Quantifying bacterial spore germination by single-cell impedance cytometry for assessment of host microbiota susceptibility to Clostridioides difficile infection

Phosphorylation-amplified synchronized droplet microfluidics sensitizes bacterial growth kinetic real-time monitoring

The necessity for rapid and accurate bacterial growth monitoring is imperative across various domains, including healthcare and environmental safety. We introduce the self-synchronized droplet-amplified electrical screening cytometry (SYNC) system, a novel meld of droplet microfluidics and electrochemical amplification tailored for precise bacterial growth kinetic monitoring. SYNC encapsulates single bacteria in picolitre droplets, enabling real-time, fluorescence-free electrochemical monitoring. A specially devised phosphorylation-amplified culture medium translates bacterial metabolic activity into discernible electrical impedance changes. The dual-channel design and a rail-based structure in SYNC facilitate parallel screening and self-synchronization of droplets, addressing the limitations of conventional impedance cytometry. SYNC showcases a 5-fold enhancement in detection sensitivity and reduces 50% of the detection time compared to traditional approaches. Notably, SYNC is pioneering in providing exact initial bacterial concentrations, achieve to 104 bacteria/ml, a capability unmatched by existing real-time techniques measuring electrochemical variations. Along with its robust performance, this earmarks SYNC as a powerful tool for applications such as antibiotic susceptibility testing, food quality monitoring, and real-time water bacteria monitoring, paving the way for enhanced microbial process management and infection control.

Electrical micro flow cytometry with LSTM and its application in leukocyte differential

This paper developed an electrical micro flow cytometry to realize leukocyte differentials leveraging a constrictional microchannel and a deep neural network. Firstly, purified granulocytes, lymphocytes or monocytes traveled through the constrictional microchannel with a cross-sectional area marginally larger than individual cells and produced large impedance variations by blocking focused electric field lines. By optimizing key elements (e.g., normalization, learning rate, batch size and neuron number) of the recurrent neural network (RNN), electrical results of purified leukocytes were analyzed to establish a leukocyte differential system with a classification accuracy of 95.2%. Then the leukocyte mixtures were forced to travel through the same constrictional microchannel, producing mixed impedance profiles which were classified into granulocytes, lymphocytes and monocytes based on the aforementioned differential system. As to the classification results, two leukocyte mixtures from the same donor were processed, producing comparable classification results, which were 57% versus 59% of granulocytes, 37% versus 34% of lymphocytes and 6% versus 7% of monocytes. These results validated the established classification system based on the constrictional microchannel and the recurrent neural network, providing a new perspective of differentiating white blood cells by electrical flow cytometry.

Correlating Antibiotic-Induced Dysbiosis to Clostridioides difficile Spore Germination and Host Susceptibility to Infection Using an Ex Vivo Assay

Antibiotic-induced microbiota disruption and its persistence create conditions for dysbiosis and colonization by opportunistic pathogens, such as those causing Clostridioides difficile (C. difficile) infection (CDI), which is the most severe hospital-acquired intestinal infection. Given the wide differences in microbiota across hosts and in their recovery after antibiotic treatments, there is a need for assays to assess the influence of dysbiosis and its recovery dynamics on the susceptibility of the host to CDI. Germination of C. difficile spores is a key virulence trait for the onset of CDI, which is influenced by the level of primary vs secondary bile acids in the intestinal milieu that is regulated by the microbiota composition. Herein, the germination of C. difficile spores in fecal supernatant from mice that are subject to varying degrees of antibiotic treatment is utilized as an ex vivo assay to predict intestinal dysbiosis in the host based on their susceptibility to CDI, as determined by in vivo CDI metrics in the same mouse model. Quantification of spore germination down to lower detection limits than the colony-forming assay is achieved by using impedance cytometry to count single vegetative bacteria that are identified based on their characteristic electrical physiology for distinction vs aggregated spores and cell debris in the media. As a result, germination can be quantified at earlier time points and with fewer spores for correlation to CDI outcomes. This sets the groundwork for a point-of-care tool to gauge the susceptibility of human microbiota to CDI after antibiotic treatments.

Opportunities in optical and electrical single-cell technologies to study microbial ecosystems

New techniques are revolutionizing single-cell research, allowing us to study microbes at unprecedented scales and in unparalleled depth. This review highlights the state-of-the-art technologies in single-cell analysis in microbial ecology applications, with particular attention to both optical tools, i.e., specialized use of flow cytometry and Raman spectroscopy and emerging electrical techniques. The objectives of this review include showcasing the diversity of single-cell optical approaches for studying microbiological phenomena, highlighting successful applications in understanding microbial systems, discussing emerging techniques, and encouraging the combination of established and novel approaches to address research questions. The review aims to answer key questions such as how single-cell approaches have advanced our understanding of individual and interacting cells, how they have been used to study uncultured microbes, which new analysis tools will become widespread, and how they contribute to our knowledge of ecological interactions.

A review on intelligent impedance cytometry systems: Development, applications and advances

Impedance cytometry is a well-established technique for counting and analyzing single cells, with several advantages, such as convenience, high throughput, and no labeling required. A typical experiment consists of the following steps: single-cell measurement, signal processing, data calibration, and particle subtype identification. At the beginning of this article, we compared commercial and self-developed options extensively and provided references for developing reliable detection systems, which are necessary for cell measurement. Then, a number of typical impedance metrics and their relationships to biophysical properties of cells were analyzed with respect to the impedance signal analysis. Given the rapid advances of intelligent impedance cytometry in the past decade, this article also discussed the development of representative machine learning-based approaches and systems, and their applications in data calibration and particle identification. Finally, the remaining challenges facing the field were summarized, and potential future directions for each step of impedance detection were discussed.

Automated biophysical classification of apoptotic pancreatic cancer cell subpopulations by using machine learning approaches with impedance cytometry

Unrestricted cell death can lead to an immunosuppressive tumor microenvironment, with dysregulated apoptotic signaling that causes resistance of pancreatic cancer cells to cytotoxic therapies. Hence, modulating cell death by distinguishing the progression of subpopulations under drug treatment from viable towards early apoptotic, late apoptotic, and necrotic states is of interest. While flow cytometry after fluorescent staining can monitor apoptosis with single-cell sensitivity, the background of non-viable cells within non-immortalized pancreatic tumors from xenografts can confound distinction of the intensity of each apoptotic state. Based on single-cell impedance cytometry of drug-treated pancreatic cancer cells that are obtained from tumor xenografts with differing levels of gemcitabine sensitivity, we identify the biophysical metrics that can distinguish and quantify cellular subpopulations at the early apoptotic versus late apoptotic and necrotic states, by using machine learning methods to train for the recognition of each phenotype. While supervised learning has previously been used for classification of datasets with known classes, our advancement is the utilization of optimal positive controls for each class, so that clustering by unsupervised learning and classification by supervised learning can occur on unknown datasets, without human interference or manual gating. In this manner, automated biophysical classification can be used to follow the progression of apoptotic states in each heterogeneous drug-treated sample, for developing drug treatments to modulate cancer cell death and advance longitudinal analysis to discern the emergence of drug resistant phenotypes.

Single-cell assessment of the modulation of macrophage activation by ex vivo intervertebral discs using impedance cytometry

Measurement of macrophage activation and its modulation for immune regulation is of great interest to arrest inflammatory responses associated with degeneration of intervertebral discs that cause chronic back pain, and with transplants that face immune rejection. Due to the phenotypic plasticity of macrophages that serve multiple immune functions, the net disease outcome is determined by a balance of subpopulations with competing functions, highlighting the need for single-cell methods to quantify heterogeneity in their activation phenotypes. However, since macrophage activation can follow several signaling pathways, cytometry after fluorescent staining of markers with antibodies does not often provide dose-dependent information on activation dynamics. We present high throughput single-cell impedance cytometry for multiparametric measurement of biophysical changes to individual macrophages for quantifying activation in a dose and duration dependent manner, without relying on a particular signaling pathway. Impedance phase metrics measured at two frequencies and the electrical diameter from impedance magnitude at lower frequencies are used in tandem to benchmark macrophage activation by degenerated discs against that from lipopolysaccharide stimulation at varying dose and duration levels, so that reversal of the activation state by curcumin can be ascertained. This label-free single-cell measurement method can form the basis for platforms to screen therapies for inflammation, thereby addressing the chronic problem of back pain.

Spores-on-a-chip: new frontiers for spore research

In recent years, microfluidic technologies have become widespread in biological science. However, the suitability of this technique for understanding different aspects of spore research has hardly been considered. Herein, we review recent developments in 'spores-on-a-chip' technologies, highlighting how they could be exploited to drive new frontiers in spore research.

Modified Red Blood Cells as Multimodal Standards for Benchmarking Single-Cell Cytometry and Separation Based on Electrical Physiology

Biophysical cellular information at single-cell sensitivity is becoming increasingly important within analytical and separation platforms that associate the cell phenotype with markers of disease, infection, and immunity. Frequency-modulated electrically driven microfluidic measurement and separation systems offer the ability to sensitively identify single cells based on biophysical information, such as their size and shape, as well as their subcellular membrane morphology and cytoplasmic organization. However, there is a lack of reliable and reproducible model particles with well-tuned subcellular electrical phenotypes that can be used as standards to benchmark the electrical physiology of unknown cell types or to benchmark dielectrophoretic separation metrics of novel device strategies. Herein, the application of red blood cells (RBCs) as multimodal standard particles with systematically modulated subcellular electrophysiology and associated fluorescence level is presented. Using glutaraldehyde fixation to vary membrane capacitance and by membrane resealing after electrolyte penetration to vary interior cytoplasmic conductivity and fluorescence in a correlated manner, each modified RBC type can be identified at single-cell sensitivity based on phenomenological impedance metrics and fitted to dielectric models to compute biophysical information. In this manner, single-cell impedance data from unknown RBC types can be mapped versus these model RBC types for facile determination of subcellular biophysical information and their dielectrophoretic separation conditions, without the need for time-consuming algorithms that often require unknown fitting parameters. Such internal standards for biophysical cytometry can advance in-line phenotypic recognition strategies.

Deep Learning Assisted Microfluidic Impedance Flow Cytometry for Label-free Foodborne Bacteria Analysis and Classification. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021;2021:7087-7090. [PMID: 34892734 DOI: 10.1109/embc46164.2021.9630684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

According to the urgent need for rapid detection and identification of foodborne bacteria to prevent public health event, a microfluidic electrical impedance flow cytometry assisted with convolutional neural network (ConvNet) based deep learning algorithm was proposed in this study to analyze the impedance signals of bacteria. With the assistance of the deep learning algorithm, Escherichia coli (EPEC), Salmonella enteritidis (SE) and Vibrio parahaemolyticus (VP) were identified with an accuracy of 100%. The proposed impedance based analysis system can be potentially applied for pre-classification of different subtypes of bacteria in a label-free manner.Clinical Relevance-The whole platform can be miniaturized and applied for point-of-care testing (POCT) of pathogenic bacteria detection.

Rapid bacteria-detection platform based on magnetophoretic concentration, dielectrophoretic separation, and impedimetric detection

Most biosensors employ small sample quantities (less than 100 μL) for bacteria detection, thereby resulting in inaccurate low-concentration measurements. Detection performed using small sample volumes with low bacteria concentration may produce false-negative results. Therefore, sample pretreatment plays a critical role in accurate bacteria detection. This paper presents an impedimetric bacteria-detection sensor integrated with bacteria concentration and separation devices for rapid bacteria detection. Post conjugation using magnetic particles (MPs), the MP-conjugated bacteria (MP/Bac) are concentrated via magnetophoresis by a factor exceeding 100. In addition, MP/Bac are separated from MPs via dielectrophoresis to prevent occurrence of signal errors caused by MPs not conjugated with bacteria. Subsequently, concentrated MP/Bac are captured on a sensor electrode, and bacteria concentration is detected by measuring signal changes caused by the impedance difference between bacteria and the medium. The performance of the proposed bacteria-detection device was evaluated using a 5-mL homogenized cabbage sample injected with Staphylococcus aureus at 30 mL/h flow rate. The observed signal change was measured for 10 min using a sample with a concentration of 5-5 × 10³ CFU/mL and was found to be approximately 0.34 mV at 50 CFU/mL; the limit of detection was 36 CFU/mL. These results confirm that the proposed device can detect low bacteria concentrations in food samples.

Zwitterions for impedance spectroscopy: The new buffers in town

Studying the role of buffers in impedance spectroscopy is a relatively unexplored area. We demonstrate a special class of biologically relevant buffers known as Good's zwitterionic buffers that show improved performance over standard electrolyte buffers (e.g. PBS) currently widely used in impedance spectroscopy measurements of bacterial suspensions. Our theoretical and experimental comparisons of conductivity of classical and zwitterionic buffers at various different concentrations show that ion-ion interaction effects are significantly higher in zwitterionic buffers as compared to classical buffers at the concentrations at which they are used. This and the fact that zwitterions have larger sizes leads to the lowering of their conductivity which significantly improves their impedance sensing ability. We illustrate through an example of heat-induced ionic release in model S. typhi and S. aureus bacteria that having a low conductivity buffer is indeed beneficial for biological impedance measurements. In fact, the best buffer for impedance studies can be chosen solely based on their electrical properties as long as they are also biologically compatible. This gives Good's zwitterionic buffers an edge over conventional media as they satisfy both these criteria.

Microfluidic impedance cytometry for single-cell sensing: Review on electrode configurations

Single-cell analysis has gained considerable attention for disease diagnosis, drug screening, and differentiation monitoring. Compared to the well-established flow cytometry, which uses fluorescent-labeled antibodies, microfluidic impedance cytometry (MIC) offers a simple, label-free, and noninvasive method for counting, classifying, and monitoring cells. Superior features including a small footprint, low reagent consumption, and ease of use have also been reported. The MIC device detects changes in the impedance signal caused by cells passing through the sensing/electric field zone, which can extract information regarding the size, shape, and dielectric properties of these cells. According to recent studies, electrode configuration has a remarkable effect on detection accuracy, sensitivity, and throughput. With the improvement in microfabrication technology, various electrode configurations have been reported for improving detection accuracy and throughput. However, the various electrode configurations of MIC devices have not been reviewed. In this review, the theoretical background of the impedance technique for single-cell analysis is introduced. Then, two-dimensional, three-dimensional, and liquid electrode configurations are discussed separately; their sensing mechanisms, fabrication processes, advantages, disadvantages, and applications are also described in detail. Finally, the current limitations and future perspectives of these electrode configurations are summarized. The main aim of this review is to offer a guide for researchers on the ongoing advancement in electrode configuration designs.

Apoptotic Bodies in the Pancreatic Tumor Cell Culture Media Enable Label-Free Drug Sensitivity Assessment by Impedance Cytometry

The ability to rapidly and sensitively predict drug response and toxicity using in vitro models of patient-derived tumors is essential for assessing chemotherapy efficacy. Currently, drug sensitivity assessment for solid tumors relies on imaging adherent cells or by flow cytometry of cells lifted from drug-treated cultures after fluorescent staining for apoptotic markers. Subcellular apoptotic bodies (ABs), including microvesicles that are secreted into the culture media under drug treatment can potentially serve as markers for drug sensitivity, without the need to lift cells under culture. However, their stratification to quantify cell disassembly is challenging due to their compositional diversity, with tailored labeling strategies currently needed for the recognition and cytometry of each AB type. It is shown that the high frequency impedance phase versus size distribution of ABs determined by high-throughput single-particle impedance cytometry of supernatants in the media of gemcitabine-treated pancreatic tumor cultures exhibits phenotypic resemblance to lifted apoptotic cells and enables shape-based stratification within distinct size ranges, which is not possible by flow cytometry. It is envisioned that this tool can be applied in conjunction with the appropriate pancreatic tumor microenvironment model to assess drug sensitivity and toxicity of patient-derived tumors, without the need to lift cells from cultures.

Self-aligned sequential lateral field non-uniformities over channel depth for high throughput dielectrophoretic cell deflection

Dielectrophoresis (DEP) enables the separation of cells based on subtle subcellular phenotypic differences by controlling the frequency of the applied field. However, current electrode-based geometries extend over a limited depth of the sample channel, thereby reducing the throughput of the manipulated sample (sub-μL min-1 flow rates and <105 cells per mL). We present a flow through device with self-aligned sequential field non-uniformities extending laterally across the sample channel width (100 μm) that are created by metal patterned over the entire depth (50 μm) of the sample channel sidewall using a single lithography step. This enables single-cell streamlines to undergo progressive DEP deflection with minimal dependence on the cell starting position, its orientation versus the field and intercellular interactions. Phenotype-specific cell separation is validated (>μL min-1 flow and >106 cells per mL) using heterogeneous samples of healthy and glutaraldehyde-fixed red blood cells, with single-cell impedance cytometry showing that the DEP collected fractions are intact and exhibit electrical opacity differences consistent with their capacitance-based DEP crossover frequency. This geometry can address the vision of an "all electric" selective cell isolation and cytometry system for quantifying phenotypic heterogeneity of cellular systems.

Label-Free Quantification of Cell Cycle Synchronicity of Human Neural Progenitor Cells Based on Electrophysiology Phenotypes

The ability to coax human-induced pluripotent stem cells (hiPSCs) into human neural progenitor cells (hNPCs) can lead to novel drug discovery and transplant therapy platforms for neurological diseases. Since hNPCs can form organoids that mimic brain development, there is emerging interest in their label-free characterization for controlling cell composition to optimize organoid formation in three-dimensional (3D) cultures. However, this requires the ability to quantify hNPCs in heterogeneous samples with subpopulations of similar phenotype. Using high-throughput (>6000 cells per condition), single-cell impedance cytometry, we present the utilization of electrophysiology for quantification of hNPC subpopulations that are altered in cell cycle synchronicity by camptothecin (CPT) exposure. Electrophysiology phenotypes are determined from impedance magnitude and phase metrics for distinguishing each cell cycle phase, as validated by flow cytometry, for a wide range of subpopulation proportions. Using multishell dielectric models for each cell cycle phase, electrophysiology alterations with CPT dose could be predicted. This label-free detection strategy can prevent loss of cell viability to speed the optimization of cellular compositions for organoid development.

Single-cell microfluidic impedance cytometry: from raw signals to cell phenotypes using data analytics

The biophysical analysis of single-cells by microfluidic impedance cytometry is emerging as a label-free and high-throughput means to stratify the heterogeneity of cellular systems based on their electrophysiology. Emerging applications range from fundamental life-science and drug assessment research to point-of-care diagnostics and precision medicine. Recently, novel chip designs and data analytic strategies are laying the foundation for multiparametric cell characterization and subpopulation distinction, which are essential to understand biological function, follow disease progression and monitor cell behaviour in microsystems. In this tutorial review, we present a comparative survey of the approaches to elucidate cellular and subcellular features from impedance cytometry data, covering the related subjects of device design, data analytics (i.e., signal processing, dielectric modelling, population clustering), and phenotyping applications. We give special emphasis to the exciting recent developments of the technique (timeframe 2017-2020) and provide our perspective on future challenges and directions. Its synergistic application with microfluidic separation, sensor science and machine learning can form an essential toolkit for label-free quantification and isolation of subpopulations to stratify heterogeneous biosystems.

An Electrokinetically-Driven Microchip for Rapid Entrapment and Detection of Nanovesicles

Electrical Impedance Spectroscopy (EIS) has been widely used as a label-free and rapid characterization method for the analysis of cells in clinical research. However, the related work on exosomes (40–150 nm) and the particles of similar size has not yet been reported. In this study, we developed a new Lab-on-a-Chip (LOC) device to rapidly entrap a cluster of sub-micron particles, including polystyrene beads, liposomes, and small extracellular vesicles (exosomes), utilizing an insulator-based dielectrophoresis (iDEP) scheme followed by measuring their impedance utilizing an integrated electrical impedance sensor. This technique provides a label-free, fast, and non-invasive tool for the detection of bionanoparticles based on their unique dielectric properties. In the future, this device could potentially be applied to the characterization of pathogenic exosomes and viruses of similar size, and thus, be evolved as a powerful tool for early disease diagnosis and prognosis.

Double-peak signal features in microfluidic impedance flow cytometry enable sensitive measurement of cell membrane capacitance

The probing of individual cells at specific frequency regimes in a microfluidic impedance flow cytometer led to the observation of unusual "double peak" features in the reactive component of the resulting signal. The phenomenon was restricted to the lower frequencies (400-800 kHz) of the β-dispersion regime and its occurrence was facilitated by the co-planar microelectrode geometry in the device. To understand the reasons for this anomalous behaviour, the system was modelled using COMSOL. The simulated model agreed well with experimental observations and provided insight into the origins of this signal profile and the effect of various parameters on its behaviour. One of the most significant observations of this study was the high sensitivity of the features in the "double peak" profile to changes in cell membrane capacitance (CMC), compared to conventional "single peaks" of reactive impedance. This was consequently exploited to accurately distinguish populations of normal and glutaraldehyde treated erythrocytes based on variations in their CMC, indicating a drastic decrease in the CMC of treated cells. Additionally, we demonstrate the applicability of using this double peak effect to identify cell populations within a mixture of PBMCs. This study is an improvement over conventional approaches of measuring CMC via impedance flow cytometry by enabling the measurement of both cell size and cell membrane properties at a single frequency rather than using multiple frequencies. Using a single frequency significantly simplifies the system and reduces the associated costs. Additionally, this technique enables the measurement of CMC at relatively low frequencies.