Graphene Biosensor Could Enable Rapid Screening of Acute Myocardial Infarction

Out of all cardiovascular diseases, acute myocardial infarction (AMI) has one of the highest mortality rates, accounting for 31.8% of all deaths worldwide.

Image Credit: Emily frost/Shutterstock.com

Probable causes of AMI include hypertension, obesity, hyperlipidemia, smoking, overwork, and drinking, which raises the percentage of the total population classed as a AMI high-risk group. Becasue of this, early disease detection is vital.

Lengthy, pre-diagnosis clinical procedures heavily rely on expensive instrumentation and require sophisticated procedures, several reagents, and experienced professionals in hospitals to be completed. Also, the proportion (∼70%) of atypical initial symptoms, includes shoulder pain, toothache, dizziness, stomachache, and left arm weakness, are the major impediments to the in-time diagnosis of AMI.

To help alleviate these issues, a rapid and simple diagnosis of AMI in a non-medical context is widely required, decreasing the diagnostic time and significantly lowering mortality risk.

Due to the high specificity and stability of aptamer and the high sensitivity of graphene, the aptameric graphene field-effect transistor (GFET) biosensor is considered to be the solution, and it may be used as portable or wearable devices in non-medical environments.

Despite significant advances, the great majority of aptameric GFETs are hard to achieve stable functioning in clinical applications until resolving specific difficulties, such as:

(1) Non-linear signal output with drastically varying sensitivity in different ranges.

(2) Hemocyte biofouling of graphene surfaces is another concern. Due to detection failure, biofouling would prevent them from being used in whole blood.

(3) As the surface of graphene is sensitive, surface-absorbed contaminants from living surroundings might have a major impact on the biosensor’s sensitivity, accuracy, and longevity.

In a new study, researchers present a three-dimensional affinity biosensor (3DA-biosensor) made up of a self-assembled hydrogel-aptamer hybrid layer (H-AHL) composition and GFET for ultra-sensitive, quick, and accurate detection of AMI in patient samples. The thickness of the H-AHL structure is modulated efficiently, enabling the adjustment of the 3DA-biosensor's detecting parameter, resulting in a customizable high-sensitivity detection range.

Furthermore, encapsulating aptamer and graphene within a hydrogel can dramatically increase the biosensor’s lifespan and activity. According to investigations, the 3DA-biosensor allows for quick detection of AMI biomarkers in whole blood, as well as the identification of positive or negative patient samples in less than 5 minutes with a 94% accuracy rate.

This biosensor might be used alongside modern wearable or portable circuits to detect disease and provide personalized health treatment.

Methodology

A commercial foundry provided the graphene, a monolayer sheet created by chemical vapor deposition. Following 15 minutes of room-temperature polymerization, the H-AHL structure was built in the open cavity over the graphene-conducting channel.

To eliminate unreacted monomers and aptamers, the produced H-AHL was soaked in 100 mM NaCl and 20 mM HEPES for 10 minutes.

The 3DA-biosensors with the H-AHL structure were calibrated in 1 x PBS until the signal output was constant and then recorded responses of different concentrations of cTnI (1 x 10^-7 to 1 nM) within 6 minutes at room temperature for each concentration. Commercial products fetal bovine serum and EDTA anticoagulated rabbit blood were used to make the cTnI solution.

A commercial source meter is used to measure the output of the 3DA-biosensor signal (Keithley 2636B). The charge screening effect caused by nonspecific adsorption on the graphene surface was mitigated in these studies by sweeping the on-chip gate voltage from -0.3–0.3 V at a modest pace with a step of 0.001 V. For real-time measurement, the drain-source bias (V_ds) was set at 0.02 V.

Results

Figure 1A shows the schematics for the manufacturing operations of the 3D biosensor with the H-AHL structure. Figure 1B shows an image of the integrated 3DA-biosensor, while the structure of H-AHL is studied using a scanning electron microscope (SEM) in Figure 1C.

The 3DA-biosensor, as shown in Figure 1D, provides quick, easy, and specific detection of the AMI biomarker in patient samples using a tailored portable analytical apparatus.

Figure 1. Schematic of the 3DA-biosensor for AMI biomarker detection. (A) Schematic of the 3DA-biosensor fabrication process. The graphene-conducting channel is modified with the mixed solution that contains gel stock solution and aptamer to form the hydrogel-aptamer hybrid layer. (B) Photograph of the fabricated 3DA-biosensor with the H-AHL structure. (C) SEM image of the H-AHL structure. A mass of nano- or microsize holes is observed in the hydrogel through the cross-section, illustrating the self-filtration capability of the biosensor. (D) 3DA-biosensor exploits the self-filtering and specific recognition capability of the H-AHL and the sensitivity of GFET to achieve detection of disease biomarkers in whole blood without pretreatment to samples. Image Credit: Wang, et al., 2022

The Dirac point (V_Dirac), the value of V_g at which drain-source current (I_ds) achieves its lowest, was seen to fall by 0.065 V from 0.132–0.067 V when the concentration of cTnI rises from 1 x 10^-7 to 1 nM. (Figure 2A).

IgG-aptamer- and insulin-aptamer-modified 3DA-biosensors had different transfer characteristic curves for rising target biomarker concentrations than cTnI-aptamer-modified devices (Figures 2B and 2C). V_Dirac is displayed as a function of biomarker concentrations (Figures 2D–2F), defined as ΔV_Dirac = V_Dirac - V_Dirac,₀, while V_Dirac,₀ is the Dirac point equivalent to the voltage recorded in 1 x PBS.

Figure 2. 3DA-biosensor selectively detects disease biomarkers in pure buffer. (A–C) After modification with corresponding aptamers, transfer characteristic curves are measured while the biosensor exposure to cTnI solution ranging from 1 × 10⁻⁷ to 1 nM (A), IgG solution ranging from 0.02 to 500 nM (B), and insulin solution ranging from 1 × 10⁻⁶ to 0.5 nM (C). (D–F) Normalized Dirac point shift ΔV_Dirac/ΔV_Dirac,_max as a function of different concentrations of cTnI protein (D), IgG protein (E), and insulin (F), respectively. Each fitted curve is a least-square fit to the Hill-Langmuir binding model. Each test was repeated three times (data are represented as mean ± SD). (G) Sensing response of the 3DA-biosensor with H-AHL in the presence of the target biomarker is significantly higher than that generated by the device with the blank hydrogel. Each test was repeated three times (data are represented as mean ± SD). (H) Signal output of the 3DA-biosensor to target cTnI protein is significantly larger compared with control proteins (CEA, myoglobin, CK-MB, and HSA). Each test was repeated three times (data are represented as mean ± SD). Image Credit: Wang, et al., 2022

Figure 3A shows that as the structure thickness increased from 0–1.4 mm (0 mm for the conventional aptameric GFET), the ΔV_Dirac,_max, which can be considered the sensing system gain, increased significantly to a maximum value of 0.069 V, accompanied by a significant drop to 0.026 V as the structure thickness increased from 1.4–2.35 mm.

Figure 3. 3DA-biosensor with adjustable sensitivity ranges of detection. (A) The maximum Dirac point shift ΔV_Dirac,_max of the 3DA-biosensor with various H-AHL structure thickness. Each test was repeated three times (data are represented as mean ± SD). (B) LOD of the biosensor showing the monotonical increase with the enhancement of H-AHL thickness. (C–E) 3DA-biosensor of different H-AHL thickness showing the adjustable sensitivity to various concentrations ranging from 0 to 5 × 10⁻⁷ nM (C), 2 × 10⁻⁴ to 6 × 10⁻⁴ nM (D), and 5 to 45 nM (E). (F) Sensing response of 3DA-biosensor with different thicknesses of H-AHL from 0 to 2.35 mm in the presence of cTnI concentration ranging from 5 × 10⁻⁹ to 50 nM. Each test was repeated three times (data are represented as mean ± SD). Image Credit: Wang, et al., 2022

The H-AHL retains good integrity and transparency, as demonstrated in Figure 4A, and no apparent blood infiltration is seen after a 60-minute test.

Figure 4. Characterization of the 3DA-biosensor self-filtration in whole blood. (A) Photograph of the H-AHL structure soaked in whole blood or blood with Mb from 0 to 60 min. (B) Top: serum dyed with Mb is significantly and rapidly immersed into the hydrogel. Bottom: hydrogel is super-oleophobic under the water with the contact angle about 178°. (C) Time-resolved measurement of bare graphene sensor (blue line) and the biosensor with H-AHL (purple line) in whole blood represents that the nonspecific adsorption of non-target molecules is minimized with the existence of H-AHL structure. Image Credit: Wang, et al., 2022

The V_Dirac changed continuously to the negative direction of the x-axis as the cTnI concentration grew from 1 x 10^-7 to 1 nM, comparable to the case of the device tested in pure buffer, with a maximum V_Dirac value of 0.051 V in serum (Figure 5A).

Figure 5. Biomarker detection of 3DA-biosensor in serum and whole blood. (A) Transfer characteristic curves measured when the biosensor is exposed to cTnI protein in serum ranging from 1 × 10⁻⁷ to 1 nM. (B) Normalized ΔV_Dirac/ΔV_Dirac,_max as a function of different concentrations of cTnI in 1 × PBS (square) and serum (circle). Each test was repeated three times (data are represented as mean ± SD). (C) Transfer characteristic curves measured when the biosensor is exposed to cTnI protein in whole blood ranging from 1 × 10⁻⁷ to 1 nM. (D) Normalized ΔV_Dirac/ΔV_Dirac,_max as a function of various cTnI concentrations in 1× PBS (square), serum (circle), and whole blood (triangle). Each test was repeated three times (data are represented as mean ± SD). (E) Real-time monitoring of cTnI in whole blood using 3DA-biosensor with 1.4 and 2 mm H-AHL structure. Inset is normalized ΔI_ds/ΔI_ds,_max as a function of different cTnI concentrations in whole blood. Image Credit: Wang, et al., 2022

The hospital provided the patient’s samples, and the concentration of the target biomarker was determined in advance using medical diagnostic equipment (Figure 6A).

Figure 6. 3DA-biosensor analysis of patient samples for AMI detection. (A) Photograph of extraction of patient samples. (B) I response obtained by 3DA-biosensor in the presence of healthy and patient samples. I response of positive samples is greater than 0. (C) Signal outputs in real time for sample detection are compared with calibration baseline to define the positive or negative. (D) 3DA-biosensor results for the presence of healthy (<0.012 ng/mL) and AMI patient samples, as defined by I response greater than 0. Image Credit: Wang, et al., 2022

Table 1 shows a comparison of the 3DA-biosensor with earlier approaches.

Table 1. Comparison of the 3DA-biosensor with existing biosensors for cTnI detection. Source: Wang, et al., 2022

Conclusion

This research highlights the ultra-sensitive and quick detection of AMI in patient samples utilizing a self-assembled 3DA-biosensor. The findings enable assessments of a wide range of AMI biomarkers in whole blood, overcoming basic problems faced by almost all graphene-based sensors, such as sensitivity limitations and interference from non-target molecules.

The H-AHL structure increases the density of aptamers per projected area while also empowering the GFET’s self-filtration capabilities, allowing for high-gain and ultra-sensitive detection of cTnI in whole blood at concentrations ranging from 1 x 10^-7 to 1 nM with a K_D of 3.20 x 10^-4 nM. Without any preprocessing, the 3DA-biosensor can determine the positive or negative for samples with an accuracy rate of more than 94%.

Researchers believe that, with further development, the 3DA-biosensor could be a very interesting approach to personalized health monitoring in people’s daily lives; it will also have a lot of applications in conjunction with artificial intelligence and big data processing.

Journal Reference:

Wang, Z., Hao, Z., Yang, C., Wang, H., Huang, C., Zhao, X. and Pan, Y. (2022) Ultra-sensitive and rapid screening of acute myocardial infarction using 3D-affinity graphene biosensor. Cell Reports Physical Science, p.100855. Available Online: https://www.sciencedirect.com/science/article/pii/S2666386422001291.

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