Highly sensitive covalently functionalized light-addressable potentiometric sensor for determination of biomarker

Jintao Liang1, Mingyuan Guan2, Guoyin Huang2

  • 1School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin, Guangxi 541004, China; Guangxi Experiment Center of Information Sciences, Guilin University of Electronic Technology, Guilin, Guangxi 541004, China.

Insights

We developed a novel light-addressable potentiometric sensor (LAPS) to detect human immunoglobulin G (hIgG), a key disease biomarker. This sensor shows promise for early disease prediction with simple, reproducible detection.

Area of Science:

  • Biosensors
  • Biomarker Detection
  • Immunoglobulin Assays

Background:

  • Biomarkers indicate biological status and aid in early disease prediction.
  • Human immunoglobulin G (hIgG) serves as a model biomarker for disease detection.

Purpose of the Study:

  • To develop and validate a novel structured light-addressable potentiometric sensor (LAPS) for quantifying human immunoglobulin G (hIgG).
  • To assess the sensor's performance, stability, and reproducibility for potential clinical applications.

Main Methods:

  • Covalently immobilizing goat anti-human immunoglobulin G antibodies onto a LAPS chip as the recognition element.
  • Utilizing a laser diode controlled by a field-programmable gate array to drive the LAPS system.
  • Monitoring potential shifts in response to varying concentrations of hIgG in a supporting electrolyte solution.

Main Results:

  • Achieved a linear correlation between potential shift and hIgG concentration (ΔV (V)=0.00714ChIgG (μg/mL)-0.0147, R²=0.9968) over a 0-150 μg/mL range.
  • Demonstrated acceptable stability and reproducibility of the LAPS system.
  • Validated the sensor's capability for sensitive and specific hIgG detection.

Conclusions:

  • The developed LAPS system offers a sensitive, stable, and reproducible method for detecting disease biomarkers like hIgG.
  • The system's simple operation and multi-sample format make it suitable for environmental, food, and clinical diagnostics.
  • This technology holds significant promise for early disease prediction and monitoring.