Perspectives on label-free microscopy of heterogeneous and dynamic biological systems.

Significance: Advancements in label-free microscopy could provide real-time, non-invasive imaging with unique sources of contrast and automated standardized analysis to characterize heterogeneous and dynamic biological processes. These tools would overcome challenges with widely used methods that are destructive (e.g., histology, flow cytometry) or lack cellular resolution (e.g., plate-based assays, whole animal bioluminescence imaging). Aim: This perspective aims to (1) justify the need for label-free microscopy to track heterogeneous cellular functions over time and space within unperturbed systems and (2) recommend improvements regarding instrumentation, image analysis, and image interpretation to address these needs. Approach: Three key research areas (cancer research, autoimmune disease, and tissue and cell engineering) are considered to support the need for label-free microscopy to characterize heterogeneity and dynamics within biological systems. Based on the strengths (e.g., multiple sources of molecular contrast, non-invasive monitoring) and weaknesses (e.g., imaging depth, image interpretation) of several label-free microscopy modalities, improvements for future imaging systems are recommended. Conclusion: Improvements in instrumentation including strategies that increase resolution and imaging speed, standardization and centralization of image analysis tools, and robust data validation and interpretation will expand the applications of label-free microscopy to study heterogeneous and dynamic biological systems.

Enzyme-powered, label-free DNA walker for Uracil-DNA glycosylase detection at single-cell level.

Uracil-DNA glycosylase (UDG) plays a crucial role in the removal of damaged uracil bases, thereby upholding genetic stability and integrity. An enzyme-powered, label-free DNA walker was devised for UDG activity detection. Initially, a label-free DNA track, incorporating a gold nanoparticle (AuNP), multiple hairpin structures, and various swing arms, was engineered for walking mechanism. The hairpin structure was meticulously crafted to include a G-quadruplex sequence, enabling the generation of a label-free fluorescence signal. The swing arm remained inert in the absence of UDG, but became activated upon the introduction of UDG, thereby initiating the enzyme-powered walking process and generating significant dissociative G-quadruplex sequences. By integrating a selective fluorescent dye into the design, an enhanced label-free fluorescence response was achieved. The proposed DNA walker presented a direct and label-free approach for UDG detection, demonstrating exceptional sensitivity with a detection limit of 0.00004 U/mL. Using the uracil glycosylase inhibitor (UGI) as an inhibitory model, inhibitor assay was conducted with satisfactory precision. Furthermore, successful analysis of cellular UDG at the single-cell level was accomplished. Consequently, the developed DNA walker serves as a label-free, selective, and sensitive tool for UDG activity assessment, showing great potential for applications in disease diagnosis, inhibitor screening, and biomedical investigations.

Label-free three-dimensional imaging and quantitative analysis of living fibroblasts and myofibroblasts by holotomographic microscopy.

Holotomography (HT) is a cutting-edge fast live-cell quantitative label-free imaging technique. Based on the principle of quantitative phase imaging, it combines holography and tomography to record a three-dimensional map of the refractive index, used as intrinsic optical and quantitative imaging contrast parameter of biological samples, at a sub-micrometer spatial resolution. In this study HT has been employed for the first time to analyze the changes of fibroblasts differentiating towards myofibroblasts - recognized as the main cell player of fibrosis - when cultured in vitro with the pro-fibrotic factor, namely transforming growth factor-ß1. In parallel, F-actin, vinculin, α-smooth muscle actin, phospho-myosin light chain 2, type-1 collagen, peroxisome proliferator-activated receptor-gamma coactivator-1α expression and mitochondria were evaluated by confocal laser scanning microscopy. Plasmamembrane passive properties and transient receptor potential canonical channels' currents were also recorded by whole-cell patch-clamp. The fluorescence images and electrophysiological results have been compared to the data obtained by HT and their congruence has been discussed. HT turned out to be a valid approach to morphologically distinguish fibroblasts from well differentiated myofibroblasts while obtaining objective measures concerning volume, surface area, projection area, surface index and dry mass (i.e., the mass of the non-aqueous content inside the cell including proteins and subcellular organelles) of the entire cell, nuclei and nucleoli with the major advantage to monitor outer and inner features in living cells in a non-invasive, rapid and label-free approach. HT might open up new research opportunities in the field of fibrotic diseases. RESEARCH HIGHLIGHTS: Holotomography (HT) is a label-free laser interferometric imaging technology exploiting the intrinsic optical property of cells namely refractive index (RI) to enable a direct imaging and analysis of whole cells or intracellular organelles. HT turned out a valid approach to distinguish morphological features of living unlabeled fibroblasts from differentiated myofibroblasts. HT provided quantitative information concerning volume, surface area, projection area, surface index and dry mass of the entire fibroblasts/myofibroblasts, nuclei and nucleoli.

Label-free electrochemical biosensor based on dual amplification of gold nanoparticles and polycaprolactones for CEA detection.

Carcinoembryonic Antigen (CEA), an acidic glycoprotein with human embryonic antigen properties, is found on the surface of cancer cells that have differentiated from endodermal cells. This paper presents a label-free electrochemical immunoassay for the dual amplification detection of CEA using gold nanoparticles loaded with polypyrrole polydopamine (Au/PPy-PDA) and polymerized polycaprolactone (Ng-PCL) prepared by ring-opening polymerization (ROP). First, the composite Au/PPy-PDA was adhered to the electrode surface. Then, gold nanoparticles form a Au-S bond with the sulfhydryl group in Apt1 to secure it on the electrode surface. Subsequently, the non-specific binding sites on the electrodes surface are closed by bovine serum albumin (BSA). Next, CEA is dropped onto the electrode surface, which is immobilized by antigen-antibody specific recognition, and the carboxyl-functionalized Apt2 forms a "sandwich structure" of antibody-antigen-antibody by specific recognition. Polymeric Ng-PCL is adhered to the electrode surface, leading to an increase in the electrochemical impedance signal, resulting in a complete chain of signal analysis. Finally, the response signal is detected by electrochemical impedance spectroscopy (EIS). Under optimal experimental conditions, the method has the advantages of high sensitivity and wide linear range (1 pg mL-1∼100 ng mL-1), and the lower limit of detection (LOD) is 0.234 pg mL-1. And it has the same high sensitivity, selectivity and interference resistance for the real samples detection. Thus, it provides a new way of thinking about biomedical and clinical diagnosis.

Label-free and ultrasensitive detection of environmental lead ions based on spatially localized DNA nanomachines driven by hyperbranched hybridization chain reaction.

A label-free fluorescent sensing strategy for the rapid and highly sensitive detection of Pb2+ was developed by integrating Pb2+ DNAzyme-specific cleavage activity and a tetrahedral DNA nanostructure (TDN)-enhanced hyperbranched hybridization chain reaction (hHCR). This strategy provides accelerated reaction rates because of the highly effective collision probability and enriched local concentrations from the spatial confinement of the TDN, thus showing a higher detection sensitivity and a more rapid detection process. Moreover, a hairpin probe based on a G-triplex instead of a G-quadruplex or chemical modification makes hybridization chain reaction more controlled and flexible, greatly improving signal amplification capacities and eliminating labeled DNA probes. The enhanced reaction rates and improved signal amplification efficiency endowed the biosensors with high sensitivity and a rapid response. The label-free detection of Pb2+ based on G-triplex combined with thioflavin T can be achieved with a detection limit as low as 1.8 pM in 25 min. The proposed Pb2+-sensing platform was also demonstrated to be applicable for Pb2+ detection in tap water, river water, shrimp, rice, and soil samples, thus showing great potential for food safety and environmental monitoring.

Label-free proteomic analysis reveals the hepatoprotective mechanism of gypenosides in liver injury rats.

Chronic liver disease, a long-term condition resulting from various causes such as alcohol abuse, metabolic disorders, and viral hepatitis, is becoming a significant global health challenge. Gypenosides (GPs), derived from the traditional Chinese medicine Gynostemma pentaphyllum (Thunb.) Makino, exhibited hepatoprotective properties in recent years, yet the precise therapeutic mechanism remains unclear. In this study, label-free and parallel reaction monitoring (PRM) proteomics were used to elucidate the hepatoprotective mechanism of GPs in liver injury rats. Through label-free proteomics, we identified 2104 differentially expressed proteins (DEPs) associated with liver injury, along with 1974 DEPs related to the effects of GPs. Bioinformatics analysis revealed that GPs primarily restored metabolic processes involving valine, leucine, and isoleucine degradation, as well as propanoate and butanoate metabolism, and steroid hormone biosynthesis during liver injury. Subsequently, overlapping the two groups of DEPs identified 1508 proteins reversed following GPs treatment, with key targets further validated by PRM. Eight target proteins were identified for GPs treatment of liver injury, including Lgals3, Psat1, Phgdh, Cyp3a9, Cyp2c11, Cyp4a2, Glul, and Ces1d. These findings not only elucidated the hepatoprotective mechanism of GPs, but may also serve as potential therapeutic targets of chronic liver disease.

From Genotype to Phenotype: Raman Spectroscopy and Machine Learning for Label-Free Single-Cell Analysis.

Raman spectroscopy has made significant progress in biosensing and clinical research. Here, we describe how surface-enhanced Raman spectroscopy (SERS) assisted with machine learning (ML) can expand its capabilities to enable interpretable insights into the transcriptome, proteome, and metabolome at the single-cell level. We first review how advances in nanophotonics-including plasmonics, metamaterials, and metasurfaces-enhance Raman scattering for rapid, strong label-free spectroscopy. We then discuss ML approaches for precise and interpretable spectral analysis, including neural networks, perturbation and gradient algorithms, and transfer learning. We provide illustrative examples of single-cell Raman phenotyping using nanophotonics and ML, including bacterial antibiotic susceptibility predictions, stem cell expression profiles, cancer diagnostics, and immunotherapy efficacy and toxicity predictions. Lastly, we discuss exciting prospects for the future of single-cell Raman spectroscopy, including Raman instrumentation, self-driving laboratories, Raman data banks, and machine learning for uncovering biological insights.

METAPHOR: Metabolic evaluation through phasor-based hyperspectral imaging and organelle recognition for mouse blastocysts and oocytes.

Only 30% of embryos from in vitro fertilized oocytes successfully implant and develop to term, leading to repeated transfer cycles. To reduce time-to-pregnancy and stress for patients, there is a need for a diagnostic tool to better select embryos and oocytes based on their physiology. The current standard employs brightfield imaging, which provides limited physiological information. Here, we introduce METAPHOR: Metabolic Evaluation through Phasor-based Hyperspectral Imaging and Organelle Recognition. This non-invasive, label-free imaging method combines two-photon illumination and AI to deliver the metabolic profile of embryos and oocytes based on intrinsic autofluorescence signals. We used it to classify i) mouse blastocysts cultured under standard conditions or with depletion of selected metabolites (glucose, pyruvate, lactate); and ii) oocytes from young and old mouse females, or in vitro-aged oocytes. The imaging process was safe for blastocysts and oocytes. The METAPHOR classification of control vs. metabolites-depleted embryos reached an area under the ROC curve (AUC) of 93.7%, compared to 51% achieved for human grading using brightfield imaging. The binary classification of young vs. old/in vitro-aged oocytes and their blastulation prediction using METAPHOR reached an AUC of 96.2% and 82.2%, respectively. Finally, organelle recognition and segmentation based on the flavin adenine dinucleotide signal revealed that quantification of mitochondria size and distribution can be used as a biomarker to classify oocytes and embryos. The performance and safety of the method highlight the accuracy of noninvasive metabolic imaging as a complementary approach to evaluate oocytes and embryos based on their physiology.

Label-free and highly sensitive detection of aflatoxin B1 by Ag IANPs via surface-enhanced Raman spectroscopy.

Aflatoxin B1 (AFB1), a pernicious constituent of the aflatoxin family, predominantly contaminates cereals, oils, and their derivatives. Acknowledged as a Class I carcinogen by the World Health Organization (WHO), the expeditious and quantitative discernment of AFB1 remains imperative. This investigation delineates that aluminum ions can precipitate the coalescence of iodine-modified silver nanoparticles, thereby engendering hot spots conducive for label-free AFB1 identification via Surface-Enhanced Raman Spectroscopy (SERS). This methodology manifests a remarkable limit of detection (LOD) at 0.47 fg/mL, surpassing the sensitivity thresholds of conventional survey techniques. Moreover, this method has good anti-interference ability, with a relative error of less than 10% and a relative standard deviation of less than 6% in quantitative results. Collectively, these findings illuminate the substantial application potential and viability of this approach in the quantitative analysis of AFB1, underpinning a significant advancement in food safety diagnostics.

Design, synthesis, antimicrobial activity, and mechanism of novel 3-(2,4-dichlorophenyl)-[1,2,4]triazolo[3,4-b][1,3,4]thiadiazole derivatives.

BACKGROUND: Plant pathogens cause substantial crop losses annually, posing a grave threat to global food security. Fungicides have usually been used for their control, but the rapid development of pesticide resistance renders many ineffective, therefore the search for novel and efficient green pesticides to prevent and control plant diseases has become the top priority in crop planting. RESULTS: The results of bioassay studies indicated that most of the target compounds showed certain antimicrobial activity in vitro. In particular, compound X7 showed high inhibitory activity against Xanthomonas oryzae pv. oryzae (Xoo), with an EC50 value of 27.47 µg mL-1, surpassing conventional control agents such as thiazole zinc (41.55 µg mL-1) and thiodiazole copper (53.39 µg mL-1). Further studies on molecular docking showed that X7 had a strong binding affinity with 2FBW. The morphological change observed by scanning electron microscopy indicated that the surface of Xoo appears wrinkled and cracked under X7 treatment and a total of 2662 proteins were identified by label-free proteomic analysis. Three experiments have elucidated the mechanism whereby X7 induced considerable changes in the physiological and biochemical properties of Xoo, which in turn affected the reproduction and growth of bacteria. CONCLUSION: This work represents a pivotal advancement, offering important reference for the research and development therapeutics in combating plant pathogens. © 2024 Society of Chemical Industry.

Label-Free Sample Preparation for Single-Cell Proteomics.

In recent years, single-cell proteomics (SCP) has become a valuable addition to other single-cell omics technologies for studying cellular heterogeneity. The amount of protein in a single cell is very limited, and in contrast to sequencing techniques, there are currently no means for protein amplification. Therefore, most single-cell proteomics approaches aim to maximize sample preparation efficiency while minimizing peptide loss. By reducing processing volumes to sub-microliters and avoiding manual transfer steps that could lead to peptide loss, peptide recovery, and the robustness of SCP workflows have been significantly improved. In this chapter, we describe a protocol for label-free SCP sample preparation using the cellenONE® platform and the proteoCHIP LF 48 substrate prior to analysis with high-performance liquid chromatography-mass spectrometry.

Efficient and Sensitive Sample Preparation, Separations, and Data Acquisition for Label-Free Single-Cell Proteomics.

We describe a sensitive and efficient workflow for label-free single-cell proteomics that spans sample preparation, liquid chromatography separations, and mass spectrometry data acquisition. The Tecan Uno Single Cell Dispenser provides rapid cell isolation and nanoliter-volume reagent dispensing within 384-well PCR plates. A newly developed sample processing workflow achieves cell lysis, protein denaturation, and digestion in 1 h with a single reagent dispensing step. Low-flow liquid chromatography coupled with wide-window data-dependent acquisition results in the quantification of nearly 3000 proteins per cell using an Orbitrap Exploris 480 mass spectrometer. This approach greatly broadens accessibility to sensitive single-cell proteome profiling for nonspecialist laboratories.

Compact Linnik-type hyperspectral quantitative phase microscope for advanced classification of cellular components.

Hyperspectral quantitative phase microscopy (HS-QPM) involves the acquisition of phase images across narrow spectral bands, which enables wavelength-dependent study of different biological samples. In the present work, a compact Linnik-type HS-QPM system is developed to reduce the instability and complexity associated with conventional HS-QPM techniques. The use of a single objective lens for both reference and sample arms makes the setup compact. The capabilities of the system are demonstrated by evaluating the HS phase map of both industrial and biological specimens. Phase maps of exfoliated cheek cells at different wavelengths are stacked to form a HS phase cube, adding spectral dimensionality to spatial phase distribution. Analysis of wavelength response of different cellular components are performed using principal component analysis to identify dominant spectral features present in the HS phase dataset. Findings of the study emphasize on the efficiency and effectiveness of HS-QPM for advancing cellular characterization in biomedical research and clinical applications.

2D Materials in Advanced Electronic Biosensors for Point-of-Care Devices.

Since two-dimensionalal (2D) materials have distinct chemical and physical properties, they are widely used in various sectors of modern technologies. In the domain of diagnostic biodevices, particularly for point-of-care (PoC) biomedical diagnostics, 2D-based field-effect transistor biosensors (bio-FETs) demonstrate substantial potential. Here, in this review article, the operational mechanisms and detection capabilities of biosensing devices utilizing graphene, transition metal dichalcogenides (TMDCs), black phosphorus, and other 2D materials are addressed in detail. The incorporation of these materials into FET-based biosensors offers significant advantages, including low detection limits (LOD), real-time monitoring, label-free diagnosis, and exceptional selectivity. The review also highlights the diverse applications of these biosensors, ranging from conventional to wearable devices, underscoring the versatility of 2D material-based FET devices. Additionally, the review provides a comprehensive assessment of the limitations and challenges faced by these devices, along with insights into future prospects and advancements. Notably, a detailed comparison of FET-based biosensors is tabulated along with various other biosensing platforms and their working mechanisms. Ultimately, this review aims to stimulate further research and innovation in this field while educating the scientific community about the latest advancements in 2D materials-based biosensors.

Integrating UPLC-MS/MS with in Silico and in Vitro Screening Accelerates the Discovery of Active Compounds in Stephania epigaea.

Traditional Chinese medicines (TCMs) are popular in clinic because of their safety and efficacy. They contain abundant natural active compounds, which are important sources of new drug discovery. However, how to efficiently identify active compounds from complex ingredients remains a challenge. In this study, a method combining UHPLC-MS/MS characterization and in silico screening was developed to discover compounds with dopamine D2 receptor (D2R) activity in Stephania epigaea (S. epigaea). By combining the compounds identified in S. epigaea by UHPLC-MS/MS with reported compounds, a virtual library of 80 compounds was constructed for in silico screening. Potentially active compounds were chosen based on screening scores and subsequently tested for in vitro activity on a transfected cell line CHO-K1-D2 model using label-free cellular phenotypic assay. Three D2R agonists and five D2R antagonists were identified. (-)-Asimilobine, N-nornuciferine and (-)-roemerine were reported for the first time as D2R agonists, with EC50 values of 0.35 ± 0.04 µM, 1.37 ± 0.10 µM and 0.82 ± 0.22 µM, respectively. Their target specificity was validated by desensitization and antagonism assay. (-)-Isocorypalmine, (-)-tetrahydropalmatine, (-)-discretine, (+)-corydaline and (-)-roemeroline showed strong antagonistic activity on D2R with IC50 values of 92 ± 9.9 nM, 1.73 ± 0.13 µM, 0.34 ± 0.02 µM, 2.09 ± 0.22 µM and 0.85 ± 0.08 µM, respectively. Their kinetic binding profiles were characterized using co-stimulation assay and they were both D2R competitive antagonists. We docked these ligands with human D2R crystal structure and analyzed the structure-activity relationship of aporphine-type D2R agonists and protoberberine-type D2R antagonists. These results would help to elucidate the mechanism of action of S. epigaea for its analgesic and sedative efficacy and benefit for D2R drug design. This study demonstrated the potential of integrating UHPLC-MS/MS with in silico and in vitro screening for accelerating the discovery of active compounds from TCMs.

A label-free fluorescence aptamer sensor for point-of-care serotonin detection.

Serotonin, a pivotal neurotransmitter regulating various physiological functions, plays a crucial role in disease diagnosis, necessitating precise monitoring of its levels in biological fluids for accurate assessment. Aptamers, known for their high specificity and affinity, have emerged as innovative molecular probes for serotonin analysis. However, existing serotonin aptamer sensing platforms exhibit limitations in terms of portability and rapid detection capabilities. In this study, we introduce a novel, portable, label-free serotonin aptamer sensor utilizing a dye replacement strategy, achieving a short sample-to-result turnaround time and convenient signal readout through a smartphone. The performance of this aptamer sensor was thoroughly assessed across diverse physiological media, demonstrating robust stability in buffer, urine, and serum. Importantly, the detection limit was in the nanomolar range, emphasizing its suitability for the rapid, sensitive, and user-friendly detection of serotonin. This research pioneers an approach for the development of a point-of-care testing (POCT) system for serotonin with practical implications, particularly in resource-limited settings.

Label-free white blood cells classification using a deep feature fusion neural network.

White blood cell (WBC) classification is a valuable diagnostic approach for identifying diseases. However, conventional methods for WBC detection, such as flow cytometers, have limitations in terms of their high cost, large system size, and laborious staining procedures. As a result, deep learning-based label-free WBC image analysis methods are gaining popularity. Nevertheless, most existing deep learning WBC classification techniques fail to effectively utilize the subtle differences in the internal structures of WBCs observed under a microscope. To address this issue, we propose a neural network with feature fusion in this study, which enables the detection of label-free WBCs. Unlike conventional convolutional neural networks (CNNs), our approach combines low-level features extracted by shallow layers with high-level features extracted by deep layers, generating fused features for accurate bright-field WBC identification. Our method achieves an accuracy of 80.3 % on the testing set, demonstrating a potential solution for deep-learning-based biomedical diagnoses. Considering the proposed method simplifies the cell detection process and eliminates the need for complex operations like fluorescent staining, we anticipate that this automatic and label-free WBC classification network could facilitate more precise and effective analysis, and it could contribute to the future adoption of miniatured flow cytometers for point-of-care (POC) diagnostics applications.

Information-Distilled Generative Label-Free Morphological Profiling Encodes Cellular Heterogeneity.

Image-based cytometry faces challenges due to technical variations arising from different experimental batches and conditions, such as differences in instrument configurations or image acquisition protocols, impeding genuine biological interpretation of cell morphology. Existing solutions, often necessitating extensive pre-existing data knowledge or control samples across batches, have proved limited, especially with complex cell image data. To overcome this, "Cyto-Morphology Adversarial Distillation" (CytoMAD), a self-supervised multi-task learning strategy that distills biologically relevant cellular morphological information from batch variations, is introduced to enable integrated analysis across multiple data batches without complex data assumptions or extensive manual annotation. Unique to CytoMAD is its "morphology distillation", symbiotically paired with deep-learning image-contrast translation-offering additional interpretable insights into label-free cell morphology. The versatile efficacy of CytoMAD is demonstrated in augmenting the power of biophysical imaging cytometry. It allows integrated label-free classification of human lung cancer cell types and accurately recapitulates their progressive drug responses, even when trained without the drug concentration information. CytoMAD  also allows joint analysis of tumor biophysical cellular heterogeneity, linked to epithelial-mesenchymal plasticity, that standard fluorescence markers overlook. CytoMAD can substantiate the wide adoption of biophysical cytometry for cost-effective diagnosis and screening.

Target-promoted autocatalytic hairpin assembly of bivalent DNAzymes for sensitive and label-free electrochemical metallothionein assay.

Metallothionein (MT) has shown to be an important biomarker for environmental monitoring and various diseases, due to its significant binding ability to heavy metal ions. On the basis of such a characteristic and the Hg2+-stabilized DNA duplex (Hg2+-dsDNA) probe, as well as a new autocatalytic hairpin assembly (aCHA)/DNAzyme cascaded signal enhancement strategy, the construction of a highly sensitive and label-free electrochemical MT biosensor is described. Target MT molecules bind Hg2+ in Hg2+-dsDNA to disrupt the duplex structure and to release ssDNA sequences, which trigger subsequent aCHA for efficient production of mimic aCHA triggering strands and many bivalent DNAzymes. The signal hairpins on the electrode are then cyclically cleaved by DNAzyme amplification cascade to liberate plenty G-quadruplex sequences, which bind hemin and yield largely enhanced currents for sensitive assay of MT with a detection limit of 0.217 nM in a label-free approach. Such sensor also shows selective discrimination capability to MT against other interfering proteins and assay of MT in normal serums with dilution has also been verified, indicating its potential for highly sensitive detection of different heavy metal ion binding molecules for various application scenarios.

Highly sensitive and label-free protein immunoassay-based biosensor comprising infrared metamaterial absorber inducing strong coupling.

A mid-infrared label-free immunoassay-based biosensor is an effective device to help identify and quantify biomolecules. This biosensor employs a surface-enhanced infrared absorption spectroscopy, which is a highly potent sensing technique for detecting minute quantities of analytes. In this study, a biosensor was constructed using a metamaterial absorber, which facilitated strong coupling effects. For maximum coupling effect, it is necessary to enhance the near-field intensity and the spatial and spectral overlap between the optical cavity resonance and the vibrational mode of the analyte. Due to significant peak splitting, conventional baseline correction methods fail to adequately analyze such a coupling system. Therefore, we employed a coupled harmonic oscillation model to analyze the spectral distortion resulting from the peak splitting induced by the strong coupling effect. The proposed biosensor with a thrombin-binding aptamer-based immunoassay could achieve a limit of detection of 267.4 pM, paving the way for more efficient protein detection in clinical practice.