Speaker
Description
Plants’ responses to external stimuli are highly complex coordinated events that depend on the plant species, genotype, and developmental stage, as well as the strength and duration of the stimuli. In addition, the responses occur on a scale ranging from molecular to organismal level, often resulting in emergent properties that require data with a clear spatial component. Attempts to analyze imaging data with spatial information across platforms face challenges such as incompatible sample preparation methods, incompatible data resolution and type, resulting in difficulties in data alignment. The Stanford Synchrotron Radiation Light Source at SLAC National Accelerator Laboratory is developing a multimodal imaging pipeline that allows for correlative imaging of samples across X-ray fluorescence, Fourier transformed infrared and brightfield image capture. In addition, as part of the multi-institute AIMS-LEAF (AI-driven Multimodal Science for Linking gEnotype to Phenotype) program, we aim to integrate AI/ML with multiscale multimodal imaging and sequencing data to predict heat stress tolerance in Arabidopsis thaliana. The project will connect genotype to phenotype across treatment conditions and developmental stages for the creation of a more accurate model of plant heat stress response. Development of multimodal imaging pipeline in this project can provide high resolution spatial information of the fluxes in plant ion homeostasis and changes in biochemistry in response to heat stress across platforms, allowing alignment of specialized tissue and cell types. The simultaneous collection of RNA from the adjacent tissue / organs is used to link the fluxes observed to gene expression, which will improve the model for systemic plant abiotic stress response and may produce novel insights in stress tolerance mechanisms.