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Cited by 436

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Isolation of rare cells from cell mixtures by dielectrophoresis

Open access
Gascoyne, Peter R. C., Noshari, Jamileh, Anderson, Thomas J., Becker, Frederick F.·2009·ELECTROPHORESIS

Abstract

The application of dielectrophoretic field‐flow fractionation (depFFF) to the isolation of circulating tumor cells (CTCs) from clinical blood specimens was studied using simulated cell mixtures of three different cultured tumor cell types with peripheral blood. The depFFF method can not only exploit intrinsic tumor cell properties so that labeling is unnecessary but can also deliver unmodified, viable tumor cells for culture and/or all types of molecular analysis. We investigated tumor cell recovery efficiency as a function of cell loading for a 25 mm wide × 300 mm long depFFF chamber. More than 90% of tumor cells were recovered for small samples but a larger chamber will be required if similarly high recovery efficiencies are to be realized for 10 mL blood specimens used CTC analysis in clinics. We show that the factor limiting isolation efficiency is cell–cell dielectric interactions and that isolation protocols should be completed within ∼15 min in order to avoid changes in cell dielectric properties associated with ion leakage.

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Metadata

Authors
Gascoyne, P. R. C., Noshari, J., Anderson, T. J., & Becker, F. F.
Venue
ELECTROPHORESIS
Publisher
Wiley
Published
2009-04
Volume
30
Issue
8
Pages
1388-1398
ISSN
  • 0173-0835 (ISSN-L)
  • 1522-2683
OpenAlex
W2059024717
Cited by
436
References
3
FWCI
14.56
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Isolation of rare cells from cell mixtures by dielectrophoresis. https://app.cochat.ai/papers/10.1002/elps.200800373
The paper
Gascoyne, P. R. C., Noshari, J., Anderson, T. J., & Becker, F. F. (2009). Isolation of rare cells from cell mixtures by dielectrophoresis. ELECTROPHORESIS. https://doi.org/10.1002/elps.200800373

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.