A Simple Phase-Sensitive Surface Plasmon Resonance Sensor Based on Simultaneous Polarization Measurement Strategy

Enhanced biosensing of tumor necrosis factor-alpha based on aptamer-functionalized surface plasmon resonance substrate and Goos-Hänchen shift

Tumor necrosis factor-alpha (TNF-α) serves as a crucial biomarker in various diseases, necessitating sensitive detection methodologies. This study introduces an innovative approach utilizing an aptamer-functionalized surface plasmon resonance (SPR) substrate together with an ultrasensitive measure, the Goos-Hänchen (GH) shift, to achieve sensitive detection of TNF-α. The developed GH-aptasensing platform has shown a commendable figure-of-merit of 1.5 × 104 μm per RIU, showcasing a maximum detectable lateral position shift of 184.7 ± 1.2 μm, as characterized by the glycerol measurement. Employing aptamers as the recognition unit, the system exhibits remarkable biomolecule detection capabilities, including the experimentally obtained detection limit of 1 aM for the model protein bovine serum albumin (BSA), spanning wide dynamic ranges. Furthermore, the system successfully detects TNF-α, a small cytokine, with an experimental detection limit of 1 fM, comparable to conventional SPR immunoassays. This achievement represents one of the lowest experimentally derived detection limits for cytokines in aptamer-based SPR sensing. Additionally, the application of the GH shift marks a ground breaking advancement in aptamer-based biosensing, holding significant promise for pushing detection limits further, especially for small cytokine targets.

Label-free biosensing with singular-phase-enhanced lateral position shift based on atomically thin plasmonic nanomaterials

Rapid plasmonic biosensing has attracted wide attention in early disease diagnosis and molecular biology research. However, it was still challenging for conventional angle-interrogating plasmonic sensors to obtain higher sensitivity without secondary amplifying labels such as plasmonic nanoparticles. To address this issue, we developed a plasmonic biosensor based on the enhanced lateral position shift by phase singularity. Such singularity presents as a sudden phase retardation at the dark point of reflection from resonating plasmonic substrate, leading to a giant position shift on reflected beam. Herein, for the first time, the atomically thin layer of Ge2Sb2Te5 (GST) on silver nanofilm was demonstrated as a novel phase-response-enhancing plasmonic material. The GST layer was not only precisely engineered to singularize phase change but also served as a protective layer for active silver nanofilm. This new configuration has achieved a record-breaking largest position shift of 439.3 μm measured in calibration experiments with an ultra-high sensitivity of 1.72 × 108 nm RIU-1 (refractive index unit). The detection limit was determined to be 6.97 × 10-7 RIU with a 0.12 μm position resolution. Besides, a large figure of merit (FOM) of 4.54 × 1011 μm (RIU∙°)-1 was evaluated for such position shift interrogation, enabling the labelfree detection of trace amounts of biomolecules. In targeted biosensing experiments, the optimized sensor has successfully detected small cytokine biomarkers (TNF-α and IL-6) with the lowest concentration of 1 × 10-16 M. These two molecules are the key proinflammatory cancer markers in clinical diagnosis, which cannot be directly screened by current clinical techniques. To further validate the selectivity of our sensing systems, we also measured the affinity of integrin binding to arginylglycylaspartic acid (RGD) peptide (a key protein interaction in cell adhesion) with different Mn2+ ion concentrations, ranging from 1 nM to 1 mM.

Thickness measurement of bimetallic film using surface plasmon resonance holographic microscopy

Bimetallic film with high stability and sensitivity is often used to excite surface plasmon resonance (SPR). The thicknesses of the bimetallic film play an important role in quantitative retrieval of the sample's parameters, and a precise measurement method is not available until now. In this paper, we propose a method for measuring the thicknesses of bimetallic film using surface plasmon resonance holographic microscopy (SPRHM). Considering that the refractive index of the dielectric upon the bimetallic film sensitively modulates the SPR phase response, the two thickness parameters of bimetallic film can be calculated by two phase-contrast SPR images with two different liquid dielectrics. The capability of this method was verified with several Ag-Au film couples by using a compact SPRHM setup. Our work provides a precise characterization method for the parameters of SPR configuration and may find wide applications in the research fields of SPR sensing and imaging.

Real-Time Ellipsometric Surface Plasmon Resonance Sensor Using Polarization Camera May Provide the Ultimate Detection Limit

Ellipsometric Surface Plasmon Resonance (SPR) sensors are known for their relatively simple optical configuration compared to interferometric and optical heterodyne phase interrogation techniques. However, most of the previously explored ellipsometric SPR sensors based on intensity measurements are limited by their real-time applications because phase or polarization shifts are conducted serially. Here we present an ellipsometric SPR sensor based on a Kretschmann-Raether (KR) diverging beam configuration and a pixelated microgrid polarization camera. The proposed methodology has the advantage of real-time and higher precision sensing applications. The short-term stability of the measurement using the ellipsometric parameters tanψ and cos(Δ) is found to be superior over direct SPR or intensity measurements, particularly with fluctuating sources such as laser diodes. Refractive index and dynamic change measurements in real-time are presented together with Bovine Serum Albumin (BSA)-anti-BSA antibody binding to demonstrate the potential of the developed sensor for biological sensing applications with a resolution of sub-nM and down to pM with additional optimization. The analysis shows that this approach may provide the ultimate detection limit for SPR sensors.

Surface Plasmon Resonance Imaging Sensor for Detection of Photolytically and Photocatalytically Degraded Glyphosate

Glyphosate is one of the most widely used pesticides, which, together with its primary metabolite aminomethylphosphonic acid, remains present in the environment. Many technologies have been developed to reduce glyphosate amounts in water. Among them, heterogeneous photocatalysis with titanium dioxide as a commonly used photocatalyst achieves high removal efficiency. Nevertheless, glyphosate is often converted to organic intermediates during its degradation. The detection of degraded glyphosate and emerging products is, therefore, an important element of research in terms of disposal methods. Attention is being paid to new sensors enabling the fast detection of glyphosate and its degradation products, which would allow the monitoring of its removal process in real time. The surface plasmon resonance imaging (SPRi) method is a promising technique for sensing emerging pollutants in water. The aim of this work was to design, create, and test an SPRi biosensor suitable for the detection of glyphosate during photolytic and photocatalytic experiments focused on its degradation. Cytochrome P450 and TiO₂ were selected as the detection molecules. We developed a sensor for the detection of the target molecules with a low molecular weight for monitoring the process of glyphosate degradation, which could be applied in a flow-through arrangement and thus detect changes taking place in real-time. We believe that SPRi sensing could be widely used in the study of xenobiotic removal from surface water or wastewater.

Boosting the detection performance of severe acute respiratory syndrome coronavirus 2 test through a sensitive optical biosensor with new superior antibody

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus emerged in late 2019 leading to the COVID-19 disease pandemic that triggered socioeconomic turmoil worldwide. A precise, prompt, and affordable diagnostic assay is essential for the detection of SARS-CoV-2 as well as its variants. Antibody against SARS-CoV-2 spike (S) protein was reported as a suitable strategy for therapy and diagnosis of COVID-19. We, therefore, developed a quick and precise phase-sensitive surface plasmon resonance (PS-SPR) biosensor integrated with a novel generated anti-S monoclonal antibody (S-mAb). Our results indicated that the newly generated S-mAb could detect the original SARS-CoV-2 strain along with its variants. In addition, a SARS-CoV-2 pseudovirus, which could be processed in BSL-2 facility was generated for evaluation of sensitivity and specificity of the assays including PS-SPR, homemade target-captured ELISA, spike rapid antigen test (SRAT), and quantitative reverse transcription polymerase chain reaction (qRT-PCR). Experimentally, PS-SPR exerted high sensitivity to detect SARS-CoV-2 pseudovirus at 589 copies/ml, with 7-fold and 70-fold increase in sensitivity when compared with the two conventional immunoassays, including homemade target-captured ELISA (4 × 103 copies/ml) and SRAT (4 × 104 copies/ml), using the identical antibody. Moreover, the PS-SPR was applied in the measurement of mimic clinical samples containing the SARS-CoV-2 pseudovirus mixed with nasal mucosa. The detection limit of PS-SPR is calculated to be 1725 copies/ml, which has higher accuracy than homemade target-captured ELISA (4 × 104 copies/ml) and SRAT (4 × 105 copies/ml) and is comparable with qRT-PCR (1250 copies/ml). Finally, the ability of PS-SPR to detect SARS-CoV-2 in real clinical specimens was further demonstrated, and the assay time was less than 10 min. Taken together, our results indicate that this novel S-mAb integrated into PS-SPR biosensor demonstrates high sensitivity and is time-saving in SARS-CoV-2 virus detection. This study suggests that incorporation of a high specific recognizer in SPR biosensor is an alternative strategy that could be applied in developing other emerging or re-emerging pathogenic detection platforms.

Easy and Rapid Approach to Obtaining the Binding Affinity of Biomolecular Interactions Based on the Deep Learning Boost

Recently, the deep learning (DL) dimension of artificial intelligence has received much attention from biochemical researchers and thus has gradually become the key approach adopted in the area of biosensing applications. Studies have shown that the use of DL techniques for sensing can not only shorten the time of data analysis but also significantly increase the accuracy of data analysis and prediction, resulting in the performance improvement of biosensing systems in comparison to conventional methods. However, obtaining reliable equilibrium and rate constants of biomolecular interactions during the detection process remains difficult and time-consuming to date. In this study, we propose a transformed model based on the deep transfer learning and sequence-to-sequence autoencoder that can successfully transfer the SPR sensorgram to the protein-binding constants, that is, the association rate constant (k_a) and dissociation rate constant (k_d), which provide crucial information to understand the mechanisms of drug action and the functional structures of biomolecules. Experimentally, we first trained and tested the pre-trained model using the Langmuir model which generated ideal SPR sensorgrams and then we fine-tuned the pre-trained model through the augmented SPR sensorgrams which were synthesized by using the synthesized minority oversampling technique (SMOTE) through the moderate-scale experiment. Next, the fine-tuned model was inputted with a short experimental SPR sensorgram that only needs 110 s, and the sensorgram was directly transformed into a reconstructed ideal sensorgram. Finally, the binding kinetic constants, that is, k_a and k_d, as outputs, were obtained through fitting the reconstructed ideal sensorgram. The results showed that the prediction errors of k_a and k_d obtained by our model were less than 12 and 24%, respectively. Based on the convenience, accuracy, and reliability of the proposed DL approach, we believe our strategy significantly boosts the feasibility to monitor the binding affinity of antibodies online during production.