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Earles, J. M.

Publications and source records attributed to Earles, J. M..

2 recordsLinked to original sources

Maximum CO2 diffusion inside leaves is limited by the scaling of cell size and genome size

Maintaining high rates of photosynthesis in leaves requires efficient movement of CO2 from the atmosphere to the chloroplasts inside the leaf where it is converted into sugar. Throughout the evolution of vascular plants, CO2 diffusion across the leaf surface was maximized by reducing the sizes of the guard cells that form stomatal pores in the leaf epidermis1,2. Once inside the leaf, CO2 must diffuse through the intercellular airspace and into the mesophyll cells where photosynthesis occurs3,4. However, the diffusive interface defined by the mesophyll cells and the airspace and its coordinated evolution with other leaf traits are not well described5. Here we show that among vascular plants variation in the total amount of mesophyll surface area per unit mesophyll volume is driven primarily by cell size, the lower limit of which is defined by genome size. The higher surface area enabled by smaller cells allows for more efficient CO2 diffusion into photosynthetic mesophyll cells. Our results demonstrate that genome downsizing among the flowering plants6 was critical to restructuring the entire pathway of CO2 diffusion, facilitating high rates of CO2 supply to the leaf mesophyll cells despite declining atmospheric CO2 levels during the Cretaceous.

plant biology

Digitally Deconstructing Leaves in 3D Using X-ray Microcomputed Tomography and Machine Learning

Premise of the studyX-ray microcomputed tomography (microCT) can be used to measure 3D leaf internal anatomy, providing a holistic view of tissue organisation. Previously, the substantial time needed for segmenting multiple tissues limited this technique to small datasets, restricting its utility for phenotyping experiments and limiting our confidence in the conclusion of these studies due to low replication numbers. Methods and ResultsWe present a Python codebase for random-forest machine learning segmentation and 3D leaf anatomical trait quantification which dramatically reduces the time required to process single leaf microCT scans into detailed segmentations. By training the model on each scan using 6 hand segmented image slices out of >1500 in the full leaf scan, it achieves >90% accuracy in background and tissue segmentation. ConclusionOverall, this 3D segmentation and quantification pipeline can reduce one of the major barriers to using microCT imaging in high-throughput plant phenotyping.

plant biology