Optical topometry and machine learning to rapidly phenotype stomatal patterning traits for maize QTL mapping

Abstract

Stomata are adjustable pores on leaf surfaces that regulate the trade-off of CO2 uptake with water vapor loss, thus having critical roles in controlling photosynthetic carbon gain and plant water use. The lack of easy, rapid methods for phenotyping epidermal cell traits have limited discoveries about the genetic basis of stomatal patterning. A high-throughput epidermal cell phenotyping pipeline is presented here and used for quantitative trait loci (QTL) mapping in field-grown maize (Zea mays). The locations and sizes of stomatal complexes and pavement cells on images acquired by an optical topometer from mature leaves were automatically determined. Computer estimated stomatal complex density (SCD; R2 = 0.97) and stomatal complex area (SCA; R2 = 0.71) were strongly correlated with human measurements. Leaf gas exchange traits were genetically correlated with the dimensions and proportion of stomatal complexes (rg = 0.39 to 0.71) but did not correlate with SCD. Heritability of epidermal traits was moderate to high (h2 = 0.42-0.82) across two field seasons. 36 QTLs were consistently identified for a given trait in both years. 24 hotspots of overlapping QTLs for multiple traits were identified, with univariate versus multivariate single marker analysis providing evidence consistent with pleiotropy in multiple cases. Putative orthologs of genes known to regulate stomatal patterning in Arabidopsis (Arabidopsis thaliana) were located within some, but not all, of these regions. This study demonstrates how discovery of the genetic basis for stomatal patterning can be accelerated in maize, a C4 model species where these processes are poorly understood.

Plasticity in stomatal behavior across a gradient of water supply is consistent among field-grown maize inbred lines with varying stomatal patterning

“…New high-throughput phenotyping and analytical techniques are providing unprecedented detail and depth of information about the suite of traits that underpin variation in WUE within C4 species (Ferguson et al, 2021;Pignon et al, 2021a,b;Xie et al, 2021). This should then in turn allow additional studies of the type presented here to quantify gs-model parameters in other genotypes and provide the parameterization data needed to inform crop improvement effort with in silico analyses (Marshall-Colon et al, 2017).…”

“…The four maize inbred lines studied display significant variation in stomatal density, other aspects of stomatal patterning and anatomy (Xie et al 2021), and A and gs (Fig. 4).…”

“…And, the limited available data suggest that intraspecific variation can be as great as interspecific variation (Miner, Bauerle, & Baldocchi, 2017). gs is determined by the stomatal dynamics (opening and closing of stomatal aperture) as well as maximum conductance via epidermal stomata patterning (stomatal density, size, and distribution) (Dow et al, 2014;Lawson & Matthews, 2020;Leakey et al, 2019;Xie et al, 2021). Stomatal patterning more broadly covaries with vein density, leaf width and canopy temperature (Brodribb & Holbrook, 2003;Brodribb & McAdam, 2011;Cano, Sharwood, Cousins, & Ghannoum, 2019;Prakash et al, 2021).…”

“…For example, the relationship between gs and stomatal density can be positive (Li, Li, Li, & Zhang, 2017;Xu & Zhou, 2008), undetectable (Zhao, Sun, Kjelgren, & Liu, 2015), or negative (Bresta et al, 2018) across different species or pools of intraspecific variation. Likewise, the relationship between gs and stomatal complex area can be positive (Galmés et al, 2013;Li et al, 2017) undetectable (Xu & Zhou, 2008;Zhao et al, 2015) or dependent on the shape of the stomatal complex rather than its size (Xie et al 2021). Therefore, it is of interest to investigate if variation in stomatal patterning contributes to intraspecific variation in the slope parameters of gs-models.…”

“…Maize (Zea mays) is a model plant for studying the genomics, genetics and physiology of complex traits in C4 plants (Buckler et al, 2009). The development of a machine-learning tool to automatically analyze microscopy images of the leaf epidermis facilitated detailed analysis of variation in stomatal patterning across genetically and anatomically diverse maize inbred lines that were consistent over two growing seasons (Xie et al, 2021). Relative to diversity in the species as a whole, B73 and MS71 were identified as inbred lines with moderate (106 mm -2 ) and low stomatal density (88 mm -2 ), respectively (Xie, 2021).…”