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Novel computed tomography-based tools reliably quantify plant reproductive investment.

ABSTRACT: The flower is a bisexual reproductive unit where both genders compete for resources. Counting pollen and ovules in flowers is essential to understand how much is invested in each gender. Classical methods to count very numerous pollen grains and ovules are inefficient when pollen grains are tightly aggregated, and when fertilization rates of ovules are unknown. In this study we have therefore developed novel counting techniques based on computed tomography. In order to demonstrate the potential of our methods in very difficult cases, we counted pollen and ovules across inflorescences of deceptive and rewarding species of European orchids, which possess both very large numbers of pollen grains (tightly aggregated) and ovules. Pollen counts did not significantly vary across inflorescences and pollination strategies, whereas deceptive flowers had significantly more ovules than rewarding flowers. The within-inflorescence variance of pollen-to-ovule ratios in rewarding flowers was four times higher than in deceptive flowers, possibly demonstrating differences in the constraints acting on both pollination strategies. We demonstrate the inaccuracies and limitations of previously established methods, and the broad applicability of our new techniques: they allow measurement of reproductive investment without restriction on object number or aggregation, and without specimen destruction.

SUBMITTER: Staedler YM

PROVIDER: S-EPMC5853293 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Project description:BackgroundLeaf cellular architecture plays an important role in setting limits for carbon assimilation and, thus, photosynthetic performance. However, the low density, fine structure, and sensitivity to desiccation of plant tissue has presented challenges to its quantification. Classical methods of tissue fixation and embedding prior to 2D microscopy of sections is both laborious and susceptible to artefacts that can skew the values obtained. Here we report an image analysis pipeline that provides quantitative descriptors of plant leaf intercellular airspace using lab-based X-ray computed tomography (microCT). We demonstrate successful visualisation and quantification of differences in leaf intercellular airspace in 3D for a range of species (including both dicots and monocots) and provide a comparison with a standard 2D analysis of leaf sections.ResultsWe used the microCT image pipeline to obtain estimates of leaf porosity and mesophyll exposed surface area (Smes) for three dicot species (Arabidopsis, tomato and pea) and three monocot grasses (barley, oat and rice). The imaging pipeline consisted of (1) a masking operation to remove the background airspace surrounding the leaf, (2) segmentation by an automated threshold in ImageJ and then (3) quantification of the extracted pores using the ImageJ 'Analyze Particles' tool. Arabidopsis had the highest porosity and lowest Smes for the dicot species whereas barley had the highest porosity and the highest Smes for the grass species. Comparison of porosity and Smes estimates from 3D microCT analysis and 2D analysis of sections indicates that both methods provide a comparable estimate of porosity but the 2D method may underestimate Smes by almost 50%. A deeper study of porosity revealed similarities and differences in the asymmetric distribution of airspace between the species analysed.ConclusionsOur results demonstrate the utility of high resolution imaging of leaf intercellular airspace networks by lab-based microCT and provide quantitative data on descriptors of leaf cellular architecture. They indicate there is a range of porosity and Smes values in different species and that there is not a simple relationship between these parameters, suggesting the importance of cell size, shape and packing in the determination of cellular parameters proposed to influence leaf photosynthetic performance.

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Novel computed tomography-based tools reliably quantify plant reproductive investment.

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