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 the use of quantitative, forward and reverse genetics to discover the genetic basis of stomatal patterning. A new high-throughput epidermal cell phenotyping pipeline is presented here and used for quantitative trait loci (QTL) mapping in field-grown maize. 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 correlated with the dimensions and proportion of stomatal complexes but, unexpectedly, did not correlate with SCD. Genetic variation in epidermal traits were consistent across two field seasons. Out of 143 QTLs in total, 36 QTLs were consistently identified for a given trait in both years. 24 hotspots of overlapping QTLs for multiple traits were identified. Orthologs of genes known to regulate stomatal patterning in Arabidopsis 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 model for C4 species where these processes are poorly understood.
One sentence summary Optical topometry and machine learning tools were developed to assess epidermal cell patterning, and applied to analyze its genetic architecture alongside leaf photosynthetic gas exchange in maize.
Footnotes
The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (www.plantphysiol.org) is: Andrew D.B. Leakey (leakey{at}illinois.edu).
A.D.B.L. and J.X. conceived of and designed the original research plans; J.X. performed the experiments; G.E. developed data collection methods and performed preliminary genotype screening; M.C. provided technical assistance; J.X. and D.M-J. conceived and developed the machine learning pipeline; J.X. and A.D.B.L. analyzed the data; J.X., D.M-J. and A.D.B.L. wrote the article with contributions from all of the authors; A.D.B.L. agrees to serve as the author responsible for contact and ensures communication.
1 This work was supported by the National Science Foundation (grant no. PGR– 1238030), the University of Illinois Center for Digital Agriculture, and a Foundation for Food and Agriculture Research Graduate Student Fellowship (to J.X.).