Rapid, quantitative therapeutic screening for Alzheimer's enzymes enabled by optimal signal transduction with transistors.
Analyst
; 145(8): 2925-2936, 2020 Apr 21.
Article
em En
| MEDLINE
| ID: mdl-32159165
We show that commercially sourced n-channel silicon field-effect transistors (nFETs) operating above their threshold voltage with closed loop feedback to maintain a constant channel current allow a pH readout resolution of (7.2 ± 0.3) × 10-3 at a bandwidth of 10 Hz, or ≈3-fold better than the open loop operation commonly employed by integrated ion-sensitive field-effect transistors (ISFETs). We leveraged the improved nFET performance to measure the change in solution pH arising from the activity of a pathological form of the kinase Cdk5, an enzyme implicated in Alzheimer's disease, and showed quantitative agreement with previous measurements. The improved pH resolution was realized while the devices were operated in a remote sensing configuration with the pH sensing element off-chip and connected electrically to the FET gate terminal. We compared these results with those measured by using a custom-built dual-gate 2D field-effect transistor (dg2DFET) fabricated with 2D semi-conducting MoS2 channels and a signal amplification of 8. Under identical solution conditions the nFET performance approached the dg2DFETs pH resolution of (3.9 ± 0.7) × 10-3. Finally, using the nFETs, we demonstrated the effectiveness of a custom polypeptide, p5, as a therapeutic agent in restoring the function of Cdk5. We expect that the straight-forward modifications to commercially sourced nFETs demonstrated here will lower the barrier to widespread adoption of these remote-gate devices and enable sensitive bioanalytical measurements for high throughput screening in drug discovery and precision medicine applications.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Transistores Eletrônicos
/
Quinase 5 Dependente de Ciclina
/
Doença de Alzheimer
Tipo de estudo:
Diagnostic_studies
/
Screening_studies
Limite:
Humans
Idioma:
En
Revista:
Analyst
Ano de publicação:
2020
Tipo de documento:
Article
País de afiliação:
Estados Unidos