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Small molecule immunosensing using surface plasmon resonance.

John Mitchell¹

¹Bioengineering Technologies Group, The New Zealand Institute for Plant and Food Research Ltd, East Street, Hamilton 3214, New Zealand. John.Mitchell@plantandfood.co.nz

Sensors (Basel, Switzerland)

|December 14, 2011

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Surface plasmon resonance (SPR) biosensors are advancing for small molecule detection. New designs overcome challenges like steric hindrance, enabling sensitive detection of hormones, toxins, drugs, and explosives.

Area of Science:

Biosensing
Analytical Chemistry
Immunotechnology

Background:

Surface plasmon resonance (SPR) biosensors excel at detecting large molecules via refractive index changes.
Small molecule detection using SPR is challenging due to low mass and potential steric hindrance.
Ultrasensitive small molecule assays require specialized antibodies or high-mass labels.

Purpose of the Study:

To review the application of SPR biosensors for small molecule immunoassays.
To discuss challenges and advancements in small molecule immunosensing.
To examine assay design, matrix effects, and multiplexing capabilities.

Main Methods:

Review of existing literature on SPR biosensor applications for small molecule detection.
Analysis of sensor interface design and signal enhancement strategies.

Keywords:

conjugation immunosensor small molecule steroid surface plasmon resonance (SPR)toxin

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Evaluation of assay performance for various small molecule classes (steroids, toxins, drugs, explosives).

Main Results:

SPR biosensors can achieve high sensitivity for small molecule detection with optimized designs.
Careful sensor interface engineering and signal amplification are crucial for overcoming limitations.
Matrix effects in complex samples and multiplexed detection are key considerations.

Conclusions:

SPR technology is increasingly capable of sensitive small molecule immunosensing.
Advances in assay design and interface engineering are critical for practical applications.
Future directions include improved stability, multiplexing, and complex sample analysis.