Micron-scale Phenotyping Techniques of Maize Vascular Bundles Based on X-ray Microcomputed Tomography

Summary

We provide a novel method to improve the X-ray absorption contrast of maize tissue suitable for ordinary microcomputed tomography scanning. Based on CT images, we introduce a set of image-processing workflows for different maize materials to effectively extract microscopic phenotypes of vascular bundles of maize.

Abstract

It is necessary to accurately quantify the anatomical structures of maize materials based on high-throughput image analysis techniques. Here, we provide a 'sample preparation protocol' for maize materials (i.e., stem, leaf, and root) suitable for ordinary microcomputed tomography (micro-CT) scanning. Based on high-resolution CT images of maize stem, leaf, and root, we describe two protocols for the phenotypic analysis of vascular bundles: (1) based on the CT image of maize stem and leaf, we developed a specific image analysis pipeline to automatically extract 31 and 33 phenotypic traits of vascular bundles; (2) based on the CT image series of maize root, we set up an image processing scheme for the three-dimensional (3-D) segmentation of metaxylem vessels, and extracted two-dimensional (2-D) and 3-D phenotypic traits, such as volume, surface area of metaxylem vessels, etc. Compared with traditional manual measurement of vascular bundles of maize materials, the proposed protocols significantly improve the efficiency and accuracy of micron-scale phenotypic quantification.

Introduction

The maize vascular system runs through the entire plant, from the root and stem to the leaves, which forms the key transportation paths for delivering water, mineral nutrients, and organic substances¹. Another important function of the vascular system is to provide mechanical support for the maize plant. For example, the morphology, number, and distribution of vascular bundles in roots and stems are closely related to the lodging resistance of maize plants^2,3. At present, studies on the anatomical structure of vascular bundles mainly utilize microscopic and ultramicroscopic techniques to display the anatomical structures of a certain part of the stem, leaf, or root, and then measure and count these structures of interest by manual investigation. Undoubtedly, manual measurement of various microscopic structures in large-scale microimages is a very tedious and inefficient work and severely limits the precision of microphenotypic traits, owing to its subjectivity and inconsistency^4,5.

Maize has no secondary growth, and the cell content essentially consists of water in the primary meristem. Without any pretreatment, fresh samples of maize tissues can be directly scanned using a micro-CT device; however, the scanning results are probably poor and rough. The main reasons are summarized as follows: (1) low attenuation densities of plant tissues, resulting in a low contrast of atomic number and high noise in images; (2) fresh plant materials are prone to dehydrate and shrink during the normal scanning environment, as reported by Du⁶. The abovementioned problems have become the main constraints for the development and application of microphenotyping technology for maize, wheat, rice, and other monocotyledons. Here, we introduce the 'sample preparation protocol' to pretreat the samples of maize stem, leaf, and root. This protocol avoids the dehydration and deformation of plant materials during the CT scanning; thus, it is beneficial to increase the preservation time of plant samples with nondeformation. Moreover, the dyeing step based on solid iodine also enhances the contrast of plant materials; thus, it makes significant improvements in the imaging quality of micro-CT. Furthermore, we developed image processing software, named VesselParser, to process the CT images of maize stems and leaves. This software integrates a set of image-processing pipelines to perform high-throughput and automatic phenotyping analysis for 2-D CT images of different plant tissues. Vascular bundles in the entire cross-section of the maize stem and leaf are detected, extracted, and identified using an automatic image-processing method. As a result, we obtain 31 microscopic phenotypes of the maize stem and 33 microscopic phenotypes of the maize leaf. For the CT image series of the maize root, we developed an image-processing scheme to acquire 3-D phenotypic traits of metaxylem vessels. This scheme is superior in efficiency of image acquisition and reconstruction compared with traditional methods.

These results indicate that the image processing pipelines considering the imaging characteristics of ordinary X-ray micro-CT provide an effective method for the microscopic phenotyping of vascular bundles; this extremely widens the applications of CT techniques in plant science and improves the automatic phenotyping of plant materials at cellular resolution^6,7.

Protocol

1. Sample Preparation Protocol

1. For the sampling, collect the stem, leaf, and root from fresh maize plants and divide them into three types of sample groups (each group with four replications). Then, cut them into small segments using a surgical blade in the following manner: (1) cut a segment of the middle stem internode 1 - 1.5 cm in length; (2) cut a segment of the maximum width of the leaf 0.5 - 3 cm in length along the vertical direction with the main vein; (3) cut a segment of the crown root 0.5 cm in length.
2. For the FAA fixation, soak the sample segments in an FAA solution (90:5:5 v/v/v, 70% ethanol:100% formaldehyde:100% acetic acid) for at least 3 d.
3. Perform the dehydration procedure in six sequential ethanol gradients (i.e., 30%, 50%, 70%, 85%, 95% and 100%) and set the processing time of each ethanol gradient as 30 min.
4. Place the plant materials in the corresponding sample baskets manufactured using a 3-D printer; then, quickly transfer the sample baskets to the sample cell of a CO₂ critical-point drying system. Set the drying parameters as follows:
   (1) CO₂ in: fast speed. Holder fillers: 100%.
   (2) Charge: CO₂ charge delay 120 s into the cycle. Exchange speed: 5. Cycle number: 12.
   ​(3) Gas out: heat, fast. Speed: slow, 50%.
   1. According to the morphological differences of maize root, stem, and leaf, design and print sample baskets using a 3-D printer (e.g., Figure 1).
5. Place the dried plant materials (maize root, stem, or leaf) in a 50-mL centrifuge tube with 2 g of solid iodine to dye the plant materials with volatile iodine vapor and, then, place the tubes in a lightproof room for 4 - 5 h.

2. Micro-CT Scanning Protocol

1. To scan into the raw CT data, set the CT scanning parameters as follows: 40 kV/250 µA (for stem and leaf) or 34 kV/210 µA (for root). Set the corresponding scanning ranges for the different sizes and volumes of the plant materials used, and adjust the imaging pixel sizes as follows: 2.0 µm (for the maize root), 6.77 µm (for the maize stem), and 10.0 µm (for the maize leaf).
2. To reconstruct slice images, convert the raw CT data into CT slice images with a 2K resolution (2,000 x 2,000 pixels) using an image reconstruction software. More details are provided in the NRecon User Manual (http://bruker-microct.com/next/NReconUserGuide.pdf).

3. Image Analysis Protocol for a Single CT Image of a Maize Stem or Leaf

NOTE: Use automatic imaging software for vascular bundles to conduct the phenotyping analysis of vascular bundles within the CT slice images of the maize stem and leaf (Figure 2). The software usage steps are described as follows.

1. Appoint the organ type to initialize different algorithm pipelines. Click the Method parameters button and select maize stem or maize leaf in the first drop-down box.
2. To import the images, click the Data management button, set the work directory, and import automatically all slice images in this directory. Select single or multi-slice images into the image pipelines.
3. Determine the actual pixel size of the image. Click the Method parameters button and enter the actual pixel size of the image in the edit item of the pixel size.
4. For the phenotyping computation, click the Phenotyping computation button to automatically extract phenotypic traits of the vascular bundles for all selected slice images.
5. Click the Statistic analysis button to output the results as a TXT or CSV file format.

4. Image Analysis Protocol for CT Image Series of a Maize Root

NOTE: The CT image series of maize roots are utilized to extract the 3-D structures of metaxylem vessels using image-processing software. The main steps are as follows.

1. Import the reconstructed images of maize roots (in BMP file format) and determine the accurate spacing parameters (the size of a voxel [i.e., x, y, z]). Use the recursive Gaussian tool to smoothen these images to improve the image quality.
2. Conduct 3-D segmentation of metaxylem vessels by adjusting the threshold parameters; this generates a uniform color label for each connected metaxylem vessel.
3. Improve and identify the metaxylem vessels interactively using morphology, bitwise, and flood-fill operations.
4. Conduct volume visualization and surface reconstruction of the vessels. Use the mask statistics tool to count and measure the phenotypic traits of a vessel in the 2-D and 3-D levels.

Results

The sample preparation protocol suitable for ordinary micro-CT scanning not only prevents the deformation of plant tissues but also enhances the X-ray absorption contrast. Pretreated plant materials are scanned using a micro-CT system into high-quality slice images, and the highest resolution can reach 2 µm/pixel. Figure 4 shows the scanned micro-CT images of stem, leaf, and root, and the image contrast has a significant improvement compared with the results ...

Discussion

With the successful application of CT technology in the fields of biomedicine and materials science, this technology has been gradually introduced into the fields of botany and agriculture, promoting researches in plant life sciences as a promising technical tool. In the late 1990s, CT technology was first used to study the morphological structures and development of plant root systems. In the past decade, synchrotron HRCT has become a powerful, nondestructive tool for plant biologists, and has been successfully used to ...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Skyscan 1172 X-ray computed tomography system	Bruker Corporation, Belgium	NA	For CT scanning
CO2 critical point drying system (Leica CPD300)	Leica Corporation, Germany	NA	For sample drying
Ethanol	Any	NA	For FAA fixation
Formaldehyde	Any	NA	For FAA fixation
Acetic acid	Any	NA	For FAA fixation
Surgical blade	Any	NA	For cutting the sample sgements
3D printer	Makerbot replicator 2, MakerBot Industries, USA	NA	For printing the sample baskets of maize root, stem, and leaf
Centrifuge tube	Corning, USA	NA	Place the root, stem, or leaf materials
Solid iodine	Any	NA	For sample dyeing
SkyScan Nrecon software	SkyScan NRecon, Version: 1.6.9.4, Bruker Corporation, Belgium	NA	For image reconstruction
VesselParser software	VesselParser, Version: 3.0, National Engineering Research Center for Information Technology in Agriculture (NERCITA), Beijing, China	NA	Image analysis protocol for single CT image of maize stem or leaf
ScanIP	ScanIP, Version: 7.0; Simpleware, Exeter, UK	NA	3D image processing software
Latex gloves	Any	NA
Tweezers	Any	NA