Next-Generation ToBRFV Diagnostics: Chemiluminescence Imaging for Enhanced Sensitivity and On-Site Assessment

Giorgi Shtenberg, Agricultural and Biosystems Engineering , ARO- Volcani Institute, Rishon Lezion, Israel (giorgi@agri.gov.il)
Narsingh R. Nirala, Institute Of Biochemistry, Food Science And Nutrition, The Robert H. Smith Faculty Of Agriculture, Food And Environment, Rehovot, Israel

Globally, the productivity of tomato and pepper protected farming is under threat from the newly discovered tomato brown rugose fruit virus (ToBRFV). The European Union governments have designated this virus as a quarantine organism. Specifically, ToBRFV-free tomato and pepper seeds must be approved for entry into the EU and for sale. As a result, chemiluminescence (CL) hydrogel imaging has become a potent, non-destructive method for very sensitive and specific plant pathogen detection. The use of CL imaging to identify ToBRFV in tomato plants is investigated in this work to visualize infection spatial distribution. The technique targets viral coat proteins using specific antibodies on a hydrogel containing copper ions, which exhibit peroxidase-mimetic behavior that catalyze luminol in the presence of H₂O₂, resulting in CL glowing. Fruit and leaf samples from both healthy and infected tomato plants were examined using a CL-imaging. The emitted photons were recorded by a smartphone camera as an inhibition of the CL signal in the presence of the viral load. Results demonstrated that the technique could detect ToBRFV at early infection stages, even in asymptomatic plants, with a detection limit surpassing traditional methods like ELISA and PCR. The imaging approach provided spatial resolution of viral distribution, aiding in understanding disease progression. Furthermore, the method is rapid, requiring minimal sample preparation, and is adaptable for high-throughput screening in agricultural settings. This study highlights the potential of CL imaging as a transformative tool for ToBRFV detection, offering advantages in sensitivity, speed, and scalability over conventional diagnostic techniques. Its integration into plant pathology could enhance early detection, improve disease management, and support sustainable tomato production.