一种快速有效鉴定CRISPR/Cas9诱导水稻突变体的方法

摘要

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 hyper-spectral 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 cali-bration set(400 samples)and a prediction set(200 samples)for each class.24 optimal wavelengths were selected by 2ndspectra 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 pre-diction maps by predicting the features of each pixel on individual hyperspectral image based on 2ndderivative-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.%突变体的筛选与鉴定是育种工作中的重要环节.该研究基于高光谱成像技术实现了水稻CRISPR/Cas9突变体种子的可视化鉴别.采集了水稻HD野生型和CRISPR/Cas9突变体种子共1200粒样本的高光谱图像数据,通过Kennard-Stone算法,按照2:1的比例构建了建模集(800)和预测集(400).对水稻种子的原始光谱经过WT预处理后,通过2ndderivative提取了24个特征波长,分别基于全谱和特征波长建立径向基函数神经网络(RBFNN),极限学习机(ELM)和K最邻近法(KNN)模型.试验结果表明,无论是基于全谱还是特征波长神经网络模型都取得了良好的识别能力.通过2ndderivative提取的特征波长结合 RBFNN模型也取得了较好的鉴别结果,其建模集和预测集分别达到了92.25% 和89.50%.基于2ndderivative-RBFNN结合图像处理技术,可以实现水稻CRISPR/Cas9突变体种子的可视化鉴别,实现种子的定位和识别.结果表明应用高光谱成像技术,结合化学计量学方法和图像处理技术对水稻CRISPR/Cas9突变体的鉴别具有可行性,可为水稻育种中大量突变体的快速、准确地筛选和鉴定提供技术手段.