Background: Despite the wide spread application of confocal and multiphoton laser scanning microscopy in plant biology, leaf phenotype assessment still relies on two-dimensional imaging with a limited appreciation of the cells' structural context and an inherent inaccuracy of cell measurements. Here, a successful procedure for the three-dimensional imaging and analysis of plant leaves is presented.
Results: The procedure was developed based on a range of developmental stages, from leaf initiation to senescence, of soil-grown Arabidopsis thaliana (L.) Heynh. Rigorous clearing of tissues, made possible by enhanced leaf permeability to clearing agents, allowed the optical sectioning of the entire leaf thickness by both confocal and multiphoton microscopy. The superior image quality, in resolution and contrast, obtained by the latter technique enabled the three-dimensional visualisation of leaf morphology at the individual cell level, cell segmentation and the construction of structural models. Image analysis macros were developed to measure leaf thickness and tissue proportions, as well as to determine for the epidermis and all layers of mesophyll tissue, cell density, volume, length and width. For mesophyll tissue, the proportion of intercellular spaces and the surface areas of cells were also estimated. The performance of the procedure was demonstrated for the expanding 6th leaf of the Arabidopsis rosette. Furthermore, it was proven to be effective for leaves of another dicotyledon, apple (Malus domestica Borkh.), which has a very different cellular organisation.
Conclusions: The pipeline for the three-dimensional imaging and analysis of plant leaves provides the means to include variables on internal tissues in leaf growth studies and the assessment of leaf phenotypes. It also allows the visualisation and quantification of alterations in leaf structure alongside changes in leaf functioning observed under environmental constraints. Data obtained using this procedure can further be integrated in leaf development and functioning models.