An updated protocol for high throughput plant tissue sectioning

Quantification of the tissue and cellular structure of plant material is essential for the study of a variety of plant sciences applications. Currently, many methods for sectioning plant material are either low throughput or involve free-hand sectioning which requires a significant amount of practice. Here, we present an updated method to provide rapid and high-quality cross sections, primarily of root tissue but which can also be readily applied to other tissues such as leaves or stems. To increase the throughput of traditional agarose embedding and sectioning, custom designed 3D printed molds were utilized to embed 5–15 roots in a block for sectioning in a single cut. A single fluorescent stain in combination with laser scanning confocal microscopy was used to obtain high quality images of thick sections. The provided CAD files allow production of the embedding molds described here from a number of online 3D printing services. Although originally developed for roots, this method provides rapid, high quality cross sections of many plant tissue types, making it suitable for use in forward genetic screens for differences in specific cell structures or developmental changes. To demonstrate the utility of the technique, the two parent lines of the wheat (Triticum aestivum) Chinese Spring × Paragon doubled haploid mapping population were phenotyped for root anatomical differences. Significant differences in adventitious cross section area, stele area, xylem, phloem, metaxylem, and cortical cell file count were found

An Updated Protocol for High Throughput Plant Tissue Sectioning

Quantification of the tissue and cellular structure of plant material is essential for the study of a variety of plant sciences applications. Currently, many methods for sectioning plant material are either low throughput or involve free-hand sectioning which requires a significant amount of practice. Here, we present an updated method to provide rapid and high-quality cross sections, primarily of root tissue but which can also be readily applied to other tissues such as leaves or stems. To increase the throughput of traditional agarose embedding and sectioning, custom designed 3D printed molds were utilized to embed 5–15 roots in a block for sectioning in a single cut. A single fluorescent stain in combination with laser scanning confocal microscopy was used to obtain high quality images of thick sections. The provided CAD files allow production of the embedding molds described here from a number of online 3D printing services. Although originally developed for roots, this method provides rapid, high quality cross sections of many plant tissue types, making it suitable for use in forward genetic screens for differences in specific cell structures or developmental changes. To demonstrate the utility of the technique, the two parent lines of the wheat (Triticum aestivum) Chinese Spring × Paragon doubled haploid mapping population were phenotyped for root anatomical differences. Significant differences in adventitious cross section area, stele area, xylem, phloem, metaxylem, and cortical cell file count were found

Phenotyping root architecture in diverse wheat germplasm

Wheat is a crop of global importance accounting for 20% of global calorie consumption and a similar percentage of the daily protein for 2.5 billion people in less developed countries. To meet the food production demands of a predicted global population of 9 billion people by 2050, wheat yields need to increase by 1.7% per annum, whilst facing the added pressures of reduced fertilizer inputs and potentially reduced land availability. Plant root systems are key for efficient water and nutrient uptake and thus have a direct impact on yield, and yet there has been competitively little research into root system improvement. A high-throughput root phenotyping pipeline for wheat seedlings was designed consisting of a germination paper-based growth system combined with image segmentation and analysis software. A number of lines from the A.E. Watkins landrace population were characterised to test the final pipeline design. The pipeline was then utilized to phenotype a population of 94 lines from a doubled haploid population for quantitative trait loci (QTL) discovery. In total, 29 root QTL were discovered, with 2 loci co-localising with QTL for grain yield and nitrogen uptake efficiency discovered in field trials. Modern wheat varieties may have limited genetic diversity, due to changes in ploidy throughout evolution and subsequent domestication. With this in mind, thirty five ancient wheat relatives and eighteen amphidiploid hybrids were phenotyped for seedling root architectural traits, to determine the amount of phenotypic variation within ancient wheat species, and whether this variation can be transferred to modern varieties. The utilization of seedling root trait phenotyping is discussed and future research directions are identified

Sturm-Liouville operators on time scales

We establish the connection between Sturm-Liouville equations on time scales and Sturm--Liouville equations with measure-valued coefficients. Based on this connection we generalize several results for Sturm-Liouville equations on time scales which have been obtained by various authors in the past.Comment: 12 page

Soil strength influences wheat root interactions with soil macropores

Deep rooting is critical for access to water and nutrients found in subsoil. However, damage to soil structure and the natural increase in soil strength with depth, often impedes root penetration. Evidence suggests that roots use macropores (soil cavities greater than 75μm) to bypass strong soil layers. If roots have to exploit structures, a key trait conferring deep rooting will be the ability to locate existing pore networks; a trait called trematotropism.In this study, artificial macropores were created in repacked soil columns at bulk densities of 1.6g cm‐3 and 1.2g cm‐3, representing compact and loose soil. Near isogenic lines of wheat, Rht‐B1a and Rht‐B1c, were planted and root‐macropore interactions were visualized and quantified using X‐ray Computed Tomography.In compact soil, 68.8% of root‐macropore interactions resulted in pore colonisation, compared to 12.5% in loose soil. Changes in root growth trajectory following pore interaction were also quantified, with 21.0% of roots changing direction (±3°) in loose soil compared to 76.0% in compact soil.These results indicate colonisation of macropores is an important strategy of wheat roots in compacted subsoil. Management practices to reduce subsoil compaction and encourage macropore formation could offer significant advantage in helping wheat roots penetrate deeper into subsoil

Low-cost automated vectors and modular environmental sensors for plant phenotyping

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. High-throughput plant phenotyping in controlled environments (growth chambers and glasshouses) is often delivered via large, expensive installations, leading to limited access and the increased relevance of “affordable phenotyping” solutions. We present two robot vectors for automated plant phenotyping under controlled conditions. Using 3D-printed components and readily-available hardware and electronic components, these designs are inexpensive, flexible and easily modified to multiple tasks. We present a design for a thermal imaging robot for high-precision time-lapse imaging of canopies and a Plate Imager for high-throughput phenotyping of roots and shoots of plants grown on media plates. Phenotyping in controlled conditions requires multi-position spatial and temporal monitoring of environmental conditions. We also present a low-cost sensor platform for environmental monitoring based on inexpensive sensors, microcontrollers and internet-of-things (IoT) protocols

RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures

© The Author(s) 2019. Published by Oxford University Press. BACKGROUND: In recent years quantitative analysis of root growth has become increasingly important as a way to explore the influence of abiotic stress such as high temperature and drought on a plant's ability to take up water and nutrients. Segmentation and feature extraction of plant roots from images presents a significant computer vision challenge. Root images contain complicated structures, variations in size, background, occlusion, clutter and variation in lighting conditions. We present a new image analysis approach that provides fully automatic extraction of complex root system architectures from a range of plant species in varied imaging set-ups. Driven by modern deep-learning approaches, RootNav 2.0 replaces previously manual and semi-automatic feature extraction with an extremely deep multi-task convolutional neural network architecture. The network also locates seeds, first order and second order root tips to drive a search algorithm seeking optimal paths throughout the image, extracting accurate architectures without user interaction. RESULTS: We develop and train a novel deep network architecture to explicitly combine local pixel information with global scene information in order to accurately segment small root features across high-resolution images. The proposed method was evaluated on images of wheat (Triticum aestivum L.) from a seedling assay. Compared with semi-automatic analysis via the original RootNav tool, the proposed method demonstrated comparable accuracy, with a 10-fold increase in speed. The network was able to adapt to different plant species via transfer learning, offering similar accuracy when transferred to an Arabidopsis thaliana plate assay. A final instance of transfer learning, to images of Brassica napus from a hydroponic assay, still demonstrated good accuracy despite many fewer training images. CONCLUSIONS: We present RootNav 2.0, a new approach to root image analysis driven by a deep neural network. The tool can be adapted to new image domains with a reduced number of images, and offers substantial speed improvements over semi-automatic and manual approaches. The tool outputs root architectures in the widely accepted RSML standard, for which numerous analysis packages exist (http://rootsystemml.github.io/), as well as segmentation masks compatible with other automated measurement tools. The tool will provide researchers with the ability to analyse root systems at larget scales than ever before, at a time when large scale genomic studies have made this more important than ever

Antithymocyte Globulin Plus G-CSF Combination Therapy Leads to Sustained Immunomodulatory and Metabolic Effects in a Subset of Responders With Established Type 1 Diabetes.

Low-dose antithymocyte globulin (ATG) plus pegylated granulocyte colony-stimulating factor (G-CSF) preserves β-cell function for at least 12 months in type 1 diabetes. Herein, we describe metabolic and immunological parameters 24 months following treatment. Patients with established type 1 diabetes (duration 4-24 months) were randomized to ATG and pegylated G-CSF (ATG+G-CSF) (N = 17) or placebo (N = 8). Primary outcomes included C-peptide area under the curve (AUC) following a mixed-meal tolerance test (MMTT) and flow cytometry. "Responders" (12-month C-peptide ≥ baseline), "super responders" (24-month C-peptide ≥ baseline), and "nonresponders" (12-month C-peptide < baseline) were evaluated for biomarkers of outcome. At 24 months, MMTT-stimulated AUC C-peptide was not significantly different in ATG+G-CSF (0.49 nmol/L/min) versus placebo (0.29 nmol/L/min). Subjects treated with ATG+G-CSF demonstrated reduced CD4+ T cells and CD4+/CD8+ T-cell ratio and increased CD16+CD56hi natural killer cells (NK), CD4+ effector memory T cells (Tem), CD4+PD-1+ central memory T cells (Tcm), Tcm PD-1 expression, and neutrophils. FOXP3+Helios+ regulatory T cells (Treg) were elevated in ATG+G-CSF subjects at 6, 12, and 18 but not 24 months. Immunophenotyping identified differential HLA-DR expression on monocytes and NK and altered CXCR3 and PD-1 expression on T-cell subsets. As such, a group of metabolic and immunological responders was identified. A phase II study of ATG+G-CSF in patients with new-onset type 1 diabetes is ongoing and may support ATG+G-CSF as a prevention strategy in high-risk subjects