Invited Paper 703118: Multimodal imaging of plant-soil-microbe interactions using synchrotron X-rays: unraveling rhizosphere chemistry and processes

Abstract:

Humans rely on soil for a wide variety of resources, most notably for agriculture. The soil surrounding roots, called rhizosphere, is affected both physically and chemically by hydraulic stresses and plant-soil-microbe interactions. Essential nutrients are often inaccessible to plants even if they are abundant in the soil because they are tightly bound to soil minerals and organic matter, but there is an intricate collaboration between plants, soil microbes and fungi which allows these nutrients to be released and used by plants, which is not yet fully understood. Mapping the complex relationships between plants, soil microbes and fungi to elucidate the spatial distribution and relationships of biogeochemistry is vital for understanding key processes to be able to predict crop response to changing environment and climate. However, as soil is opaque in vivo measurements of these key processes are challenging and ex vivo sample preparation techniques significantly alter the soil chemistry resulting in loss of information. We use a combination of synchrotron XCT and XRF imaging combined with spectroscopy to address i) how the soil around the roots is altered, ii) what changes in chemistry we can detect, iii) the effects of root exudates on chemistry and microbiome, and iv) the mutualism between mycorrhiza and plants and its effect on chemical speciation. Our results show that root-induced soil deformation impacts P, S and Fe chemistry. These chemical changes were most notable within the altered region and has impacts on the current nutrient uptake models. I will discuss our current P and S gradients and speciation results in the rhizosphere of barley and wheat roots. In addition, I will address the current method development of in situ and in vivo plant-soil-microbiome imaging, limitations and opportunities for using these state-of-the-art techniques. Finally, I will highlight how this information is being used to inform and validate existing and newly developed mathematical predictive models for nutrient uptake.