Electrochemiluminescence Imaging for the Morphological and Quantitative Analysis of Living Cells under External Stimulation.
Hongfang GaoWeijuan HanHonglan QiQiang GaoChengxiao ZhangPublished in: Analytical chemistry (2020)
In this work, a simple electrochemiluminescence (ECL) imaging method based on the cell shield of the ECL emission was developed for the morphological and quantitative analysis of living cells under external stimulation. ECL images of MCF-7 cells cultured on or captured at the glassy carbon electrode (GCE) surface in a solution of tris(2,2'-bipyridyl)ruthenium(II)-tri-n-propylamine were recorded. Important morphological characteristics of living cells, including cell shape, cell area, average cell boundary, and junction distance between two adjacent cells, were directly obtained using the developed negative ECL imaging method. The ECL images revealed gradual morphological changes in cells on the GCE surface. During the course of H2O2 stimulation of cells on the GCE surface, cells shrunk, rounded up, disengaged from surrounding cells, and finally detached from the electrode surface. During the course of electrical stimulation (0.8 V), the cells on the GCE surface exhibited aggregation as demonstrated by increases in the average cell boundary and decreases in the junction distance between two adjacent cells. Additionally, a quantitative method for the sensitive determination of MCF-7 cells with a limit of detection of 29 cells/mL was developed using the negative ECL imaging strategy. This work demonstrates that the proposed negative ECL imaging strategy is a promising approach to assess important morphological characteristics of living cells during the course of external stimulation and to obtain quantitative information on cell concentrations in solution.
Keyphrases
- induced apoptosis
- living cells
- cell cycle arrest
- high resolution
- single cell
- healthcare
- fluorescent probe
- stem cells
- endoplasmic reticulum stress
- signaling pathway
- cell therapy
- spinal cord injury
- oxidative stress
- mass spectrometry
- endothelial cells
- machine learning
- deep learning
- pi k akt
- health information
- label free
- energy transfer