A Review of Advanced Impedance Biosensors with Microfluidic Chips for Single-Cell Analysis.
Soojung KimHyerin SongHeesang AhnTaeyeon KimJihyun JungSoo Kyung ChoDong-Myeong ShinJong-Ryul ChoiYoon-Hwae HwangKyujung KimPublished in: Biosensors (2021)
Electrical impedance biosensors combined with microfluidic devices can be used to analyze fundamental biological processes for high-throughput analysis at the single-cell scale. These specialized analytical tools can determine the effectiveness and toxicity of drugs with high sensitivity and demonstrate biological functions on a single-cell scale. Because the various parameters of the cells can be measured depending on methods of single-cell trapping, technological development ultimately determine the efficiency and performance of the sensors. Identifying the latest trends in single-cell trapping technologies afford opportunities such as new structural design and combination with other technologies. This will lead to more advanced applications towards improving measurement sensitivity to the desired target. In this review, we examined the basic principles of impedance sensors and their applications in various biological fields. In the next step, we introduced the latest trend of microfluidic chip technology for trapping single cells and summarized the important findings on the characteristics of single cells in impedance biosensor systems that successfully trapped single cells. This is expected to be used as a leading technology in cell biology, pathology, and pharmacological fields, promoting the further understanding of complex functions and mechanisms within individual cells with numerous data sampling and accurate analysis capabilities.
Keyphrases
- single cell
- high throughput
- induced apoptosis
- rna seq
- cell cycle arrest
- endoplasmic reticulum stress
- magnetic resonance imaging
- randomized controlled trial
- oxidative stress
- circulating tumor cells
- palliative care
- gold nanoparticles
- cell death
- label free
- high resolution
- low cost
- quantum dots
- data analysis
- cell therapy