Prof. Christian Hardtke, Director of the Department of Plant Biology at the University of Lausanne, with the help of SIB’s High Performance Computing Group led by Ioannis Xenarios, established an “Automated Quantitative Histology” approach to analyze high-resolution images of hypocotyl cross-sections in an automated manner.
The approach, funded by SystemsX.ch, combines high-resolution imaging with automated image segmentation and supervised machine learning to achieve accurate cellular feature extraction and automated cell type recognition in a large-scale developmental process.
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.
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 six 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.
After it has emerged from the seed, the plant’s first stem will develop from a few dozen cells in its radial dimension 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.
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 recognise 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 hours of computing time to complete—reveals the changes to cells and the tissues they form as they develop to allow the transport of water and the distribution of sugars and nutrients between 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.
The results of the research are to be published in February in the journal eLife.