一种快速有效鉴定CRISPR/Cas9诱导水稻突变体的方法

doi:10.3964/j.issn.1000-0593(2018)02-0570-05

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Abstract Mutant screening is an important step for CRISPR/Cas9 gene editing technology employed in crop breeding program. The present study proposes a visual identification method of CRISPR/Cas9-induced rice mutants based on near-infrared hyperspectral image technology. A total of 1 200 samples of rice seeds were collected, comprising 600 wide types and 600 CRISPR/Cas9-induced mutant samples. The whole data set was divided into two groups according to the Kennard-Stone algorithm, a calibration set (400 samples) and a prediction set (200 samples) for each class. 24 optimal wavelengths were selected by 2^nd spectra algorithm after preprocessing the selection spectral region with absolute noises by wavelet transform. Radial basis function neural network (RBFNN), extreme learning machine (ELM) and K-nearest neighbor (KNN) were used to build discrimination models based on the preprocessed full spectra and feature wavelengths. The results demonstrated that neural networks models achieved good recognition ability. The RBFNN model calculated on the optimal wavelength showed classification rates of 92.25% and 89.50% for calibration set and prediction set, respectively. Finally, the classification of mutant seeds could be visualized on prediction maps by predicting the features of each pixel on individual hyperspectral image based on 2^nd derivative-RBFNN model. It was concluded that hyperspectral imaging together with chemometric data analysis was a promising technique to identify CRISPR/Cas9-induced rice mutants, which offered a powerful tool for evaluating large number of samples from CRISPR/Cas9 gene editing performance trials and breeding programs.

Key words：NIR hyperspectral imaging; CRISPR/Cas9; Radial basis function neural network; Visualization

Received: 2017-05-15 Accepted: 2017-10-09

ZTFLH:

S123

Corresponding Authors: HE Yong E-mail: yhe@zju.edu.cn

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http://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2018)02-0570-05 OR http://www.gpxygpfx.com/EN/Y2018/V38/I02/570

[1] Platt R J, Chen S, Zhou Y, et al. Cell, 2014，159(2): 440.
[2] Endo M, Mikami M, Toki S. Plant & Cell Physiology, 2015，56(1): 41.
[3] Alishahi A, Farahmand H, Prieto N, et al. Spectrochimica Acta Part A Molecular & Biomolecular Spectroscopy, 2010，75(1): 1.
[4] WANG Hai-long, YANG Xiang-dong, ZHANG Chu, et al(王海龙, 杨向东, 张初，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2016，36(6): 1843.
[5] Liu C, Liu W, Lu X, et al. Food Chemistry, 2014，153: 87.
[6] Munck L, Moller B, Jacobsen S, et al. Journal of Cereal Science, 2004，40(3): 213.
[7] Biradar K S, Nadaf H L, Kenganal M. Indian Journal of Plant Physiology, 2010.
[8] Ishimaru K, Hirotsu N, Madoka Y，et al. Nature genetics, 2013，45(6): 707.
[9] Jetter K, Depczynski U, Molt K et al. Analytica Chimica Acta, 2000，420(2): 169.
[10] Tadé M O. Chemical Product & Process Modeling, 2012，7(1).
[11] Moore B. IEEE Transactions on Automatic Control, 2003，26(1): 17.
[12] Lorente D, Aleixos N, Gómez-Sanchis, et al. Food and Bioprocess Technology, 2013，6(2): 530.
[13] HE Yong(何勇). Application of Spectroscopy and Imaging Technology in Agriculture(光谱及成像技术在农业中的应用). Beijing: Science Press(北京: 科学出版社)，2016.
[14] Workman Jand Weyer L. Practical Guide to Interpretive Near-Infrared Spectroscopy. CRC Press, Inc., 2007.
[15] JRJJW. Interpretive Spectroscopy for Near Infrared. Applied Spectroscopy Reviews, 1996. 31(3): 251.
[16] Selvaraju K, Kirubavathi K，Kumararaman S. Journal of Minerals & Materials Characterization & Engineering, 2012，11(3): 303.
[17] Buscema M. Back Propagation Neural Networks. Substance Use & Misuse, 1998，33(2): 233.
[18] Moreno R, Corona F, Lendasse A, et al. Neurocompution, 2014, 128(5): 207.
[19] Xie C, Chen J, Liu F, et al. Scientific Reports, 2015, 5: 16564.
[20] Zhang X, Liu F, He Y, et al. Sensors, 2012, 12(12): 17234.