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Picomolar detection of carbohydrate-lectin interactions on piezoelectrically printed microcantilever array
Oren Cooper1, Hoang-Phuong Phan2, Tom Fitzpatrick3
1Institute for Glycomics, Griffith University, Gold Coast Campus, QLD, 4215, Australia.
Biosensors & Bioelectronics
|February 27, 2022
Summary
This study introduces a new method for immobilizing biomolecules on micro-electromechanical systems (MEMS) sensors using 3-(glycidyloxypropyl)-trimethoxysilane (GOPTS) self-assembled monolayers (SAMs). This approach enables highly sensitive, label-free detection of carbohydrate-lectin interactions.
Area of Science:
- Biotechnology
- Materials Science
- Sensor Technology
Background:
- Micro-electromechanical systems (MEMS) offer potential for label-free detection (LFD) of analytes.
- Immobilizing biomolecules on inorganic surfaces without losing sensing ability is critical for LFD technologies.
- Covalent functionalization of self-assembled monolayers (SAMs) enhances assay sensitivity, reproducibility, and surface stability.
Purpose of the Study:
- To investigate the use of 3-(glycidyloxypropyl)-trimethoxysilane (GOPTS) as a versatile SAM for covalent functionalization of SiO2 microcantilever arrays (MCAs).
- To demonstrate high-throughput immobilization of glycans onto MCAs for analyzing carbohydrate-lectin interactions.
- To establish MCAs as a robust, label-free, and scalable platform for analyzing these interactions.
Main Methods:
- Chemical vapor deposition of GOPTS SAMs onto SiO2 MCAs.
- Utilizing piezoelectric microarray printer technology for glycan immobilization.
- Assessing carbohydrate-lectin interactions with picogram sensitivity using LFD.
Main Results:
- GOPTS SAMs enable covalent functionalization of MCAs for sensitive detection.
- Glycan immobilization on MCAs is feasible using standard microarray printing.
- The developed MCA platform demonstrates picogram sensitivity for carbohydrate-lectin interactions.
Conclusions:
- GOPTS SAMs provide a suitable biofunctionalization strategy for MEMS-based LFD.
- This work presents a proof of principle for scalable, high-resolution, high-throughput LFD platforms.
- The methodology can be extended to various LFD technologies for diverse biological and chemical sensing applications.

