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Dielectrophoresis-Based SERS Sensors for the Detection of Cancer Cells in Microfluidic Chips
| Content Provider | MDPI |
|---|---|
| Author | Joanna, Trzcińska-Danielewicz Agnieszka, Kamińska Szymborski, Tomasz R. Czaplicka, Marta Nowicka, Ariadna B. Girstun, Agnieszka |
| Copyright Year | 2022 |
| Description | The detection of freely circulating cancer cells (CTCs) is one of the greatest challenges of modern medical diagnostics. For several years, there has been increased attention on the use of surface-enhanced Raman spectroscopy (SERS) for the detection of CTCs. SERS is a non-destructive, accurate and precise technique, and the use of special SERS platforms even enables the amplification of weak signals from biological objects. In the current study, we demonstrate the unique arrangement of the SERS technique combined with the deposition of CTCs cells on the surface of the SERS platform via a dielectrophoretic effect. The appropriate frequencies of an alternating electric field and a selected shape of the electric field can result in the efficient deposition of CTCs on the SERS platform. The geometry of the microfluidic chip, the type of the cancer cells and the positive dielectrophoretic phenomenon resulted in the trapping of CTCs on the surface of the SERS platform. We presented results for two type of breast cancer cells, MCF-7 and MDA-MB-231, deposited from the 0.1 PBS solution. The limit of detection (LOD) is 20 cells/mL, which reflects the clinical potential and usefulness of the developed approach. We also provide a proof-of-concept for these CTCs deposited on the SERS platform from blood plasma. |
| Starting Page | 681 |
| e-ISSN | 20796374 |
| DOI | 10.3390/bios12090681 |
| Journal | Biosensors |
| Issue Number | 9 |
| Volume Number | 12 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-08-25 |
| Access Restriction | Open |
| Subject Keyword | Biosensors Analytical Chemistry Surface-enhanced Raman Spectroscopy (sers) Dielectrophoresis Cancer Cells Microfluidics Mcf-7 Mda-mb-231 |
| Content Type | Text |
| Resource Type | Article |
