Literature DB >> 35015306

Numerical study of dielectrophoresis-modified inertial migration for overlapping sized cell separation.

Mohammed Khan1, Xiaolin Chen1.   

Abstract

Circulating tumor cells (CTCs) have been proven to have significant prognostic, diagnostic, and clinical values in early-stage cancer detection and treatment. The efficient separation of CTCs from peripheral blood can ensure intact and viable CTCs and can, thus, give proper genetic characterization and drug innovation. In this study, continuous and high-throughput separation of MDA-231 CTCs from overlapping sized white blood cells (WBCs) is achieved by modifying inertial cell focusing with dielectrophoresis (DEP) in a single-stage microfluidic platform by numeric simulation. The DEP is enabled by embedding interdigitated electrodes with alternating field control on a serpentine microchannel to avoid creating two-stage separation. Rather than using the electrokinetic migration of cells which slows down the throughput, the system leverages the inertial microfluidic flow to achieve high-speed continuous separation. The cell migration and cell positioning characteristics are quantified through coupled physics analyses to evaluate the effects of the applied voltages and Reynolds numbers (Re) on the separation performance. The results indicate that the introduction of DEP successfully migrates WBCs away from CTCs and that separation of MDA-231 CTCs from similar sized WBCs at a high Re of 100 can be achieved with a low voltage of magnitude 4 ×106  V/m. Additionally, the viability of MDA-231 CTCs is expected to be sustained after separation due to the short-term DEP exposure. The developed technique could be exploited to design active microchips for high-throughput separation of mixed cell beads despite their significant size overlap, using DEP-modified inertial focusing controlled simply by adjusting the applied external field.
© 2022 Wiley-VCH GmbH.

Entities:  

Keywords:  Circulating tumor cells; Dielectrophoresis; High-throughput; Inertial focusing; Label-free separation

Mesh:

Year:  2022        PMID: 35015306     DOI: 10.1002/elps.202100187

Source DB:  PubMed          Journal:  Electrophoresis        ISSN: 0173-0835            Impact factor:   3.535


  1 in total

1.  Deterministic Lateral Displacement (DLD) Analysis Tool Utilizing Machine Learning towards High-Throughput Separation.

Authors:  Eric Gioe; Mohammed Raihan Uddin; Jong-Hoon Kim; Xiaolin Chen
Journal:  Micromachines (Basel)       Date:  2022-04-23       Impact factor: 3.523

  1 in total

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