High-Speed Single-Cell Dielectric Spectroscopy

Long-Term Stable and Multifeature Microfluidic Impedance Flow Cytometry Based on a Constricted Channel for Single-Cell Mechanical Phenotyping

The microfluidic impedance flow cytometer (m-IFC) using constricted microchannels is an appealing choice for the high-throughput measurement of single-cell mechanical properties. However, channels smaller than the cells are susceptible to irreversible blockage, extremely affecting the stability of the system and the throughput. Meanwhile, the common practice of extracting a single quantitative index, i.e., total cell passage time, through the constricted part is inadequate to decipher the complex mechanical properties of individual cells. Herein, this study presents a long-term stable and multifeature m-IFC based on a constricted channel for single-cell mechanical phenotyping. The blockage problem is effectively overcome by adding tiny xanthan gum (XG) polymers. The cells can pass through the constricted channel at a flow rate of 500 μL/h without clogging, exhibiting high throughput (∼240 samples per second) and long-term stability (∼2 h). Moreover, six detection regions were implemented to capture the multiple features related to the whole process of a single cell passing through the long-constricted channel, e.g., creep, friction, and relaxation stages. To verify the performance of the multifeature m-IFC, cells treated with perturbations of microtubules and microfilaments within the cytoskeleton were detected, respectively. It suggests that the extracted features provide more comprehensive clues for single-cell analysis in structural and mechanical transformation. Overall, our proposed multifeature m-IFC exhibits the advantages of nonclogging and high throughput, which can be extended to other cell types for nondestructive and real-time mechanical phenotyping in cost-effective applications.

Discrimination of Microplastics and Phytoplankton Using Impedance Cytometry

Both microplastics and phytoplankton are found together in the ocean as suspended microparticles. There is a need for deployable technologies that can identify, size, and count these particles at high throughput to monitor plankton community structure and microplastic pollution levels. In situ analysis is particularly desirable as it avoids the problems associated with sample storage, processing, and degradation. Current technologies for phytoplankton and microplastic analysis are limited in their capability by specificity, throughput, or lack of deployability. Little attention has been paid to the smallest size fraction of microplastics and phytoplankton below 10 μm in diameter, which are in high abundance. Impedance cytometry is a technique that uses microfluidic chips with integrated microelectrodes to measure the electrical impedance of individual particles. Here, we present an impedance cytometer that can discriminate and count microplastics sampled directly from a mixture of phytoplankton in a seawater-like medium in the 1.5-10 μm size range. A simple machine learning algorithm was used to classify microplastic particles based on dual-frequency impedance measurements of particle size (at 1 MHz) and cell internal electrical composition (at 500 MHz). The technique shows promise for marine deployment, as the chip is sensitive, rugged, and mass producible.

Neural Network-Enabled Multiparametric Impedance Signal Templating for High throughput Single-Cell Deformability Cytometry Under Viscoelastic Extensional Flows

Cellular biophysical metrics exhibit systematic alterations during processes, such as metastasis and immune cell activation, which can be used to identify and separate live cell subpopulations for targeting drug screening. Image-based biophysical cytometry under extensional flows can accurately quantify cell deformability based on cell shape alterations but needs extensive image reconstruction, which limits its inline utilization to activate cell sorting. Impedance cytometry can measure these cell shape alterations based on electric field screening, while its frequency response offers functional information on cell viability and interior structure, which are difficult to discern by imaging. Furthermore, 1-D temporal impedance signal trains exhibit characteristic shapes that can be rapidly templated in near real-time to extract single-cell biophysical metrics to activate sorting. We present a multilayer perceptron neural network signal templating approach that utilizes raw impedance signals from cells under extensional flow, alongside its training with image metrics from corresponding cells to derive net electrical anisotropy metrics that quantify cell deformability over wide anisotropy ranges and with minimal errors from cell size distributions. Deformability and electrical physiology metrics are applied in conjunction on the same cell for multiparametric classification of live pancreatic cancer cells versus cancer associated fibroblasts using the support vector machine model.

Microfluidic-Based Electrical Operation and Measurement Methods in Single-Cell Analysis

Cellular heterogeneity plays a significant role in understanding biological processes, such as cell cycle and disease progression. Microfluidics has emerged as a versatile tool for manipulating single cells and analyzing their heterogeneity with the merits of precise fluid control, small sample consumption, easy integration, and high throughput. Specifically, integrating microfluidics with electrical techniques provides a rapid, label-free, and non-invasive way to investigate cellular heterogeneity at the single-cell level. Here, we review the recent development of microfluidic-based electrical strategies for single-cell manipulation and analysis, including dielectrophoresis- and electroporation-based single-cell manipulation, impedance- and AC electrokinetic-based methods, and electrochemical-based single-cell detection methods. Finally, the challenges and future perspectives of the microfluidic-based electrical techniques for single-cell analysis are proposed.

A microfluidic impedance cytometry device for robust identification of H. pluvialis

H. pluvialis contains rich oleic acid and astaxanthin, which have important applications in the fields of biodiesel and biomedicine. Detection of live H. pluvialis is the prerequisite to obtaining oleic acid and astaxanthin. For this purpose, we successfully developed a reliable microfluidic impedance cytometry for the identification of live H. pluvialis. Firstly, we established a simulation model for detecting H. pluvialis based on their morphology and studied the effect of medium conductivity on the impedance of H. pluvialis at different frequencies. From the simulations, we determined that the optimal solution conductivity for the detection of H. pluvialis was 1500 μS cm-1 and studied the frequency responses of the impedance of H. pluvialis. Secondly, we fabricated the microchannels and stainless-steel detection electrodes and assembled them into microfluidic impedance cytometry. The frequency dependence of live and dead H. pluvialis was explored under different frequencies, and live and dead H. pluvialis were distinguished at a frequency of 1 MHz. The impedance of live H. pluvialis at the frequency of 1 MHz ranges from 33.73 to 52.23 Ω, while that of dead ones ranges from 13.05 to 19.59 Ω. Based on these findings, we accomplished the identification and counting of live H. pluvialis in the live and dead sample solutions. Furthermore, we accomplished the identification and counting of live H. pluvialis in the mixed samples containing Euglena and H. pluvialis. This approach possesses the promising capacity to serve as a robust tool in the identification of target microalgae, addressing a challenge in the fields of biodiesel and biomedicine.

Biosensor with Microchannel for Broadband Dielectric Characterization of Nanoliter Cell Suspensions up to 110 GHz

Cell dielectric property measurement holds significant potential for application in cell detection and diagnosis due to its label-free and noninvasive nature. In this study, we developed a biosensor designed to measure the permittivity of liquid samples, particularly cell suspensions at the nanoliter scale, utilizing microwave and millimeter wave coplanar waveguides in conjunction with a microchannel. This biosensor facilitates the measurement of scattering parameters within a frequency domain ranging from 1 GHz to 110 GHz. The obtained scattering parameters are then converted into dielectric constants using specific algorithms. A cell capture structure within the microchannel ensures that cell suspensions remain stable within the measurement zone. The feasibility of this biosensor was confirmed by comparison with a commercial Keysight probe. We measured the dielectric constants of three different cell suspensions (HepG2, A549, MCF-7) using our biosensor. We also counted the number of cells captured in multiple measurements for each cell type and compared the corresponding changes in permittivity. The results indicated that the real part of the permittivity of HepG2 cells is 0.2-0.8 lower than that of the other two cell types. The difference between A549 and MCF-7 was relatively minor, only 0.2-0.4. The fluctuations in the dielectric spectrum caused by changes in cell numbers during measurements were smaller than the differences observed between different cell types. Thus, the sensor is suitable for measuring cell suspensions and can be utilized for label-free, noninvasive studies in identifying biological cell suspensions.

On the compatibility of single-cell microcarriers (nanovials) with microfluidic impedance cytometry

We investigate for the first time the compatibility of nanovials with microfluidic impedance cytometry (MIC). Nanovials are suspendable crescent-shaped single-cell microcarriers that enable specific cell adhesion, the creation of compartments for undisturbed cell growth and secretion, as well as protection against wall shear stress. MIC is a label-free single-cell technique that characterizes flowing cells based on their electrical fingerprints and it is especially targeted to cells that are naturally in suspension. Combining nanovial technology with MIC is intriguing as it would represent a robust framework for the electrical analysis of single adherent cells at high throughput. Here, as a proof-of-concept, we report the MIC analysis of mesenchymal stromal cells loaded in nanovials. The electrical analysis is supported by numerical simulations and validated by means of optical analysis. We demonstrate that the electrical diameter can discriminate among free cells, empty nanovials, cell-loaded nanovials, and clusters, thus grounding the foundation for the use of nanovials in MIC. Furthermore, we investigate the potentiality of MIC to assess the electrical phenotype of cells loaded in nanovials and we draw directions for future studies.

Microwave Flow Cytometric Detection and Differentiation of Escherichia coli

Label-free measurement and analysis of single bacterial cells are essential for food safety monitoring and microbial disease diagnosis. We report a microwave flow cytometric sensor with a microstrip sensing device with reduced channel height for bacterial cell measurement. Escherichia coli B and Escherichia coli K-12 were measured with the sensor at frequencies between 500 MHz and 8 GHz. The results show microwave properties of E. coli cells are frequency-dependent. A LightGBM model was developed to classify cell types at a high accuracy of 0.96 at 1 GHz. Thus, the sensor provides a promising label-free method to rapidly detect and differentiate bacterial cells. Nevertheless, the method needs to be further developed by comprehensively measuring different types of cells and demonstrating accurate cell classification with improved machine-learning techniques.

Optimizing Microfluidic Impedance Cytometry by Bypass Electrode Layout Design

Microfluidic impedance cytometry (MIC) has emerged as a popular technique for single-cell analysis. Traditional MIC electrode designs consist of a pair of (or three) working electrodes, and their detection performance needs further improvements for microorganisms. In this study, we designed an 8-electrode MIC device in which the center pair was defined as the working electrode, and the connection status of bypass electrodes could be changed. This allowed us to compare the performance of layouts with no bypasses and those with floating or grounding electrodes by simulation and experiment. The results of detecting Φ 5 μm beads revealed that both the grounding and the floating electrode outperformed the no bypass electrode, and the grounding electrode demonstrated the best signal-to-noise ratio (SNR), coefficient of variation (CV), and detection sensitivity. Furthermore, the effects of different bypass grounding areas (numbers of grounding electrodes) were investigated. Finally, particles passing at high horizontal positions can be detected, and Φ 1 μm beads can be measured in a wide channel (150 μm) using a fully grounding electrode, with the sensitivity of bead volume detection reaching 0.00097%. This provides a general MIC electrode optimization technology for detecting smaller particles, even macromolecular proteins, viruses, and exosomes in the future.

Microfluidic impedance cytometry with flat-end cylindrical electrodes for accurate and fast analysis of marine microalgae

Marine microalgae play an increasingly significant role in addressing the issues of environmental monitoring and disease treatment, making the analysis of marine microalgae at the single-cell level an essential technique. For this, we put forward accurate and fast microfluidic impedance cytometry to analyze microalgal cells by assembling two cylindrical electrodes and microchannels to form a three-dimensional detection zone. Firstly, we established a mathematical model of microalgal cell detection based on Maxwell's mixture theory and numerically investigated the effects of the electrode gap, microalgal positions, and ion concentrations of the solution on detection to optimize detection conditions. Secondly, 80 μm stainless steel wires were used to construct flat-ended cylindrical electrodes and were then inserted into two collinear channels fabricated using standard photolithography techniques to form a spatially uniform electric field to promote the detection throughput and sensitivity. Thirdly, based on the validation of this method, we measured the impedance of living Euglena and Haematococcus pluvialis to study parametric influences, including ion concentration, cell density and electrode gap. The throughput of this method was also investigated, which reached 1800 cells per s in the detection of Haematococcus pluvialis. Fourthly, we analyzed live and dead Euglena to prove the ability of this method to detect the physiological status of cells and obtained impedances of 124.3 Ω and 31.0 Ω with proportions of 15.9% and 84.1%, respectively. Finally, this method was engineered for the analysis of marine microalgae, measuring living Euglena with an impedance of 159.61 Ω accounting for 3.9%, dead Euglena with an impedance of 36.43 Ω accounting for 10.1% and Oocystis sp. with an impedance of 55.00 Ω accounting for about 81.0%. This method could provide a reliable tool to analyze marine microalgae for monitoring the marine environment and treatment of diseases owing to its outstanding advantages of low cost, high throughput and high corrosion resistance.

Amyloid-β Oligomer-Induced Electrophysiological Mechanisms and Electrical Impedance Changes in Neurons

Amyloid plays a critical role in the pathogenesis of Alzheimer's disease (AD) and can aggregate to form oligomers and fibrils in the brain. There is increasing evidence that highly toxic amyloid-β oligomers (AβOs) lead to tau protein aggregation, hyperphosphorylation, neuroinflammation, neuronal loss, synaptic loss, and dysfunction. Although the effects of AβOs on neurons have been investigated using conventional biochemical experiments, there are no established criteria for electrical evaluation. To this end, we explored electrophysiological changes in mouse hippocampal neurons (HT22) following exposure to AβOs and/or naringenin (Nar, a flavonoid compound) using electrical impedance spectroscopy (EIS). AβO-induced HT22 showed a decreased impedance amplitude and increased phase angle, and the addition of Nar reversed these changes. The characteristic frequency was markedly increased with AβO exposure, which was also reversed by Nar. The AβOs decreased intranuclear and cytoplasmic resistance and increased nucleus resistance and extracellular capacitance. Overall, the innovative construction of the eight-element CPE-equivalent circuit model further reflects that the pseudo-capacitance of the cell membrane and cell nucleus was increased in the AβO-induced group. This study conclusively revealed that AβOs induce cytotoxic effects by disrupting the resistance characteristics of unit membranes. The results further support that EIS is an effective technique for evaluating AβO-induced neuronal damage and microscopic electrical distinctions in the sub-microscopic structure of reactive cells.

Advance of microfluidic flow cytometry enabling high-throughput characterization of single-cell electrical and structural properties

This paper reported a micro flow cytometer capable of high-throughput characterization of single-cell electrical and structural features based on constrictional microchannels and deep neural networks. When single cells traveled through microchannels with constricted cross-sectional areas, they effectively blocked concentrated electric field lines, producing large impedance variations. Meanwhile, the traveling cells were confined within the cross-sectional areas of the constrictional microchannels, enabling the capture of high-quality images without losing focuses. Then single-cell features from impedance profiles and optical images were extracted from customized recurrent and convolution networks (RNN and CNN), which were further fused for cell-type classification based on support vector machines (SVM). As a demonstration, two leukemia cell lines (e.g., HL60 vs. Jurkat) were analyzed, producing high-classification accuracies of 99.3% based on electrical features extracted from Long Short-Term Memory (LSTM) of RNN, 96.7% based on structural features extracted from Resnet18 of CNN and 100.0% based on combined features enabled by SVM. The microfluidic flow cytometry developed in this study may provide a new perspective for the field of single-cell analysis.

Current perspectives, challenges, and future directions in the electrochemical detection of microplastics

Microplastics (5 μm) are a developing threat that contaminate every environmental compartment. The detection of these contaminants is undoubtedly an important topic of study because of their high potential to cause harm to ecosystems. For many years, scientists have been assiduously striving to surmount the obstacle of detection restrictions and minimize the likelihood of receiving results that are either false positives or false negatives. This study covers the current state of electrochemical sensing technology as well as its application as a low-cost analytical platform for the detection and characterization of novel contaminants. Examples of detection mechanisms, electrode modification procedures, device configuration, and performance are given to show how successful these approaches are for monitoring microplastics in the environment. Additionally included are the recent developments in nanoimpact techniques. Compared to electrochemical methods for microplastic remediation, the use of electrochemical sensors for microplastic detection has received very little attention. With an overview of microplastic electrochemical sensors, this review emphasizes the promise of existing electrochemical remediation platforms toward sensor design and development. In order to enhance the monitoring of these substances, a critical assessment of the requirements for future research, challenges associated with detection, and opportunities is provided. In addition to-or instead of-the now-in-use laboratory-based analytical equipment, these technologies can be utilized to support extensive research and manage issues pertaining to microplastics in the environment and other matrices.

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.

An impedance flow cytometry with integrated dual microneedle for electrical properties characterization of single cell

Electrical characteristics of living cells have been proven to reveal important details about their internal structure, charge distribution and composition changes in the cell membrane, as well as the extracellular context. An impedance flow cytometry is a common approach to determine the electrical properties of a cell, having the advantage of label-free and high throughput. However, the current techniques are complex and costly for the fabrication process. For that reason, we introduce an integrated dual microneedle-microchannel for single-cell detection and electrical properties extraction. The dual microneedles utilized a commercially available tungsten needle coated with parylene. When a single cell flows through the parallel-facing electrode configuration of the dual microneedle, the electrical impedance at multiple frequencies is measured. The impedance measurement demonstrated the differential of normal red blood cells (RBCs) with three different sizes of microbeads at low and high frequencies, 100 kHz and 2 MHz, respectively. An electrical equivalent circuit model (ECM) was used to determine the unique membrane capacitance of individual cells. The proposed technique demonstrated that the specific membrane capacitance of an RBC is 9.42 mF/m-2, with the regression coefficients, ρ at 0.9895. As a result, this device may potentially be used in developing countries for low-cost single-cell screening and detection.

Impedance-Based Multimodal Electrical-Mechanical Intrinsic Flow Cytometry

Reflecting various physiological states and phenotypes of single cells, intrinsic biophysical characteristics (e.g., mechanical and electrical properties) are reliable and important, label-free biomarkers for characterizing single cells. However, single-modal mechanical or electrical properties alone are not specific enough to characterize single cells accurately, and it has been long and challenging to couple the conventionally image-based mechanical characterization and impedance-based electrical characterization. In this work, the spatial-temporal characteristics of impedance sensing signal are leveraged, and an impedance-based multimodal electrical-mechanical flow cytometry framework for on-the-fly high-dimensional intrinsic measurement is proposed, that is, Young's modulus E, fluidity β, radius r, cytoplasm conductivity σi , and specific membrane capacitance Csm , of single cells. With multimodal high-dimensional characterization, the electrical-mechanical flow cytometry can better reveal the difference in cell types, demonstrated by the experimental results with three types of cancer cells (HepG2, MCF-7, and MDA-MB-468) with 93.4% classification accuracy and pharmacological perturbations of the cytoskeleton (fixed and Cytochalasin B treated cells) with 95.1% classification accuracy. It is envisioned that multimodal electrical-mechanical flow cytometry provides a new perspective for accurate label-free single-cell intrinsic characterization.

Permittivity-Based Microparticle Classification by the Integration of Impedance Cytometry and Microwave Resonators

Permittivity of microscopic particles can be used as a classification parameter for applications in materials and environmental sciences. However, directly measuring the permittivity of individual microparticles has proven to be challenging due to the convoluting effect of particle size on capacitive signals. To overcome this challenge, a sensing platform is built to independently obtain both the geometric and electric size of a particle, by combining impedance cytometry and microwave resonant sensing in a microfluidic chip. This way the microwave signal, which contains both permittivity and size effects, can be normalized by the size information provided by impedance cytometry to yield an intensive parameter that depends only on permittivity. The technique allows to differentiate between polystyrene and soda lime glass microparticles-below 22 µm in diameter-with more than 94% accuracy, despite their similar sizes and electrical characteristics. Furthermore, it is shown that the same technique can be used to differentiate between normal healthy cells and fixed cells of the same geometric size. The technique offers a potential route for targeted applications such as environmental monitoring of microplastic pollution or quality control in pharmaceutical industry.

A High-Throughput Microfluidic Cell Sorter Using a Three-Dimensional Coupled Hydrodynamic-Dielectrophoretic Pre-Focusing Module

Dielectrophoresis (DEP) is a powerful tool for label-free sorting of cells, even those with subtle differences in morphological and dielectric properties. Nevertheless, a major limitation is that most existing DEP techniques can efficiently sort cells only at low throughputs (<1 mL h-1). Here, we demonstrate that the integration of a three-dimensional (3D) coupled hydrodynamic-DEP cell pre-focusing module upstream of the main DEP sorting region enables cell sorting with a 10-fold increase in throughput compared to conventional DEP approaches. To better understand the key principles and requirements for high-throughput cell separation, we present a comprehensive theoretical model to study the scaling of hydrodynamic and electrostatic forces on cells at high flow rate regimes. Based on the model, we show that the critical cell-to-electrode distance needs to be ≤10 µm for efficient cell sorting in our proposed microfluidic platform, especially at flow rates ≥ 1 mL h-1. Based on those findings, a computational fluid dynamics model and particle tracking analysis were developed to find optimum operation parameters (e.g., flow rate ratios and electric fields) of the coupled hydrodynamic-DEP 3D focusing module. Using these optimum parameters, we experimentally demonstrate live/dead K562 cell sorting at rates as high as 10 mL h-1 (>150,000 cells min-1) with 90% separation purity, 85% cell recovery, and no negative impact on cell viability.

DUPLETS: Deformability-Assisted Dual-Particle Encapsulation Via Electrically Activated Sorting

Co-encapsulation of bead carriers and biological cells in microfluidics has become a powerful technique for various biological assays in single-cell genomics and drug screening because of its distinct capability of single-cell confinement. However, current co-encapsulation approaches exist a trade-off between cell/bead pairing rate and probability of multiple cells in individual droplets, significantly limiting the effective throughput of single-paired cell-bead droplets production. Deformability-assisted dUal-Particle encapsuLation via Electrically acTivated Sorting (DUPLETS) system is reported to overcome this problem. The DUPLETS can differentiate the encapsulated content in individual droplets and sort out targeted droplets via a combined screening of mechanical and electrical characteristics of single droplets in label-free manners and with the highest effective throughput in comparison to current commercial platforms. The DUPLETS has been demonstrated to enrich single-paired cell-bead droplets to over 80% (above eightfold higher than current co-encapsulation techniques). It eliminates multicell droplets to 0.1% whereas up to ≈24% in 10× Chromium. It is believed that merging DUPLETS into the current co-encapsulation platforms can meaningfully elevate sample quality in terms of high purity of single-paired cell-bead droplets, low fraction of multicell droplets, and high cell viability, which can benefit a multitude of biological assay applications.

Bio-inspired microfluidics: A review

Biomicrofluidics, a subdomain of microfluidics, has been inspired by several ideas from nature. However, while the basic inspiration for the same may be drawn from the living world, the translation of all relevant essential functionalities to an artificially engineered framework does not remain trivial. Here, we review the recent progress in bio-inspired microfluidic systems via harnessing the integration of experimental and simulation tools delving into the interface of engineering and biology. Development of "on-chip" technologies as well as their multifarious applications is subsequently discussed, accompanying the relevant advancements in materials and fabrication technology. Pointers toward new directions in research, including an amalgamated fusion of data-driven modeling (such as artificial intelligence and machine learning) and physics-based paradigm, to come up with a human physiological replica on a synthetic bio-chip with due accounting of personalized features, are suggested. These are likely to facilitate physiologically replicating disease modeling on an artificially engineered biochip as well as advance drug development and screening in an expedited route with the minimization of animal and human trials.

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.

Coaxial Dielectric Spectroscopy as an In-Line Process Analytical Technique for Reaction Monitoring

The suitability of broadband dielectric spectroscopy (DS) as a tool for in-line (in situ) reaction monitoring is demonstrated. Using the esterification of 4-nitrophenol as a test-case, we show that multivariate analysis of time-resolved DS data-collected across a wide frequency range with a coaxial dip-probe-allows reaction progress to be measured with both high precision and high accuracy. In addition to the workflows for data collection and analysis, we also establish a convenient method for rapidly assessing the applicability of DS to previously untested reactions or processes. We envisage that, given its orthogonality to other spectroscopic methods, its low cost, and its ease of implementation, DS will be a valuable addition to the process chemist's analytical toolbox.

piRT-IFC: Physics-informed real-time impedance flow cytometry for the characterization of cellular intrinsic electrical properties

Real-time transformation was important for the practical implementation of impedance flow cytometry. The major obstacle was the time-consuming step of translating raw data to cellular intrinsic electrical properties (e.g., specific membrane capacitance Csm and cytoplasm conductivity σcyto). Although optimization strategies such as neural network-aided strategies were recently reported to provide an impressive boost to the translation process, simultaneously achieving high speed, accuracy, and generalization capability is still challenging. To this end, we proposed a fast parallel physical fitting solver that could characterize single cells' Csm and σcyto within 0.62 ms/cell without any data preacquisition or pretraining requirements. We achieved the 27000-fold acceleration without loss of accuracy compared with the traditional solver. Based on the solver, we implemented physics-informed real-time impedance flow cytometry (piRT-IFC), which was able to characterize up to 100,902 cells' Csm and σcyto within 50 min in a real-time manner. Compared to the fully connected neural network (FCNN) predictor, the proposed real-time solver showed comparable processing speed but higher accuracy. Furthermore, we used a neutrophil degranulation cell model to represent tasks to test unfamiliar samples without data for pretraining. After being treated with cytochalasin B and N-Formyl-Met-Leu-Phe, HL-60 cells underwent dynamic degranulation processes, and we characterized cell's Csm and σcyto using piRT-IFC. Compared to the results from our solver, accuracy loss was observed in the results predicted by the FCNN, revealing the advantages of high speed, accuracy, and generalizability of the proposed piRT-IFC.

iSort enables automated complex microfluidic droplet sorting in an effort to democratize technology

Fluorescence-activated droplet sorting (FADS) is a widely used microfluidic technique for high-throughput screening. However, it requires highly trained specialists to determine optimal sorting parameters, and this results in a large combinatorial space that is challenging to optimize systematically. Additionally, it is currently challenging to track every single droplet within a screen, leading to compromised sorting and "hidden" false-positive events. To overcome these limitations, we have developed a setup in which the droplet frequency, spacing, and trajectory at the sorting junction are monitored in real time using impedance analysis. The resulting data are used to continuously optimize all parameters automatically and to counteract perturbations, resulting in higher throughput, higher reproducibility, increased robustness, and a beginner-friendly character. We believe this provides a missing piece for the spreading of phenotypic single-cell analysis methods, similar to what we have seen for single-cell genomics platforms.

Rapid Phenotypic Antimicrobial Susceptibility Testing Using a Coulter Counter and Proliferation Rate Discrepancy

The rapid determination of antimicrobial susceptibility and evidence-based antimicrobial prescription is necessary to combat widespread antimicrobial resistance and promote effectively treatment for bacterial infections. This study developed a rapid phenotypic antimicrobial susceptibility determination method competent for seamless clinical implementation. A laboratory-friendly Coulter counter-based antimicrobial susceptibility testing (CAST) was developed and integrated with bacterial incubation, population growth monitoring, and result analysis to quantitatively detect differences in bacterial growth between resistant and susceptible strains following a 2 h exposure to antimicrobial agents. The distinct proliferation rates of the different strains enabled the rapid determination of their antimicrobial susceptibility phenotypes. We evaluated the performance efficacy of CAST for 74 clinically isolated Enterobacteriaceae subjected to 15 antimicrobials. The results were consistent with those obtained via the 24 h broth microdilution method, showing 90.18% absolute categorical agreement.

Performance-enhanced clogging-free viscous sheath constriction impedance flow cytometry

As a label-free and high-throughput single cell analysis platform, impedance flow cytometry (IFC) suffers from clogging caused by a narrow microchannel as mechanical constriction (MC). Current sheath constriction (SC) solutions lack systematic evaluation of the performance and proper guidelines for the sheath fluid. Herein, we hypothesize that the viscosity of the non-conductive liquid is the key to the performance of SC, and propose to employ non-conductive viscous sheath flow in SC to unlock the tradeoff between sensitivity and throughput, while ensuring measurement accuracy. By placing MC and SC in series in the same microfluidic chip, we established an evaluation platform to prove the hypothesis. Through modeling analysis and experiments, we confirmed the accuracy (error < 1.60% ± 4.71%) of SC w.r.t. MC, and demonstrated that viscous non-conductive PEG solution achieved an improved sensitivity (7.92×) and signal-to-noise ratio (1.42×) in impedance measurement, with the accuracy maintained and free of clogging. Viscous SC IFC also shows satisfactory ability to distinguish different types of cancer cells and different subtypes of human breast cancer cells. It is envisioned that viscous SC IFC paves the way for IFC to be really usable in practice with clogging-free, accurate, and sensitive performance.

Machine learning empowered multi-stress level electromechanical phenotyping for high-dimensional single cell analysis

Microfluidics provides a powerful platform for biological analysis by harnessing the ability to precisely manipulate fluids and microparticles with integrated microsensors. Here, we introduce an imaging and impedance cell analyzer (IM2Cell), which implements single cell level impedance analysis and hydrodynamic mechanical phenotyping simultaneously. For the first time, IM2Cell demonstrates the capability of multi-stress level mechanical phenotyping. Specifically, IM2Cell is capable of characterizing cell diameter, three deformability responses, and four electrical properties. It presents high-dimensional information to give insight into subcellular components such as cell membrane, cytoplasm, cytoskeleton, and nucleus. In this work, we first validate imaging and impedance-based cell analyses separately. Then, the two techniques are combined to obtain both imaging and impedance data analyzed by machine learning method, exhibiting an improved prediction accuracy from 83.1% to 95.4% between fixed and living MDA-MB-231 breast cancer cells. Next, IM2Cell demonstrates 91.2% classification accuracy in a mixture of unlabeled MCF-10A, MCF-7, and MDA-MB-231 cell lines. Finally, an application demonstrates the potential of IM2Cell for the deformability studies of peripheral blood mononuclear cells (PBMCs) subpopulations without cumbersome isolation or labeling steps.

Using Impedance Flow Cytometry for Rapid Viability Classification of Heat-Treated Bacteria

In the future, rapid electrical characterization of cells with impedance flow cytometry promises to be a fast and accurate method for the evaluation of cell properties. In this paper, we investigate how the conductivity of the suspending medium along with the heat exposure time affects the viability classification of heat-treated E. coli. Using a theoretical model, we show that perforation of the bacteria membrane during heat exposure changes the impedance of the bacterial cell from effectively less conducting than the suspension medium to effectively more conducting. Consequently, this results in a shift in the differential argument of the complex electrical current that can be measured with impedance flow cytometry. We observe this shift experimentally through measurements on E. coli samples with varying medium conductivity and heat exposure times. We show that increased exposure time and lower medium conductivity results in improved classification between untreated and heat-treated bacteria. The best classification was achieved with a medium conductivity of 0.045 S/m after 30 min of heat exposure.

A Simple Micromilled Microfluidic Impedance Cytometer with Vertical Parallel Electrodes for Cell Viability Analysis

Microfluidic impedance cytometry has been demonstrated as an effective platform for single cell analysis, taking advantage of microfabricated features and dielectric cell sensing methods. In this study, we present a simple microfluidic device to improve the sensitivity, accuracy, and throughput of single suspension cell viability analysis using vertical sidewall electrodes fabricated by a widely accessible negative manufacturing method. A microchannel milled through a 75 µm platinum wire, which was embedded into poly-methyl-methacrylate (PMMA), created a pair of parallel vertical sidewall platinum electrodes. Jurkat cells were interrogated in a custom low-conductivity buffer (1.2 ± 0.04 mS/cm) to reduce current leakage and increase device sensitivity. Confirmed by live/dead staining and electron microscopy, a single optimum excitation frequency of 2 MHz was identified at which live and dead cells were discriminated based on the disruption in the cell membrane associated with cell death. At this frequency, live cells were found to exhibit changes in the impedance phase with no appreciable change in magnitude, while dead cells displayed the opposite behavior. Correlated with video microscopy, a computational algorithm was created that could identify cell detection events and determine cell viability status by application of a mathematical correlation method.

Single-Cell Classification Based on Population Nucleus Size Combining Microwave Impedance Spectroscopy and Machine Learning

Many recent efforts in the diagnostic field address the accessibility of cancer diagnosis. Typical histological staining methods identify cancer cells visually by a larger nucleus with more condensed chromatin. Machine learning (ML) has been incorporated into image analysis for improving this process. Recently, impedance spectrometers have been shown to generate all-inclusive lab-on-a-chip platforms to detect nucleus abnormities. In this paper, a wideband electrical sensor and data analysis paradigm that can identify nuclear changes shows the realization of a single-cell microfluidic device to detect nuclei of altered sizes. To model cells of altered nucleus, Jurkat cells were treated to enlarge or shrink their nucleus followed by broadband sensing to obtain the S-parameters of single cells. The ability to deduce important frequencies associated with nucleus size is demonstrated and used to improve classification models in both binary and multiclass scenarios, despite a heterogeneous and overlapping cell population. The important frequency features match those predicted in a double-shell circuit model published in prior work, demonstrating a coherent new analytical technique for electrical data analysis. The electrical sensing platform assisted by ML with impressive accuracy of cell classification looks forward to a label-free and flexible approach to cancer diagnosis.

Parallel Impedance Cytometry for Real-Time Screening of Bacterial Single Cells from Nano- to Microscale

The benefits of impedance cytometry include high-throughput and label-free detection, while long-term calibration is required to remove the effects of the detection circuits. This study presents a novel impedance cytometry system, called parallel impedance cytometry, to simplify the calibration and analysis of the impedance signals. Furthermore, target objects can be detected even when benchmarked against similar objects. Parallel dual microchannels allow the simultaneous detection of reference and target particles in two separate microchannels, without the premixing of reference and target suspensions. The impedance pulses of both can appear separately on the opposite sides of the same time series, which have been verified via simulation and experimental results. Raw impedance signals can easily distinguish target particles from reference ones. Polystyrene beads with different sizes ranging from nano- to microscale (e.g., 500, 750 nm, 1, 2, 3, and 4.5 μm) confirm the nanosensitivity of the system. In addition, the detection of antibiotic-treated Escherichia coli cells demonstrates that our system can be used for the quantitative assessment of the dielectric properties of individual cells, as well as for the proportion of susceptible cells. Through benchmarking against untreated E. coli cells in the other channel, our method enables the discrimination of susceptible cells from others and the comparison of susceptible and insusceptible cells in the target suspension. Those findings indicate that the parallel impedance cytometry can greatly facilitate the measurement and calibration of the impedances of various particles or cells and provide a means to compare their dielectric properties.

A new approach for accurate determination of particle sizes in microfluidic impedance cytometry

In microfluidic impedance cytometry, the change in impedance is recorded as an individual cell passes through a channel between electrodes deposited on its walls, and the particle size is inferred from the amplitude of the impedance signal using calibration. However, because the current density is nonuniform between electrodes of finite width, there could be an error in the particle size measurement because of uncertainty about the location of the particle in the channel cross section. Here, a correlation is developed relating the particle size to the signal amplitude and the velocity of the particle through the channel. The latter is inferred from the time interval between the two extrema in the impedance curve as the particle passes through a channel with cross-sectional dimensions of 50 μm (width) × 30 μm (height) with two pairs of parallel facing electrodes. The change in impedance is predicted using 3D COMSOL finite-element simulations, and a theoretical correlation that is independent of particle size is formulated to correct the particle diameter for variations in the cross-sectional location. With this correlation, the standard deviation in the experimental data is reduced by a factor of two to close to the standard deviation reported in the manufacturer specifications.

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.

Label-Free Microfluidic Impedance Cytometry for Acrosome Integrity Assessment of Boar Spermatozoa

Microfluidics and lab-on-chip technologies have been used in a wide range of biomedical applications. They are known as versatile, rapid, and low-cost alternatives for expensive equipment and time-intensive processing. The veterinary industry and human fertility clinics could greatly benefit from label-free and standardized methods for semen analysis. We developed a tool to determine the acrosome integrity of spermatozoa using microfluidic impedance cytometry. Spermatozoa from boars were treated with the calcium ionophore A23187 to induce acrosome reaction. The magnitude, phase and opacity of individual treated and non-treated (control) spermatozoa were analyzed and compared to conventional staining for acrosome integrity. The results show that the opacity at 19 MHz over 0.5 MHz is associated with acrosome integrity with a cut-off threshold at 0.86 (sensitivity 98%, specificity 97%). In short, we have demonstrated that acrosome integrity can be determined using opacity, illustrating that microfluidic impedance cytometers have the potential to become a versatile and efficient alternative in semen analysis and for fertility treatments in the veterinary industry and human fertility clinics.

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.

Emerging electrochemical techniques for identifying and removing micro/nanoplastics in urban waters

The ubiquitous micro/nanoplastics (MPs/NPs) in urban waters are priority pollutants due to their toxic effects on living organisms. Currently, great efforts have been made to realize a plastic-free urban water system, and the identification and removal of MPs/NPs are two primary issues. Among diverse methods, emerging electrochemical techniques have gained growing interests owing to their facile implementation, high efficiency, eco-compatibility, onsite operation, etc. Herein, recent progress in the electrochemical identification and removal of MPs/NPs in urban waters are comprehensively reviewed. The electrochemical sensing of MPs/NPs and their released pollutants (e.g., bisphenol A (BPA)) has been analyzed, and the sensing principles and the featured electrochemical devices/electrodes are examined. Afterwards, recent applications of electrochemical methods (i.e., electrocoagulation, electroadsorption, electrokinetic separation and electrochemical degradation) in MPs/NPs removal are discussed in detail. The influences of critical parameters (e.g., plastics' property, current density and electrolyte) in the electrochemical identification and removal of MPs/NPs are also analyzed. Finally, the current challenges and prospects in electrochemical sensing and removal of MPs/NPs in urban waters are elaborated. This review would advance efficient electrochemical technologies for future MPs/NPs pollutions management in urban waters.

Developments of Conventional and Microfluidic Flow Cytometry Enabling High-Throughput Characterization of Single Cells

This article first reviews scientific meanings of single-cell analysis by highlighting two key scientific problems: landscape reconstruction of cellular identities during dynamic immune processes and mechanisms of tumor origin and evolution. Secondly, the article reviews clinical demands of single-cell analysis, which are complete blood counting enabled by optoelectronic flow cytometry and diagnosis of hematologic malignancies enabled by multicolor fluorescent flow cytometry. Then, this article focuses on the developments of optoelectronic flow cytometry for the complete blood counting by comparing conventional counterparts of hematology analyzers (e.g., DxH 900 of Beckman Coulter, XN-1000 of Sysmex, ADVIA 2120i of Siemens, and CELL-DYN Ruby of Abbott) and microfluidic counterparts (e.g., microfluidic impedance and imaging flow cytometry). Future directions of optoelectronic flow cytometry are indicated where intrinsic rather than dependent biophysical parameters of blood cells must be measured, and they can replace blood smears as the gold standard of blood analysis in the near future.

Deciphering impedance cytometry signals with neural networks

Microfluidic impedance cytometry is a label-free technique for high-throughput single-cell analysis. Multi-frequency impedance measurements provide data that allows full characterisation of cells, linking electrical phenotype to individual biophysical properties. To efficiently extract the information embedded in the electrical signals, potentially in real-time, tailored signal processing is needed. Artificial intelligence approaches provide a promising new direction. Here we demonstrate the ability of neural networks to decipher impedance cytometry signals in two challenging scenarios: (i) to determine the intrinsic dielectric properties of single cells directly from raw impedance data streams, (ii) to capture single-cell signals that are hidden in the measured signals of coincident cells. The accuracy of the results and the high processing speed (fractions of ms per cell) demonstrate that neural networks can have an important role in impedance-based single-cell analysis.

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.

Dual-frequency impedance assays for intracellular components in microalgal cells

Intracellular components (including organelles and biomolecules) at the submicron level are typically analyzed in situ by special preparation or expensive setups. Here, a label-free and cost-effective approach of screening microalgal single-cells at a subcellular resolution is available based on impedance cytometry. To the best of our knowledge, it is the first time that the relationships between impedance signals and submicron intracellular organelles and biomolecules are shown. Experiments were performed on Euglena gracilis (E. gracilis) cells incubated under different incubation conditions (i.e., aerobic and anaerobic) and 15 μm polystyrene beads (reference) at two distinct stimulation frequencies (i.e., 500 kHz and 6 MHz). Based on the impedance detection of tens of thousands of samples at a throughput of about 900 cells per second, three metrics were used to track the changes in biophysical properties of samples. As a result, the electrical diameters of cells showed a clear shrinkage in cell volume and intracellular components, as observed under a microscope. The morphology metric of impedance pulses (i.e., tilt index) successfully characterized the changes in cell shape and intracellular composition distribution. Besides, the electrical opacity showed a stable ratio of the intracellular components to cell volume under the cellular self-regulation. Additionally, simulations were used to support these findings and to elucidate how submicron intracellular components and cell morphology affect impedance signals, providing a basis for future improvements. This work opens up a label-free and high-throughput way to analyze single-cell intracellular components by impedance cytometry.

Neural network-enhanced real-time impedance flow cytometry for single-cell intrinsic characterization

Single-cell impedance flow cytometry (IFC) is emerging as a label-free and non-invasive method for characterizing the electrical properties and revealing sample heterogeneity. At present, most IFC studies utilize phenomenological parameters (e.g., impedance amplitude, phase and opacity) to characterize single cells instead of intrinsic biophysical metrics (e.g., radius r, cytoplasm conductivity σ_i and specific membrane capacitance C_sm). Intrinsic parameters are normally calculated off-line by time-consuming model-fitting methods. Here, we propose to employ neural network (NN)-enhanced IFC to achieve both real-time single-cell intrinsic characterization and intrinsic parameter-based cell classification at high throughput. Three intrinsic parameters (r, σ_i and C_sm) can be obtained online and in real-time via a trained NN at 0.3 ms per single-cell event, achieving significant improvement in calculation speed. Experiments involving four cancer cells and one lymphocyte cell demonstrated 91.5% classification accuracy in the cell type for a test group of 9751 cell samples. By performing a viability assay, we provide evidence that the IFC test per se would not substantially affect the cell property. We envision that the NN-enhanced real-time IFC will provide a new platform for high-throughput, real-time and online cell intrinsic electrical characterization.

Assessment of the electrical penetration of cell membranes using four-frequency impedance cytometry

The electrical penetration of the cell membrane is vital for determining the cell interior via impedance cytometry. Herein, we propose a method for determining the conductivity of the cell membrane through the tilting levels of impedance pulses. When electrical penetration occurs, a high-frequency current freely passes through the cell membrane; thus, the intracellular distribution can directly act on the high-frequency impedance pulses. Numerical simulation shows that an uneven intracellular component distribution can affect the tilting levels of impedance pulses, and the tilting levels start increasing when the cell membrane is electrically penetrated. Experimental evidence shows that higher detection frequencies (>7 MHz) lead to a wider distribution of the tilting levels of impedance pulses when measuring cell populations with four-frequency impedance cytometry. This finding allows us to determine that a detection frequency of 7 MHz is able to pass through the membrane of Euglena gracilis (E. gracilis) cells. Additionally, we provide a possible application of four-frequency impedance cytometry in the biomass monitoring of single E. gracilis cells. High-frequency impedance (≥7 MHz) can be applied to monitor these biomass changes, and low-frequency impedance (<7 MHz) can be applied to track the corresponding biovolume changes. Overall, this work demonstrates an easy determination method for the electrical penetration of the cell membrane, and the proposed platform is applicable for the multiparameter assessment of the cell state during cultivation.

Electro-optical classification of pollen grains via microfluidics and machine learning

In aerobiological monitoring and agriculture there is a pressing need for accurate, label-free and automated analysis of pollen grains, in order to reduce the cost, workload and possible errors associated to traditional approaches. Methods: We propose a new multimodal approach that combines electrical sensing and optical imaging to classify pollen grains flowing in a microfluidic chip at a throughput of 150 grains per second. Electrical signals and synchronized optical images are processed by two independent machine learning-based classifiers, whose predictions are then combined to provide the final classification outcome. Results: The applicability of the method is demonstrated in a proof-of-concept classification experiment involving eight pollen classes from different taxa. The average balanced accuracy is 78.7 % for the electrical classifier, 76.7 % for the optical classifier and 84.2 % for the multimodal classifier. The accuracy is 82.8 % for the electrical classifier, 84.1 % for the optical classifier and 88.3 % for the multimodal classifier. Conclusion: The multimodal approach provides better classification results with respect to the analysis based on electrical or optical features alone. Significance: The proposed methodology paves the way for automated multimodal palynology. Moreover, it can be extended to other fields, such as diagnostics and cell therapy, where it could be used for label-free identification of cell populations in heterogeneous samples.

Submicron-precision particle characterization in microfluidic impedance cytometry with double differential electrodes

Submicron-precision particle characterization is crucial for counting, sizing and identifying a variety of biological particles, such as bacteria and apoptotic bodies. Microfluidic impedance cytometry has been attractive in current research for microparticle characterization due to its advantages of label-free detection, ease of miniaturization and affordability. However, conventional electrode configurations of three electrodes and floating electrodes have not yet demonstrated the capability of probing submicron particles or microparticles with a submicron size difference. In this study, we present a label-free high-throughput (∼800 particles per second) impedance-based microfluidic flow cytometry system integrated with a novel design of a double differential electrode configuration, enabling submicron particle detection (down to 0.4 μm) with a minimum size resolution of 200 nm. The signal-to-noise ratio has been boosted from 13.98 dB to 32.64 dB compared to a typical three-electrode configuration. With the proposed microfluidic impedance cytometry, we have shown results of sizing microparticles that accurately correlate with manufacturers' datasheets (R² = 0.99938). It also shows that population ratios of differently sized beads in mixture samples are consistent with the results given by commercial fluorescence-based flow cytometry (within ∼1% difference). This work provides a label-free approach with submicron precision for sizing and counting microscale and submicron particles, and a new avenue of designing electrode configurations with a feature of suppressing the electrical noise for accomplishing a high signal-to-noise ratio in a wide range of frequencies. This novel double differential impedance sensing system paves a new pathway for real-time analysis and accurate particle screening in pathological and pharmacological research.

An easy-fabricated and disposable polymer-film microfluidic impedance cytometer for cell sensing

We report here an easy-fabricated and disposable polymer-film microfluidic impedance cytometer (PMIC) integrated with inertial focusing and parallel facing electrodes for cell sensing. The cells are first focused in an asymmetric serpentine channel, and then their impedance signals are measured when passing through the electrode region. The proposed PMIC device is the first impedance cytometer that is fabricated into a flexible sheet (with a thickness of 0.45 mm) by using the materials of commonly-available ITO-coated polymer films and double-sided adhesive tapes, the whole fabrication process is shortened from traditional 3-4 days to less than 5 min by using UV laser cutting. To verify the feasibility of our device for cell sensing, we explore the focusing behaviors of three differently sized particles and two types of tumor cells, and analyze their impedance signals. The results show that our device is capable of obtaining impedance information on numbers, diameters, and longitudinal positions of cells. We envision that our PMIC device is promising in label-free cell sensing owning to the advantages of low cost, small footprint, and simple fabrication.