[Advances in Technology]

Development of high-throughput phenotyping software for measuring seed shape

Takanari Tanabata1, Taeko Shibaya1, Kiyosumi Hori1, Kaworu Ebana2, Masahiro Yano3
1Rice Applied Genomics Research Unit, 2Biodiversity Research Unit, 3Agrogenomics Research Center
[Abstract]
We developed an efficient software for high-throughput measurement of seed shape and size. The SmartGrain software facilitates accurate and efficient measurement of various parameters that can be used in detailed phenotypic characterization of seeds for genetic analysis. SmartGrain was successfully used in the detection of QTL for seed size among Japanese rice varieties.
[Keywords]
phenotyping, image analysis, seed shape

[Background]

Recent progress of molecular techniques enables us to overcome several concerns about expensive and laborious genotyping works in DNA marker assisted selection (MAS). However, phenotyping methodology is still low-throughput and remains as a major bottleneck for broader use of MAS. Here, we developed a high-throughput phenotyping software to facilitate accurate and fast measurements of seed shape and size which are some of the most important traits used for crop genetic analysis.
[Results and Discussion]
  1. SmartGrain can analyze images captured on a scanner with the supplied software and can recognize individual seeds for measurement. The process takes only a few minutes per image so that several hundred batches of seeds can be analysed in a short time (Fig. 1).
  2. In addition to seed area, it can also be used for measurement of perimeter length (PL), seed length (L), seed width (W), circularity (CS), length to width ratio (LWR), intersection of length and width (IS), center of gravity (CG), and distance (DS) between IS and CG (Fig. 2). The data can be exported as a CSV file.
  3. To verify the accuracy of SmartGrain, we analyzed the seeds of 127 inbred lines derived from Nipponbare/Koshihikari backcrossed with Koshihikari, followed by QTL analysis. Four QTLs were detected for L, LWR, CS, and PL on the long arm of chromosome 11 and a candidate genomic region of the QTL was narrowed down (Fig. 3).
  4. SmartGrain can also be used for measuring the seeds of other plant species such as Arabidopsis, soybean (Glycine max), Setaria italica and Setaria viridis with sufficient accuracy as in rice.
[Future prospects]
  1. SmartGrain is a freeware available at http://www.naro.affrc.go.jp/archive/nias/qtl/SmartGrain/ and currently runs on Microsoft Windows Vista, 7 and 8,
  2. Applications in genomic analysis involving grain size will accelerate phenotypic measurements and the isolation of associated genes.
  3. As it can discriminate lesion area on the hull as well as seed shape, it can be used for evaluation of the level of disease resistance.
  4. SmartGrain can also be used as a tool for evaluation of genetic resources, phenotype analysis in various breeding programs, and in test cultivation under various fertilizer conditions.

Fig.1. Scheme of high-throughput measurement Fof isgeed. s1hape using SmartGrain


Fig.2. SmartGrain automatically identifies seeds within an image and measures the seed shape profiles. (A) Image of grains on scanner. Red arrow indicates a pedicel. (B) SmartGrain image of grains. (C) Grain shape parameters: AS, within red line; PL, red line; CS, from red line; L, yellow line; W, green line; IS, white circle; CG, red circle. (D) Algorithm for image analysis and measurement of seed shape profiles.


Fig.3. Seed L and W of SL635, SL636, and SL637 CSSLs with a cv Nipponbare segment of chromosome 11 in Koshihikari background. (A) Graphical genotypes of chromosome 11 in the CSSLs. Gray, homozygous for Koshihikari; white, homozygous for Nipponbare. (B) Seeds of the three CSSLs and Koshihikari; bar = 5 mm. L (C) and 1,000-grain weight (D; dehulled) of parents and CSSLs. Asterisks indicate significant differences between Koshihikari and the CSSLs: *P < 0.05, **P < 0.01; ns, not significant. L was measured in five plants per line, and 1,000-seed weight was measured independently three times in bulked grains of each line.

 

[Reference]

  1. Tanabata T, Shibaya T, Hori K, Ebana K, Yano M (2012) SmartGrain: High-throughput phenotyping software for measuring seed shape through image analysis. Plant Physiology 160(4):1871-1880.
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