New Growth-Related Features of Wheat Grain Pericarp Revealed by Synchrotron-Based X-ray Micro-Tomography and 3D Reconstruction

Wheat (Triticum aestivum L.) is one of the most important crops as it provides 20% of calories and proteins to the human population. To overcome the increasing demand in wheat grain production, there is a need for a higher grain yield, and this can be achieved in particular through an increase in the grain weight. Moreover, grain shape is an important trait regarding the milling performance. Both the final grain weight and shape would benefit from a comprehensive knowledge of the morphological and anatomical determinism of wheat grain growth. Synchrotron-based phase-contrast X-ray microtomography (X-ray µCT) was used to study the 3D anatomy of the growing wheat grain during the first developmental stages. Coupled with 3D reconstruction, this method revealed changes in the grain shape and new cellular features. The study focused on a particular tissue, the pericarp, which has been hypothesized to be involved in the control of grain development. We showed considerable spatio-temporal diversity in cell shape and orientations, and in tissue porosity associated with stomata detection. These results highlight the growth-related features rarely studied in cereal grains, which may contribute significantly to the final grain weight and shape.

A Hitchhiker's Guide through the Bio-image Analysis Software Universe

Modern research in the life sciences is unthinkable without computational methods for extracting, quantifying and visualizing information derived from microscopy imaging data of biological samples. In the past decade, we observed a dramatic increase in available software packages for these purposes. As it is increasingly difficult to keep track of the number of available image analysis platforms, tool collections, components and emerging technologies, we provide a conservative overview of software that we use in daily routine and give insights into emerging new tools. We give guidance on which aspects to consider when choosing the platform that best suits the user's needs, including aspects such as image data type, skills of the team, infrastructure and community at the institute and availability of time and budget.

Brain virtual histology with X-ray phase-contrast tomography Part II:3D morphologies of amyloid- plaques in Alzheimer's disease models

While numerous transgenic mouse strains have been produced to model the formation of amyloid-β (Aβ) plaques in the brain, efficient methods for whole-brain 3D analysis of Aβ deposits have to be validated and standardized. Moreover, routine immunohistochemistry performed on brain slices precludes any shape analysis of Aβ plaques, or require complex procedures for serial acquisition and reconstruction. The present study shows how in-line (propagation-based) X-ray phase-contrast tomography (XPCT) combined with ethanol-induced brain sample dehydration enables hippocampus-wide detection and morphometric analysis of Aβ plaques. Performed in three distinct Alzheimer mouse strains, the proposed workflow identified differences in signal intensity and 3D shape parameters: 3xTg displayed a different type of Aβ plaques, with a larger volume and area, greater elongation, flatness and mean breadth, and more intense average signal than J20 and APP/PS1. As a label-free non-destructive technique, XPCT can be combined with standard immunohistochemistry. XPCT virtual histology could thus become instrumental in quantifying the 3D spreading and the morphological impact of seeding when studying prion-like properties of Aβ aggregates in animal models of Alzheimer's disease. This is Part II of a series of two articles reporting the value of in-line XPCT for virtual histology of the brain; Part I shows how in-line XPCT enables 3D myelin mapping in the whole rodent brain and in human autopsy brain tissue.

Esmraldi: efficient methods for the fusion of mass spectrometry and magnetic resonance images

Background

Mass spectrometry imaging (MSI) is a family of acquisition techniques producing images of the distribution of molecules in a sample, without any prior tagging of the molecules. This makes it a very interesting technique for exploratory research. However, the images are difficult to analyze because the enclosed data has high dimensionality, and their content does not necessarily reflect the shape of the object of interest. Conversely, magnetic resonance imaging (MRI) scans reflect the anatomy of the tissue. MRI also provides complementary information to MSI, such as the content and distribution of water.

Results

We propose a new workflow to merge the information from 2D MALDI–MSI and MRI images. Our workflow can be applied to large MSI datasets in a limited amount of time. Moreover, the workflow is fully automated and based on deterministic methods which ensures the reproducibility of the results. Our methods were evaluated and compared with state-of-the-art methods. Results show that the images are combined precisely and in a time-efficient manner.

Conclusion

Our workflow reveals molecules which co-localize with water in biological images. It can be applied on any MSI and MRI datasets which satisfy a few conditions: same regions of the shape enclosed in the images and similar intensity distributions.

Darkfield and Fluorescence Macrovision of a Series of Large Images to Assess Anatomical and Chemical Tissue Variability in Whole Cross-Sections of Maize Stems

The proportion and composition of plant tissues in maize stems vary with genotype and agroclimatic factors and may impact the final biomass use. In this manuscript, we propose a quantitative histology approach without any section labelling to estimate the proportion of different tissues in maize stem sections as well as their chemical characteristics. Macroscopic imaging was chosen to observe the entire section of a stem. Darkfield illumination was retained to visualise the whole stem cellular structure. Multispectral autofluorescence images were acquired to detect cell wall phenolic compounds after UV and visible excitations. Image analysis was implemented to extract morphological features and autofluorescence pseudospectra. By assimilating the internode to a cylinder, the relative proportions of tissues in the internode were estimated from their relative areas in the sections. The approach was applied to study a series of 14 maize inbred lines. Considerable variability was revealed among the 14 inbred lines for both anatomical and chemical traits. The most discriminant morphological descriptors were the relative amount of rind and parenchyma tissues together with the density and size of the individual bundles, the area of stem and the parenchyma cell diameter. The rind, as the most lignified tissue, showed strong visible-induced fluorescence which was line-dependant. The relative amount of para-coumaric acid was associated with the UV-induced fluorescence intensity in the rind and in the parenchyma near the rind, while ferulic acid amount was significantly correlated mainly with the parenchyma near the rind. The correlation between lignin and the tissue pseudospectra showed that a global higher amount of lignin resulted in a higher level of lignin fluorescence whatever the tissues. We demonstrated here the potential of darkfield and autofluorescence imaging coupled with image analysis to quantify histology of maize stem and highlight variability between different lines.

Parametric mapping of cellular morphology in plant tissue sections by gray level granulometry

BACKGROUND

The cellular morphology of plant organs is strongly related to other physical properties such as shape, size, growth, mechanical properties or chemical composition. Cell morphology often vary depending on the type of tissue, or on the distance to a specific tissue. A common challenge in quantitative plant histology is to quantify not only the cellular morphology, but also its variations within the image or the organ. Image texture analysis is a fundamental tool in many areas of image analysis, that was proven efficient for plant histology, but at the scale of the whole image.

RESULTS

This work presents a method that generates a parametric mapping of cellular morphology within images of plant tissues. It is based on gray level granulometry from mathematical morphology for extracting image texture features, and on Centroidal Voronoi Diagram for generating a partition of the image. Resulting granulometric curves can be interpreted either through multivariate data analysis or by using summary features corresponding to the local average cell size. The resulting parametric maps describe the variations of cellular morphology within the organ.

CONCLUSIONS

We propose a methodology for the quantification of cellular morphology and of its variations within images of tissue sections. The results should help understanding how the cellular morphology is related to genotypic and / or environmental variations, and clarify the relationships between cellular morphology and chemical composition of cell walls.

Changes in cell walls lignification, feruloylation and p-coumaroylation throughout maize internode development

Plant cell walls development is an integrated process during which several components are deposited successively. In the cell walls in grass, the accessibility of structural polysaccharides is limited by the cell walls structure and composition mainly as a result of phenolic compounds. Here, we studied the patterns of cell walls establishment in the internode supporting the ear in three distinct maize genotypes. The developmental patterns observed in the internode cell walls in terms of its composition are reported with an emphasis on lignification, p-coumaroylation and feruloylation. We combined biochemical and histological approaches and revealed that internode cell walls development in maize before flowering is characterized by the rapid deposition of secondary cell walls components and robust lignification in both the pith and the rind. After flowering and until silage maturity, the slow deposition of secondary walls components occurs in the cortical region, and the deposited lignins are rich in β-O-4 bonds and are highly p-coumaroylated. We conclude the paper by proposing a revised spatiotemporal model based on that proposed by Terashima et al. (1993) for cell walls development in grass.

Use of X-ray micro computed tomography imaging to analyze the morphology of wheat grain through its development

BACKGROUND

Wheat is one of the most important staple source in the world for human consumption, animal feed and industrial raw materials. To deal with the global and increasing population demand, enhancing crop yield by increasing the final weight of individual grain is considered as a feasible solution. Morphometric analysis of wheat grain plays an important role in tracking and understanding developmental processes by assessing potential impacts on grains properties, size and shape that are major determinants of final grain weight. X-ray micro computed tomography (μCT) is a very powerful non-invasive imaging tool that is able to acquire 3D images of an individual grain, enabling to assess the morphology of wheat grain and of its different compartments. Our objective is to quantify changes of morphology during growth stages of wheat grain from 3D μCT images.

METHODS

3D μCT images of wheat grains were acquired at various development stages ranging from 60 to 310 degree days after anthesis. We developed robust methods for the identification of outer and inner tissues within the grains, and the extraction of morphometric features using 3D μCT images. We also developed a specific workflow for the quantification of the shape of the grain crease.

RESULTS

The different compartments of the grain could be semi-automatically segmented. Variations of volumes of the compartments adequately describe the different stages of grain developments. The evolution of voids within wheat grain reflects lysis of outer tissues and growth of inner tissues. The crease shape could be quantified for each grain and averaged for each stage of development, helping us understand the genesis of the grain shape.

CONCLUSION

This work shows that μCT acquisitions and image processing methodologies are powerful tools to extract morphometric parameters of developing wheat grain. The results of quantitative analysis revealed remarkable features of wheat grain growth. Further work will focus on building a computational model of wheat grain growth based on real 3D imaging data.

Cell geometry determines symmetric and asymmetric division plane selection in Arabidopsis early embryos

Plant tissue architecture and organ morphogenesis rely on the proper orientation of cell divisions. Previous attempts to predict division planes from cell geometry in plants mostly focused on 2D symmetric divisions. Using the stereotyped division patterns of Arabidopsis thaliana early embryogenesis, we investigated geometrical principles underlying plane selection in symmetric and in asymmetric divisions within complex 3D cell shapes. Introducing a 3D computational model of cell division, we show that area minimization constrained on passing through the cell centroid predicts observed divisions. Our results suggest that the positioning of division planes ensues from cell geometry and gives rise to spatially organized cell types with stereotyped shapes, thus underlining the role of self-organization in the developing architecture of the embryo. Our data further suggested the rule could be interpreted as surface minimization constrained by the nucleus position, which was validated using live imaging of cell divisions in the stomatal cell lineage.

The proper positioning of division planes is key for correct development and morphogenesis of organs, in particular in plants were cellular walls prevent cell rearrangements. Elucidating how division planes are selected is therefore essential to decipher the cellular bases of plant morphogenesis. Previous attempts to identify geometrical rules relating cell shape and division plane positioning in plants mostly focused on symmetric divisions in tissues reduced to 2D geometries. Here, we combined 3D quantitative image analysis and a new 3D cell division model to evaluate the existence of geometrical rules in asymmetrical and symmetrical divisions of complex cell shapes. We show that in the early embryo of the model plant Arabidopsis thaliana, which presents stereotyped but complex cell division patterns, a single geometrical rule (area minimization constrained on passing through the cell centroid) recapitulates the complete sequence of division events. This new rule, valid for both symmetrical and asymmetrical divisions, generalizes previously proposed cell division rules and can be interpreted based on the dynamics of the cytoskeleton and on the positioning of the nucleus, a hypothesis that we validated using leaf cell division patterns. This work highlights the importance of self-organization in plant early morphogenesis and the emergence of robust cellular organizations based on geometrical feedback loops between cell geometry and division plane selection.

Quantitative dissection of variations in root growth rate: a matter of cell proliferation or of cell expansion?

Plant organ growth results from cell production and cell expansion. Deciphering the contribution of each of these processes to growth rate is an important issue in developmental biology. Here, we investigated the cellular processes governing root elongation rate, considering two sources of variation: genotype and disturbance by chemicals (NaCl, polyethylene glycol, H2O2, abscisic acid). Exploiting the adventitious rooting capacity of the Populus genus, and using time-lapse imaging under infrared-light, particle image velocimetry, histological analysis, and kinematics, we quantified the cellular processes involved in root growth variation, and analysed the covariation patterns between growth parameters. The rate of cell production by the root apical meristem and the number of dividing cells were estimated in vivo without destructive measurement. We found that the rate of cell division contributed more to the variation in cell production rate than the number of dividing cells. Regardless of the source of variation, the length of the elongation zone was the best proxy for growth rate, summarizing rates of cell production and cell elongation into a single parameter. Our results demonstrate that cell production rate is the main driver of growth rate, whereas elemental elongation rate is a key driver of short-term growth adjustments.

Tissue Lignification, Cell Wall p-Coumaroylation and Degradability of Maize Stems Depend on Water Status

Water supply and valorization are two urgent issues in the utilization of maize biomass in the context of climate change and replacement of fossil resources. Maximizing maize biomass valorization is of interest to make biofuel conversion competitive, and to increase forage energetic value for animal fodder. One way to estimate biomass valorization is to quantify cell wall degradability. In this study, we evaluated the impact of water supply on cell wall degradability, cell wall contents and structure, and distribution of lignified cell types in maize internodes using dedicated high-throughput tools to effectively phenotype maize internodes from 11 inbred lines under two contrasting irrigation scenarios in field trials over three years. Overall, our results clearly showed that water deficit induced significant changes in lignin content and distribution along with a reduction in lignin p-coumaroylation, thereby impacting cell wall degradability. Additionally, we also observed that responses to a water deficit varied between the lines examined, underscoring biochemical and histological target traits for plant breeding.

Quantitative imaging of plants: multi-scale data for better plant anatomy

This article comments on: Staedler YM, Kreisberger T, Manafzadeh S, Chartier M, Handschuh S, Pamperl S, Sontag S, Paun O, Schönenberger J. 2017. Novel computed tomography-based tools reliably quantify plant reproductive investment. Journal of Experimental Botany 69, 525–535.

The preprophase band of microtubules controls the robustness of division orientation in plants

Controlling cell division plane orientation is essential for morphogenesis in multicellular organisms. In plant cells, the future cortical division plane is marked before mitotic entry by the preprophase band (PPB). Here, we characterized an Arabidopsis trm (TON1 Recruiting Motif) mutant that impairs PPB formation but does not affect interphase microtubules. Unexpectedly, PPB disruption neither abolished the capacity of root cells to define a cortical division zone nor induced aberrant cell division patterns but rather caused a loss of precision in cell division orientation. Our results advocate for a reassessment of PPB function and division plane determination in plants and show that a main output of this microtubule array is to limit spindle rotations in order to increase the robustness of cell division.

Histological quantification of maize stem sections from FASGA-stained images

BACKGROUND

Crop species are of increasing interest both for cattle feeding and for bioethanol production. The degradability of the plant material largely depends on the lignification of the tissues, but it also depends on histological features such as the cellular morphology or the relative amount of each tissue fraction. There is therefore a need for high-throughput phenotyping systems that quantify the histology of plant sections.

RESULTS

We developed custom image processing and an analysis procedure for quantifying the histology of maize stem sections coloured with FASGA staining and digitalised with whole microscopy slide scanners. The procedure results in an automated segmentation of the input images into distinct tissue regions. The size and the fraction area of each tissue region can be quantified, as well as the average coloration within each region. The measured features can discriminate contrasted genotypes and identify changes in histology induced by environmental factors such as water deficit.

CONCLUSIONS

The simplicity and the availability of the software will facilitate the elucidation of the relationships between the chemical composition of the tissues and changes in plant histology. The tool is expected to be useful for the study of large genetic populations, and to better understand the impact of environmental factors on plant histology.

KymoRod: a method for automated kinematic analysis of rod-shaped plant organs

A major challenge in plant systems biology is the development of robust, predictive multiscale models for organ growth. In this context it is important to bridge the gap between the, rather well-documented molecular scale and the organ scale by providing quantitative methods to study within-organ growth patterns. Here, we describe a simple method for the analysis of the evolution of growth patterns within rod-shaped organs that does not require adding markers at the organ surface. The method allows for the simultaneous analysis of root and hypocotyl growth, provides spatio-temporal information on curvature, growth anisotropy and relative elemental growth rate and can cope with complex organ movements. We demonstrate the performance of the method by documenting previously unsuspected complex growth patterns within the growing hypocotyl of the model species Arabidopsis thaliana during normal growth, after treatment with a growth-inhibiting drug or in a mechano-sensing mutant. The method is freely available as an intuitive and user-friendly Matlab application called KymoRod.

Color quantification of stained maize stem section describes lignin spatial distribution within the whole stem

This work presents a method to quantify the lignification of maize tissues by automated color image analysis of stained maize stem cross sections. Safranin and Alcian blue staining makes lignified tissues appear red, and nonlignified tissues appear blue. Lignification is assessed by the ratio of red intensity over blue intensity. A rough quantification of global lignification is computed as the surface ratio of lignified tissues. A more precise quantification is obtained by computing profiles of red/blue intensity ratio in relation to the distance to the epidermis, depicting the spatial distribution of lignified walls within the stem. Lignification profiles are analyzed through summary parameters describing the evolution of lignification in three specific regions. The distribution of lignification can be quickly assessed depending on the position and the development stage, allowing the screening of genetic variations to be envisioned.

Stereological estimation of cell wall density of DR12 tomato mutant using three-dimensional confocal imaging

BACKGROUND AND AIMS

The cellular structure of fleshy fruits is of interest to study fruit shape, size, mechanical behaviour or sensory texture. The cellular structure is usually not observed in the whole fruit but, instead, in a sample of limited size and volume. It is therefore difficult to extend measurements to the whole fruit and/or to a specific genotype, or to describe the cellular structure heterogeneity within the fruit.

METHODS

An integrated method is presented to describe the cellular structure of the whole fruit from partial three-dimensional (3D) observations, involving the following steps: (1) fruit sampling, (2) 3D image acquisition and processing and (3) measurement and estimation of relevant 3D morphological parameters. This method was applied to characterize DR12 mutant and wild-type tomatoes (Solanum lycopersicum).

KEY RESULTS

The cellular structure was described using the total volume of the pericarp, the surface area of the cell walls and the ratio of cell-wall surface area to pericarp volume, referred to as the cell-wall surface density. The heterogeneity of cellular structure within the fruit was investigated by estimating variations in the cell-wall surface density with distance to the epidermis.

CONCLUSIONS

The DR12 mutant presents a greater pericarp volume and an increase of cell-wall surface density under the epidermis.

Robust incremental compensation of the light attenuation with depth in 3D fluorescence microscopy

Summary Fluorescent signal intensities from confocal laser scanning microscopes (CLSM) suffer from several distortions inherent to the method. Namely, layers which lie deeper within the specimen are relatively dark due to absorption and scattering of both excitation and fluorescent light, photobleaching and/or other factors. Because of these effects, a quantitative analysis of images is not always possible without correction. Under certain assumptions, the decay of intensities can be estimated and used for a partial depth intensity correction. In this paper we propose an original robust incremental method for compensating the attenuation of intensity signals. Most previous correction methods are more or less empirical and based on fitting a decreasing parametric function to the section mean intensity curve computed by summing all pixel values in each section. The fitted curve is then used for the calculation of correction factors for each section and a new compensated sections series is computed. However, these methods do not perfectly correct the images. Hence, the algorithm we propose for the automatic correction of intensities relies on robust estimation, which automatically ignores pixels where measurements deviate from the decay model. It is based on techniques adopted from the computer vision literature for image motion estimation. The resulting algorithm is used to correct volumes acquired in CLSM. An implementation of such a restoration filter is discussed and examples of successful restorations are given.