Multimodal correlative imaging and modelling of phosphorus uptake from soil by hyphae of mycorrhizal fungi

Statistical Effective Diffusivity Estimation in Porous Media Using an Integrated On-site Imaging Workflow for Synchrotron Users

Transport in porous media plays an essential role for many physical, engineering, biological and environmental processes. Novel synchrotron imaging techniques and image-based models have enabled more robust quantification of geometric structures that influence transport through the pore space. However, image-based modelling is computationally expensive, and end users often require, while conducting imaging campaign, fast and agile bulk-scale effective parameter estimates that account for the pore-scale details. In this manuscript we enhance a pre-existing image-based model solver known as OpenImpala to estimate bulk-scale effective transport parameters. In particular, the boundary conditions and equations in OpenImpala were modified in order to estimate the effective diffusivity in an imaged system/geometry via a formal multi-scale homogenisation expansion. Estimates of effective pore space diffusivity were generated for a range of elementary volume sizes to estimate when the effective diffusivity values begin to converge to a single value. Results from OpenImpala were validated against a commercial finite element method package COMSOL Multiphysics (abbreviated as COMSOL). Results showed that the effective diffusivity values determined with OpenImpala were similar to those estimated by COMSOL. Tests on larger domains comparing a full image-based model to a homogenised (geometrically uniform) domain that used the effective diffusivity parameters showed differences below 2 % error, thus verifying the accuracy of the effective diffusivity estimates. Finally, we compared OpenImpala's parallel computing speeds to COMSOL. OpenImpala consistently ran simulations within fractions of minutes, which was two orders of magnitude faster than COMSOL providing identical supercomputing specifications. In conclusion, we demonstrated OpenImpala's utility as part of an on-site tomography processing pipeline allowing for fast and agile assessment of porous media processes and to guide imaging campaigns while they are happening at synchrotron beamlines.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11242-023-01993-7.

Arbuscular mycorrhizal fungi enhance plant phosphorus uptake through stimulating hyphosphere soil microbiome functional profiles for phosphorus turnover

The extraradical hyphae of arbuscular mycorrhizal (AM) fungi are colonized by different bacteria in natural and agricultural systems, but the mechanisms by which AM fungi interact with the hyphosphere soil microbiome and influence soil organic phosphorus (P) mobilization remain unclear. We grew Medicago in two-compartment microcosms, inoculated with Rhizophagus irregularis, or not, in the root compartment and set up P treatments (without P, with P addition as KH₂ PO₄ or nonsoluble phytate) in the hyphal compartment. We studied the processes of soil P turnover and characterized the microbiome functional profiles for P turnover in the hyphosphere soil by metagenomic sequencing. Compared with the bulk soil, the hyphosphere soil of R. irregularis was inhabited by a specific bacterial community and their functional profiles for P turnover was stimulated. At the species level, the shift in hyphosphere soil microbiome was characterized by the recruitment of the genome bin2.39 harbouring both gcd and phoD genes and genome bin2.97 harbouring the phoD gene, which synergistically drove nonsoluble phytate mobilization in the hyphosphere soil. Our results suggest that AM fungi recruits a specific hyphosphere soil microbiome and stimulated their functional profiles for P turnover to enhance utilization of phytate.

Realizing the Full Potential of Advanced Microscopy Approaches for Interrogating Plant-Microbe Interactions

Microscopy has served as a fundamental tool for insight and discovery in plant-microbe interactions for centuries. From classical light and electron microscopy to corresponding specialized methods for sample preparation and cellular contrasting agents, these approaches have become routine components in the toolkit of plant and microbiology scientists alike to visualize, probe and understand the nature of host-microbe relationships. Over the last three decades, three-dimensional perspectives led by the development of electron tomography, and especially, confocal techniques continue to provide remarkable clarity and spatial detail of tissue and cellular phenomena. Confocal and electron microscopy provide novel revelations that are now commonplace in medium and large institutions. However, many other cutting-edge technologies and sample preparation workflows are relatively unexploited yet offer tremendous potential for unprecedented advancement in our understanding of the inner workings of pathogenic, beneficial, and symbiotic plant-microbe interactions. Here, we highlight key applications, benefits, and challenges of contemporary advanced imaging platforms for plant-microbe systems with special emphasis on several recently developed approaches, such as light-sheet, single molecule, super-resolution, and adaptive optics microscopy, as well as ambient and cryo-volume electron microscopy, X-ray microscopy, and cryo-electron tomography. Furthermore, the potential for complementary sample preparation methodologies, such as optical clearing, expansion microscopy, and multiplex imaging, will be reviewed. Our ultimate goal is to stimulate awareness of these powerful cutting-edge technologies and facilitate their appropriate application and adoption to solve important and unresolved biological questions in the field. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Effect of Digestate Modified with Amendments on Soil Health and Plant Biomass under Varying Experimental Durations

A digestate with amendments provides plants with available nutrients and improves the microbiological properties of treated soil. Modification of a digestate through the addition of a biochar and sulphur source is less well-known. This pot experiment aimed at comparing the short- and long-time fertilization effects of a digestate enriched with biochar, with elemental sulphur, or with a combination of both on soil health and plant biomass. The experiment was carried out with maize, cultivated twice (1st-12th week = pre-cultivation; re-sowing after shoot harvest, 13th-24th = main cultivation) in soil amended with prepared digestate. The digestate used in pre-cultivation was incubated untreated (D) and was then treated with biochar (D + B), with elemental sulphur at a low (LS) and high (HS) dose, or with a combination of both (D + B + LS and D + B + HS). An additional unamended digestate (D) was added to each soil variant before the main cultivation. The application of digestate with a high dose of elemental sulphur and biochar mediated the most significant differences in the soil. The increase (compared to the unamended soil) was of short-term type (+11% and +6% increased total nitrogen and carbon after 12 weeks), then of long-term type (+54% and +30% increased sulphur and arylsulfatase activity after 24 weeks), and later emerged in the 13th to the 24th week of the experiment (+57% and +32% non-inhibited urease, increased N-acetyl-β-D-glucosaminidase and phosphatase). No significant differences in the effect of the applied amendments on dry aboveground plant biomass were observed.

Correlative Imaging of the Rhizosphere─A Multimethod Workflow for Targeted Mapping of Chemical Gradients

Examining in situ processes in the soil rhizosphere requires spatial information on physical and chemical properties under undisturbed conditions. We developed a correlative imaging workflow for targeted sampling of roots in their three-dimensional (3D) context and assessed the imprint of roots on chemical properties of the root-soil contact zone at micrometer to millimeter scale. Maize (Zea mays) was grown in 15N-labeled soil columns and pulse-labeled with 13CO2 to visualize the spatial distribution of carbon inputs and nitrogen uptake together with the redistribution of other elements. Soil columns were scanned by X-ray computed tomography (X-ray CT) at low resolution (45 μm) to enable image-guided subsampling of specific root segments. Resin-embedded subsamples were then analyzed by X-ray CT at high resolution (10 μm) for their 3D structure and chemical gradients around roots using micro-X-ray fluorescence spectroscopy (μXRF), nanoscale secondary ion mass spectrometry (NanoSIMS), and laser-ablation isotope ratio mass spectrometry (LA-IRMS). Concentration gradients, particularly of calcium and sulfur, with different spatial extents could be identified by μXRF. NanoSIMS and LA-IRMS detected the release of 13C into soil up to a distance of 100 μm from the root surface, whereas 15N accumulated preferentially in the root cells. We conclude that combining targeted sampling of the soil-root system and correlative microscopy opens new avenues for unraveling rhizosphere processes in situ.

A workflow for segmenting soil and plant X-ray computed tomography images with deep learning in Google's Colaboratory

X-ray micro-computed tomography (X-ray μCT) has enabled the characterization of the properties and processes that take place in plants and soils at the micron scale. Despite the widespread use of this advanced technique, major limitations in both hardware and software limit the speed and accuracy of image processing and data analysis. Recent advances in machine learning, specifically the application of convolutional neural networks to image analysis, have enabled rapid and accurate segmentation of image data. Yet, challenges remain in applying convolutional neural networks to the analysis of environmentally and agriculturally relevant images. Specifically, there is a disconnect between the computer scientists and engineers, who build these AI/ML tools, and the potential end users in agricultural research, who may be unsure of how to apply these tools in their work. Additionally, the computing resources required for training and applying deep learning models are unique, more common to computer gaming systems or graphics design work, than to traditional computational systems. To navigate these challenges, we developed a modular workflow for applying convolutional neural networks to X-ray μCT images, using low-cost resources in Google's Colaboratory web application. Here we present the results of the workflow, illustrating how parameters can be optimized to achieve best results using example scans from walnut leaves, almond flower buds, and a soil aggregate. We expect that this framework will accelerate the adoption and use of emerging deep learning techniques within the plant and soil sciences.