A versatile, rapid Agrobacterium-mediated transient expression system for functional genomics studies in cannabis seedling.

We have developed and optimized a rapid, versatile Agrobacterium-mediated transient expression system for cannabis seedlings that can be used in functional genomics studies of both hemp-type and drug-type cannabis. Cannabis (Cannabis sativa L.) holds great promise in the medical and food industries due to its diverse chemical composition, including specialized cannabinoids.

Deep learning-based quantification and transcriptomic profiling reveal a methyl jasmonate-mediated glandular trichome formation pathway in Cannabis sativa.

Cannabis glandular trichomes (GTs) are economically and biotechnologically important structures that have a remarkable morphology and capacity to produce, store, and secrete diverse classes of secondary metabolites. However, our understanding of the developmental changes and the underlying molecular processes involved in cannabis GT development is limited. In this study, we developed Cannabis Glandular Trichome Detection Model (CGTDM), a deep learning-based model capable of differentiating and quantifying three types of cannabis GTs with a high degree of efficiency and accuracy.