High-resolution microCT reveals relationships between stomata and interior leaf anatomy in Sorghum

Abstract

Stomata are pores in the leaf epidermis that regulate the trade-off between CO2 uptake for photosynthesis and water vapor loss to the atmosphere. Stomatal patterning therefore influences water use efficiency and is a target for engineering to avoid drought stress. However, there is limited understanding of how internal leaf anatomy is coordinated with stomatal development, in part due to the technical challenges of assessing three-dimensional anatomy with sufficient resolution. C4 grasses are understudied, and this is a significant knowledge gap given their file-like stomatal distribution and unique mesophyll organization. In this study, wild-type sorghum and a low-stomatal density transgenic line expressing a synthetic Epidermal Patterning Factor (EPFsyn) were studied. High-resolution microCT was paired with machine learning to characterize three-dimensional traits of mesophyll, epidermis, and airspace, which together determine gias. Sorghum internal leaf airspace is an arrangement of large sub-stomatal airspaces with thin air passageways. Adaxial and abaxial surfaces differed in stomatal patterning relative to mesophyll structures, sub-stomatal crypts and airspace CO2 conductance (gias). Surprisingly, adaxial stomata were consistently located above rather than between vascular bundles. Unexpectedly, gias was not significantly different in wild-type versus EPFsyn. EPFsyn plants had larger crypts and shifts in internal leaf anatomy, indicating a potential compensation mechanism for predicted impacts of reduced stomatal density on gias. These findings provide a new understanding of the interplay between leaf surface specific anatomy and internal structural patterning of the mesophyll in a C4 species, and provides knowledge relevant to engineering water use efficiency in crop species.

Plant phenotyping relevance

高解像度microCTと機械学習を組み合わせ、葉の三次元形態・気腔などの植物形質を抽出する手法が研究の主要部分であり、単なる生物学的測定ではない。

Code and data availability

The supplied blocks contain only literature-cited reference lists and a supplementary figure caption. No public phenotype/trait datasets, microCT image data, author analysis code, or trained models are described with deposit or availability language, so no paper-specific public reproduction asset is identifiable.

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