Supporting data for “Digital immunochromatography: A bead-based bilateral zigzag flow immuno-assay with adaptive differential noise correction for rapid and precise protein detection”
The early detection of biomarkers at ultra-low concentrations is critical for diagnosing and managing diseases such as neurodegenerative disorders, and ocular conditions. However, current diagnostic techniques face significant limitations in sensitivity, accuracy, and throughput, especially in point-of-care (POC) settings where rapid and reliable detection is essential. These challenges include inefficient detection of low-abundance biomarkers, high background noise, and the need for complex calibration processes. This thesis addresses these limitations by presenting a novel digital immunochromatography platform that integrates a bead-based Bilateral Zigzag Flow Immunoassay (BiZi-FIA) with an Adaptive Differential Noise Correction (ADNC) algorithm, offering significant advancements in single-molecule diagnostics.
To improve capture efficiency and imaging throughput, the BiZi-FIA platform features a Flow and Trap Junction Array (FT-JA) with a bilateral zigzag flow and high-density grids. This design incorporates inclined trap architectures to enhance microbead packing and alignment, reducing the imaging area to a single frame and drastically improving imaging speed. These improvements allow the platform to achieve a 93% capture efficiency with an array density of 9.7 × 10³ beads/mm² within 180 seconds, significantly enhancing the sampling efficiency and enabling the reliable detection of biomarkers at extremely low concentrations.
To further improve the signal-to-noise ratio and reduce optical noise, the platform replaces magnetic beads with silica beads as capture substrates. Silica beads, with superior optical properties than magnetic beads, enable larger field-of-view imaging. The BiZi-FIA integrates on-chip filtering and washing mechanisms to efficiently remove nonspecific labels and impurities, ensuring only specific immunocomplexes remain on the beads. This mechanism simplifies the workflow by eliminating reliance on magnetic separation and reducing the need for additional manual washing steps, enhancing the suitability for high-throughput and POC applications.
To obtain rapid and precise detection results, the ADNC algorithm was developed based on the BiZi-FIA, which dynamically corrects background noise variations by leveraging differential analysis between test and control samples, enabling precise quantification of biomarkers without reliance on external calibration curves. The ADNC employs gradient descent methods to determine precise signal gradients for rapid concentration calculations, thereby streamlining the workflow for clinical applications.
These innovations resulted in a highly efficient digital immunochromatography platform validated through the detection of three protein biomarkers, namely IgE, LCN-1, and NFL, in laboratory and human tear samples. Specifically, the system achieved a limit of detection (LOD) of 0.013 fM for IgE, 0.12 fM for LCN-1, and 0.81 fM for NFL, the total assay process included an incubation period of 30 minutes, followed by a molecule array and signal measurement time of less than 10 minutes per assay, significantly reducing overall processing time compared to conventional methods. In a preliminary evaluation using 2.2 µL human tear samples diluted in PBS, the system accurately detected and quantified all three biomarkers with such a small volume sample, underscoring its multiplexing capability. These results highlight the excellent potential of the platform for non-invasive disease diagnostics and real-time disease monitoring, offering significant advantages in early detection, personalized medicine, and the management of chronic conditions.
