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

Rane, Aditya; Jarmoshti, Javad; Siddique, Abdullah-Bin; Adair, Sara; Torres-Castro, Karina; Honrado, Carlos; Bauer, Todd W.; Swami, Nathan S.

doi:10.1039/d3lc00804e

Lab Chip

2024

DOI: 10.1039/d3lc00804e

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

Aditya Rane,

Javad Jarmoshti,

Abdullah-Bin Siddique

et al.

Abstract: Due to rare numbers of circulating tumor cells (CTCs) in liquid biopsies, there is much interest in enrichment of alternate circulating-like mesenchymal cancer cell subpopulations from in vitro tumor cultures...

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2024

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Cited by 4 publications

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Neural Network‐Enabled Multiparametric Impedance Signal Templating for High throughput Single‐Cell Deformability Cytometry Under Viscoelastic Extensional Flows

Jarmoshti,

Siddique,

Rane

et al. 2024

Small

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Cellular biophysical metrics exhibit systematic alterations during processes, such as metastasis and immune cell activation, which can be used to identify and separate live cell subpopulations for targeting drug screening. Image‐based biophysical cytometry under extensional flows can accurately quantify cell deformability based on cell shape alterations but needs extensive image reconstruction, which limits its inline utilization to activate cell sorting. Impedance cytometry can measure these cell shape alterations based on electric field screening, while its frequency response offers functional information on cell viability and interior structure, which are difficult to discern by imaging. Furthermore, 1‐D temporal impedance signal trains exhibit characteristic shapes that can be rapidly templated in near real‐time to extract single‐cell biophysical metrics to activate sorting. We present a multilayer perceptron neural network signal templating approach that utilizes raw impedance signals from cells under extensional flow, alongside its training with image metrics from corresponding cells to derive net electrical anisotropy metrics that quantify cell deformability over wide anisotropy ranges and with minimal errors from cell size distributions. Deformability and electrical physiology metrics are applied in conjunction on the same cell for multiparametric classification of live pancreatic cancer cells versus cancer associated fibroblasts using the support vector machine model.

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Neural Network‐Enabled Multiparametric Impedance Signal Templating for High throughput Single‐Cell Deformability Cytometry Under Viscoelastic Extensional Flows

Jarmoshti,

Siddique,

Rane

et al. 2024

Small

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Recent developments in the use of electrokinetic methods for rapid cell viability assessments and separations

Lapizco-Encinas

2024

Journal of Chromatography Open

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Real-time impedance-activated dielectrophoretic actuation for reconfigurable manipulation of single flowing particles

Lefevre,

Brandi,

De Ninno

et al. 2024

Lab Chip

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

Abstract: Due to rare numbers of circulating tumor cells (CTCs) in liquid biopsies, there is much interest in enrichment of alternate circulating-like mesenchymal cancer cell subpopulations from in vitro tumor cultures...

Cited by 4 publications

References 52 publications

Neural Network‐Enabled Multiparametric Impedance Signal Templating for High throughput Single‐Cell Deformability Cytometry Under Viscoelastic Extensional Flows

Neural Network‐Enabled Multiparametric Impedance Signal Templating for High throughput Single‐Cell Deformability Cytometry Under Viscoelastic Extensional Flows

Recent developments in the use of electrokinetic methods for rapid cell viability assessments and separations

Real-time impedance-activated dielectrophoretic actuation for reconfigurable manipulation of single flowing particles

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