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Regenerative therapies, including the transplantation of spinal cord progenitor cells (SCPCs) derived from induced pluripotent stem cells (iPSCs), are promising treatment strategies for spinal cord injuries. However, the risk of tumorigenicity from residual iPSCs advocates an unmet need for rapid SCPCs safety profiling. Herein, a rapid (≈3000 cells min‐1) electrical‐based microfluidic biophysical cytometer is reported to detect low‐abundance iPSCs from SCPCs at single‐cell resolution. Based on multifrequency impedance measurements (0.3 to 12 MHz), biophysical features including cell size, deformability, membrane, and nucleus dielectric properties are simultaneously quantified as a cell is hydrodynamically stretched at a cross junction under continuous flow. A supervised uniform manifold approximation and projection (UMAP) model is further developed for impedance‐based quantification of undifferentiated iPSCs with high sensitivity (≈1% spiked iPSCs) and shows good correlations with SCPCs differentiation outcomes using two iPSC lines. Cell membrane opacity (day 1) is also identified as a novel early intrinsic predictive biomarker that exhibits a strong correlation with SCPC differentiation efficiency (day 10). Overall, it is envisioned that this label‐free and optic‐free platform technology can be further developed as a versatile cost‐effective process analytical tool to monitor or assess stem cell quality and safety in regenerative medicine. [ABSTRACT FROM AUTHOR]

Particle classification plays a crucial role in various scientific and technological applications, such as differentiating between bacteria and viruses in healthcare applications or identifying and classifying cancer cells. This technique requires accurate and efficient analysis of particle properties. In this study, we investigated the integration of electrical and optical features through a multimodal approach for particle classification. Machine learning classifier algorithms were applied to evaluate the impact of combining these measurements. Our results demonstrate the superiority of the multimodal approach over analyzing electrical or optical features independently. We achieved an average test accuracy of 94.9% by integrating both modalities, compared to 66.4% for electrical features alone and 90.7% for optical features alone. This highlights the complementary nature of electrical and optical information and its potential for enhancing classification performance. By leveraging electrical sensing and optical imaging techniques, our multimodal approach provides deeper insights into particle properties and offers a more comprehensive understanding of complex biological systems. [ABSTRACT FROM AUTHOR]

Cancer is a fatal disease and a significant cause of millions of deaths. Traditional methods for cancer detection often have limitations in identifying the disease in its early stages, and they can be expensive and time-consuming. Since cancer typically lacks symptoms and is often only detected at advanced stages, it is crucial to use affordable technologies that can provide quick results at the point of care for early diagnosis. Biosensors that target specific biomarkers associated with different types of cancer offer an alternative diagnostic approach at the point of care. Recent advancements in manufacturing and design technologies have enabled the miniaturization and cost reduction of point-of-care devices, making them practical for diagnosing various cancer diseases. Furthermore, machine learning (ML) algorithms have been employed to analyze sensor data and extract valuable information through the use of statistical techniques. In this review paper, we provide details on how various machine learning algorithms contribute to the ongoing development of advanced data processing techniques for biosensors, which are continually emerging. We also provide information on the various technologies used in point-of-care cancer diagnostic biosensors, along with a comparison of the performance of different ML algorithms and sensing modalities in terms of classification accuracy. [ABSTRACT FROM AUTHOR]

Individual cells have many unique properties that can be quantified to develop a holistic understanding of a population. This can include understanding population characteristics, identifying subpopulations, or elucidating outlier characteristics that may be indicators of disease. Electrical impedance measurements are rapid and label-free for the monitoring of single cells and generate large datasets of many cells at single or multiple frequencies. To increase the accuracy and sensitivity of measurements and define the relationships between impedance and biological features, many electrical measurement systems have incorporated machine learning (ML) paradigms for control and analysis. Considering the difficulty capturing complex relationships using traditional modelling and statistical methods due to population heterogeneity, ML offers an exciting approach to the systemic collection and analysis of electrical properties in a data-driven way. In this work, we discuss incorporation of ML to improve the field of electrical single cell analysis by addressing the design challenges to manipulate single cells and sophisticated analysis of electrical properties that distinguish cellular changes. Looking forward, we emphasize the opportunity to build on integrated systems to address common challenges in data quality and generalizability to save time and resources at every step in electrical measurement of single cells. [ABSTRACT FROM AUTHOR]

Determining nucleic acid concentrations in a sample is an important step prior to proceeding with downstream analysis in molecular diagnostics. Given the need for testing DNA amounts and its purity in many samples, including in samples with very small input DNA, there is utility of novel machine learning approaches for accurate and high-throughput DNA quantification. Here, we demonstrated the ability of a neural network to predict DNA amounts coupled to paramagnetic beads. To this end, a custom-made microfluidic chip is applied to detect DNA molecules bound to beads by measuring the impedance peak response (IPR) at multiple frequencies. We leveraged electrical measurements including the frequency and imaginary and real parts of the peak intensity within a microfluidic channel as the input of deep learning models to predict DNA concentration. Specifically, 10 different deep learning architectures are examined. The results of the proposed regression model indicate that an R_Squared of 97% with a slope of 0.68 is achievable. Consequently, machine learning models can be a suitable, fast, and accurate method to measure nucleic acid concentration in a sample. The results presented in this study demonstrate the ability of the proposed neural network to use the information embedded in raw impedance data to predict the amount of DNA concentration. [ABSTRACT FROM AUTHOR]

Improving biosensor performance which utilize impedance cytometry is a highly interested research topic for many clinical and diagnostic settings. During development, a sensor's design and external factors are rigorously optimized, but improvements in signal quality and interpretation are usually still necessary to produce a sensitive and accurate product. A common solution involves digital signal processing after sample analysis, but these methods frequently fall short in providing meaningful signal outcome changes. This shortcoming may arise from a lack of investigative research into selecting and using signal processing functions, as many choices in current sensors are based on either theoretical results or estimated hypotheses. While a ubiquitous condition set is improbable across diverse impedance cytometry designs, there lies a need for a streamlined and rapid analytical method for discovering those conditions for unique sensors. Herein, we present a comprehensive dissemination of digital filtering parameters applied on experimental impedance cytometry data for determining the limits of signal processing on signal quality improvements. Various filter orders, cutoff frequencies, and filter types are applied after data collection for highest achievable noise reduction. After designing and fabricating a microfluidic impedance cytometer, 9 µm polystyrene particles were measured under flow and signal quality improved by 6.09 dB when implementing digital filtering. This approached was then translated to isolated human neutrophils, where similarly, signal quality improved by 7.50 dB compared to its unfiltered original data. By sweeping all filtering conditions and devising a system to evaluate filtering performance both by signal quality and object counting accuracy, this may serve as a framework for future systems to determine their appropriately optimized filtering configuration. [ABSTRACT FROM AUTHOR]

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. HIGHLIGHTS: • Measurement of particle size through microfluidic-based impedance cytometry using parallel facing electrodes. • Development of an analytical formula for a rectangular channel via 3D COMSOL simulation. • Implementation of the analytical formula for accurate determination of particle size irrespective of value. [ABSTRACT FROM AUTHOR]

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. [Display omitted] [ABSTRACT FROM AUTHOR]

Cancer is a fatal disease and a significant cause of millions of deaths. Traditional methods for cancer detection often have limitations in identifying the disease in its early stages, and they can be expensive and time-consuming. Since cancer typically lacks symptoms and is often only detected at advanced stages, it is crucial to use affordable technologies that can provide quick results at the point of care for early diagnosis. Biosensors that target specific biomarkers associated with different types of cancer offer an alternative diagnostic approach at the point of care. Recent advancements in manufacturing and design technologies have enabled the miniaturization and cost reduction of point-of-care devices, making them practical for diagnosing various cancer diseases. Furthermore, machine learning (ML) algorithms have been employed to analyze sensor data and extract valuable information through the use of statistical techniques. In this review paper, we provide details on how various machine learning algorithms contribute to the ongoing development of advanced data processing techniques for biosensors, which are continually emerging. We also provide information on the various technologies used in point-of-care cancer diagnostic biosensors, along with a comparison of the performance of different ML algorithms and sensing modalities in terms of classification accuracy.

Impedance cytometry is wildly used in single-cell detection, and its sensitivity is essential for determining the status of single cells. In this work, we focus on the effect of electrode gap on detection sensitivity. Through comparing the electrode span of 1 µm and 5 µm, our work shows that narrowing the electrode span could greatly improve detection sensitivity. The mechanism underlying the sensitivity improvement was analyzed via numerical simulation. The small electrode gap (1 µm) allows the electric field to concentrate near the detection area, resulting in a high sensitivity for tiny particles. This finding is also verified with the mixture suspension of 1 µm and 3 µm polystyrene beads. As a result, the electrodes with 1 µm gap can detect more 1 µm beads in the suspension than electrodes with 5 µm gap. Additionally, for single yeast cells analysis, it is found that impedance cytometry with 1 µm electrodes gap can easily distinguish budding yeast cells, which cannot be realized by the impedance cytometry with 5 µm electrodes gap. All experimental results support that narrowing the electrode gap is necessary for tiny particle detection, which is an important step in the development of submicron and nanoscale impedance cytometry. [ABSTRACT FROM AUTHOR]

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.

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., (© 2024 The Author(s). Small published by Wiley‐VCH GmbH.)

Individual cells have many unique properties that can be quantified to develop a holistic understanding of a population. This can include understanding population characteristics, identifying subpopulations, or elucidating outlier characteristics that may be indicators of disease. Electrical impedance measurements are rapid and label-free for the monitoring of single cells and generate large datasets of many cells at single or multiple frequencies. To increase the accuracy and sensitivity of measurements and define the relationships between impedance and biological features, many electrical measurement systems have incorporated machine learning (ML) paradigms for control and analysis. Considering the difficulty capturing complex relationships using traditional modelling and statistical methods due to population heterogeneity, ML offers an exciting approach to the systemic collection and analysis of electrical properties in a data-driven way. In this work, we discuss incorporation of ML to improve the field of electrical single cell analysis by addressing the design challenges to manipulate single cells and sophisticated analysis of electrical properties that distinguish cellular changes. Looking forward, we emphasize the opportunity to build on integrated systems to address common challenges in data quality and generalizability to save time and resources at every step in electrical measurement of single cells.

Objective: 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. [ABSTRACT FROM AUTHOR]

Determining nucleic acid concentrations in a sample is an important step prior to proceeding with downstream analysis in molecular diagnostics. Given the need for testing DNA amounts and its purity in many samples, including in samples with very small input DNA, there is utility of novel machine learning approaches for accurate and high-throughput DNA quantification. Here, we demonstrated the ability of a neural network to predict DNA amounts coupled to paramagnetic beads. To this end, a custom-made microfluidic chip is applied to detect DNA molecules bound to beads by measuring the impedance peak response (IPR) at multiple frequencies. We leveraged electrical measurements including the frequency and imaginary and real parts of the peak intensity within a microfluidic channel as the input of deep learning models to predict DNA concentration. Specifically, 10 different deep learning architectures are examined. The results of the proposed regression model indicate that an R_Squared of 97% with a slope of 0.68 is achievable. Consequently, machine learning models can be a suitable, fast, and accurate method to measure nucleic acid concentration in a sample. The results presented in this study demonstrate the ability of the proposed neural network to use the information embedded in raw impedance data to predict the amount of DNA concentration.

There is a paradigm shift in biomanufacturing toward continuous bioprocessing but cell-based manufacturing using adherent and suspension cultures, including microcarriers, hydrogel microparticles, and 3D cell aggregates, remains challenging due to the lack of efficient in-line bioprocess monitoring and cell harvesting tools. Herein, a novel label-free microfluidic platform for high throughput (≈50 particles/sec) impedance bioanalysis of biomass, cell viability, and stem cell differentiation at single particle resolution is reported. The device is integrated with a real-time piezo-actuated particle sorter based on user-defined multi-frequency impedance signatures. Biomass profiling of Cytodex-3 microcarriers seeded with adipose-derived mesenchymal stem cells (ADSCs) is first performed to sort well-seeded or confluent microcarriers for downstream culture or harvesting, respectively. Next, impedance-based isolation of microcarriers with osteogenic differentiated ADSCs is demonstrated, which is validated with a twofold increase of calcium content in sorted ADSCs. Impedance profiling of heterogenous ADSCs-encapsulated hydrogel (alginate) microparticles and 3D ADSC aggregate mixtures is also performed to sort particles with high biomass and cell viability to improve cell quality. Overall, the scalable microfluidic platform technology enables in-line sample processing from bioreactors directly and automated analysis of cell quality attributes to maximize cell yield and improve the control of cell quality in continuous cell-based manufacturing., (© 2024 Wiley‐VCH GmbH.)

Impedance cytometry is wildly used in single-cell detection, and its sensitivity is essential for determining the status of single cells. In this work, we focus on the effect of electrode gap on detection sensitivity. Through comparing the electrode span of 1 µm and 5 µm, our work shows that narrowing the electrode span could greatly improve detection sensitivity. The mechanism underlying the sensitivity improvement was analyzed via numerical simulation. The small electrode gap (1 µm) allows the electric field to concentrate near the detection area, resulting in a high sensitivity for tiny particles. This finding is also verified with the mixture suspension of 1 µm and 3 µm polystyrene beads. As a result, the electrodes with 1 µm gap can detect more 1 µm beads in the suspension than electrodes with 5 µm gap. Additionally, for single yeast cells analysis, it is found that impedance cytometry with 1 µm electrodes gap can easily distinguish budding yeast cells, which cannot be realized by the impedance cytometry with 5 µm electrodes gap. All experimental results support that narrowing the electrode gap is necessary for tiny particle detection, which is an important step in the development of submicron and nanoscale impedance cytometry.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer lacking specific biomarkers that can be correlated to disease onset, promotion and progression. To assess whether tumor cell electrophysiology may serve as a marker for PDAC tumorigenicity, we use multi-frequency impedance cytometry at high throughput (∼350 cells/s) to measure the electrical phenotype of single PDAC tumor cells from xenografts, which are derived from primary pancreatic tumors versus those from liver metastases of different patients. A novel phase contrast metric based on variations in the high and low frequency impedance phase responses that is related to electrophysiology of the cell interior is found to be systematically altered as a function of tumorigenicity. PDAC cells of higher tumorigenicity exhibited lowered interior conductivity and enhanced permittivity, which is validated by the dielectrophoresis on the respective cell types. Using genetic analysis, we suggest the role of dysregulated Na+ transport and removal of Ca2+ ions from the cytoplasm on key oncogenic KRAS -driven processes that may be responsible for lowering of the interior cell conductivity. We envision that impedance cytometry can serve as a tool to quantify phenotypic heterogeneity for rapidly stratifying tumorigenicity. It can also aid in protocols for dielectrophoretic isolation of cells with a particular phenotype for prognostic studies on patient survival and to tailor therapy selection to specific patients. Image 1 • High-throughput (∼350 cells/s) impedance cytometry to measure ∼10,000 cells/sample. • Samples of patient-derived pancreatic tumor xenografts of varying tumorigenicity. • Impedance phase due to electrophysiology of cell interior varies with tumorigenicity. • Lowered interior conductivity and enhanced permittivity for cells of higher tumorigenicity. • Validated based on level of positive dielectrophoresis and its crossover frequency. [ABSTRACT FROM AUTHOR]

The growing need for personalized, accurate, and non-invasive diagnostic technology has resulted in significant advancements, from pushing current mechanistic limitations to innovative modality developments across various disease-related biomarkers. However, there still lacks clinical solutions for analyzing multiple biomarkers simultaneously, limiting prognosis for patients suffering with complicated diseases or comorbidities. Here, we conceived, fabricated, and validated a multifrequency impedance cytometry apparatus with novel frequency-sensitive barcoded metal oxide Janus particles (MOJPs) as cell-receptor targeting agents. These microparticles are modulated by a metal oxide semi-coating which exhibit electrical property changes in a multifrequency electric field and are functionalized to target CD11b and CD66b membrane proteins on neutrophils. A multi-modal system utilizing supervised machine learning and simultaneous high-speed video microscopy classifies immune-specific surface receptors targeted by MOJPs as they form neutrophil-MOJP conjugates, based on multivariate multifrequency electrical recordings. High precision and sensitivity were determined based on the type of MOJPs conjugated with cells (>90% accuracy between neutrophil-MOJP conjugates versus cells alone). Remarkably, the design could differentiate the number of MOJPs conjugated per cell within the same MOJP class (>80% accuracy); which also improved comparing electrical responses across different MOJP types (>75% accuracy) as well. Such trends were consistent in individual blood samples and comparing consolidated data across multiple samples, demonstrating design robustness. The configuration may further expand to include more MOJP types targeting critical biomarker receptors in one sample and increase the modality's multiplexing potential. [ABSTRACT FROM AUTHOR]

Microfluidic impedance flow cytometers enable high-throughput, non-invasive, and label-free detection of single-cells. Cytometers with coplanar electrodes are easy and cheap to fabricate, but are sensitive to positional differences of passing particles, owing to the inhomogeneous electric field. We present a novel particle height compensation method, which employs the dependence of measured electrical opacity on particle height. The measured electrical opacity correlates with the particle height as a result of the constant electrical double layer series capacitance of the electrodes. As an alternative to existing compensation methods, we use only two coplanar electrodes and multi-frequency analysis to determine the particle size of a mixture of 5, 6, and 7 µm polystyrene beads with an accuracy (CV) of 5.8%, 4.0%, and 2.9%, respectively. Additionally, we can predict the bead height with an accuracy of 1.5 µm (8% of channel height) using the measured opacity and we demonstrate its application in flow cytometry with yeast. The use of only two electrodes is of special interest for simplified, easy-to-use chips with a minimum amount of instrumentation and of limited size.

The isolation, analysis, and enumeration of circulating tumor cells (CTCs) from cancer patient blood samples are a paradigm shift for cancer patient diagnosis, prognosis, and treatment monitoring. Most methods used to isolate and enumerate these target cells rely on the expression of cell surface markers, which varies between patients, cancer types, tumors, and stages. Here, we propose a label‐free high‐throughput platform to isolate, enumerate, and size CTCs on two coupled microfluidic devices. Cancer cells were purified through a Vortex chip and subsequently flowed in‐line to an impedance chip, where a pair of electrodes measured fluctuations of an applied electric field generated by cells passing through. A proof‐of‐concept of the coupling of those two devices was demonstrated with beads and cells. First, the impedance chip was tested as a stand‐alone device: (1) with beads (mean counting error of 1.0%, sizing information clearly separated three clusters for 8, 15, and 20 um beads, respectively) as well as (2) with cancer cells (mean counting error of 3.5%). Second, the combined setup was tested with beads, then with cells in phosphate‐buffered saline, and finally with cancer cells spiked in healthy blood. Experiments demonstrated that the Vortex HT chip enriched the cancer cells, which then could be counted and differentiated from smaller blood cells by the impedance chip based on size information. Further discrimination was shown with dual high‐frequency measurements using electric opacity, highlighting the potential application of this combined setup for a fully integrated label‐free isolation and enumeration of CTCs from cancer patient samples. © 2019 International Society for Advancement of Cytometry [ABSTRACT FROM AUTHOR]

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., (© 2023 The Authors. Advanced Materials published by Wiley-VCH GmbH.)

Circulating leukocytes comprise of approximately 1% of all blood cells and efficient enrichment of these cells from whole blood is critical for understanding cellular heterogeneity and biological significance in health and diseases. In this work, we report a novel microfluidic strategy for rapid (< 1 h) label-free leukocyte sorting and impedance-based profiling to determine cell activation in type 2 diabetes mellitus (T2DM) using whole blood. Leukocytes were first size-fractionated into different subtypes (neutrophils, monocytes, lymphocytes) using an inertial spiral sorter prior to single-cell impedance measurement in a microfluidic device with coplanar electrode design. Significant changes in membrane dielectric properties (size and opacity) were detected between healthy and activated leukocytes (TNF-α/LPS stimulated), during monocyte differentiation and among different monocyte subsets (classical, intermediate, non-classical). As proof-of-concept for diabetes testing, neutrophil/monocyte dielectric properties in T2DM subjects (n = 8) were quantified which were associated with cardiovascular risk factors including lipid levels, C-reactive protein (CRP) and vascular functions (LnRHI) (P < 0.05) were observed. Overall, these results clearly showed that T2DM subjects have pro-inflammatory leukocyte phenotypes and suggest leukocyte impedance signature as a novel surrogate biomarker for inflammation. [ABSTRACT FROM AUTHOR]

While there is a great interest in microfluidic impedance cytometry as a label-free approach for single-particle analysis, the accuracy of the technique is challenged by the positional dependence issue, i.e. identical particles flowing in the microchannel along different trajectories provide different signals. We solve this issue without resorting to particle focusing, by means of a straightforward modification of the conventional wiring scheme for the standard impedance chip comprising two pairs of facing electrodes. Instead of applying the AC voltage to electrodes on the same side of the channel and collecting the differential current flowing through the electrodes on the other side, we apply the AC voltage to diagonally opposite electrodes and collect the differential current flowing through the remaining ones. Therefore, the bipolar signal recorded upon the passage of a particle shows opposite pulses with different amplitude. The relative difference of the latter is a new metric enabling a simple compensation procedure of the signal impedance for off-center particles. Impedance data for 5.2, 6, and 7 μm particles are collected, and coefficients of variation in (electrical) diameter of particles respectively of 2.8%, 1%, and 1.2%, similar to the manufacturers’ quoted values, are obtained. The novel operation mode is successfully implemented also in a coplanar electrode configuration, exploiting two pairs of liquid electrodes. [ABSTRACT FROM AUTHOR]

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. [Display omitted] • Self-developed impedance cytometry systems and their comparison to commercial setups. • Impedance metrics quantification and their relationships to biophysical properties of single cells. • Representative intelligent impedance cytometry systems: applications and remaining challenges. [ABSTRACT FROM AUTHOR]

Chemotherapy failure in pancreatic cancer patients is widely attributed to cancer cell reprogramming towards drug resistance by cancer associated fibroblasts (CAFs), which are the abundant cell type in the tumor microenvironment. Association of drug resistance to specific cancer cell phenotypes within multicellular tumors can advance isolation protocols for enabling cell-type specific gene expression markers to identify drug resistance. This requires the distinction of drug resistant cancer cells versus CAFs, which is challenging since permeabilization of CAF cells during drug treatment can cause non-specific uptake of cancer cell-specific stains. Cellular biophysical metrics, on the other hand, can provide multiparametric information to assess the gradual alteration of target cancer cells towards drug resistance, but these phenotypes need to be distinguished versus CAFs. Using pancreatic cancer cells and CAFs from a metastatic patient-derived tumor that exhibits cancer cell drug resistance under CAF co-culture, the biophysical metrics from multifrequency single-cell impedance cytometry are utilized for distinction of the subpopulation of viable cancer cells versus CAFs, before and after gemcitabine treatment. This is accomplished through supervised machine learning after training the model using key impedance metrics for cancer cells and CAFs from transwell co-cultures, so that an optimized classifier model can recognize each cell type and predict their respective proportions in multicellular tumor samples, before and after gemcitabine treatment, as validated by their confusion matrix and flow cytometry assays. In this manner, an aggregate of the distinguishing biophysical metrics of viable cancer cells after gemcitabine treatment in co-cultures with CAFs can be used in longitudinal studies, to classify and isolate the drug resistant subpopulation for identifying markers. [Display omitted] [ABSTRACT FROM AUTHOR]

Microfluidic impedance-based devices with coplanar electrode layout represent an attractive tool for low-cost, label-free, single-cell analysis. However, their usefulness has long been limited by the positional dependence of the measured signals, i.e., identical particles traveling along different paths provide different traces. In this paper we show that it is possible to significantly reduce this unwanted effect via straightforward signal processing, by exploiting the richness of the information contained in the recorded traces. [ABSTRACT FROM AUTHOR]

Urinary tract infection (UTI) diagnosis based on urine culture for bacteriuria analysis is time-consuming and often leads to wastage of hospital resources due to false-positive UTI cases. Direct cellular phenotyping (e.g., RBCs, neutrophils, epithelial cells) of urine samples remains a technical challenge as low cell concentrations, and urine characteristics (conductivities, pH, microbes) can affect the accuracy of cell measurements. In this work, we report a microfluidic inertial-impedance cytometry technique for label-free rapid (<5 min) neutrophil sorting and impedance profiling from urine directly. Based on size-based inertial focusing effects, neutrophils are isolated, concentrated, and resuspended in saline (buffer exchange) to improve consistency in impedance-based single-cell analysis. We first observed that both urine pH and the presence of bacteria can affect neutrophil high-frequency impedance measurements possibly due to changes in nucleus morphology as neutrophils undergo NETosis and phagocytosis, respectively. As a proof-of-concept for clinical testing, we report for the first time, rapid UTI testing based on multiparametric impedance profiling of putative neutrophils (electrical size, membrane properties, and distribution) in urine samples from non-UTI ( n = 20) and UTI patients ( n = 20). A significant increase in cell count was observed in UTI samples, and biophysical parameters were used to develop a UTI classifier with an area under the receiver operating characteristic curve of 0.84. Overall, the developed platform facilitates rapid culture-free urine screening which can be further developed to assess disease severity in UTI and other urologic diseases based on neutrophil electrical signatures.

Impedance cytometry has enabled label-free and fast antibiotic susceptibility testing of bacterial single cells. Here, a machine learning-based impedance system is provided to score the phenotypic response of bacterial single cells to antibiotic treatment, with a high throughput of more than one thousand cells per min. In contrast to other impedance systems, an online training method on reference particles is provided, as the parallel impedance cytometry can distinguish reference particles from target particles, and label reference and target particles as the training and test set, respectively, in real time. Experiments with polystyrene beads of two different sizes (3 and 4.5 µm) confirm the functionality and stability of the system. Additionally, antibiotic-treated Escherichia coli cells are measured every two hours during the six-hour drug treatment. All results successfully show the capability of real-time characterizing the change in dielectric properties of individual cells, recognizing single susceptible cells, as well as analyzing the proportion of susceptible cells within heterogeneous populations in real time. As the intelligent impedance system can perform all impedance-based characterization and recognition of particles in real time, it can free operators from the post-processing and data interpretation. [Display omitted] • Real-time and high-throughput assessment of single bacteria. • Parallel impedance detection for the separate measurement of target and reference samples. • Artificial intelligence-based impedance system for fast antibiotic sensitivity testing. [ABSTRACT FROM AUTHOR]

In this manuscript, we present three different micro-impedance sensing architectures for electronic counting of cells and beads. The first method of sensing is based on using an open circuit sensing electrode integrated in a micro-pore, which measures the shift in potential as a micron-sized particle passes through. Our micro-pore, based on a funnel shaped microchannel, was fabricated in PDMS and was bound covalently to a glass substrate patterned with a gold open circuit electrode. The amplification circuitry was integrated onto a battery-powered custom printed circuit board. The second method is based on a three electrode differential measurement, which opens up the potential of using signal processing techniques to increase signal to noise ratio post measurement. The third architecture uses a contactless sensing approach, which significantly minimizes the cost of the consumable component of the impedance cytometer. We demonstrated proof of concept for the three sensing architectures by measuring the detected signal due to the passage of micron-sized beads through the pore. [ABSTRACT FROM AUTHOR]

Microfluidic impedance cytometry is emerging as a powerful label-free technique for the characterization of single biological cells. In order to increase the sensitivity and the specificity of the technique, suited digital signal processing methods are required to extract meaningful information from measured impedance data. In this study, a simple and robust event-detection algorithm for impedance cytometry is presented. Since a differential measuring scheme is generally adopted, the signal recorded when a cell passes through the sensing region of the device exhibits a typical odd-symmetric pattern. This feature is exploited twice by the proposed algorithm: first, a preliminary segmentation, based on the correlation of the data stream with the simplest odd-symmetric template, is performed; then, the quality of detected events is established by evaluating their \textE_{2}$O}$ index, that is, a measure of the ratio between their even and odd parts. A thorough performance analysis is reported, showing the robustness of the algorithm with respect to parameter choice and noise level. In terms of sensitivity and positive predictive value, an overall performance of 94.9% and 98.5%, respectively, was achieved on two datasets relevant to microfluidic chips with very different characteristics, considering three noise levels. The present algorithm can foster the role of impedance cytometry in single-cell analysis, which is the new frontier in “Omics.” [ABSTRACT FROM AUTHOR]

Microfluidic impedance cytometry shows a great value in biomedical diagnosis. However, the crosstalk between neighboring microelectrodes strongly weakens the impedance signal. Hereby, we demonstrate a novel microfluidic impedance cytometer consisted of sensing electrodes and ground electrodes (GNDs). The simulation reveals a signal enhancement by more than five times with GNDs compared to that without ones. We also found that the linear correlation between the impedance at a high frequency and that at a low frequency varies as microparticle size changes, which can be used for microparticle classification. The study can help with microelectrode optimization and signal processing for microfluidic impedance analysis., (© 2022 Wiley-VCH GmbH.)

In this work, we demonstrate the differentiation of demodulated multifrequency signals from impedance sensitive microparticles when targeting surface receptors on neutrophils in a microfluidic impedance cytometer. These scheme uses a single signal input and detection configuration, and machine learning can differentiate particle types with up to 82% accuracy.

Cell staining is a typical procedure for assessing the distribution and density of intracellular components. In this work, a label-free alternative is developed and verified using impedance cytometry to characterize the loss of intracellular components in Euglena gracilis (E. gracilis) cells. By inhibiting chloroplast synthesis, the number of chloroplasts in single cells are reduced gradually, as is the density of intracellular components. As a result, low-frequency impedance signals (0.5 MHz) are shown to assess the cell morphology. With increasing voltage frequency (i.e., > 1 MHz), the resistance of the cell membrane lowers, and the cell membrane eventually becomes transparent to impedance detection. The magnitude and morphology of impedance signals (6 MHz) get the dependance on the density and distribution of intracellular components, respectively. Additionally, impedance-based cell phenotyping reveals that the shrinking of intracellular components and cell volume can cause two distinct declines in the high- and low-frequency electrical diameter of single cells, respectively. This conclusion is confirmed by simulation results and the time course of the changes in electrical diameters and electrical opacity. To sum up, our findings indicate that impedance cytometry and our analysis method can be further refined to serve as a powerful and non-invasive tool for assessing intracellular components at the single cell level. • Impact analysis of intracellular density and distribution on the impedance signals. • Characterization of cellular morphology by the low-frequency impedance detection. • Intracellular phenotyping via the magnitude and shape of high-frequency impedance pulses. [ABSTRACT FROM AUTHOR]

The intrinsic biophysical states of neutrophils are associated with immune dysfunctions in diseases. While advanced image-based biophysical flow cytometers can probe cell deformability at high throughput, it is nontrivial to couple different sensing modalities (e.g., electrical) to measure other critical cell attributes including cell viability and membrane integrity. Herein, an "optics-free" impedance-deformability cytometer for multiparametric single cell mechanophenotyping is reported. The microfluidic platform integrates hydrodynamic cell pinching, and multifrequency impedance quantification of cell size, deformability, and membrane impedance (indicative of cell viability and activation). A newly-defined "electrical deformability index" is validated by numerical simulations, and shows strong correlations with the optical cell deformability index of HL-60 experimentally. Human neutrophils treated with various biochemical stimul are further profiled, and distinct differences in multimodal impedance signatures and UMAP analysis are observed. Overall, the integrated cytometer enables label-free cell profiling at throughput of >1000 cells min -1 without any antibodies labeling to facilitate clinical diagnostics., (© 2022 Wiley-VCH GmbH.)