VIBRANT: spectral profiling for single-cell drug responses

High-content cell profiling has proven invaluable for single-cell phenotyping in response to chemical perturbations. However, methods with improved throughput, information content and affordability are still needed. We present a new high-content spectral profiling method named vibrational painting (VIBRANT), integrating mid-infrared vibrational imaging, multiplexed vibrational probes and an optimized data analysis pipeline for measuring single-cell drug responses. Three infrared-active vibrational probes were designed to measure distinct essential metabolic activities in human cancer cells. More than 20,000 single-cell drug responses were collected, corresponding to 23 drug treatments. The resulting spectral profile is highly sensitive to phenotypic changes under drug perturbation. Using this property, we built a machine learning classifier to accurately predict drug mechanism of action at single-cell level with minimal batch effects. We further designed an algorithm to discover drug candidates with new mechanisms of action and evaluate drug combinations. Overall, VIBRANT has demonstrated great potential across multiple areas of phenotypic screening.

Cellular subpopulations identified using an ensemble average of multiple dielectrophoresis measurements

While the average value measurement approach can successfully analyze and predict the general behavior and biophysical properties of an isogenic cell population, it fails when significant differences among individual cells are generated in the population by intracellular changes such as the cell cycle, or different cellular responses to certain stimuli. Detecting such single-cell differences in a cell population has remained elusive. Here, we describe an easy-to-implement and generalizable platform that measures the dielectrophoretic cross-over frequency of individual cells by decreasing measurement noise with a stochastic method and computing ensemble average statistics. This platform enables multiple, real-time, label-free detection of individual cells with significant dielectric variations over time within an isogenic cell population. Using a stochastic method in combination with the platform, we distinguished cell subpopulations from a mixture of drug-untreated and -treated isogenic cells. Furthermore, we demonstrate that our platform can identify drug-treated isogenic cells with different recovery rates.

Analysis of cellular response to drugs with a microfluidic single-cell platform based on hyperspectral imaging

BACKGROUND

Cellular response to pharmacological action of drugs is significant for drug development. Traditional detection method for cellular response to drugs normally rely on cell proliferation assay and metabolomics examination. In principle, these analytical methods often required cell labeling, invasion analysis, and hours of co-culture with drugs, which are relatively complex and time-consuming. Moreover, these methods can only indicate the drug effectiveness on cell colony rather than single cells. Thus, to meet the requirements of personal precision medicine, the development of drug response analysis on the high resolution of single cell is demanded.

RESULTS

To provide precise result for drug response on single-cell level, a microfluidic platform coupled with the label-free hyperspectral imaging was developed. With the help of horizontal single-cell trapping sieves, hundreds of single cells were trapped independently in microfluidic channels for the purposes of real-time drug delivery and single-cell hyperspectral image recording. To significantly identify the cellular hyperspectral change after drug stimulation, the differenced single-cell spectrum was proposed. Compared with the deep learning classification method based on hyperspectral images, an optimal performance can be achieved by the classification strategy based on differenced spectra. And the cellular response to different reagents, for example, K+, Epidermal Growth Factor (EGF), and Gefitinib at different concentrations can be accurately characterized by the differenced single-cell spectra analysis.

SIGNIFICANCE AND NOVELTY

The high-throughput, rapid analysis of cellular response to drugs at the single-cell level can be accurately performed by our platform. After systematically analyzing the materials and the structures of the single-cell microfluidic chip, the optimal single-cell trapping method was proposed to contribute to the further application of hyperspectral imaging on microfluidic single-cell analysis. And the hyperspectral characterization of single-cell with cancer drug stimulation proved the application potential of our method in personal cancer medication.

Amplification-free detection of telomerase activity at the single-cell level via Cas12a-lighting-up single microbeads (Cas12a-LSMBs)

Telomerase overexpresses in almost all cancer cells and has been deemed a universal biomarker for cancer diagnosis and therapy. However, simple and ultrasensitive detection of telomerase activity in single-cells is still a huge challenge. Herein, we wish to report Cas12a-lighting up single microbeads (Cas12a-LSMBs) for ultrasensitive detection of telomerase activity without nucleic acid amplification. In this platform, single-strand DNA reporter (ssDNA reporter)-functionalized single-microbeads (functionalized-SMBs) are employed as a reactor for the trans-cleavage of telomerase-activated CRISPR/Cas12a as well as a reporting unit for fluorescence signal enrichment and visualization. Due to the space-confined effect and signal enrichment mechanism on the surface of the functionalized SMBs, the Cas12a-LSMBs can accurately detect telomerase activity in crude cell lysates with high specificity. Importantly, we have demonstrated that the Cas12a-LSMBs are a reliable and practical tool to detect telomerase activity in single cells and investigate cellular heterogeneity of telomerase activity from cell-to-cell variations.

Deep learning applications in single-cell genomics and transcriptomics data analysis

Traditional bulk sequencing methods are limited to measuring the average signal in a group of cells, potentially masking heterogeneity, and rare populations. The single-cell resolution, however, enhances our understanding of complex biological systems and diseases, such as cancer, the immune system, and chronic diseases. However, the single-cell technologies generate massive amounts of data that are often high-dimensional, sparse, and complex, thus making analysis with traditional computational approaches difficult and unfeasible. To tackle these challenges, many are turning to deep learning (DL) methods as potential alternatives to the conventional machine learning (ML) algorithms for single-cell studies. DL is a branch of ML capable of extracting high-level features from raw inputs in multiple stages. Compared to traditional ML, DL models have provided significant improvements across many domains and applications. In this work, we examine DL applications in genomics, transcriptomics, spatial transcriptomics, and multi-omics integration, and address whether DL techniques will prove to be advantageous or if the single-cell omics domain poses unique challenges. Through a systematic literature review, we have found that DL has not yet revolutionized the most pressing challenges of the single-cell omics field. However, using DL models for single-cell omics has shown promising results (in many cases outperforming the previous state-of-the-art models) in data preprocessing and downstream analysis. Although developments of DL algorithms for single-cell omics have generally been gradual, recent advances reveal that DL can offer valuable resources in fast-tracking and advancing research in single-cell.

The 2D microfluidics cookbook - modeling convection and diffusion in plane flow devices

A growing number of microfluidic systems operate not through networks of microchannels but instead through using 2D flow fields. While the design rules for channel networks are already well-known and exposed in microfluidics textbooks, the knowledge underlying transport in 2D microfluidics remains scattered piecemeal and is not easily accessible to experimentalists and engineers. In this tutorial review, we formulate a unified framework for understanding, analyzing and designing 2D microfluidic technologies. We first show how a large number of seemingly different devices can all be modelled using the same concepts, namely flow and diffusion in a Hele-Shaw cell. We then expose a handful of mathematical tools, accessible to any engineer with undergraduate level mathematics knowledge, namely potential flow, superposition of charges, conformal transforms and basic convection-diffusion. We show how these tools can be combined to obtain a simple "recipe" that models almost any imaginable 2D microfluidic system. We end by pointing to more advanced topics beyond 2D microfluidics, namely interface problems and flow and diffusion in the third dimension. This forms the basis of a complete theory allowing for the design and operation of new microfluidic systems.

Tuneable Cell-Laden Double-Emulsion Droplets for Enhanced Signal Detection

Water-in-oil-in-water (w/o/w) or double-emulsion (DE) droplets have been widely used for cellular assays at a single-cell level because of their stability and biocompatibility. The oil shell of w/o/w droplets plays the role of a semipermeable membrane that allows substances with low molecular weight (e.g., water) to travel through but restricts those with high molecular weight (e.g., fluorescent biomarkers). Therefore, the core of DEs can be manipulated using osmosis, resulting in the shrinking or swelling of the core. Water leaves the inner aqueous phase to the outer phase via the oil shell when the osmotic pressure of the outer phase is higher than that in the inner phase, causing the shrinkage of DEs and vice versa. These processes can be achieved by transferring the DEs to hypertonic or hypotonic solutions. Manipulation of the core size of DEs can be beneficial to cellular assays. First, due to the selectivity of the oil shell of DEs, the concentration of biomarkers in the core increases when the inner aqueous phase is shrunk, resulting in the enhancement of biosignals. We demonstrate this by encapsulating the Bgl3 enzyme-secreting yeast with a substrate that displays fluorescence after hydrolyzation. In a second application, a single GFP-tagged yeast cell was encapsulated in DEs. After swelling the core of DEs, we observe that the larger core of DEs promotes cell growth compared to those with the smaller cores, leading to more intracellular proteins (green-fluorescent protein) for screening. These osmotic manipulations provide new tools for droplet-based biochemistry.

Droplets microfluidics platform-A tool for single cell research

Cells are the most basic structural and functional units of living organisms. Studies of cell growth, differentiation, apoptosis, and cell-cell interactions can help scientists understand the mysteries of living systems. However, there is considerable heterogeneity among cells. Great differences between individuals can be found even within the same cell cluster. Cell heterogeneity can only be clearly expressed and distinguished at the level of single cells. The development of droplet microfluidics technology opens up a new chapter for single-cell analysis. Microfluidic chips can produce many nanoscale monodisperse droplets, which can be used as small isolated micro-laboratories for various high-throughput, precise single-cell analyses. Moreover, gel droplets with good biocompatibility can be used in single-cell cultures and coupled with biomolecules for various downstream analyses of cellular metabolites. The droplets are also maneuverable; through physical and chemical forces, droplets can be divided, fused, and sorted to realize single-cell screening and other related studies. This review describes the channel design, droplet generation, and control technology of droplet microfluidics and gives a detailed overview of the application of droplet microfluidics in single-cell culture, single-cell screening, single-cell detection, and other aspects. Moreover, we provide a recent review of the application of droplet microfluidics in tumor single-cell immunoassays, describe in detail the advantages of microfluidics in tumor research, and predict the development of droplet microfluidics at the single-cell level.

Validation of Easy Fabrication Methods for PDMS-Based Microfluidic (Bio)Reactors

The common method for producing casting molds for the fabrication of polydimethylsiloxane (PDMS) chips is standard photolithography. This technique offers high resolution from hundreds of nanometers to a few micrometers. However, this mold fabrication method is costly, time-consuming, and might require clean room facilities. Additionally, there is a need for non-micromechanics experts, who do not have specialized equipment to easily and quickly prototype chips themselves. Simple, so-called, makerspace technologies are increasingly being explored as alternatives that have potential to enable anyone to fabricate microfluidic structures. We therefore tested simple fabrication methods for a PDMS-based microfluidic device. On the one hand, channels were replicated from capillaries and tape. On the other hand, different mold fabrication methods, namely laser cutting, fused layer 3D printing, stereolithographic 3D printing, and computer numerical control (CNC) milling, were validated in terms of machine accuracy and tightness. Most of these methods are already known, but the incorporation and retention of particles with sizes in the micrometer range have been less investigated. We therefore tested two different types of particles, which are actually common carriers for the immobilization of enzymes, so that the resulting reactor could ultimately be used as a microfluidic bioreactor. Furthermore, CNC milling provide the most reliable casting mold fabrication method. After some optimization steps with regard to manufacturing settings and post-processing polishing, the chips were tested for the retention of two different particle types (spherical and non-spherical particles). In this way, we successfully tested the obtained PDMS-based microfluidic chips for their potential applicability as (bio)reactors with enzyme immobilization carrier beads.

Microfluidic Droplet-SERS Platform for Single-Cell Cytokine Analysis via a Cell Surface Bioconjugation Strategy

A microfluidic-based surface-enhanced Raman scattering (SERS) platform for analyzing cytokines secreted by single cells is reported based on the elaborate bioconjugation of the immuno-sandwich complex on the probed cell surface. This platform integrates the dual functions of microfluidic droplet separation of single cells and SERS measurement. Two immune nanoprobes (capture probe and SERS probe) are introduced into a microfluidic droplet along with a single cell. They were anchored to the cell membrane protein surface by capturing secreted cytokines to form an immune sandwich structure, realizing the enrichment effect of cytokines above the cell membrane surface and the amplification effect of SERS detection probes. This single-cell analytical platform was applied to track specific cell-secreted vascular endothelial growth factor (VEGF) of different cell lines (MCF-7, SGC, and T24), and highly sensitive detection of VEGF was achieved. Chemometric methods (principal component analysis and t-distributed stochastic neighbor embedding) were adopted for the SERS data analysis, and the support vector machine (SVM) discriminant model was established to test the data. These chemometric methods successfully identify significant differences in the secreting ability of cytokines among three kinds of cancer cell lines, revealing cell heterogeneity. In addition, the behavior of single cells secreting VEGF was monitored time-dependently and was shown to increase with time. This work demonstrates the importance of tracking specific cells secreting cytokines based on the cell surface bioconjugation strategy. Our developed platform provides guidelines for using the single-cell exocytosis factors as biomarkers to assess the early diagnosis of cancer and provide physiological cues for learning single-cell secretions.

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

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

Assessing the growth kinetics and stoichiometry of Escherichia coli at the single-cell level

Microfluidic cultivation and single-cell analysis are inherent parts of modern microbial biotechnology and microbiology. However, implementing biochemical engineering principles based on the kinetics and stoichiometry of growth in microscopic spaces remained unattained. We here present a novel integrated framework that utilizes distinct microfluidic cultivation technologies and single-cell analytics to make the fundamental math of process-oriented biochemical engineering applicable at the single-cell level. A combination of non-invasive optical cell mass determination with sub-pg sensitivity, microfluidic perfusion cultivations for establishing physiological steady-states, and picoliter batch reactors, enabled the quantification of all physiological parameters relevant to approximate a material balance in microfluidic reaction environments. We determined state variables (biomass concentration based on single-cell dry weight and mass density), biomass synthesis rates, and substrate affinities of cells grown in microfluidic environments. Based on this data, we mathematically derived the specific kinetics of substrate uptake and growth stoichiometry in glucose-grown Escherichia coli with single-cell resolution. This framework may initiate microscale material balancing beyond the averaged values obtained from populations as a basis for integrating heterogeneous kinetic and stoichiometric single-cell data into generalized bioprocess models and descriptions.

Label-Free Profiling of up to 200 Single-Cell Proteomes per Day Using a Dual-Column Nanoflow Liquid Chromatography Platform

Single-cell proteomics (SCP) has great potential to advance biomedical research and personalized medicine. The sensitivity of such measurements increases with low-flow separations (<100 nL/min) due to improved ionization efficiency, but the time required for sample loading, column washing, and regeneration in these systems can lead to low measurement throughput and inefficient utilization of the mass spectrometer. Herein, we developed a two-column liquid chromatography (LC) system that dramatically increases the throughput of label-free SCP using two parallel subsystems to multiplex sample loading, online desalting, analysis, and column regeneration. The integration of MS1-based feature matching increased proteome coverage when short LC gradients were used. The high-throughput LC system was reproducible between the columns, with a 4% difference in median peptide abundance and a median CV of 18% across 100 replicate analyses of a single-cell-sized peptide standard. An average of 621, 774, 952, and 1622 protein groups were identified with total analysis times of 7, 10, 15, and 30 min, corresponding to a measurement throughput of 206, 144, 96, and 48 samples per day, respectively. When applied to single HeLa cells, we identified nearly 1000 protein groups per cell using 30 min cycles and 660 protein groups per cell for 15 min cycles. We explored the possibility of measuring cancer therapeutic targets with a pilot study comparing the K562 and Jurkat leukemia cell lines. This work demonstrates the feasibility of high-throughput label-free single-cell proteomics.

Single-cell droplet microfluidics for biomedical applications

This review focuses on the recent advances in the fundamentals of single-cell droplet microfluidics and its applications in biomedicine, providing insights into design and establishment of single-cell microsystems and their further performance.

Flow radiocytometry using droplet optofluidics

Flow-based cytometry methods are widely used to analyze heterogeneous cell populations. However, their use for small molecule studies remains limited due to bulky fluorescent labels that often interfere with biochemical activity in cells. In contrast, radiotracers require minimal modification of their target molecules and can track biochemical processes with negligible interference and high specificity. Here, we introduce flow radiocytometry (FRCM) that broadens the scope of current cytometry methods to include beta-emitting radiotracers as probes for single cell studies. FRCM uses droplet microfluidics and radiofluorogenesis to translate the radioactivity of single cells into a fluorescent signal that is then read out using a high-throughput optofluidic device. As a proof of concept, we quantitated [18F]fluorodeoxyglucose radiotracer uptake in single human breast cancer cells and successfully assessed the metabolic flux of glucose and its heterogeneity at the cellular level. We believe FRCM has potential applications ranging from analytical assays for cancer and other diseases to development of small-molecule drugs.

Measurement methods of single cell drug response

In the last decades, a wide multitude of research activity has been focused on the development of new drugs, and devoted to overcome the challenges of high cost and low efficiency in drug evaluation. The measurement of drug response at the single cell level is a quicker, more direct and more accurate way to reflect drug efficacy, which can shorten the drug development period and reduce research costs. Therefore, the single cell drug response (SCDR) measurement technology has aroused extensive attention from researchers, and has become a hot topic in the fields of drug research and cell biology. Recent years have seen the emergence of various SCDR measurement technologies that feature different working principles and different levels of measurement performance. To better examine, compare and summarize the characteristics and functions of these technologies, we select signal-to-noise ratio, throughput, content, invasion, and device complexity as the criteria to evaluate them from the drug efficacy perspective. This review aims to highlight sixteen kinds of SCDR measurement technologies, including patch-clamp technique, live-cell interferometry, capillary electrophoresis, secondary ion mass spectrometry, and more, and report widespread representative examples of SCDR measurement the recent approaches for over the past forty years. Based on their reaction principles, these technologies are classified into four categories: electrical, optical, electrochemical, and mass spectrometry, and a detailed comparison is made between them. After in-depth understanding of these technologies, it is expected to improve or integrate these technologies to propose better SCDR measurement strategies, and explore methods in new drug development and screening, as well as disease diagnosis and treatment.

Real-time capture of single particles in controlled flow by a rapidly generated foci array with adjustable intensity and pattern

We propose a new, to the best of our knowledge, technique to capture single particles in real-time in a microfluidic system with controlled flow using micro-pillar traps fabricated by one-step. The micro pillars are fabricated in parallel by femtosecond multi-foci laser beams, which are generated by multiplexing gratings. As the generation process does not need integration loops, the pattern and the intensity distribution of the foci array can be controlled in real-time by changing the parameters of gratings. The real-time control of the foci array enables rapidly fabricating microtraps in the microchannel with adjustment of the pillar spaces and patterns according to the sizes and shapes of target particles. This technology provides an important step towards using platforms based on single-particle analysis, and it paves the way for the development of innovative microfluidic devices for single-cell analysis.

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

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

Droplet flow cytometry for single-cell analysis

The interrogation of single cells has revolutionised biology and medicine by providing crucial unparalleled insights into cell-to-cell heterogeneity. Flow cytometry (including fluorescence-activated cell sorting) is one of the most versatile and high-throughput approaches for single-cell analysis by detecting multiple fluorescence parameters of individual cells in aqueous suspension as they flow past through a focus of excitation lasers. However, this approach relies on the expression of cell surface and intracellular biomarkers, which inevitably lacks spatial and temporal phenotypes and activities of cells, such as secreted proteins, extracellular metabolite production, and proliferation. Droplet microfluidics has recently emerged as a powerful tool for the encapsulation and manipulation of thousands to millions of individual cells within pico-litre microdroplets. Integrating flow cytometry with microdroplet architectures surrounded by aqueous solutions (e.g., water-in-oil-in-water (W/O/W) double emulsion and hydrogel droplets) opens avenues for new cellular assays linking cell phenotypes to genotypes at the single-cell level. In this review, we discuss the capabilities and applications of droplet flow cytometry (DFC). This unique technique uses standard commercially available flow cytometry instruments to characterise or select individual microdroplets containing single cells of interest. We explore current challenges associated with DFC and present our visions for future development.

A Monte Carlo method to estimate cell population heterogeneity from cell snapshot data

Variation is characteristic of all living systems. Laboratory techniques such as flow cytometry can probe individual cells, and, after decades of experimentation, it is clear that even members of genetically identical cell populations can exhibit differences. To understand whether variation is biologically meaningful, it is essential to discern its source. Mathematical models of biological systems are tools that can be used to investigate causes of cell-to-cell variation. From mathematical analysis and simulation of these models, biological hypotheses can be posed and investigated, then parameter inference can determine which of these is compatible with experimental data. Data from laboratory experiments often consist of "snapshots" representing distributions of cellular properties at different points in time, rather than individual cell trajectories. These data are not straightforward to fit using hierarchical Bayesian methods, which require the number of cell population clusters to be chosen a priori. Nor are they amenable to standard nonlinear mixed effect methods, since a single observation per cell is typically too few to estimate parameter variability. Here, we introduce a computational sampling method named "Contour Monte Carlo" (CMC) for estimating mathematical model parameters from snapshot distributions, which is straightforward to implement and does not require that cells be assigned to predefined categories. The CMC algorithm fits to snapshot probability distributions rather than raw data, which means its computational burden does not, like existing approaches, increase with the number of cells observed. Our method is appropriate for underdetermined systems, where there are fewer distinct types of observations than parameters to be determined, and where observed variation is mostly due to variability in cellular processes rather than experimental measurement error. This may be the case for many systems due to continued improvements in resolution of laboratory techniques. In this paper, we apply our method to quantify cellular variation for three biological systems of interest and provide Julia code enabling others to use this method.

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

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

Recent Development of Microfluidic Technology for Cell Trapping in Single Cell Analysis: A Review

Microfluidic technology has emerged from the MEMS (Micro-Electro-Mechanical System)-technology as an important research field. During the last decade, various microfluidic technologies have been developed to open up a new era for biological studies. To understand the function of single cells, it is very important to monitor the dynamic behavior of a single cell in a living environment. Cell trapping in single cell analysis is urgently demanded There have been some review papers focusing on drug screen and cell analysis. However, cell trapping in single cell analysis has rarely been covered in the previous reviews. The present paper focuses on recent developments of cell trapping and highlights the mechanisms, governing equations and key parameters affecting the cell trapping efficiency by contact-based and contactless approach. The applications of the cell trapping method are discussed according to their basic research areas, such as biology and tissue engineering. Finally, the paper highlights the most promising cell trapping method for this research area.

Single-Cell Analysis

Cells are known to be the most fundamental building block of life[...].

Bioprospecting Microbial Diversity for Lignin Valorization: Dry and Wet Screening Methods

Lignin is an abundant cell wall component, and it has been used mainly for generating steam and electricity. Nevertheless, lignin valorization, i.e. the conversion of lignin into high value-added fuels, chemicals, or materials, is crucial for the full implementation of cost-effective lignocellulosic biorefineries. From this perspective, rapid screening methods are crucial for time- and resource-efficient development of novel microbial strains and enzymes with applications in the lignin biorefinery. The present review gives an overview of recent developments and applications of a vast arsenal of activity and sequence-based methodologies for uncovering novel microbial strains with ligninolytic potential, novel enzymes for lignin depolymerization and for unraveling the main metabolic routes during growth on lignin. Finally, perspectives on the use of each of the presented methods and their respective advantages and disadvantages are discussed.

Impedance-Based Single-Cell Pipetting

Many biological methods are based on single-cell isolation. In single-cell line development, the gold standard involves the dilution of cells by means of a pipet. This process is time-consuming as it is repeated over several weeks to ensure clonality. Here, we report the modeling, designing, and testing of a disposable pipet tip integrating a cell sensor based on the Coulter principle. We investigate, test, and discuss the effects of design parameters on the sensor performances with an analytical model. We also describe a system that enables the dispensing of single cells using an instrumented pipet coupled with the sensing tip. Most importantly, this system allows the recording of an impedance trace to be used as proof of single-cell isolation. We assess the performances of the system with beads and cells. Finally, we show that the electrical detection has no effect on cell viability.

Development and characterization of Escherichia coli triple reporter strains for investigation of population heterogeneity in bioprocesses

Background

Today there is an increasing demand for high yielding robust and cost efficient biotechnological production processes. Although cells in these processes originate from isogenic cultures, heterogeneity induced by intrinsic and extrinsic influences is omnipresent. To increase understanding of this mechanistically poorly understood phenomenon, advanced tools that provide insights into single cell physiology are needed.

Results

Two Escherichia coli triple reporter strains have been designed based on the industrially relevant production host E. coli BL21(DE3) and a modified version thereof, E. coli T7E2. The strains carry three different fluorescence proteins chromosomally integrated. Single cell growth is followed with EmeraldGFP (EmGFP)-expression together with the ribosomal promoter rrnB. General stress response of single cells is monitored by expression of sigma factor rpoS with mStrawberry, whereas expression of the nar-operon together with TagRFP657 gives information about oxygen limitation of single cells. First, the strains were characterized in batch operated stirred-tank bioreactors in comparison to wildtype E. coli BL21(DE3). Afterwards, applicability of the triple reporter strains for investigation of population heterogeneity in bioprocesses was demonstrated in continuous processes in stirred-tank bioreactors at different growth rates and in response to glucose and oxygen perturbation simulating gradients on industrial scale. Population and single cell level physiology was monitored evaluating general physiology and flow cytometry analysis of fluorescence distributions of the triple reporter strains. Although both triple reporter strains reflected physiological changes that were expected based on the expression characteristics of the marker proteins, the triple reporter strain based on E. coli T7E2 showed higher sensitivity in response to environmental changes. For both strains, noise in gene expression was observed during transition from phases of non-growth to growth. Apparently, under some process conditions, e.g. the stationary phase in batch cultures, the fluorescence response of EmGFP and mStrawberry is preserved, whereas TagRFP657 showed a distinct response.

Conclusions

Single cell growth, general stress response and oxygen limitation of single cells could be followed using the two triple reporter strains developed in this study. They represent valuable tools to study population heterogeneity in bioprocesses significantly increasing the level of information compared to the use of single reporter strains.

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.

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

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

Molecular and living cell dynamic assays with optical microscopy imaging techniques

Generally, the message elucidated by the conventional analytical methods overlooks the heterogeneity of single objects, where the behavior of individual molecules is shielded. With the advent of optical microscopy imaging techniques, it is possible to identify, visualize and track individual molecules or nanoparticles under a biological environment with high temporal and spatial resolution. In this work, we summarize the commonly adopted optical microscopy techniques for bio-analytical assays in living cells, including total internal reflection fluorescence microscopy (TIRFM), super-resolution optical microscopy (SRM), and dark-field optical microscopy (DFM). The basic principles of these methods and some recent interesting applications in molecular detection and single-particle tracking are introduced. Moreover, the development in high-dimensional optical microscopy to achieve three-dimensional (3-D) as well as sub-diffraction localization and tracking of biomolecules is also highlighted.

The effect of acetate on population heterogeneity in different cellular characteristics of Escherichia coli in aerobic batch cultures

Acetate as the major by-product in industrial-scale bioprocesses with Escherichia coli is found to decrease process efficiency as well as to be toxic to cells, which has several effects like a significant induction of cellular stress responses. However, the underlying phenomena are poorly explored. Therefore, we studied time-resolved population heterogeneity of the E. coli growth reporter strain MG1655/pGS20PrrnBGFPAAV expressing destabilized green fluorescent protein during batch growth on acetate and glucose as sole carbon sources. Additionally, we applied five fluorescent stains targeting different cellular properties (viability as well as metabolic and respiratory activity). Quantitative analysis of flow cytometry data verified that bacterial populations in the bioreactor are more heterogeneous in growth as well as stronger metabolically challenged during growth on acetate as sole carbon source, compared to growth on glucose or acetate after diauxic shift. Interestingly, with acetate as sole carbon source, significant subpopulations were found with some cells that seem to be more robust than the rest of the population. In conclusion, following batch cultures population heterogeneity was evident in all measured parameters. Our approach enabled a deeper study of heterogeneity during growth on the favored substrate glucose as well as on the toxic by-product acetate. Using a combination of activity fluorescent dyes proved to be an accurate and fast alternative as well as a supplement to the use of a reporter strain. However, the choice of combination of stains should be well considered depending on which population traits to aim for.

Estimation methods for heterogeneous cell population models in systems biology

Heterogeneity among individual cells is a characteristic and relevant feature of living systems. A range of experimental techniques to investigate this heterogeneity is available, and multiple modelling frameworks have been developed to describe and simulate the dynamics of heterogeneous populations. Measurement data are used to adjust computational models, which results in parameter and state estimation problems. Methods to solve these estimation problems need to take the specific properties of data and models into account. The aim of this review is to give an overview on the state of the art in estimation methods for heterogeneous cell population data and models. The focus is on models based on the population balance equation, but stochastic and individual-based models are also discussed. It starts with a brief discussion of common experimental approaches and types of measurement data that can be obtained in this context. The second part describes computational modelling frameworks for heterogeneous populations and the types of estimation problems occurring for these models. The third part starts with a discussion of observability and identifiability properties, after which the computational methods to solve the various estimation problems are described.

Current Trends of Microfluidic Single-Cell Technologies

The investigation of human disease mechanisms is difficult due to the heterogeneity in gene expression and the physiological state of cells in a given population. In comparison to bulk cell measurements, single-cell measurement technologies can provide a better understanding of the interactions among molecules, organelles, cells, and the microenvironment, which can aid in the development of therapeutics and diagnostic tools. In recent years, single-cell technologies have become increasingly robust and accessible, although limitations exist. In this review, we describe the recent advances in single-cell technologies and their applications in single-cell manipulation, diagnosis, and therapeutics development.

Particle-Templated Emulsification for Microfluidics-Free Digital Biology

The compartmentalization of reactions in monodispersed droplets is valuable for applications across biology. However, the requirement of microfluidics to partition the sample into monodispersed droplets is a significant barrier that impedes implementation. Here, we introduce particle-templated emulsification, a method to encapsulate samples in monodispersed emulsions without microfluidics. By vortexing a mixture of hydrogel particles and sample solution, we encapsulate the sample in monodispersed emulsions that are useful for most droplet applications. We illustrate the method with ddPCR and single cell culture. The ability to encapsulate samples in monodispersed droplets without microfluidics should facilitate the implementation of compartmentalized reactions in biology.

Fundamental principles in bacterial physiology-history, recent progress, and the future with focus on cell size control: a review

Bacterial physiology is a branch of biology that aims to understand overarching principles of cellular reproduction. Many important issues in bacterial physiology are inherently quantitative, and major contributors to the field have often brought together tools and ways of thinking from multiple disciplines. This article presents a comprehensive overview of major ideas and approaches developed since the early 20th century for anyone who is interested in the fundamental problems in bacterial physiology. This article is divided into two parts. In the first part (sections 1-3), we review the first 'golden era' of bacterial physiology from the 1940s to early 1970s and provide a complete list of major references from that period. In the second part (sections 4-7), we explain how the pioneering work from the first golden era has influenced various rediscoveries of general quantitative principles and significant further development in modern bacterial physiology. Specifically, section 4 presents the history and current progress of the 'adder' principle of cell size homeostasis. Section 5 discusses the implications of coarse-graining the cellular protein composition, and how the coarse-grained proteome 'sectors' re-balance under different growth conditions. Section 6 focuses on physiological invariants, and explains how they are the key to understanding the coordination between growth and the cell cycle underlying cell size control in steady-state growth. Section 7 overviews how the temporal organization of all the internal processes enables balanced growth. In the final section 8, we conclude by discussing the remaining challenges for the future in the field.

Analysis of population structures of the microalga Acutodesmus obliquus during lipid production using multi-dimensional single-cell analysis

Microalgae bear a great potential to produce lipids for biodiesel, feed, or even food applications. To understand the still not well-known single-cell dynamics during lipid production in microalgae, a novel single-cell analytical technology was applied to study a well-established model experiment. Multidimensional single-cell dynamics were investigated with a non-supervised image analysis technique that utilizes data from epi-fluorescence microscopy. Reliability of this technique was successfully proven via reference analysis. The technique developed was used to determine cell size, chlorophyll amount, neutral lipid amount, and deriving properties on a single-cellular level in cultures of the biotechnologically promising alga Acutodesmus obliquus. The results illustrated a high correlation between cell size and chlorophyll amount, but a very low and dynamic correlation between cell size, lipid amount, and lipid density. During growth conditions under nitrogen starvation, cells with low chlorophyll content tend to start the lipid production first and the cell suspension differentiated in two subpopulations with significantly different lipid contents. Such quantitative characterization of single-cell dynamics of lipid synthesizing algae was done for the first time and the potential of such simple technology is highly relevant to other biotechnological applications and to deeper investigate the process of microalgal lipid accumulation.

Single cell assessment of yeast metabolic engineering for enhanced lipid production using Raman and AFM-IR imaging

BACKGROUND

Biodiesel is a valuable renewable fuel made from derivatized fatty acids produced in plants, animals, and oleaginous microbes. Of the latter, yeasts are of special interest due to their wide use in biotechnology, ability to synthesize fatty acids and store large amounts of triacylglycerols while utilizing non-food carbon sources. While yeast efficiently produce lipids, genetic modification and indeed, lipid pathway metabolic engineering, is usually required for cost-effective production. Traditionally, gas chromatography (GC) is used to measure fatty acid production and to track the success of a metabolic engineering strategy in a microbial culture; here we have employed vibrational spectroscopy approaches at population and single cell level of engineered yeast while simultaneously investigating metabolite levels in subcellular structures.

RESULTS

Firstly, a strong correlation (r² > 0.99) was established between Fourier transform infrared (FTIR) lipid in intact cells and GC analysis of fatty acid methyl esters in the differently engineered strains. Confocal Raman spectroscopy of individual cells carrying genetic modifications to enhance fatty acid synthesis and lipid accumulation revealed changes to the lipid body (LB), the storage organelle for lipids in yeast, with their number increasing markedly (up to tenfold higher); LB size was almost double in the strain that also expressed a LB stabilizing gene but considerable variation was also noted between cells. Raman spectroscopy revealed a clear trend toward reduced unsaturated fatty acid content in lipids of cells carrying more complex metabolic engineering. Atomic force microscopy-infrared spectroscopy (AFM-IR) analysis of individual cells indicated large differences in subcellular constituents between strains: cells of the most highly engineered strain had elevated lipid and much reduced carbohydrate in their cytoplasm compared with unmodified cells.

CONCLUSIONS

Vibrational spectroscopy analysis allowed the simultaneous measurement of strain variability in metabolite production and impact on cellular structures as a result of different gene introductions or knockouts, within a lipid metabolic engineering strategy and these inform the next steps in comprehensive lipid engineering. Additionally, single cell spectroscopic analysis measures heterogeneity in metabolite production across microbial cultures under genetic modification, an emerging issue for efficient biotechnological production.

Population heterogeneity in microbial bioprocesses: origin, analysis, mechanisms, and future perspectives

Population heterogeneity is omnipresent in all bioprocesses even in homogenous environments. Its origin, however, is only so well understood that potential strategies like bet-hedging, noise in gene expression and division of labour that lead to population heterogeneity can be derived from experimental studies simulating the dynamics in industrial scale bioprocesses. This review aims at summarizing the current state of the different parts of single cell studies in bioprocesses. This includes setups to visualize different phenotypes of single cells, computational approaches connecting single cell physiology with environmental influence and special cultivation setups like scale-down reactors that have been proven to be useful to simulate large-scale conditions. A step in between investigation of populations and single cells is studying subpopulations with distinct properties that differ from the rest of the population with sub-omics methods which are also presented here. Moreover, the current knowledge about population heterogeneity in bioprocesses is summarized for relevant industrial production hosts and mixed cultures, as they provide the unique opportunity to distribute metabolic burden and optimize production processes in a way that is impossible in traditional monocultures. In the end, approaches to explain the underlying mechanism of population heterogeneity and the evidences found to support each hypothesis are presented. For instance, population heterogeneity serving as a bet-hedging strategy that is used as coordinated action against bioprocess-related stresses while at the same time spreading the risk between individual cells as it ensures the survival of least a part of the population in any environment the cells encounter.

Microdosing, isotopic labeling, radiotracers and metabolomics: relevance in drug discovery, development and safety

This review discusses the use of stable (¹³C, ²D) or radioactive isotopes (¹⁴C, ¹¹C, ¹⁸F, ¹³¹I, ⁶⁴Cu, ⁶⁸Ga) incorporated into the molecular structure of new drug entities for the purpose of pharmacokinetic or -dynamic studies. Metabolite in safety testing requires the administration of pharmacologically active doses. In such studies, radiotracers find application mainly in preclinical animal investigations, whereby LC-MS/MS is used to identify metabolite structure and drug-related effects. In contrast, first-in-human metabolite studies have to be carried out at nonpharmacological doses not exceeding 100 μg (microdose), which is generally too low for metabolite detection by LC-MS/MS. This short-coming can be overcome by specific radio- or isotopic labeling of the drug of interest and measurements using accelerator mass spectroscopy, single-photon emission computed tomography and positron emission tomography. Such combined radioisotope-based approaches permit Phase 0, first-in-human metabolite study.

Beyond the bulk: disclosing the life of single microbial cells

Microbial single cell analysis has led to discoveries that are beyond what can be resolved with population-based studies. It provides a pristine view of the mechanisms that organize cellular physiology, unbiased by population heterogeneity or uncontrollable environmental impacts. A holistic description of cellular functions at the single cell level requires analytical concepts beyond the miniaturization of existing technologies, defined but uncontrolled by the biological system itself. This review provides an overview of the latest advances in single cell technologies and demonstrates their potential. Opportunities and limitations of single cell microbiology are discussed using selected application-related examples.

Functional and Biomimetic Materials for Engineering of the Three-Dimensional Cell Microenvironment

The cell microenvironment has emerged as a key determinant of cell behavior and function in development, physiology, and pathophysiology. The extracellular matrix (ECM) within the cell microenvironment serves not only as a structural foundation for cells but also as a source of three-dimensional (3D) biochemical and biophysical cues that trigger and regulate cell behaviors. Increasing evidence suggests that the 3D character of the microenvironment is required for development of many critical cell responses observed in vivo, fueling a surge in the development of functional and biomimetic materials for engineering the 3D cell microenvironment. Progress in the design of such materials has improved control of cell behaviors in 3D and advanced the fields of tissue regeneration, in vitro tissue models, large-scale cell differentiation, immunotherapy, and gene therapy. However, the field is still in its infancy, and discoveries about the nature of cell-microenvironment interactions continue to overturn much early progress in the field. Key challenges continue to be dissecting the roles of chemistry, structure, mechanics, and electrophysiology in the cell microenvironment, and understanding and harnessing the roles of periodicity and drift in these factors. This review encapsulates where recent advances appear to leave the ever-shifting state of the art, and it highlights areas in which substantial potential and uncertainty remain.

Host transcriptional responses following ex vivo re-challenge with Mycobacterium tuberculosis vary with disease status

The identification of immune correlates that are predictive of disease outcome for tuberculosis remains an ongoing challenge. To address this issue, we evaluated gene expression profiles from peripheral blood mononuclear cells following ex vivo challenge with Mycobacterium tuberculosis, among participants with active TB disease (ATBD, n = 10), latent TB infection (LTBI, n = 10), and previous active TB disease (after successful treatment; PTBD, n = 10), relative to controls (n = 10). Differential gene expression profiles were assessed by suppression-subtractive hybridization, dot blot, real-time polymerase chain reaction, and the comparative cycle threshold methods. Comparing ATBD to control samples, greater fold-increases of gene expression were observed for a number of chemotactic factors (CXCL1, CXCL3, IL8, MCP1, MIP1α). ATBD was also associated with higher IL1B gene expression, relative to controls. Among LTBI samples, gene expression of several chemotactic factors (CXCL2, CXCL3, IL8) was similarly elevated, compared to individuals with PTBD. Our results demonstrated that samples from participants with ATBD and LTBI have distinct gene expression profiles in response to ex vivo M. tuberculosis infection. These findings indicate the value in further characterizing the peripheral responses to M. tuberculosis challenge as a route to defining immune correlates of disease status or outcome.

Generation of ribosome imprinted polymers for sensitive detection of translational responses

Whilst the profiling of the transcriptome and proteome even of single-cells becomes feasible, the analysis of the translatome, which refers to all messenger RNAs (mRNAs) engaged with ribosomes for protein synthesis, is still an elaborate procedure requiring millions of cells. Herein, we report the generation and use of "smart materials", namely molecularly imprinted polymers (MIPs) to facilitate the isolation of ribosomes and translated mRNAs from merely 1,000 cells. In particular, we show that a hydrogel-based ribosome imprinted polymer could recover ribosomes and associated mRNAs from human, simian and mice cellular extracts, but did not selectively enrich yeast ribosomes, thereby demonstrating selectivity. Furthermore, ribosome imprinted polymers enabled the sensitive measurement of an mRNA translational regulatory event, requiring 1,000-fold less cells than current methodologies. These results provide first evidence for the suitability of MIPs to selectively recover ribonucleoprotein complexes such as ribosomes, founding a novel means for sensitive detection of gene regulation.

Single-Cell Transcriptional Analysis

Despite being a relatively recent technological development, single-cell transcriptional analysis through high-throughput sequencing has already been used in hundreds of fruitful studies to make exciting new biological discoveries that would otherwise be challenging or even impossible. Consequently, this has fueled a virtuous cycle of even greater interest in the field and compelled development of further improved technical methodologies and approaches. Thanks to the combined efforts of the research community, including the fields of biochemistry and molecular biology, technology and instrumentation, data science, computational biology, and bioinformatics, the single-cell RNA-sequencing field is advancing at a pace that is both astounding and unprecedented. In this review, we provide a broad introduction to this revolutionary technology by presenting the state-of-the-art in sample preparation methodologies, technology platforms, and computational analysis methods, while highlighting the key considerations for designing, executing, and interpreting a study using single-cell RNA sequencing.