Conference proceeding
BAEC adhesion analysis using Thickness Shear Mode sensor
2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, v 7, pp 1047-1050
2005
PMID: 17282367
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
A thickness shear mode (TSM) sensor of which acoustic shear wave has a nano-scale penetration depth was used in detecting biological activities of living cells. The initial cell adhesion process was characterized by analyzing multi-harmonic responses of the TSM sensor. A suspension of bovine aortic endothelial cells (BAECs) was placed on the TSM sensor, and changes in impedance magnitude during sedimentation and attachment processes were monitored. For the validation of cell adhesion, the sensor surface was rinsed at various time points during the adhesion process, and the number of cells remaining attached was counted. By comparing sensor signals with the independently measured cell adhesion, sensor readings were correlated with physical adhesion process. In addition, by coating the TSM sensors with gelatin and by blocking the integrin bindings with Arg-Gly-Asp peptides (RGD), we investigated cell adhesion process under different environments. The time course of sensor readings were characterized with delay time, slope and maximum value of impedance magnitude changes. This study demonstrates the ability of the TSM sensor to detect distinct phases in the cell adhesion process
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Details
- Title
- BAEC adhesion analysis using Thickness Shear Mode sensor
- Creators
- S Hong - Drexel UniversityE Ergezen - Drexel UniversityK.A Barbee - Drexel UniversityR Lec - Drexel UniversityIEEE
- Publication Details
- 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, v 7, pp 1047-1050
- Publisher
- IEEE
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems
- Web of Science ID
- WOS:000238998400272
- Scopus ID
- 2-s2.0-33846921068
- Other Identifier
- 991019168815504721
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Engineering, Biomedical