Addressing Hemolysis-Induced Loss of Sensitivity in Lateral Flow Assays of Blood Samples with Platinum-Coated Gold...

Gold nanoparticles (GNPs), which appear red, are widely used as labels in lateral flow assays (LFAs) for visual detection. However, in blood-derived samples, hemolysis─caused by the rupture of red blood cells and the release of hemoglobin─creates a red-colored background that obscures the test line, significantly raising the limit of detection (LOD) and reducing the diagnostic sensitivity of LFAs. The degree of hemolysis can vary across samples due to differences in blood collection techniques, such as varying finger prick pressure, leading to inconsistent sensitivity. A practical solution to hemolysis-induced sensitivity loss should be easy for LFA manufacturers to implement without complicating the assay's use. We propose modifying the color of GNPs from red to black by coating the nanoparticles with platinum, creating a platinum shell around a gold core (GNPs@Pt). This simple and cost-effective modification produces black nanoparticles, enhancing the contrast between the test line and background in hemolyzed samples. Using manual threshold-based image analysis, (scanning test strips and calculating the signal-to-noise ratio as a reference technique), we found that GNPs@Pt improved the LOD in LFAs of hemolyzed samples, with greater relative improvements observed at higher levels of hemolysis. In experiments assessing visual detection by human subjects, GNPs@Pt significantly outperformed standard GNPs; however, human accuracy in recognizing the test line was consistently lower than that achieved with threshold-based image analysis. To address the impracticality of manual threshold-based image analysis and the unreliability of visual detection, we developed a machine learning (ML) model for analyzing images of the LFA test strips. The integration of GNPs@Pt with ML-based image analysis achieved an assay LOD performance comparable to manual threshold-based image analysis, demonstrating 100% sensitivity and 100% specificity when benchmarked against the latter as a reference standard. Given the ease of substituting GNPs with GNPs@Pt, we recommend adopting this modification for assays involving blood samples. Additionally, we advocate using ML models, which can be integrated into mobile applications, as a standard readout tool for LFAs.