Search for: All records

SUMMARY A major challenge in global crop production is mitigating yield loss due to plant diseases. One of the best strategies to control these losses is through breeding for disease resistance. One barrier to the identification of resistance genes is the quantification of disease severity, which is typically based on the determination of a subjective score by a human observer. We hypothesized that image‐based, non‐destructive measurements of plant morphology over an extended period after pathogen infection would capture subtle quantitative differences between genotypes, and thus enable identification of new disease resistance loci. To test this, we inoculated a genetically diverse biparental mapping population of tomato (Solanum lycopersicum) withRalstonia solanacearum, a soilborne pathogen that causes bacterial wilt disease. We acquired over 40 000 time‐series images of disease progression in this population, and developed an image analysis pipeline providing a suite of 10 traits to quantify bacterial wilt disease based on plant shape and size. Quantitative trait locus (QTL) analyses using image‐based phenotyping for single and multi‐traits identified QTLs that were both unique and shared compared with those identified by human assessment of wilting, and could detect QTLs earlier than human assessment. Expanding the phenotypic space of disease with image‐based, non‐destructive phenotyping both allowed earlier detection and identified new genetic components of resistance. 

The root cap, a small tissue at the tip of the root, protects the root from environmental stress and functions in gravity perception. To perform its functions, the position and size of the root cap remains stable throughout root growth. This occurs due to constant root cap cell turnover, in which the last layer of the root cap is released, and new root cap cells are produced. Cells in the last root cap layer are known as border cells or border-like cells, and have important functions in root protection against bacterial and fungal pathogens. Despite the importance of root cap cell release to root health and plant growth, the mechanisms regulating this phenomenon are not well understood. Recent work identified several factors including transcription factors, auxin, and small peptides with roles in the production and release of root cap cells. Here, we review the involvement of the known players in root cap cell release, compare the release of border-like cells and border cells, and discuss the importance of root cap cell release to root health and survival. 

The acquisition of quantitative information on plant development across a range of temporal and spatial scales is essential to understand the mechanisms of plant growth. Recent years have shown the emergence of imaging methodologies that enable the capture and analysis of plant growth, from the dynamics of molecules within cells to the measurement of morphometricand physiological traits in field-grown plants. In some instances, these imaging methods can be parallelized across multiple samples to increase throughput. When high throughput is combined with high temporal and spatial resolution, the resulting image-derived data sets could be combined with molecular large-scale data sets to enable unprecedented systems-level computational modeling. Such image-driven functional genomics studies may be expected to appear at an accelerating rate in the near future given the early success of the foundational efforts reviewed here. We present new imaging modalities and review how they have enabled a better understanding of plant growth from the microscopic to the macroscopic scale.