Early detection of late blight in potato by whole‐plant redox imaging

SUMMARY Late blight caused by the oomycete Phytophthora infestans is a most devastating disease of potatoes (Solanum tuberosum). Its early detection is crucial for suppressing disease spread. Necrotic lesions are normally seen in leaves at 4 days post‐inoculation (dpi) when colonized cells are dead, but early detection of the initial biotrophic growth stage, when the pathogen feeds on living cells, is challenging. Here, the biotrophic growth phase of P. infestans was detected by whole‐plant redox imaging of potato plants expressing chloroplast‐targeted reduction–oxidation sensitive green fluorescent protein (chl‐roGFP2). Clear spots on potato leaves with a lower chl‐roGFP2 oxidation state were detected as early as 2 dpi, before any visual symptoms were recorded. These spots were particularly evident during light‐to‐dark transitions, and reflected the mislocalization of chl‐roGFP2 outside the chloroplasts. Image analysis based on machine learning enabled systematic identification and quantification of spots, and unbiased classification of infected and uninfected leaves in inoculated plants. Comparing redox with chlorophyll fluorescence imaging showed that infected leaf areas that exhibit mislocalized chl‐roGFP2 also showed reduced non‐photochemical quenching and enhanced quantum PSII yield (ΦPSII) compared with the surrounding leaf areas. The data suggest that mislocalization of chloroplast‐targeted proteins is an efficient marker of late blight infection, and demonstrate how it can be utilized for non‐destructive monitoring of the disease biotrophic stage using whole‐plant redox imaging. Significance Statement The late blight disease is the most damaging disease affecting potato plants, triggered by the oomycete plant pathogen Phytophthora infestans. In order to unravel the molecular mechanisms involved in this plant–pathogen interaction, it is important to spatially resolve the early stages of disease development. Using a redox genetically encoded biosensor, redox imaging and an AI algorithm, we develop a monitoring approach that detects infection spots non‐destructively before visual symptoms occur.