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

Three-dimensional inertial focusing based impedance cytometer enabling high-accuracy characterization of electrical properties of tumor cells

The differences in the cross-sectional positions of cells in the detection area have a severe negative impact on achieving accurate characterization of the impedance spectra of cells. Herein, we proposed a three-dimensional (3D) inertial focusing based impedance cytometer integrating sheath fluid compression and inertial focusing for the high-accuracy electrical characterization and identification of tumor cells. First, we studied the effects of the particle initial position and the sheath fluid compression on particle focusing. Then, the relationship of the particle height and the signal-to-noise ratio (SNR) of the impedance signal was explored. The results showed that efficient single-line focusing of 7-20 μm particles close to the electrodes was achieved and impedance signals with a high SNR and a low coefficient of variation (CV) were obtained. Finally, the electrical properties of three types of tumor cells (A549, MDA-MB-231, and UM-UC-3 cells) were accurately characterized. Machine learning algorithms were implemented to accurately identify tumor cells based on the amplitude and phase opacities at multiple frequencies. Compared with traditional two-dimensional (2D) inertial focusing, the identification accuracy of A549, MDA-MB-231, and UM-UC-3 cells using our 3D inertial focusing increased by 57.5%, 36.4% and 36.6%, respectively. The impedance cytometer enables the detection of cells with a wide size range without causing clogging and obtains high SNR signals, improving applicability to different complex biological samples and cell identification accuracy.

Liquid Biopsy Instrument for Ultra-Fast and Label-Free Detection of Circulating Tumor Cells

Rapid diagnosis and real-time monitoring are of great important in the fight against cancer. However, most available diagnostic technologies are time-consuming and labor-intensive and are commonly invasive. Here, we describe CytoExam, an automatic liquid biopsy instrument designed based on inertial microfluidics and impedance cytometry, which uses a deep learning algorithm for the analysis of circulating tumor cells (CTCs). In silico and in vitro experiments demonstrated that CytoExam could achieve label-free detection of CTCs in the peripheral blood of cancer patients within 15 min. The clinical applicability of CytoExam was also verified using peripheral blood samples from 10 healthy donors and >50 patients with breast, colorectal, or lung cancer. Significant differences in the number of collected cells and predicted CTCs were observed between the 2 groups, with variations in the dielectric properties of the collected cells from cancer patients also being observed. The ultra-fast and minimally invasive features of CytoExam may pave the way for new paths for cancer diagnosis and scientific research.

Single-cell impedance cytometry of anticancer drug-treated tumor cells exhibiting mitotic arrest state to apoptosis using low-cost silver-PDMS microelectrodes

Chemotherapeutic drugs such as paclitaxel and vinblastine interact with microtubules and thus induce complex cell states of mitosis arrest at the G2/M phase followed by apoptosis dependent on drug exposure time and concentration. Microfluidic impedance cytometry (MIC), as a label-free and high-throughput technology for single-cell analysis, has been applied for viability assay of cancer cells post drug exposure at fixed time and dosage, yet verification of this technique for varied tumor cell states after anticancer drug treatment remains a challenge. Here we present a novel MIC device and for the first time perform impedance cytometry on carcinoma cells exhibiting progressive states of G2/M arrest followed by apoptosis related to drug concentration and exposure time, after treatments with paclitaxel and vinblastine, respectively. Our results from impedance cytometry reveal increased amplitude and negative phase shift at low frequency as well as higher opacity for HeLa cells under G2/M mitotic arrest compared to untreated cells. The cells under apoptosis, on the other hand, exhibit opposite changes in these electrical parameters. Therefore, the impedance features differentiate the HeLa cells under progressive states post anticancer drug treatment. We also demonstrate that vinblastine poses a more potent drug effect than paclitaxel especially at low concentrations. Our device is fabricated using a unique sacrificial layer-free soft lithography process as compared to the existing MIC device, which gives rise to readily aligned parallel microelectrodes made of silver-PDMS embedded in PDMS channel sidewalls with one molding step. Our results uncover the potential of the MIC device, with a fairly simple and low-cost fabrication process, for cellular state screening in anticancer drug therapy.

Apoptotic bodies: bioactive treasure left behind by the dying cells with robust diagnostic and therapeutic application potentials

Apoptosis, a form of programmed cell death, is essential for growth and tissue homeostasis. Apoptotic bodies (ApoBDs) are a form of extracellular vesicles (EVs) released by dying cells in the last stage of apoptosis and were previously regarded as debris of dead cells. Recent studies unraveled that ApoBDs are not cell debris but the bioactive treasure left behind by the dying cells with an important role in intercellular communications related to human health and various diseases. Defective clearance of ApoBDs and infected-cells-derived ApoBDs are possible etiology of some diseases. Therefore, it is necessary to explore the function and mechanism of the action of ApoBDs in different physiological and pathological conditions. Recent advances in ApoBDs have elucidated the immunomodulatory, virus removal, vascular protection, tissue regenerative, and disease diagnostic potential of ApoBDs. Moreover, ApoBDs can be used as drug carriers enhancing drug stability, cellular uptake, and targeted therapy efficacy. These reports from the literature indicate that ApoBDs hold promising potential for diagnosis, prognosis, and treatment of various diseases, including cancer, systemic inflammatory diseases, cardiovascular diseases, and tissue regeneration. This review summarizes the recent advances in ApoBDs-related research and discusses the role of ApoBDs in health and diseases as well as the challenges and prospects of ApoBDs-based diagnostic and therapeutic applications.

Rapid Assessment of Susceptibility of Bacteria and Erythrocytes to Antimicrobial Peptides by Single-Cell Impedance Cytometry

Antimicrobial peptides (AMPs) represent a promising class of compounds to fight antibiotic-resistant infections. In most cases, they kill bacteria by making their membrane permeable and therefore exhibit low propensity to induce bacterial resistance. In addition, they are often selective, killing bacteria at concentrations lower than those at which they are toxic to the host. However, clinical applications of AMPs are hindered by a limited understanding of their interactions with bacteria and human cells. Standard susceptibility testing methods are based on the analysis of the growth of a bacterial population and therefore require several hours. Moreover, different assays are required to assess the toxicity to host cells. In this work, we propose the use of microfluidic impedance cytometry to explore the action of AMPs on both bacteria and host cells in a rapid manner and with single-cell resolution. Impedance measurements are particularly well-suited to detect the effects of AMPs on bacteria, due to the fact that the mechanism of action involves perturbation of the permeability of cell membranes. We show that the electrical signatures of Bacillus megaterium cells and human red blood cells (RBCs) reflect the action of a representative antimicrobial peptide, DNS-PMAP23. In particular, the impedance phase at high frequency (e.g., 11 or 20 MHz) is a reliable label-free metric for monitoring DNS-PMAP23 bactericidal activity and toxicity to RBCs. The impedance-based characterization is validated by comparison with standard antibacterial activity assays and absorbance-based hemolytic activity assays. Furthermore, we demonstrate the applicability of the technique to a mixed sample of B. megaterium cells and RBCs, which paves the way to study AMP selectivity for bacterial versus eukaryotic cells in the presence of both cell types.

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.

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.

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.

On‐chip microfluidic buffer swap of biological samples in‐line with downstream dielectrophoresis

Microfluidic cell enrichment by dielectrophoresis, based on biophysical and electrophysiology phenotypes, requires that cells be resuspended from their physiological media into a lower conductivity buffer for enhancing force fields and enabling the dielectric contrast needed for separation. To ensure that sensitive cells are not subject to centrifugation for resuspension and spend minimal time outside of their culture media, we present an on‐chip microfluidic strategy for swapping cells into media tailored for dielectrophoresis. This strategy transfers cells from physiological media into a 100‐fold lower conductivity media by using tangential flows of low media conductivity at 200‐fold higher flow rate versus sample flow to promote ion diffusion over the length of a straight channel architecture that maintains laminarity of the flow‐focused sample and minimizes cell dispersion across streamlines. Serpentine channels are used downstream from the flow‐focusing region to modulate hydrodynamic resistance of the central sample outlet versus flanking outlets that remove excess buffer, so that cell streamlines are collected in the exchanged buffer with minimal dilution in cell numbers and at flow rates that support dielectrophoresis. We envision integration of this on‐chip sample preparation platform prior to or post‐dielectrophoresis, in‐line with on‐chip monitoring of the outlet sample for metrics of media conductivity, cell velocity, cell viability, cell position, and collected cell numbers, so that the cell flow rate and streamlines can be tailored for enabling dielectrophoretic separations from heterogeneous samples.

The multi-molecular mechanisms of tumor-targeted drug resistance in precision medicine

Clinically, cancer drug therapy is still dominated by chemotherapy drugs. Although the emergence of targeted drugs has greatly improved the survival rate of patients with advanced cancer, drug resistance has always been a difficult problem in clinical cancer treatment. At the current level of medicine, most drugs cannot escape the fate of drug resistance. With the emergence and development of gene detection, liquid biopsy ctDNA technology, and single-cell sequencing technology, the molecular mechanism of tumor drug resistance has gradually emerged. Drugs can also be updated in response to drug resistance mechanisms and bring higher survival benefits. The use of new drugs often leads to new mechanisms of resistance. In this review, the multi-molecular mechanisms of drug resistance are introduced, and the overcoming of drug resistance is discussed from the perspective of the tumor microenvironment.

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.

What is the future of electrical impedance spectroscopy in flow cytometry?

More than 20 years ago, electrical impedance spectroscopy (EIS) was proposed as a potential characterization method for flow cytometry. As the setup is comparably simple and the method is label-free, EIS has attracted considerable interest from the research community as a potential alternative to standard optical methods, such as fluorescence-activated cell sorting (FACS). However, until today, FACS remains by and large the laboratory standard with highly developed capabilities and broad use in research and clinical settings. Nevertheless, can EIS still provide a complement or alternative to FACS in specific applications? In this Perspective, we will give an overview of the current state of the art of EIS in terms of technologies and capabilities. We will then describe recent advances in EIS-based flow cytometry, compare the performance to that of FACS methods, and discuss potential prospects of EIS in flow cytometry.