Discrimination of CRISPR/Cas9-induced mutants of rice seeds using near-infrared hyperspectral imaging. Issue 1 (December 2017)

• Record Type: Journal Article
• Authors: Feng, Xuping; Peng, Cheng; Chen, Yue; Liu, Xiaodan; Feng, Xujun; He, Yong
• Is Part Of: Scientific reports. Volume 7:Issue 1(2017)
• Page Start: 1
• Page End: 10
• Publication Date: 2017-12
• Subjects: Natural history -- Research -- Periodicals; Biology -- Research -- Periodicals; Physical sciences -- Research -- Periodicals; Earth sciences -- Research -- Periodicals; Environmental sciences -- Research -- Periodicals; 502.85
• DOI: 10.1038/s41598-017-16254-z
• Languages: English
• ISSNs: 2045-2322

Abstract

Abstract Identifying individuals with target mutant phenotypes is a significant procedure in mutant exploitation for implementing genome editing technology in a crop breeding programme. In the present study, a rapid and non-invasive method was proposed to identify CRISPR/Cas9-induced rice mutants from their acceptor lines (huaidao-1 and nanjing46) using hyperspectral imaging in the near-infrared (NIR) range (874.41–1733.91 nm) combined with chemometric analysis. The hyperspectral imaging data were analysed using principal component analysis (PCA) for exploratory purposes, and a support vector machine (SVM) and an extreme learning machine (ELM) were applied to build discrimination models for classification. Meanwhile, PCA loadings and a successive projections algorithm (SPA) were used for extracting optimal spectral wavelengths. The SVM-SPA model achieved best performance, with classification accuracies of 93% and 92.75% being observed for calibration and prediction sets for huaidao-1 and 91.25% and 89.50% for nanjing46, respectively. Furthermore, the classification of mutant seeds was visualized on prediction maps by predicting the features of each pixel on individual hyperspectral images based on the SPA-SVM model. The above results indicated that NIR hyperspectral imaging together with chemometric data analysis could be a reliable tool for identifying CRISPR/Cas9-induced rice mutants, which would help to accelerate selection and crop breeding processes.