Sankar Martial, Nieminen Kaisa, Ragni Laura, Xenarios Ioannis, Hardtke Christian S
Department of Plant Molecular Biology, University of Lausanne, Lausanne, Switzerland.
Elife. 2014 Feb 11;3:e01567. doi: 10.7554/eLife.01567.
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. DOI: http://dx.doi.org/10.7554/eLife.01567.001.
在诸多优势中，其体积小使得模式生物成为备受青睐的研究对象。然而，即便在模式系统中，要在细胞水平对众多发育过程进行详细分析，也会因它们的尺度而受到严重阻碍。例如，拟南芥下胚轴的次生生长会形成一种由高度特化组织构成的径向模式，该模式从几十细胞开始，最终包含数千个细胞。由于其尺度以及只能通过侵入性方式进行研究，这一动态过程很难追踪，从而妨碍了对其中涉及的细胞增殖、分化和模式形成事件的全面理解。为克服此类限制，我们建立了一种自动化定量组织学方法。我们从拼接的高分辨率图像中获取下胚轴横截面，并使用定制的高通量图像处理和分割技术提取其中的信息内容。通过机器学习实现自动细胞类型识别，我们能够建立一个细胞分辨率图谱，揭示次生生长过程中的维管形态动力学，例如等距韧皮部极的形成。DOI: http://dx.doi.org/10.7554/eLife.01567.001
