Biology subjects

Meline, V.

Publications and source records attributed to Meline, V..

3 recordsLinked to original sources

bioRxiv2026

Automated Landmark-Based Root Inoculation in Arabidopsis Using Computer Vision and Robotics

Manual inoculation of plant roots is labor-intensive, spatially imprecise, and limits experimental throughput in plant-microbe interaction studies. Here, we present an integrated computer vision and robotics pipeline for automated, landmark-based root inoculation in Arabidopsis thaliana. Seedlings grown on Gelrite plates were imaged using the HADES automated phenotyping platform at the Netherlands Plant Eco-Phenotyping Centre, Utrecht University. A U-Net-based segmentation model (RootNet, F1 = 0.80) identified root structures, from which primary root tips were localized with a mean error of 0.25 mm. An affine coordinate transformation (mean target registration error: 1.09 mm) mapped image coordinates to the workspace of an Opentrons OT-2 liquid handling robot for targeted dispensing of 10 {micro}L volumes. The system achieved successful inoculation in all 17 benchmark seedlings, corresponding to 100% accuracy (95% CI: 80-100%, Clopper-Pearson), and biological validation with fluorescent bacteria confirmed successful colonization along the root axis in 9 of 10 seedlings. To our knowledge, this is the first reported demonstration of automated, landmark-based root inoculation, extending the concept of automated phenotyping from passive measurement to active robotic intervention in real-time. The pipeline is generalizable to other root landmarks and organisms, enabling precise and reproducible delivery of microorganisms to specific root locations for systematic investigation of localized plant-microbe interactions.

plant biology↗

bioRxiv2021

Trehalose induces tomato defenses and increases drought tolerance and bacterial wilt disease resistance

Ralstonia solanacearum causes bacterial wilt disease, leading to severe crop losses. Xylem sap from R. solanacearum-infected tomato is enriched in the disaccharide trehalose. Water-stressed plants also accumulate trehalose, which increases drought tolerance via abscisic acid (ABA) signaling. Because R. solanacearum-infected plants suffer reduced water flow, we hypothesized that bacterial wilt physiologically mimics drought stress, which trehalose could mitigate. We found that R. solanacearum-infected plants differentially expressed drought-associated genes, including those involved in ABA and trehalose metabolism, and had more ABA in xylem sap. Consistent with this, treating tomato roots with ABA reduced both stomatal conductance and stem colonization by R. solanacearum. Treating roots with trehalose increased xylem sap ABA and reduced plant water use by lowering stomatal conductance and temporarily improving water use efficiency. Trehalose treatment also upregulated expression of salicylic acid (SA)-dependent tomato defense genes; increased xylem sap levels of SA and other antimicrobial compounds; and increased bacterial wilt resistance of SA-insensitive NahG tomato plants. Additionally, trehalose treatment increased xylem concentrations of jasmonic acid and related oxylipins. Finally, trehalose-treated plants were substantially more resistant to bacterial wilt disease. Together, these data show that exogenous trehalose reduced both water stress and bacterial wilt disease and triggered systemic disease resistance, possibly through a Damage Associated Molecular Pattern (DAMP) response pathway. This suite of responses revealed unexpected linkages between plant responses to biotic and abiotic stress and suggested that R. solanacearum-infected plants increase trehalose to improve water use efficiency and increase wilt disease resistance. The pathogen may degrade trehalose to counter these efforts. Together, these results suggest that treating tomatoes with exogenous trehalose could be a practical strategy for bacterial wilt management.

plant biology↗

bioRxiv2021

Image-based assessment of plant disease progression identifies new genetic loci for resistance

A major challenge in global crop production is mitigating yield loss due to plant diseases. One of the best means of disease control is plant resistance, but the identification of genes that promote resistance has been limited by the subjective quantification of disease, which is typically scored by the human eye. We hypothesized that image-based, non-destructive quantification of disease phenotypes would enable the rapid identification of new disease resistance loci. We tested this using the interaction between tomato and Ralstonia solanacearum, a soilborne pathogen that causes bacterial wilt disease. We acquired over 40,000 time-series images of disease progression in a tomato recombinant inbred line population, and developed an image analysis pipeline providing a suite of ten traits to quantify wilt disease based on plant shape and size. Quantitative trait loci (QTL) analyses using image-based phenotyping identified QTL that were both unique and shared compared with those identified by human assessment of wilting. When shared loci were identified, image-based phenotyping could detect some QTL several days earlier than human assessment. Thus, expanding the phenotypic space of disease with image-based, non- destructive phenotyping allowed both earlier detection and identified new genetic components of resistance.

plant biology↗