The Antibody-Free Recognition of Cancer Cells Using Plasmonic Biosensor Platforms with the Anisotropic Resonant Metasurfaces.
Zhang ZhangMaosheng YangXin YanXinyue GuoJie LiYue YangDequan WeiLonghai LiuJianhua XieYufei LiuLanju LiangJianquan YaoPublished in: ACS applied materials & interfaces (2020)
It is vital and promising for portable and disposable biosensing devices to achieve on-site detection and analysis of cancer cells. Although traditional labeling techniques provide an accurate quantitative measurement, the complicated cell staining and high-cost measurements limit their further development. Here, we demonstrate a nonimmune biosensing technology. The plasmonic biosensors, which are based on anisotropic resonant split ring resonators in the terahertz range, successfully realize the antibody-free recognition of cancer cells. The dependences of Δf and the fitted phase slope on the cancer cell concentration at different polarizations give new perspective in hexagonal radar maps. The results indicate that the lung cancer cell A549 and liver cancer cell HepG2 can be distinguished and determined simply based on the enclosed shapes in the radar maps without any antibody introduction. The minimum concentration of identification reduces to as low as 1 × 104 cells/mL and such identification can be kept valid in a wide range of cell concentration, ranging from 104 to 105. The construction of two-dimensional extinction intensity cards of corresponding cancer cells based on the wavelet transform method also supplies corresponding information for the antibody-free recognition and determination of two cancer cells. Our plasmonic metasurface biosensors show a great potential in the determination and recognition of label-free cancer cells, being an alternative to nonimmune biosensing technology.
Keyphrases
- label free
- single cell
- cell therapy
- high resolution
- solid phase extraction
- healthcare
- energy transfer
- stem cells
- oxidative stress
- high intensity
- cell cycle arrest
- convolutional neural network
- cell proliferation
- mesenchymal stem cells
- cell death
- bioinformatics analysis
- machine learning
- signaling pathway
- mass spectrometry
- climate change
- liquid chromatography