Research on the evolutionary history of the morphological structure of cotton seeds: a new perspective based on high-resolution micro-CT technology

Abstract

Cotton (Gossypium hirsutum L.) seed morphological structure has a significant impact on the germination, growth and quality formation. However, the wide variation of cotton seed morphology makes it difficult to achieve quantitative analysis using traditional phenotype acquisition methods. In recent years, the application of micro-CT technology has made it possible to analyze the three-dimensional morphological structure of seeds, and has shown technical advantages in accurate identification of seed phenotypes. In this study, we reconstructed the seed morphological structure based on micro-CT technology, deep neural network Unet-3D model, and threshold segmentation methods, extracted 11 basics phenotypes traits, and constructed three new phenotype traits of seed coat specific surface area, seed coat thickness ratio and seed density ratio, using 102 cotton germplasm resources with clear year characteristics. Our results show that there is a significant positive correlation (P< 0.001) between the cotton seed size and that of the seed kernel and seed coat volume, with correlation coefficients ranging from 0.51 to 0.92, while the cavity volume has a lower correlation with other phenotype indicators (r<0.37, P< 0.001). Comparison of changes in Chinese self-bred varieties showed that seed volume, seed surface area, seed coat volume, cavity volume and seed coat thickness increased by 11.39%, 10.10%, 18.67%, 115.76% and 7.95%, respectively, while seed kernel volume, seed kernel surface area and seed fullness decreased by 7.01%, 0.72% and 16.25%. Combining with the results of cluster analysis, during the hundred-year cultivation history of cotton in China, it showed that the specific surface area of seed structure decreased by 1.27%, the relative thickness of seed coat increased by 8.70%, and the compactness of seed structure increased by 50.17%. Furthermore, the new indicators developed based on micro-CT technology can fully consider the three-dimensional morphological structure and cross-sectional characteristics among the indicators and reflect technical advantages. In this study, we constructed a microscopic phenotype research system for cotton seeds, revealing the morphological changes of cotton seeds with the year in China and providing a theoretical basis for the quantitative analysis and evaluation of seed morphology.

Keywords: cotton; micro-CT; phenotypic analysis; seed morphological structure; temporal succession.

Figure 1

Micro-CT-based cotton seed imaging processing flow. (A) micro-CT image sequence. (B) U-net network. (C) Segment seed kernel mask. (D) Segment seed coat and cavity. (E) Loss and accuracy curves on training set.

Figure 2

Three-dimensional reconstruction of 102 cotton seed (green) and three-dimensional reconstruction images and three-dimensional view of seed coat (blue), cavity (yellow), and seed kernel (red) of 12 seeds. The seeds shown in the figure are represented using the RAS coordinate system and demonstrate consistent orientation, measured in mm. The three-dimensional morphological structure was scaled to 25 cm, and the image scale for the three orthographic views was set to 5 cm.

Figure 3

Three types of the inner structures and seed damage in cotton seeds are depicted. Among them, (A) represents the cavity between the seed coat and the kernel, (B) depicts the cavity inside the seed coat and endosperm remnants, (C) shows the cavity inside the seed kernel, (D) represents damage to the seed coat, (E) shows the breach between seed kernels, and (F) represents damage to the seed kernel.

Figure 4

Data evaluation of seed length (A), seed width (B) and seed thickness (C) measured values extracted based on CT images. N=306. Date represents mean ± SE (3 biological replicates, n=9, 15 and 26 plants, respectively), letters above the bars indicate significant differences at the level of P<0.05.

Figure 5

Correlation analysis of 11 phenotypic indicators (Seed Length, Seed Width, Seed Thickness, Seed Volume, Seed Surface Area, Kernel Volume, Kernel Surface Area, Seed Coat Volume, Seed Cavity Volume, Average Seed Coat Thickness and Seed Fullness). Significance *< 0.05, **< 0.01; ***< 0.001.

Figure 6

The trend of seed morphological structure change of cotton varieties independently cultivated in China. SSA, Seed Surface Area; KSA, Seed Kernel Surface Area; SCV, Seed Cavity Volume; ASCT, Average Seed Coat Thickness. The cultivars released years involve cotton varieties cultivated from the country in 1958-1970, 1970-1990 and 1990-2020, respectively.

Figure 7

Clustering of 102 cotton varieties. Ten seed phenotypic indicators were standardized using the Z-score method and classified using the Ward method in combination with squared Euclidean distance as the similarity measure, to categorize the indicators of different cotton varieties. The 102 varieties are divided into three categories. The first cluster comprised 46 varieties, accounting for 45.1% of the total varieties. Of these, one variety was from 1904-1958, three varieties from 1958-1970, four varieties from 1970-1990, 32 varieties from 1990-2020, and six varieties had unknown Years. The second cluster consisted of 41 varieties, accounting for 40.2% of the total varieties, including 2 varieties from 1904-1958, 3 varieties from 1970-1990, 30 varieties from 1990-2020, and 6 varieties with unknown Years. The third cluster encompassed 15 varieties, accounting for 14.7% of the total varieties, including 2 varieties from 1904-1958, 1 variety from 1958-1970, 4 varieties from 1970-1990, 3 varieties from, and 5 varieties with unknown Years.

Figure 8

The three clustering results from the statistical analysis of the variations in cotton seed morphology were Seed Length, Width, and Thickness (A), Seed Volume and Seed Kernel Volume (B), Cavity Volume and Seed Coat Volume (C), Seed Surface Area and Seed Kernel Surface Area (D), and Seed Fullness (E). LSD test was used for normal distribution data. Date represents mean ± SE (3 biological replicates, n=46,41 and 15 varieties, respectively), letters above the bars indicate significant differences at the level of P<0.05.

Figure 9

Comparison of differences in Seed Coat Specific Surface Area (A), Seed Coat Thickness Ratio (B), and Seed Density Raito (C) among four Years. Given are the means ± SEM. Boxes represent first and third quartile (upper and lower margins), and median (horizontal line). N=306, significance: *<0.05, **<0.01.