Molecular marker discovery and detection for blinding eye disease

Abstract

Accurate, sensitive, and specific detection of molecular markers in intraocular fluid will facilitate the early discovery, diagnosis, and intervention of eye diseases. In this study, a total of 168 participants were recruited and divided into two distinct cohorts: discovery and verification. In the discovery phase, proteomic analysis identified MCP-1 in aqueous humor as a potential molecular marker for blinding eye disease. We further developed a molecular detection technology for the marker based on biolayer interference sensing. The technology utilizes a sandwich strategy with one-to-one pairing of two different biorecognition molecules for MCP-1. It also incorporates automation, high throughput, and real-time monitoring, achieving highly selective recognition and accurate analysis of MCP-1. It demonstrates a low detection limit (0.16 pM), good reliability (R² = 0.995), and a wide analytical range (0.244-1000 pM) for MCP-1 in human aqueous humor samples. Crucially, in the verification phase with 150 subjects, the technology achieved a high detection rate (95.0%) for patients with age-related macular degeneration and high myopia cataract in under 30 min, and was able to further differentiate between them with a specificity of 86.0%. Therefore, the developed molecular detection technology may provide a robust, convenient, and valuable solution for widespread screening, early discovery, and differential diagnosis of blinding eye diseases.

Keywords: Biorecognition molecules; Blinding eye diseases; Molecular detection technology; Molecular markers.

Scheme 1

Workflow of a discovery and verification cohort study for early discovery and differential diagnosis of blinding eye diseases

Fig. 1

Screening and identification of markers for blinding eye diseases. (A) Schematic diagram of proteomics analysis of human AH samples. (B) PCA score plot showing clear separation among the AMD, HMC, and NC groups, reflecting distinct protein expression profiles. (C) Volcano plot of differential proteins between the AMD and NC groups, and (D) the HMC and NC groups, with significantly altered proteins defined by a Bonferroni-adjusted P-value < 0.05 and|Log₂ Fold Change| >1. (E) Heatmap depicting the expression patterns of significantly upregulated and downregulated proteins in the AMD vs. NC, and (F) HMC vs. NC comparison. (G) Top 20 most important proteins identified based on SHAP values derived from multiple machine learning algorithms in the AMD vs. NC and (H) HMC vs. NC comparison. (I) Venn diagram highlighting MCP-1 as a vital differential protein identified as a candidate marker in both comparisons (AMD vs. NC and HMC vs. NC)

Fig. 2

MCP-1 aptamer screening and optimization. (A) Schematic diagram of magnetic bead–based SELEX. (B) Melting curve analysis of enriched screening libraries using the QuantStudio™ Real-Time PCR System. (C) Monitoring of ssDNA recovery from screening libraries via Qubit^® fluorometry. (D) Biolayer interferometry analysis of binding strength in the enriched screening libraries. (E, F) Biolayer interferometry assessment of intermolecular interactions of the original aptamers Seq2 and Seq39 with MCP-1. (G, H) Schematic diagram of the folded structures of Seq2 and Seq39 before and after truncation optimization. (I, J) Biolayer interferometry analysis of the affinity and specificity of the truncated aptamers Seq2T and Seq39T for MCP-1

Fig. 3

Dimeric aptamer construction and identification. (A) Schematic diagram of the constructed heterodimeric aptamer DA2TN39T. (B) Biolayer interferometry analysis of the affinity and specificity of DA2TN39T for MCP-1. (C) RF/6A cells were untreated (Ctrl; I) or pretreated with MCP-1 (II) with or without the Seq2T (III), Seq39T (IV), or DA2TN39T (V) for 24 h. Transwell assays were then conducted to assess the migration of RF/6A cells (n = 3; VI). (D) RF/6A cells were untreated (Ctrl; I) or pretreated with MCP-1 (II) with or without the aptamers Seq2T (III), Seq39T (IV), or DA2TN39T (V) for 24 h. Tube formation ability was detected and analyzed (n = 3; VI). *p  0.05, ***p  0.0005, and ****p  0.0001 compared with MCP-1 treatment

Fig. 4

Recognition mechanism analysis. (A) Conformation of the DA2TN39T–MCP-1 complex before and after MD simulations. Variations in RMSD (B), Rg (C), and SASA (D) of MCP-1 and DA2TN39T according to simulation time. (E) Hydrophobic and hydrophilic surface areas of the MCP-1 as a function of MD simulation time. (F) RMSF distributions of MCP-1 and DA2TN39T (G depict flexibility distributions of the corresponding protein and aptamer structures). (H) Patterns of DA2TN39T binding to hydrophilic and hydrophobic surfaces of MCP-1 (blue and orange regions on protein surface represent hydrophilic and hydrophobic regions, respectively). (I) Sites of binding between aptamer bases and MCP-1 amino acid residues (green dashed lines indicate hydrogen bonds). (J) Numbers of hydrogen bonds and (K) binding energies between DA2TN39T and MCP-1 according to MD simulation time

Fig. 5

Construction and principle of molecular detection technology. (A) BLI sandwich assay–based screening of antibodies that bind MCP-1 in conjunction with aptamers. (B) Biolayer interferometry analysis of the binding affinity and targeting specificity of MAb5 for MCP-1. (C) Schematic diagram of molecular detection technology based on antibody–aptamer pairing binding and BLI sensing for MCP-1. (D) Representative scanning electron micrograph of a biosensor with insoluble precipitates on its surface

Fig. 6

Optimization and application of molecular detection technology. Optimization of MAb5 (A) assembly concentration and (B) assembly time. (C) Amount of MCP-1 captured on MAb5-coated sensors. (D) Optimization of HRP–DA2TN39T concentration (****p < 0.0001; ns, no significance) and (E) signal amplification time in the detection system. (F) Time dependence of the response signal within the sensing system after the addition of MCP-1. The signal was recorded in the final step of the assay. (G) Calibration curve for MCP-1, plotting response signal against MCP-1 concentration, in the range of 0.244–1000 pM. Inset: Linear relationship between the signal and MCP-1 concentration (from 0.244 to 62.5 pM). (H) Selectivity of the molecular detection technology (****p < 0.0001 compared with signal induced by MCP-1)

Fig. 7

Molecular detection technology for blinding eye diseases. (A) Schematic representation of sample acquisition, preprocessing, and analysis using the developed molecular detection technology. (B) Correlation coefficients between detection results obtained by ELISA and those obtained using the developed technology among patients with blinding eye diseases. (C) MCP-1 concentrations in AH samples from NC, and AMD, and HMC patients, as measured using our developed detection technology. (D) Receiver operating characteristic curves demonstrating the diagnostic performance of MCP-1 concentrations in blinding eye disease patients. (E) Receiver operating characteristic curve of MCP-1 to discriminate HMC from AMD patients