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                <full_title>eLife</full_title>
                <issn media_type="electronic">2050-084X</issn>
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                  <title>Automated quantitative histology reveals vascular morphodynamics during Arabidopsis hypocotyl secondary growth</title>
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                  <person_name contributor_role="author" sequence="first">
                    <given_name>Martial</given_name>
                    <surname>Sankar</surname>
                    <affiliation>Department of Plant Molecular Biology, University of Lausanne, Lausanne, Switzerland</affiliation>
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                    <given_name>Kaisa</given_name>
                    <surname>Nieminen</surname>
                    <affiliation>Department of Plant Molecular Biology, University of Lausanne, Lausanne, Switzerland</affiliation>
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                    <given_name>Laura</given_name>
                    <surname>Ragni</surname>
                    <affiliation>Department of Plant Molecular Biology, University of Lausanne, Lausanne, Switzerland</affiliation>
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                  <person_name contributor_role="author" sequence="additional">
                    <given_name>Ioannis</given_name>
                    <surname>Xenarios</surname>
                    <affiliation>Vital-IT, Swiss Institute of Bioinformatics, Lausanne, Switzerland</affiliation>
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                  <person_name contributor_role="author" sequence="additional">
                    <given_name>Christian S</given_name>
                    <surname>Hardtke</surname>
                    <affiliation>Department of Plant Molecular Biology, University of Lausanne, Lausanne, Switzerland</affiliation>
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                  <jats:p>Among various advantages, their small size makes model organisms preferred subjects of investigation. Yet, even in model systems detailed analysis of numerous developmental processes at cellular level is severely hampered by their scale. For instance, secondary growth of Arabidopsis hypocotyls creates a radial pattern of highly specialized tissues that comprises several thousand cells starting from a few dozen. This dynamic process is difficult to follow because of its scale and because it can only be investigated invasively, precluding comprehensive understanding of the cell proliferation, differentiation, and patterning events involved. To overcome such limitation, we established an automated quantitative histology approach. We acquired hypocotyl cross-sections from tiled high-resolution images and extracted their information content using custom high-throughput image processing and segmentation. Coupled with automated cell type recognition through machine learning, we could establish a cellular resolution atlas that reveals vascular morphodynamics during secondary growth, for example equidistant phloem pole formation.</jats:p>
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                  <jats:p>Our understanding of the living world has been advanced greatly by studies of ‘model organisms’, such as mice, zebrafish, and fruit flies. Studying these creatures has been crucial to uncovering the genes that control how our bodies develop and grow, and also to discover the genetic basis of diseases such as cancer.</jats:p>
                  <jats:p>Thale cress—or Arabidopsis thaliana to give its formal name—is the model organism of choice for many plant biologists. This tiny weed has been widely studied because it can complete its lifecycle, from seed to seed, in about 6 weeks, and because its relatively small genome simplifies the search for genes that control specific traits. However, as with other much-studied model systems, understanding the changes that underpin the development of some of the more complex tissues in Arabidopsis has been severely hampered by the shear number of cells involved.</jats:p>
                  <jats:p>After it has emerged from the seed, the plant’s first stem will develop from a few dozen cells in width to several thousand cells with highly specialized tissues arranged in a complex pattern of concentric circles. Although this stem thickening process represents a major developmental change in many plants—from Arabidopsis to oak trees—it has been under-researched. This is partly because it involves so many different cells, and also because it can only be observed in thin sections cut out of the plant’s stem.</jats:p>
                  <jats:p>Now Sankar, Nieminen, Ragni et al. have developed a novel approach, termed ‘automated quantitative histology’, to overcome these problems. This strategy involves ‘teaching’ a computer to automatically recognize different plant cells and to measure their important features in high-resolution images of tissue sections. The resulting ‘map’ of the developing stem—which required over 800 hr of computing time to complete—reveals the changes to cells and tissues as they develop that allow the transport of water, sugars and nutrients between the above- and below-ground organs. Sankar, Nieminen, Ragni et al. suggest that their novel approach could, in the future, also be applied to study the development of other tissues and organisms, including animals.</jats:p>
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                      <title>Figure 1. Cellular level analysis of Arabidopsis hypocotyl secondary growth.</title>
                      <subtitle>(A) Light microscopy of cross sections obtained from Arabidopsis hypocotyls (organ position illustrated for a 9-day-old seedling, lower left) at 9 dag (upper left) and 35 dag (right). Size bars are 100 μm. Blue GUS staining due to the presence of an APL::GUS reporter gene in this Col-0 background line marks phloem bundles. (B) Overview of the developmental series (time points and distinct samples per genotype) analyzed in this study. (C) Example of a high-resolution hypocotyl section image assembled from 11 × 11 tiles. (D) The same image after pre-processing and binarization, and (E) subsequent segmentation using a watershed algorithm. (F) Number of mis-segmented cells as determined by careful visual inspection in 12 sections, plotted against the total number of cells per section (log scale).</subtitle>
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                      <title>Figure 2. The ‘Quantitative Histology’ approach.</title>
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                      <title>Figure 2—figure supplement 1. An example of classifier selection through V-fold cross validation.</title>
                      <subtitle>The green arrow points out the selected feature combination according to the criteria of minimum number of features with the highest performance and the lowest variation (the radiusV feature was excluded due to its putative variation in tissue location).</subtitle>
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                      <title>Figure 3. Progression of tissue proliferation.</title>
                      <subtitle>(A) Principal component analysis (PCA) of the phenoprints shown in Figure 2B, performed with normalized values (Supplementary file 4). The inlay screeplot displays the proportion of total variation explained by each principal component. (B–E) Comparative plots of parameter progression in the two genotypes. In (D), xylem represents combined vessel, parenchyma, and fiber cells, phloem represents combined phloem parenchyma and bundle cells. Error bars indicate standard error.</subtitle>
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                    <doi_data>
                      <doi>10.7554/eLife.01567.006</doi>
                      <resource>https://elifesciences.org/articles/01567#fig3</resource>
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                      <title>Figure 4. Bimodal distribution of incline angle according to position.</title>
                      <subtitle>(A and B) Spatial distribution of cell incline angle illustrates the vascular organization in Ler (B) as compared to Col-0 (A) at later stages of development, for example 30 dag. The size of the disc increases with the area of the cell. Blue color indicates radial cell orientation, red orthoradial. (C and D) Violin plots of incline angle distribution, illustrating increasingly bimodal distribution coincident with refined vascular organization and different dynamics of the process in the two genotypes.</subtitle>
                    </titles>
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                      <doi>10.7554/eLife.01567.007</doi>
                      <resource>https://elifesciences.org/articles/01567#fig4</resource>
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                      <title>Figure 4—figure supplement 1. An illustration of the incline angle.</title>
                      <subtitle>The incline is the angle between the section radius through the center of an ellipse fit to a cell and the major axis of that ellipse extended towards the x axis.</subtitle>
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                    <doi_data>
                      <doi>10.7554/eLife.01567.008</doi>
                      <resource>https://elifesciences.org/articles/01567/figures#fig4s1</resource>
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                    <titles>
                      <title>Figure 5. Distinct local organization of incline angle during hypocotyl secondary growth progression.</title>
                      <subtitle>(A–J) Density plots of cell incline angle vs radial position for the two genotypes at the indicated developmental stages, representing all cells across all sections for a given time point. The red lines represent the fit of these cloud distributions with locally weighted linear regression (i.e., lowess), revealing the essential data trends. All sections were normalized from 0.0 (the manually defined center) to 1.0 (the average radius in a set of sections as determined by the average distance of the outermost cells from the center for individual sections). Box plots indicate the quartiles of the radian distribution for each cell-type class and are placed at the average position of the cell type with respect to the y axis. Outliers are shown as circles.</subtitle>
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                      <doi>10.7554/eLife.01567.009</doi>
                      <resource>https://elifesciences.org/articles/01567#fig5</resource>
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                    <titles>
                      <title>Figure 5—figure supplement 1. Analysis of cell number in defined xylem regions of different size.</title>
                      <subtitle>Cell number in a circle of 200–500 pixels around the section centers for Col-0. Cell count in a constant area of xylem over time across all averaged across all sections.</subtitle>
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                    <doi_data>
                      <doi>10.7554/eLife.01567.010</doi>
                      <resource>https://elifesciences.org/articles/01567/figures#fig5s1</resource>
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                    <titles>
                      <title>Figure 6. Mapping of phloem pole patterning.</title>
                      <subtitle>(A) Example of Gaussian kernel density estimate of the location of predicted phloem bundles cells in a 30 dag Col-0 section. High density represents phloem poles. (B) Example of an analysis of emerging phloem pole position in a 30 dag Col-0 section. The plot represents a pixel intensity map after noise reduction along a circular region of interest across the emerging phloem poles. Intensity peaks are due to GUS staining conferred to phloem bundles by an APL::GUS reporter construct. (C) Probability density function of the data shown in (B) obtained from an automated Bayesian model. The dominant single peak indicates a constant arc distance of ca. 62 pixel between the phloem poles.</subtitle>
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                    <doi_data>
                      <doi>10.7554/eLife.01567.011</doi>
                      <resource>https://elifesciences.org/articles/01567#fig6</resource>
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                    <titles>
                      <title>Supplementary file 1.</title>
                      <subtitle>(A) An explanation of the extracted parameters that describe the cellular features. (B) Summary information of the hand-labeled training set for supervised machine learning. (C) Definition of the classifiers selected for analysis. (D) Summary of the classifier parameters for supervised machine learning. (E) Overview of the cell type classes recognized by the supervised machine learning approach and their assignment codes used in Data Files 3 and 4.</subtitle>
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                    <doi_data>
                      <doi>10.7554/eLife.01567.012</doi>
                      <resource>https://elifesciences.org/articles/01567/figures#SD1-data</resource>
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                    <titles>
                      <title>Supplementary file 2.</title>
                      <subtitle>Quality control files for the Col-0 sections.</subtitle>
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                    <doi_data>
                      <doi>10.7554/eLife.01567.013</doi>
                      <resource>https://elifesciences.org/articles/01567/figures#SD2-data</resource>
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                    <titles>
                      <title>Supplementary file 3.</title>
                      <subtitle>Quality control files for the Ler sections.</subtitle>
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                    <doi_data>
                      <doi>10.7554/eLife.01567.014</doi>
                      <resource>https://elifesciences.org/articles/01567/figures#SD3-data</resource>
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                    <titles>
                      <title>Supplementary file 4.</title>
                      <subtitle>The normalized values of the phenoprints (Figure 2B) used for PCA.</subtitle>
                    </titles>
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                    <doi_data>
                      <doi>10.7554/eLife.01567.015</doi>
                      <resource>https://elifesciences.org/articles/01567/figures#SD4-data</resource>
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                    <titles>
                      <title>Decision letter</title>
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                    <doi_data>
                      <doi>10.7554/eLife.01567.016</doi>
                      <resource>https://elifesciences.org/articles/01567#SA1</resource>
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                      <title>Author response</title>
                    </titles>
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                    <doi_data>
                      <doi>10.7554/eLife.01567.017</doi>
                      <resource>https://elifesciences.org/articles/01567#SA2</resource>
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