Graphene biosensors outcompete ELISA and Simoa for point-of-care diagnostics

Graphenea’s S20 graphene field effect transistor (GFET) chip has been used to demonstrate biosensors that are simple, fast, having an ultralow limit of detection (LOD), directly outcompeting traditional ELISA and state of the art Simoa biosensors.

Researchers from the UK and from Graphenea have used the graphene field-effect biosensor platform to detect glial fibrillary acidic protein (GFAP), a discriminative biomarker for many neurological diseases, such as traumatic brain injury (TBI). The research was published in ACS Sensors.

Image: On-chip GFET biosensing outcompetes ELISA and Simoa. From Xu et al, ACS Sens. 2021, DOI: 10.1021/acssensors.1c02232. Reproduced under a CC BY 4.0 license.

The GFET S-20 chip was prepared by modification with PBASE and GFAP antibodies, which are sensitive to the presence of GFAP. Human blood plasma samples were collected from patients participating in a clinical study, each patient having moderate to severe TBI. When the plasma was introduced onto the GFET biosensor, binding of GFAP with the antibodies caused a shift in the Dirac point of the graphene, which is reflected in changes in the source-drain current of the graphene device. The described method is simple to implement, requiring very little time and equipment. Furthermore, reliable GFAP detection was achieved in just several minutes. The sensor showed analytical resolution down to the order of 10-1 pg/mL in patient plasma samples. Furthermore, the GFET technology is the first with which ultrahigh sensitivity down to the 2.3 x 102 fg/mL level was demonstrated.

For comparison, the GFET technology was gauged against the gold standard Simoa, and the traditional ELISA tests. Compared to those established alternatives, the graphene technology is the only one that can detect GFAP in patient plasma samples with a limit of detection down to femtomolar levels, without signal amplification, within minutes. The extreme sensitivity implies that diseases could be detected much before symptoms start, which would assist epidemiological control. At production volumes anticipated when this technology is put to widespread use, the cost is also expected to be very competitive. Modified with different antibodies, the platform can be used to detect other disease biomarkers and is expected to be used in point-of-care and hospital bedside disease diagnosis in the near future.

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