Deep learning-based high-throughput phenotyping accelerates gene discovery for stomatal traits

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. By profiling at eight different time points, we captured dynamic changes in gene expression, phenotypes, and metabolic processes associated with GT development. By integrating weighted gene co-expression network analysis with CGTDM measurements, we established correlations between phenotypic variations in GT traits and the global transcriptome profiles across the developmental gradient. Notably, we identified a module containing methyl jasmonate (MeJA)-responsive genes that significantly correlated with stalked GT density and cannabinoid content during development, suggesting the existence of a MeJA-mediated GT formation pathway. Our findings were further supported by the successful promotion of GT development in cannabis through exogenous MeJA treatment. Importantly, we have identified CsMYC4 as a key transcription factor that positively regulates GT formation via MeJA signaling in cannabis. These findings provide novel tools for GT detection and counting, as well as valuable information for understanding the molecular regulatory mechanism of GT formation, which has the potential to facilitate the molecular breeding, targeted engineering, informed harvest timing, and manipulation of cannabinoid production.

Plant microphenotype: from innovative imaging to computational analysis

The microphenotype plays a key role in bridging the gap between the genotype and the complex macro phenotype. In this article, we review the advances in data acquisition and the intelligent analysis of plant microphenotyping and present applications of microphenotyping in plant science over the past two decades. We then point out several challenges in this field and suggest that cross-scale image acquisition strategies, powerful artificial intelligence algorithms, advanced genetic analysis, and computational phenotyping need to be established and performed to better understand interactions among genotype, environment, and management. Microphenotyping has entered the era of Microphenotyping 3.0 and will largely advance functional genomics and plant science.

Data-driven approaches to improve water-use efficiency and drought resistance in crop plants

With the increasing population, there lies a pressing demand for food, feed and fibre, while the changing climatic conditions pose severe challenges for agricultural production worldwide. Water is the lifeline for crop production; thus, enhancing crop water-use efficiency (WUE) and improving drought resistance in crop varieties are crucial for overcoming these challenges. Genetically-driven improvements in yield, WUE and drought tolerance traits can buffer the worst effects of climate change on crop production in dry areas. While traditional crop breeding approaches have delivered impressive results in increasing yield, the methods remain time-consuming and are often limited by the existing allelic variation present in the germplasm. Significant advances in breeding and high-throughput omics technologies in parallel with smart agriculture practices have created avenues to dramatically speed up the process of trait improvement by leveraging the vast volumes of genomic and phenotypic data. For example, individual genome and pan-genome assemblies, along with transcriptomic, metabolomic and proteomic data from germplasm collections, characterised at phenotypic levels, could be utilised to identify marker-trait associations and superior haplotypes for crop genetic improvement. In addition, these omics approaches enable the identification of genes involved in pathways leading to the expression of a trait, thereby providing an understanding of the genetic, physiological and biochemical basis of trait variation. These data-driven gene discoveries and validation approaches are essential for crop improvement pipelines, including genomic breeding, speed breeding and gene editing. Herein, we provide an overview of prospects presented using big data-driven approaches (including artificial intelligence and machine learning) to harness new genetic gains for breeding programs and develop drought-tolerant crop varieties with favourable WUE and high-yield potential traits.

Stomata conductance as a goalkeeper for increased photosynthetic efficiency

100-120 words. References should not be included. Abbreviations should be avoided as far as possible. Low stomatal conductance (g_s) poses a major constraint for improving photosynthetic efficiency for greater yield. Options at the molecular, leaf, canopy, and even the whole plant scales can be developed to enhance g_s for greater light and water use efficiencies. Among these, many genes regulating stomatal development and stomatal movement have been discovered and manipulated to increase light and water use efficiencies under well-watered, drought, or facility agriculture conditions with the manual-controlled growth environmental. Optimization of canopy conductance to increase whole plant photosynthesis with full consideration of the heterogeneities in g_s, microclimates and leaf ontology inside the canopy represents a largely uncharted area to improve crop efficiency.