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.

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.

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.

Self-aligned microfluidic contactless dielectrophoresis device fabricated by single-layer imprinting on cyclic olefin copolymer.

The trapping and deflection of biological cells by dielectrophoresis (DEP) at field non-uniformities in a microfluidic device is often conducted in a contactless dielectrophoresis (cDEP) mode, wherein the electrode channel is in a different layer than the sample channel, so that field penetration through the interceding barrier causes DEP above critical cut-off frequencies. In this manner, through physical separation of the electrode and sample channels, it is possible to spatially modulate electric fields with no electrode-induced damage to biological cells in the sample channel. However, since this device requires interlayer alignment of the electrode to sample channel and needs to maintain a thin interceding barrier (~ 15 µm) over the entire length over which DEP is needed (~ 1 cm), variations in alignment and microstructure fidelity cause wide variations in cDEP trapping level and frequency response across devices. We present a strategy to eliminate interlayer alignment by fabricating self-aligned electrode and sample channels, simultaneously with the interceding barrier layer (14-µm width and 50-µm depth), using a single-layer imprint and bond process on cyclic olefin copolymer. Specifically, by designing support structures, we preserve fidelity of the high aspect ratio insulating posts in the sample channel and the interceding barrier between the sample and electrode channels over the entire device footprint (~ 1 cm). The device operation is validated based on impedance measurements to quantify field penetration through the interceding barrier and by DEP trapping measurements. The presented fabrication strategy can eventually improve cDEP device manufacturing protocols to enable more reproducible DEP performance. Graphical abstract.

On-Chip Impedance for Quantifying Parasitic Voltages During AC Electrokinetic Trapping.

OBJECTIVE: Assessing the effectiveness of microfluidic device structures for enabling electrokinetic or acoustic trapping requires imaging of model particles within each device under the requisite force fields. To avoid the need for extensive microscopy, the use of valuable biological samples, and reliance on a trained operator to assess efficacy of trapping, we explore electrical means to identify device geometry variations that are responsible for the poor trapping. RESULTS: Using the example of AC electrokinetic trapping over an insulated channel in a contact-less dielectrophoresis mode, we present an on-chip method to acquire impedance spectra on the microfluidic device for quantifying the parasitic voltage drops. CONCLUSION: Based on the parasitic voltage drops, device geometries can be designed to maximize fraction of the applied voltage that is available for dielectrophoretic manipulation and the measured on-chip impedance can rapidly inform downstream decisions on particle manipulation.

Crystallization of high aspect ratio HKUST-1 thin films in nanoconfined channels for selective small molecule uptake.

We present the ability to create unique morphologies of a prototypical metal organic framework (MOF), HKUST-1, by carrying out its crystallization within a set of nano-confined fluidic channels. These channels are fabricated on cyclic olefin copolymer by the high-fidelity hot embossing imprinting method. The picoliter volume synthesis in the nanochannels is hypothesized to bias the balance between nucleation and growth rates to obtain high aspect ratio large-crystalline domains of HKUST-1, which are grown in defined morphologies due to the patterned nanochannels. Confined crystal growth is achieved in nanofluidic channels as shallow as 50 nm. HKUST-1 crystalline domains with aspect ratios greater than 2500, and lengths up to 144 µm are obtained using the nanochannels, exceeding values obtained using chemical modulation and other confinement methods. HKUST-1 crystals are characterized using optical microscopy and scanning electron microscopy with energy dispersive spectroscopy. Porosity of the MOF and selective molecular uptake is demonstrated through inclusion of anthracene and methylene blue within the HKUST-1 framework, and with exclusion of rhodamine B and riboflavin, characterized using a confocal fluorescence microscope. We attribute this selectivity to the analyte size and electrostatic characteristics. Nanoconfined crystallization of MOFs can thus yield control over crystalline morphology to create ideal MOF crystals for enabling selective molecular enrinchment and sensing.

Frequency-selective electrokinetic enrichment of biomolecules in physiological media based on electrical double-layer polarization.

Proteomic biomarkers of interest to the early diagnosis of diseases and infections are present at trace levels versus interfering species. Hence, their selective enrichment is needed within bio-assays for speeding binding kinetics with receptors and for reducing signal interferences. While DC fields can separate biomolecules based on their electrokinetic mobilities, they are unable to selectively enrich biomarkers versus interfering species, which may possess like-charges. We present the utilization of AC electrokinetics to enable frequency-selective enrichment of nanocolloidal biomolecules, based on the characteristic time constant for polarization of their electrical double-layer, since surface conduction in their ion cloud depends on colloidal size, shape and surface charge. In this manner, using DC-offset AC fields, differences in frequency dispersion for negative dielectrophoresis are balanced against electrophoresis in a nanoslit channel to enable the selective enrichment of prostate specific antigen (PSA) versus anti-mouse immunoglobulin antibodies that cause signal interferences to immunoassays. Through coupling enrichment to capture by receptors on graphene-modified surfaces, we demonstrate the elimination of false positives caused by anti-mouse immunoglobulin antibodies to the PSA immunoassay.