small size of bacteria compared to the micron-range infrared wavelengths of the probing photons. To record infrared spectra of bacteria in an aqueous environment, at different spatial resolutions, two setups were developed. A custom-built attenuated total reflection inverted microscope was coupled to a synchrotron-based FTIR spectrometer, using a germanium hemisphere. With such a setup, a projected spot size of 1 × 1 μm² was achieved, which allowed spectral acquisition at the single-cell level in the 1800-1300 cm^-1 region. The second setup used a demountable liquid micro-chamber with a thermal source-powered FTIR microscope, in transmission geometry, for probing clusters of a few thousands of live cells in the mid-IR region (4000-975 cm^-1). Both setups were applied for studying two strains of a model lactic acid bacterium exhibiting different cryo-resistances. The two approaches allowed the discrimination of both strains and revealed population heterogeneity among bacteria at different spatial resolutions. The multivariate analysis of spectra indicated that the cryo-sensitive cells presented the highest cell heterogeneity and the highest content of proteins with the α-helix structure. Furthermore, the results from clusters of bacterial cells evidenced phosphate and peptidoglycan vibrational bands associated with the cell envelope, as potential markers of resistance to environmental conditions. Graphical Abstract.

Photodegradable Hydrogels for Rapid Screening, Isolation, and Genetic Characterization of Bacteria with Rare Phenotypes

Screening mutant libraries (MLs) of bacteria for strains with specific phenotypes is often a slow and laborious process that requires assessment of tens of thousands of individual cell colonies after plating and culturing on solid media. In this report, we develop a three-dimensional, photodegradable hydrogel interface designed to dramatically improve the throughput of ML screening by combining high-density cell culture with precision extraction and the recovery of individual, microscale colonies for follow-up genetic and phenotypic characterization. ML populations are first added to a hydrogel precursor solution consisting of polyethylene glycol (PEG) o-nitrobenzyl diacrylate and PEG-tetrathiol macromers, where they become encapsulated into 13 μm thick hydrogel layers at a density of 90 cells/mm², enabling parallel monitoring of 2.8 × 10⁴ mutants per hydrogel. Encapsulated cells remain confined within the elastic matrix during culture, allowing one to track individual cells that grow into small, stable microcolonies (45 ± 4 μm in diameter) over the course of 72 h. Colonies with rare growth profiles can then be identified, extracted, and recovered from the hydrogel in a sequential manner and with minimal damage using a high-resolution, 365 nm patterned light source. The light pattern can be varied to release motile cells, cellular aggregates, or microcolonies encapsulated in protective PEG coatings. To access the benefits of this approach for ML screening, an Agrobacterium tumefaciens C58 transposon ML was screened for rare, resistant mutants able to grow in the presence of cell free culture media from Rhizobium rhizogenes K84, a well-known inhibitor of C58 cell growth. Subsequent genomic analysis of rare cells (9/28,000) that developed into microcolonies identified that seven of the resistant strains had mutations in the acc locus of the Ti plasmid. These observations are consistent with past research demonstrating that the disruption of this locus confers resistance to agrocin 84, an inhibitory molecule produced by K84. The high-throughput nature of the screen allows the A. tumefaciens genome (approximately 5.6 Mbps) to be screened to saturation in a single experimental trial, compared to hundreds of platings required by conventional plating approaches. As a miniaturized version of the gold-standard plating assay, this materials-based approach offers a simple, inexpensive, and highly translational screening technique that does not require microfluidic devices or complex liquid handling steps. The approach is readily adaptable to other applications that require isolation and study of rare or phenotypically pure cell populations.

Variability in microbial inactivation: From deterministic Bigelow model to probability distribution of single cell inactivation times

Phenotypic heterogeneity seems to be an important component leading to biological individuality and is of great importance in the case of microbial inactivation. Bacterial cells are characterized by their own resistance to stresses. This inherent stochasticity is reflected in microbial survival curve which, in this context, can be considered as cumulative probability distribution of lethal events. The objective of the present study was to present an overview on the assessment and quantification of variability in microbial inactivation originating from single cells and discuss this heterogeneity in the context of predicting microbial behavior and Risk assessment studies. The detailed knowledge of the distribution of the single cells' inactivation times can be the basis for stochastic inactivation models which, in turn, may be employed in a risk - based food safety approach.

6-Color/1-Target Immuno-SERS Microscopy on the Same Single Cancer Cell

There is an urgent clinical need for multicolor imaging of single cancer cells (no ensemble averaging) for identifying heterogenous expression of predictive biomarkers. Specifically, the comprehensive characterization of single disseminated tumor cells (sDTCs) responsible for metastatic relapse is the key to personalized therapy for patients. Current bioimaging methods lack the necessary multicolor capacity and suffer from background/autofluorescence. Both these central limitations can be overcome by immuno-SERS microscopy using SERS nanotags conjugated to antibodies. Here, we demonstrate the proof of concept for 6-color iSERS microscopy on the same single cancer cell. Human epidermal growth factor receptor 2 (HER2), the most prominent breast cancer marker, is localized on the membrane of single SkBr-3 cells, which overexpress HER2 and are an accepted model for sDTCs in breast cancer. This work paves the way for future multicolor/multitarget imaging for characterizing heterogeneous protein expression at the single-cell level.

Microfluidics as a Novel Tool for Biological and Toxicological Assays in Drug Discovery Processes: Focus on Microchip Electrophoresis

The last decades of biological, toxicological, and pharmacological research have deeply changed the way researchers select the most appropriate 'pre-clinical model'. The absence of relevant animal models for many human diseases, as well as the inaccurate prognosis coming from 'conventional' pre-clinical models, are among the major reasons of the failures observed in clinical trials. This evidence has pushed several research groups to move more often from a classic cellular or animal modeling approach to an alternative and broader vision that includes the involvement of microfluidic-based technologies. The use of microfluidic devices offers several benefits including fast analysis times, high sensitivity and reproducibility, the ability to quantitate multiple chemical species, and the simulation of cellular response mimicking the closest human in vivo milieu. Therefore, they represent a useful way to study drug-organ interactions and related safety and toxicity, and to model organ development and various pathologies 'in a dish'. The present review will address the applicability of microfluidic-based technologies in different systems (2D and 3D). We will focus our attention on applications of microchip electrophoresis (ME) to biological and toxicological studies as well as in drug discovery and development processes. These include high-throughput single-cell gene expression profiling, simultaneous determination of antioxidants and reactive oxygen and nitrogen species, DNA analysis, and sensitive determination of neurotransmitters in biological fluids. We will discuss new data obtained by ME coupled to laser-induced fluorescence (ME-LIF) and electrochemical detection (ME-EC) regarding the production and degradation of nitric oxide, a fundamental signaling molecule regulating virtually every critical cellular function. Finally, the integration of microfluidics with recent innovative technologies-such as organoids, organ-on-chip, and 3D printing-for the design of new in vitro experimental devices will be presented with a specific attention to drug development applications. This 'composite' review highlights the potential impact of 2D and 3D microfluidic systems as a fast, inexpensive, and highly sensitive tool for high-throughput drug screening and preclinical toxicological studies.

Image-Based Single Cell Sorting Automation in Droplet Microfluidics

The recent boom in single-cell omics has brought researchers one step closer to understanding the biological mechanisms associated with cell heterogeneity. Rare cells that have historically been obscured by bulk measurement techniques are being studied by single cell analysis and providing valuable insight into cell function. To support this progress, novel upstream capabilities are required for single cell preparation for analysis. Presented here is a droplet microfluidic, image-based single-cell sorting technique that is flexible and programmable. The automated system performs real-time dual-camera imaging (brightfield & fluorescent), processing, decision making and sorting verification. To demonstrate capabilities, the system was used to overcome the Poisson loading problem by sorting for droplets containing a single red blood cell with 85% purity. Furthermore, fluorescent imaging and machine learning was used to load single K562 cells amongst clusters based on their instantaneous size and circularity. The presented system aspires to replace manual cell handling techniques by translating expert knowledge into cell sorting automation via machine learning algorithms. This powerful technique finds application in the enrichment of single cells based on their micrographs for further downstream processing and analysis.

Single-cell sequencing of miRNAs: A modified technology

The recent development of next-generation sequencing technologies has offered valuable insights into individual cells. This technology is centered on the characterization of single cells for epigenomics, genomics, and transcriptomics. Ever since the first report appeared in 2009, the single-cell RNA-sequencing saga started to explore deeper into the mechanics intrigued within a single cell. microRNA (miRNA) has been increasingly recognized as an essential molecule triggering an additional layer for gene regulation. Therefore, single-cell sequencing of miRNAs is crucial to explore the logical riddles surrounding the epigenomics, genomics, and transcriptomics of an individual cell. Scientists from the Vienna Biocenter Campus have lately performed single-cell sequencing of miRNAs in the fly, Drosophila, and nematode, Caenorhabditis elegans. In this review, we present the latest scientific explorations supported by all-inclusive data on this novel subject matter of next-generation sequencing.

Plasmid expression level heterogeneity monitoring via heterologous eGFP production at the single-cell level in Cupriavidus necator

A methodology for plasmid expression level monitoring of eGFP expression suitable for dynamic processes was assessed during fermentation. This technique was based on the expression of a fluorescent biosensor (eGFP) encoded on a recombinant plasmid coupled to single-cell analysis. Fluorescence intensity at single-cell level was measured by flow cytometry. We demonstrated that promoter evaluation based on single-cell analysis versus classic global analysis brings valuable insights. Single-cell analysis pointed out the fact that intrinsic fluorescence increased with the strength of the promoter up to a threshold. Beyond that, cell permeability increases to excrete the fluorescent protein in the medium. The metabolic load due to the increase in the eGFP production in the case of strong constitutive promoters leads to slower growth kinetics compared with plasmid-free cells. With the strain Cupriavidus necator Re2133, growth rate losses were measured from 3% with the weak constitutive promoter P_lac to 56% with the strong constitutive promoter P_j5. Through this work, it seems crucial to find a compromise between the fluorescence intensity in single cells and the metabolic load; in our conditions, the best compromise found was the weak promoter P_lac. The plasmid expression level monitoring method was tested in the presence of a heterogeneous population induced by plasmid-curing methods. For all the identified subpopulations, the plasmid expression level heterogeneity was significantly detected at the level of fluorescence intensity in single cells. After cell sorting, growth rate and cultivability were assessed for each subpopulation. In conclusion, this eGFP biosensor makes it possible to follow the variations in the level of plasmid expression under conditions of population heterogeneity.Key Points•Development of a plasmid expression level monitoring method at the single-cell level by flow cytometry.•Promoter evaluation by single-cell analysis: cell heterogeneity and strain robustness.•Reporter system optimization for efficient subpopulation detection in pure cultures.