Classical phenotyping and deep learning concur on genetic control of stomatal density and area in sorghum

Abstract

Stomatal density (SD) and stomatal complex area (SCA) are important traits that regulate gas exchange and abiotic stress response in plants. Despite sorghum (Sorghum bicolor) adaptation to arid conditions, the genetic potential of stomata-related traits remains unexplored due to challenges in available phenotyping methods. Hence, identifying loci that control stomatal traits is fundamental to designing strategies to breed sorghum with optimized stomatal regulation. We implemented both classical and deep learning methods to characterize genetic diversity in 311 grain sorghum accessions for stomatal traits at two different field environments. Nearly 12,000 images collected from abaxial (Ab) and adaxial (Ad) leaf surfaces revealed substantial variation in stomatal traits. Our study demonstrated significant accuracy between manual and deep learning methods in predicting SD and SCA. In sorghum, SD was 32%-39% greater on the Ab versus the Ad surface, while SCA on the Ab surface was 2%-5% smaller than on the Ad surface. Genome-Wide Association Study identified 71 genetic loci (38 were environment-specific) with significant genotype to phenotype associations for stomatal traits. Putative causal genes underlying the phenotypic variation were identified. Accessions with similar SCA but carrying contrasting haplotypes for SD were tested for stomatal conductance and carbon assimilation under field conditions. Our findings provide a foundation for further studies on the genetic and molecular mechanisms controlling stomata patterning and regulation in sorghum. An integrated physiological, deep learning, and genomic approach allowed us to unravel the genetic control of natural variation in stomata traits in sorghum, which can be applied to other plants.

Figure 1

Schematic overview of the study. A, Phenotyping of the SAP for SD and SCA in two environments (Env. 1—Manhattan and Env. 2—Hays) for two years (Exp. 1 in 2017 and Exp. 2 in 2018; see Supplemental Figure S1). B, Mask R-CNN models trained for predicting Ab and Ad stomatal number and complex area. Train and validate (val) images indicate the number of images used for training and validating the Mask R-CNN model trained. C, Mask R-CNN, a deep learning framework for stomata instance segmentation and stomata count. The network architecture contains convolutional layers (left) and fully connected layers (right), shown as rectangular cuboids in the figure. The size of each cuboid indicates the dimensionality of the corresponding layer. The connections between layers are represented through arrows. Detailed procedure followed to train, validate, and select the best model is provided in the Supplemental Figure S2.

Figure 2

Results of SD (per image) following manual and deep learning methods. Comparison of ground-truth images (A and B) and deep learning segmentation results (C and D, predicted stomata highlighted in colors). Relationship of the SD obtained from manual count with predicted count obtained from the deep learning method (E and G—Ab; F and H—Ad). SAP was characterized in two environments (Env. 1 and Env. 2). A total of 11,196 (in Exp. 1) and 828 (Exp. 2) images were used to manually count stomata and generate the observational ground-truth SD data. The same sets of images were used to predict the SD with the deep learning method, as illustrated in Figure 1. A–D, bars = 100 µm.

Figure 3

Relationship of observed SCA (μm²) with the corresponding data obtained using deep learning method (A). SCA was predicted using the deep learning approach on the entire SAP grown in Env. 1 and Env. 2 in 2017. Panels “B and C” show the distribution (Env. 1—blue line, dark gray bars; Env. 2—red line, light gray bars; intermediate gray bars indicate the overlap between the environments) of Ab and Ad stomata complex area, respectively. The vertical dotted lines on the histograms show population mean values in Env. 1 (blue) and Env. 2 (red). Values represent the positive percentage change in mean phenotypic value with respect to Env. 1 = [(mean trait value of Env. 1 − mean trait value of Env. 2)/mean trait value of Env. 1]×100.

Figure 4

Summary of the associated genetic loci for all the investigated traits, as revealed by GWAS. The lines (black, green, and blue) in chromosomes denote the physical position (Mb) of the cSNPs that were identified in the study. The position (in Mb) of the locus is presented on the left side of each chromosome. The locus name is shown on the right: loci found in Env. 1 [green], Env. 2, [blue] in the current study and previously reported QTLs (underlined in black) were associated with similar or closely related traits, including gas exchange, leaf morphology, and yield traits. Previously reported genomic regions or QTL IDs given in the map were obtained from https://aussorgm.org.au/sorghum-qtl-atlas/ (Mace et al., 2019), see Supplemental Table S4. SDAb, abaxial stomatal density; SDAd, adaxial stomatal density; SNAb_LA, abaxial stomatal number per single leaf (×10⁶); SNAd_LA, adaxial stomatal number per single leaf (×10⁶); SCAAb, stomata complex area of abaxial; SCAAd, stomata complex area of adaxial; and SinLA, single leaf area. # QTL (Q) acronym for the previously reported traits.

Figure 5

Regional plot of GWAS signal and pattern of pairwise LD (heatmap) for Ab SD per mm² on chromosome 6. A, The −log10 (y-axis) of the P-values are plotted against their physical chromosomal position. The red dashed line indicates the significance threshold (−log10 = 4). The yellow bar indicates the most promising genomic region in Env. 1 selected for haplotype analysis. B, The LD heatmap was constructed using Haploview 4.2 software. The color intensity of the box corresponds with the r² (light to dark gray indicate low to high recombination rate, respectively) between significant SNPs, S6_50396762, and S6_50462533. The SNP marked by dashed blue rectangle was the cSNP detected by MLM for Ab SD. C and D, SNPs highlighted in red in A are the five SNPs from which the haplotypes were formed. The whiskers indicate the interquartile range, and the outliers for Ab SD (mm⁻²) in Env. 1 (C) and Env. 2 (D). The dashed lines represent the mean, solid lines represent the median, and the whiskers indicate the 95% confidence interval. The haplotypes with a frequency (values in the parentheses) of >5% (AACCT, GGTCG, and GGTGG) were included for phenotypic reconfirmation (C and D). Means followed by a common letter are not significantly different by Tukey’s test at the 5% level of significance.

Figure 6

Stomatal conductance (A), transpiration (B), photosynthesis (C), and iWUE (D) of sorghum accessions carrying contrasting haplotypes for Ab SD under field conditions. Bars ± se (n = 3). Means followed by a common letter are not significantly different by Tukey’s test at the 5% level of significance. Gas exchange parameters were measured on a fully opened young leaf for two days (66 and 67th days after planting) in Env. 1 under field conditions in Exp. 2.