Electrophysiology-based stratification of pancreatic tumorigenicity by label-free single-cell impedance cytometry

McGrath, John S.; Honrado, Carlos; Moore, J. H.; Adair, Sara J.; Varhue, Walter; Salahi, Armita; Farmehini, Vahid; Goudreau, Bernadette J.; Nagdas, Sarbajeet; Blais, Edik M.; Bauer, Todd W.

doi:10.1016/j.aca.2019.12.033

Cited by 54 publications

(61 citation statements)

References 52 publications

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“…Ultrahigh resolution spectra of 100 data points were demonstrated across several cell types, as well as robust characterization of platelets in suspending solutions of conductivities as high as 800 mS/m. This system has also demonstrated circadian rhythms in cell electrophysiology present in human RBCs [126] and has been applied by others to characterize mitochondria [127] and complement impedance analysis of Pancreatic ductal adenocarcinoma (PDAC) [37]. The reliance of this technique on light intensity changes has presented challenges with characterizing cells and particles that are less opaque as well as sub‐2 μm sized, and is currently not adaptable in its commercial form for fluorescent spectroscopy.…”

Section: Dep Characterization Methods and Parametersmentioning

confidence: 99%

“…DEP characterization of multidrug resistance (MDR) human breast cancer cell lines revealed cytoplasmic conductivity to be an indicator of drug sensitivity [133]. McGrath et al demonstrated a lowered level of interior cell conductivity of metastatic versus primary PDAC cell types [37]. Previously mentioned, Han et al found increased intracellular lipid content resulted in decreased conductivity as well as decreased permittivity of cytoplasm in microalgae [79].…”

Section: Dep Characterization Methods and Parametersmentioning

confidence: 99%

“…Current strategies to implement cell focusing and microfluidics to meet these challenges are often complex or optimized for a particular cell size [34–36]. Recent work has shown a marked increase in throughput from less than 100 cells/s to reports of over 300 cells/s [35–37]. Further, results have compared well to DEP measurements taken in parallel [37].…”

Section: Introductionmentioning

confidence: 99%

“…Recent work has shown a marked increase in throughput from less than 100 cells/s to reports of over 300 cells/s [35–37]. Further, results have compared well to DEP measurements taken in parallel [37].…”

Section: Introductionmentioning

confidence: 99%

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Review: Dielectrophoresis in cell characterization

Henslee

2020

Electrophoresis

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Many cellular functions are affected by and thus can be characterized by a cell's electrophysiology. This has also been found to correspond to other biophysical parameters such as cell morphology and mechanical properties. Dielectrophoresis (DEP) is an electrostatic technique which can be used to examine cellular biophysical parameters through the measuring of single or multiple cell response to electric field induced forces. This label‐free method offers many advantages in characterizing a cell population over conventional electrophysiology methods such as patch clamping; however, it has yet to see mainstream pharmacological application. Challenges such as the transdisciplinary nature of the field bridging engineering and the biological sciences, throughput, specificity as well as standardization are being addressed in current literature. This review focuses on the developments of DEP‐based cell electrophysiological characterization where determining cellular properties such as membrane conductance and capacitance, and cytoplasmic conductivity are the primary motivation. A brief theoretical review, techniques for obtaining these cell parameters, as well as the resulting cell parameters and their applications are included in this review. This review aims to further support the development of DEP‐based cell characterization as an important part of the future of DEP and electrophysiology research.

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Section: Dep Characterization Methods and Parametersmentioning

confidence: 99%

Section: Dep Characterization Methods and Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Review: Dielectrophoresis in cell characterization

Henslee

2020

Electrophoresis

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“…For microfluidic applications, such as active particle sorting and selective enrichment [19,20], there is a need for high-throughput real-time signal processing methodologies. Similarly, for distinction of particular cell phenotypes from complex samples with similar-sized cells [21], there is a need for routines that can be trained using model cell types to enable impedance-based signal recognition. Machine learning offers a route to enable automated monitoring of microfluidic systems by converting routinely collected sensor and image data into actionable information in real time [22,23].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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 crosssectional 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.

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

et al. 2021

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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.

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Electrophysiology-based stratification of pancreatic tumorigenicity by label-free single-cell impedance cytometry

Cited by 54 publications

References 52 publications

Review: Dielectrophoresis in cell characterization

Review: Dielectrophoresis in cell characterization

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

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

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