Discriminatory Detection of ssDNA by Surface-Enhanced Raman Spectroscopy (SERS) and Tree-Based Support Vector Machine (Tr-SVM)
Discriminatory Detection of ssDNA by Surface-Enhanced Raman Spectroscopy (SERS) and Tree-Based Support Vector Machine (Tr-SVM)
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DOI：
10.1021/acs.analchem.0c04576
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发表时间：
2021-07-01
影响因子：
7.4
通讯作者：
Vikesland, Peter J.
中科院分区：
文献类型：
--
作者：
Kang, Seju;Kim, Inyoung;Vikesland, Peter J.
本文报道了利用表面增强拉曼光谱（Sers）对86个碱基的单链DNA（ssDNA）基因片段进行无标记检测。使用光滑的液体注入的多孔（SLIP）膜诱导聚集的43 nm的金纳米粒子和ssDNA后针自由液滴蒸发。组合SLIPSERS方法产生大量Sers热点，并能够在100 nM水平检测mecA和intI 1基因片段-抗生素耐药性背景下的两个感兴趣的基因。建立了基于树的多类支持向量机（Tr-SVM）分类器，对SLIPSERS获得的12个不同基因序列（mecA、intI 1、mecA和intI 1的类似物、2-10个碱基错配）和2个随机序列的Sers光谱进行了分类。训练的预测TrSVM分类器正确识别每个基因序列，预测准确率接近90%。这项研究说明了一种新的手段，歧视性的无标记Sers检测ssDNA使Tr-SVM。
We report label-free detection of 86-base singlestranded DNA (ssDNA) gene segments by surface-enhanced Raman spectroscopy (SERS). The use of a slippery liquid infused porous (SLIP) membrane induced aggregation of 43 nm gold nanoparticles and ssDNA upon pin-free droplet evaporation. The combined SLIPSERS approach generates significant numbers of SERS hot-spots and enabled detection at the 100 nM level of mecA and intI1 gene segments-two genes of interest in the context of antibiotic resistance. Tree-based multiclass support vector machine (Tr-SVM) classifiers were built to discriminate SERS spectra of 12 different gene sequences obtained by SLIPSERS: mecA, intI1, as well as analogues of mecA and intI1, respectively, with 2-10 base mismatches, and two random sequences. The trained predictive TrSVM classifiers correctly identified each gene sequence with a prediction accuracy of similar to 90%. This study illustrates a novel means for discriminatory label-free SERS detection of ssDNA enabled by Tr-SVM.