Nanopore-Based Single-Entity Electrochemistry for the Label-Free Monitoring of Single-Molecule Glycoprotein-Boronate Affinity Interaction and Its Sensing Application.
Haoran TangHao WangDandan ZhaoMengya CaoYanyan ZhuYongxin LiPublished in: Analytical chemistry (2022)
Nanopipettes provide a promising confined space that enables advances in single-molecule analysis, and their unique conical tubular structure is also suitable for single-cell analysis. In this work, functionalized-nanopore-based single-entity electrochemistry (SEE) analysis tools were developed for the label-free monitoring of single-molecule glycoprotein-boronate affinity interaction for the first time, and immunoglobulin G (IgG, one of the important biomarkers for many diseases such as COVID-19 and cancers) was employed as the model glycoprotein. The principle of this method is based on a single glycoprotein molecule passing through 4-mercaptophenylboronic acid (4-MPBA)-modified nanopipettes under a bias voltage and in the meantime interacting with the boronate group from modified 4-MPBA. This translocation and affinity interaction process can generate distinguishable current blockade signals. Based on the statistical analysis of these signals, the equilibrium association constant (κ a ) of single-molecule glycoprotein-boronate affinity interaction was obtained. The results show that the κ a of IgG in the confined nanopore at the single-molecule level is much larger than that measured in the open system at the ensemble level, which is possibly due to the enhanced multivalent synergistic binding in the restricted space. Moreover, the functionalized-nanopore-based SEE analysis tools were further applied for the label-free detection of IgG, and the results indicate that our method has potential application value for the detection of glycoproteins in real samples, which also paves way for the single-cell analysis of glycoproteins.
Keyphrases
- single molecule
- label free
- atomic force microscopy
- living cells
- single cell
- coronavirus disease
- sars cov
- rna seq
- machine learning
- high resolution
- young adults
- molecular dynamics
- quantum dots
- endothelial cells
- binding protein
- simultaneous determination
- deep learning
- transcription factor
- convolutional neural network
- fluorescent probe
- tandem mass spectrometry
- childhood cancer