Dielectrophoretic enrichment of live chemo-resistant circulating-like pancreatic cancer cells from media of drug-treated adherent cultures of solid tumors.

Due to low numbers of circulating tumor cells (CTCs) in liquid biopsies, there is much interest in enrichment of alternative circulating-like mesenchymal cancer cell subpopulations from in vitro tumor cultures for utilization within molecular profiling and drug screening. Viable cancer cells that are released into the media of drug-treated adherent cancer cell cultures exhibit anoikis resistance or anchorage-independent survival away from their extracellular matrix with nutrient sources and waste sinks, which serves as a pre-requisite for metastasis. The enrichment of these cell subpopulations from tumor cultures can potentially serve as an in vitro source of circulating-like cancer cells with greater potential for scale-up in comparison with CTCs. However, these live circulating-like cancer cell subpopulations exhibit size overlaps with necrotic and apoptotic cells in the culture media, which makes it challenging to selectively enrich them, while maintaining them in their suspended state. We present optimization of a flowthrough high frequency (1 MHz) positive dielectrophoresis (pDEP) device with sequential 3D field non-uniformities that enables enrichment of the live chemo-resistant circulating cancer cell subpopulation from an in vitro culture of metastatic patient-derived pancreatic tumor cells. Central to this strategy is the utilization of single-cell impedance cytometry with gates set by supervised machine learning, to optimize the frequency for pDEP, so that live circulating cells are selected based on multiple biophysical metrics, including membrane physiology, cytoplasmic conductivity and cell size, which is not possible using deterministic lateral displacement that is solely based on cell size. Using typical drug-treated samples with low levels of live circulating cells (<3%), we present pDEP enrichment of the target subpopulation to ∼44% levels within 20 minutes, while rejecting >90% of dead cells. This strategy of utilizing single-cell impedance cytometry to guide the optimization of dielectrophoresis has implications for other complex biological samples.

Multichannel impedance cytometry downstream of cell separation by deterministic lateral displacement to quantify macrophage enrichment in heterogeneous samples.

The integration of on-chip biophysical cytometry downstream of microfluidic enrichment for inline monitoring of phenotypic and separation metrics at single-cell sensitivity can allow for active control of separation and its application to versatile sample sets. We present integration of impedance cytometry downstream of cell separation by deterministic lateral displacement (DLD) for enrichment of activated macrophages from a heterogeneous sample, without the problems of biased sample loss and sample dilution caused by off-chip analysis. This required designs to match cell/particle flow rates from DLD separation into the confined single-cell impedance cytometry stage, the balancing of flow resistances across the separation array width to maintain unidirectionality, and the utilization of co-flowing beads as calibrated internal standards for inline assessment of DLD separation and for impedance data normalization. Using a heterogeneous sample with un-activated and activated macrophages, wherein macrophage polarization during activation causes cell size enlargement, on-chip impedance cytometry is used to validate DLD enrichment of the activated subpopulation at the displaced outlet, based on the multiparametric characteristics of cell size distribution and impedance phase metrics. This hybrid platform can monitor separation of specific subpopulations from cellular samples with wide size distributions, for active operational control and enhanced sample versatility.

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

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

Supervised learning on impedance cytometry data for label-free biophysical distinction of pancreatic cancer cells versus their associated fibroblasts under gemcitabine treatment.

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.

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.

Biophysical quantification of reorganization dynamics of human pancreatic islets during co-culture with adipose-derived stem cells.

Islet transplantation is a potential therapy for type 1 diabetes, but it is expensive due to limited pancreas donor numbers and the variability in islet quality. The latter is often addressed by co-culture of harvested islets with stem cells to promote in vitro remodeling of their basement membrane and enable expression of angiogenic factors for enhancing vascularization. However, given the heterogeneity in islet size, shape and function, there is a need for metrics to assess the reorganization dynamics of single islets over the co-culture period. Based on shape-evolution of individual multi-cell aggregates formed during co-culture of human islets with adipose derived stem cells and the pressures required for their bypass through microfluidic constrictions, we present size-normalized biomechanical metrics for monitoring the reorganization. Aggregates below a threshold size exhibit faster reorganization, as evident from rise in their biomechanical opacity and tightening of their size distribution, but this size threshold increases over culture time to include a greater proportion of the aggregates. Such biomechanical metrics can quantify the subpopulation of reorganized aggregates by distinguishing them versus those with incomplete reorganization, over various timepoints during the co-culture.

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.

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.

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

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

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

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

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

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

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

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

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

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.

Rapid in Vitro Assessment of Clostridioides difficile Inhibition by Probiotics Using Dielectrophoresis to Quantify Cell Structure Alterations.

Clostridioides difficile (C. difficile) infection (CDI) is the primary cause of nosocomial antibiotic-associated diarrhea, with high recurrence rates following initial antibiotic treatment regimens. Restoration of the host gut microbiome through probiotic therapy is under investigation to reduce recurrence. Current in vitro methods to assess C. difficile deactivation by probiotic microorganisms are based on C. difficile growth inhibition, but the cumbersome and time-consuming nature of the assay limits the number of assessed permutations. Phenotypic alterations to the C. difficile cellular structure upon interaction with probiotics can potentially enable rapid assessment of the inhibition without the need for extended culture. Because supernatants from cultures of commensal microbiota reflect the complex metabolite milieu that deactivates C. difficile, we explore coculture of C. difficile with an optimal dose of supernatants from probiotic culture to speed growth inhibition assays and enable correlation with alterations to its prolate ellipsoidal structure. Based on sensitivity of electrical polarizability to C. difficile cell shape and subcellular structure, we show that the inhibitory effect of Lactobacillus spp. supernatants on C. difficile can be determined based on the positive dielectrophoresis level within just 1 h of culture using a highly toxigenic strain and a clinical isolate, whereas optical and growth inhibition measurements require far greater culture time. We envision application of this in vitro coculture model, in conjunction with dielectrophoresis, to rapidly screen for potential probiotic combinations for the treatment of recurrent CDI.

High-throughput dynamical analysis of dielectrophoretic frequency dispersion of single cells based on deflected flow streamlines.

Phenotypic quantification of cells based on their plasma membrane capacitance and cytoplasmic conductivity, as determined by their dielectrophoretic frequency dispersion, is often used as a marker for their biological function. However, due to the prevalence of phenotypic heterogeneity in many biological systems of interest, there is a need for methods capable of determining the dielectrophoretic dispersion of single cells at high throughput and without the need for sample dilution. We present a microfluidic device methodology wherein localized constrictions in the microchannel are used to enhance the field delivered by adjoining planar electrodes, so that the dielectrophoresis level and direction on flow-focused cells can be determined on each traversing cell in a high-throughput manner based on their deflected flow streamlines. Using a sample of human red blood cells diluted to 2.25 × 108 cells/mL, the dielectrophoretic translation of single cells traversing at a flow rate of 1.68 µL/min is measured at a throughput of 1.1 × 105 cells/min, to distinguish positive versus negative dielectrophoresis and determine their crossover frequency in media of differing conductivity for validation of the computed membrane capacitance to that from prior methods. We envision application of this dynamic dielectrophoresis (Dy-DEP) method towards high-throughput measurement of the dielectric dispersion of single cells to stratify phenotypic heterogeneity of a particular sample based on their DEP crossover frequency, without the need for significant sample dilution. Grapical abstract.

A neural network approach for real-time particle/cell characterization in microfluidic impedance cytometry.

Microfluidic applications such as active particle sorting or selective enrichment require particle classification techniques that are capable of working in real time. In this paper, we explore the use of neural networks for fast label-free particle characterization during microfluidic impedance cytometry. A recurrent neural network is designed to process data from a novel impedance chip layout for enabling real-time multiparametric analysis of the measured impedance data streams. As demonstrated with both synthetic and experimental datasets, the trained network is able to characterize with good accuracy size, velocity, and cross-sectional position of beads, red blood cells, and yeasts, with a unitary prediction time of 0.4 ms. The proposed approach can be extended to other device designs and cell types for electrical parameter extraction. This combination of microfluidic impedance cytometry and machine learning can serve as a stepping stone to real-time single-cell analysis and sorting. Graphical Abstract.

AC electrokinetic biased deterministic lateral displacement for tunable particle separation.

We describe a novel particle separation technique that combines deterministic lateral displacement (DLD) with orthogonal electrokinetic forces. DLD is a microfluidic technique for continuous flow particle separation based on size. We describe new tunable devices that use a combination of AC electric fields with DLD to separate particles below the critical diameter. Planar electrodes were integrated into a classical DLD device to produce a force orthogonal to the fluid flow direction. Experiments with 3.0 µm, 1.0 µm and 500 nm diameter microspheres show that at low frequencies (up to 500 Hz) particles oscillate in the direction of the field due to electrophoretic (EP)/electroosmotic (EO) forces. As the frequency of the field increases, the amplitude of these oscillations vanishes and, eventually dielectrophoresis (DEP) becomes the dominant electrokinetic force on the particles (DEP arises from electric field inhomogeneities caused by the presence of the DLD posts). Both mechanisms alter the paths of the particles inside the DLD devices leading to enhanced sorting of particles below the critical diameter of the device.

A capacitive touch screen sensor for detection of urinary tract infections in portable biomedical devices.

Incidence of urinary tract infections (UTIs) is the second highest among all infections; thus, there is a high demand for bacteriuria detection. Escherichia coli are the main cause of UTIs, with microscopy methods and urine culture being the detection standard of these bacteria. However, the urine sampling and analysis required for these methods can be both time-consuming and complex. This work proposes a capacitive touch screen sensor (CTSS) concept as feasible alternative for a portable UTI detection device. Finite element method (FEM) simulations were conducted with a CTSS model. An exponential response of the model to increasing amounts of E. coli and liquid samples was observed. A measurable capacitance change due to E. coli presence and a tangible difference in the response given to urine and water samples were also detected. Preliminary experimental studies were also conducted on a commercial CTSS using liquid solutions with increasing amounts of dissolved ions. The CTSS was capable of distinguishing different volumes of liquids, also giving an exponential response. Furthermore, the CTSS gave higher responses to solutions with a superior amount of ions. Urine samples gave the top response among tested liquids. Thus, the CTSS showed the capability to differentiate solutions by their ionic content.