Ultrasensitive dopamine detection with graphene aptasensor multitransistor arrays

Area of Science:

• Neuroscience
• Materials Science
• Biotechnology

Background:

• Accurate detection of neurotransmitters like dopamine is crucial for understanding brain disorders.
• Existing sensors often struggle with sensitivity and reliability in complex biological samples.
• Developing ultrasensitive and robust neurotransmitter detection platforms is a key challenge.

Purpose of the Study:

• To develop a highly sensitive and reproducible platform for detecting dopamine at physiological levels.
• To demonstrate the utility of graphene multitransistor arrays (gMTAs) functionalized with DNA aptamers for neurotransmitter sensing.
• To overcome limitations of current sensors in detecting low analyte concentrations in complex biological matrices.

Main Methods:

• Fabrication of wafer-level graphene multitransistor arrays (gMTAs) on small chips.
• Functionalization of gMTAs with DNA aptamers for selective dopamine binding.
• Utilizing a multiple sensor array configuration for robust, simultaneous replicate measurements.
• Testing sensor performance in various buffers, complex biological samples, and CSF from a Parkinson's disease mouse model.

Main Results:

• Achieved an ultra-low limit-of-detection (LOD) of 1 aM (10^-18 M) for dopamine.
• Demonstrated a wide dynamic detection range from 1 aM to 100 µM (10 orders of magnitude).
• Exhibited high sensitivity (22 mV/decade) and reproducibility in complex biological samples, including brain homogenates and mouse CSF.
• Successfully detected minimal dopamine concentration changes in small-volume CSF samples from a Parkinson's disease model.

Conclusions:

• The developed gMTA platform offers robust and ultrasensitive dopamine detection.
• This technology overcomes sensitivity limitations in complex biological samples, paving the way for improved diagnostics.
• The platform holds significant potential for real-world research, pre-clinical studies, and clinical diagnosis of brain disorders.