Asymmetric LAMP–gold nanoparticle biosensing for rapid detection of Kenyan tomato leaf curl virus isolates from crude extracts

Abstract

Tomato leaf curl virus (ToLCV) is a significant constraint to tomato production, particularly in resource-limited agricultural settings where access to sensitive, decentralized diagnostic tools is limited. While PCR-based detection methods require specialized equipment and exhibit reduced sensitivity when applied to crude field samples, many isothermal methods generate double stranded amplicons that are suboptimal for downstream signal transduction. To address these issues, an asymmetric loop-mediated isothermal amplification (ASYLAMP) strategy was developed to preferentially generate single-stranded amplicons compatible with gold nanoparticle (AuNP) hybridization, enabling rapid visual detection. The optimized ASYLAMP assay achieved shorter time-to-positive results compared to symmetric LAMP while maintaining equivalent specificity, as supported by melt curve analysis and statistical comparison. The assay exhibited a limit of detection of 0.0008 fg µL⁻¹ and showed no cross-reactivity with other tomato-infecting viruses. Successful AuNP functionalization and target hybridization were confirmed by characteristic plasmonic shifts in UV-visible spectra. Evaluation of 79 field samples using crude DNA extracts demonstrated that the ASYLAMP–AuNP biosensor detected more positives than PCR, with all biosensor-positive samples independently confirmed by real-time qPCR. The biosensor demonstrated high diagnostic sensitivity and specificity, substantial agreement with the gold standard (κ = 0.92), and strong discriminatory performance (binary ROC AUC = 0.96), supporting its reliability as a qualitative diagnostic tool. This study introduces a proof-of-concept chemically integrated ASYLAMP–AuNP biosensor combining high sensitivity, specificity, and analytical performance. By aligning amplification chemistry with plasmonic signal transduction, the platform offers a potential diagnostic solution for plant virus surveillance in resource-constrained settings.