Abstract
Huanglongbing (citrus greening disease) is caused by the bacterium 'Candidatus Liberibacter asiaticus' (CLas) (Alphaproteobacteria) and is one of the most destructive bacterial-vector diseases affecting the citrus industry. The bacterium is transmitted by the Asian citrus psyllid (ACP; Diaphorina citri). Early detection in citrus trees is challenging due to uneven distribution of CLas throughout the tree and a long pre-symptomatic phase of the disease. Due to these limitations, ACP sampling has been suggested as a more reliable early detection strategy. The objective of this study was to develop and optimize approaches for detecting CLas in ACP adults and nymphs collected in citrus groves in California using real-time quantitative PCR (qPCR) and droplet digital PCR (ddPCR). The goal was to establish the optimal number of ACP adults and nymphal instar life stages (stages 1-2, 3, or 4-5) that yielded the most reliable detection of CLas (Cq values ≤ 38). Results indicated that CLas detection correlated with psyllid developmental stage, with the 4th-5th instar nymphs (sample size of five to ten per tube) or adult ACP (sample size of three to ten per tube) providing the most consistent qPCR detection. While CLas detection rates increased with adult ACP age, nymphs were preferred for field sampling as adult ACP might have dispersed from non-infected trees, potentially misrepresenting the grove's CLas status. Detection by droplet digital PCR confirmed the presence and genome copies of CLas in a subset of ACP across life stages. In field populations, detection rates in nymphs were consistent or stable throughout the year, whereas CLas detection in adults exhibited seasonal variation, with CLas detection and genome load peaking in January. These targeted ACP sampling strategies and optimized laboratory processing methods will facilitate CLas detection in psyllids for streamlining citrus greening disease management.