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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]

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]

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]

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]

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 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. [ABSTRACT FROM AUTHOR]

• A label-free microfluidic impedance assay for activated lymphocyte profiling. • Inertial-based single stream particle focusing for enhanced impedance detection. • Differential impedance count to assess antigen-specific T-lymphocyte responses. Circulating lymphocytes are integral components of our adaptive immunity with emerging clinical applications in immune status monitoring in infectious diseases and cell-mediated cancer immunotherapies. Herein we present a novel impedance-based microfluidic assay for label-free lymphocyte activation profiling based on native or antigen-specific T-lymphocyte biophysical responses. Single cell impedance profiling of T-lymphocytes first revealed distinct biophysical differences in cell size and membrane electrical impedance of healthy, activated (CD3/CD28) and dead lymphocyte populations. Impedance characterization of peripheral blood mononuclear cells (PBMCs) stimulated with mitogen phytohemagglutinin (PHA) or Tuberculin Purified Protein Derivative antigen (PPD) after 24 h also showed an increase in lymphocyte cell size (∼8 to 10 μm) which corresponded to activated lymphocytes (CD69+CD137+). We next developed a spiral inertial microfluidics cell sorter integrated with coplanar electrodes for direct impedance quantification of activated lymphocytes. By removing non-activated smaller lymphocytes (< 8 μm) and employing hydrodynamic-based single stream particle focusing, we demonstrated significant enrichment of activated lymphocytes (∼11.7-fold) to electrically detect low levels of lymphocyte activation (< 5%). Finally, the developed biochip is coupled with magnetic activated cell sorting (MACS) to quantify CD4+ T-lymphocytes response in PBMCs stimulated with PPD. A differential impedance cell count ratio (stimulated/unstimulated) was defined to distinguish activated T-lymphocytes, which showed better sensitivity as compared to immunophenotyping by flow cytometry. Taken together, the integrated impedance biosensor can be further developed as a rapid multiplexed screening assay to detect antigen-specific T-lymphocyte responses to characterize host immunity and diagnosis of infectious diseases (e.g tuberculosis, dengue and COVID-19). [ABSTRACT FROM AUTHOR]

The germination of ingested spores is often a necessary first step required for enabling bacterial outgrowth and host colonization, as in the case of Clostridioides difficile (C. difficile) infection. Spore germination rate in the colon depends on microbiota composition and its level of disruption by antibiotic treatment since secretions by commensal bacteria modulate primary to secondary bile salt levels to control germination. Assessment of C. difficile spore germination typically requires measurement of colony-forming units, which is labor intensive and takes at least 24 h to perform but is regularly required due to the high recurrence rates of nosocomial antibiotic-associated diarrhea. We present a rapid method to assess spore germination by using high throughput single-cell impedance cytometry (>300 events/s) to quantify live bacterial cells, by gating for their characteristic electrophysiology versus spores, so that germination can be assessed after just 4 h of culture at a detection limit of ~100 live cells per 50 μL sample. To detect the phenotype of germinated C. difficile bacteria, we utilize its characteristically higher net conductivity versus that of spore aggregates and non-viable C. difficile forms, which causes a distinctive high-frequency (10 MHz) impedance phase dispersion within moderately conductive media (0.8 S/m). In this manner, we can detect significant differences in spore germination rates within just 4 h, with increasing primary bile salt levels in vitro and using ex vivo microbiota samples from an antibiotic-treated mouse model to assess susceptibility to C. difficile infection. We envision a rapid diagnostic tool for assessing host microbiota susceptibility to bacterial colonization after key antibiotic treatments. Image 1 • Rapid assessment of bacterial spore germination by single-cell impedance cytometry. • Germination assessed after 4 h of culture based on detection limit of ~100 live cells. • Optimized impedance measurement (300 events/s) over multiple frequencies (0.5–10 MHz). • Sensitive to spore germination alterations with bile salt metabolites within just 4 h. • Detection of antibiotic-induced alterations in microbiota susceptibility to spore germination. [ABSTRACT FROM AUTHOR]