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Simultaneous on-chip DC dielectrophoretic cell separation and quantitative separation performance characterization.

ABSTRACT: Through integration of a MOSFET-based microfluidic Coulter counter with a dc-dielectrophoretic cell sorter, we demonstrate simultaneous on-chip cell separation and sizing with three different samples including 1) binary mixtures of polystyrene beads, 2) yeast cells of continuous size distribution, and 3) mixtures of 4T1 tumor cells and murine bone marrow cells. For cells with continuous size distribution, it is found that the receiver operator characteristic analysis is an ideal method to characterize the separation performance. The characterization results indicate that dc-DEP separation performance degrades as the sorting throughput (cell sorting rate) increases, which provides insights into the design and operation of size-based microfluidic cell separation.

SUBMITTER: Sun J

PROVIDER: S-EPMC7458407 | biostudies-literature | 2012 Feb

REPOSITORIES: biostudies-literature

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Simultaneous on-chip DC dielectrophoretic cell separation and quantitative separation performance characterization.

Sun Jiashu J Gao Yandong Y Isaacs Richard J RJ Boelte Kimberly C KC Lin P Charles PC Boczko Erik M EM Li Deyu D

Analytical chemistry 20120201 4

Through integration of a MOSFET-based microfluidic Coulter counter with a dc-dielectrophoretic cell sorter, we demonstrate simultaneous on-chip cell separation and sizing with three different samples including 1) binary mixtures of polystyrene beads, 2) yeast cells of continuous size distribution, and 3) mixtures of 4T1 tumor cells and murine bone marrow cells. For cells with continuous size distribution, it is found that the receiver operator characteristic analysis is an ideal method to charac ...[more]

PMID: 22229858

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Project description:Dielectrophoresis (DEP) represents an electrokinetic approach for discriminating and separating suspended cells based on their intrinsic dielectric characteristics without the need for labeling procedure. A good practice, beyond the physical and engineering components, is the selection of a buffer that does not hinder cellular and biochemical parameters as well as cell recovery. In the present work the impact of four buffers on biochemical, morphological, and mechanical parameters was evaluated in two different cancer cell lines (Caco-2 and K562). Specifically, MTT ([3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide]) assay along with flow cytometry analysis were used to evaluate the occurring changes in terms of cell viability, morphology, and granulocyte stress formation, all factors directly influencing DEP sorting capability. Quantitative real-time PCR (qRT-PCR) was instead employed to evaluate the gene expression levels of interleukin-6 (IL-6) and inducible nitric oxide synthase (iNOS), two well-known markers of inflammation and oxidative stress, respectively. An additional marker representing an index of cellular metabolic status, i.e. the expression of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) gene, was also evaluated. Among the four buffers considered, two resulted satisfactory in terms of cell viability and growth recovery (24 h), with no significant changes in cell morphology for up to 1 h in suspension. Of note, gene expression analysis showed that in both cell lines the apparently non-cytotoxic buffers significantly modulated IL-6, iNOS, and GAPDH markers, underlining the importance to deeply investigate the molecular and biochemical changes occurring during the analysis, even at apparently non-toxic conditions. The selection of a useful buffer for the separation and analysis of cells without labeling procedures, preserving cell status, represents a key factor for DEP analysis, giving the opportunity to further use cells for additional analysis.

Dataset Information

Simultaneous on-chip DC dielectrophoretic cell separation and quantitative separation performance characterization.

Publications

Simultaneous on-chip DC dielectrophoretic cell separation and quantitative separation performance characterization.

Similar Datasets

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