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.

The effect of topographical and mechanical stimulation on the structural and functional anisotropy of cardiomyocytes grown on a circular PDMS diaphragm.

Due to their immature morphology and functional immaturity, cardiomyocytes have limited use as an in vitro disease model of the native heart. Mechanical stimulation induces structural growth in cardiomyocytes in vitro by addressing the electrical-mechanical interactions between the tissues. However, current in vitro models are restricted in their capacity to replicate the milieu observed in natural myocardium. Herein, we proposed a Galinstan strain sensor integrated nanogrooved circular PDMS diaphragm to mimic the native cardiac tissues. The impact of combined topographical and mechanical stimulation on cultured cardiomyocytes at various strain areas on a circular PDMS diaphragm is studied in detail. An inverted microscope is used to image live cells and video acquisition to study the contractility of cultured cardiomyocytes. The structural changes of the cultured cardiomyocytes are investigated by its sarcomere length and connexin-43 (Cx43) expression using immunocytochemistry analysis. Cyclic strain is found to promote structural development in cultured cardiomyocytes, and diaphragms with nano-groove patterns displayed increased contractile activity and gene expression (sarcomere length ∼1.97 ± 0.03 µm and normalized Cx43-1.57) as compared to flat diaphragms (sarcomere length ∼1.82 ± 0.02 µm and normalized Cx43-1.32). The nanogrooved circular diaphragm exhibited distinct stretching mechanisms at various places, with the equibi-axial stretching regions providing the optimal structural growth and formation of natural myocardium at the diaphragm's center. Cardiomyocytes that are more mature have the potential to produce a more realistic in vitro cardiac model for disease modeling and medication development.