Discriminating single-bacterial shape using low-aspect-ratio pores
Discriminating single-bacterial shape using low-aspect-ratio pores
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DOI：
10.1038/s41598-017-17443-6
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发表时间：
2017-12-12
影响因子：
4.6
通讯作者：
Kawai, Tomoji
中科院分区：
文献类型：
--
作者：
Tsutsui, Makusu;Yoshida, Takeshi;Kawai, Tomoji
阻性脉冲分析的传统概念是通过比较离子电流阻塞量来区分液体中颗粒的大小差异。与此形成鲜明对比的是，我们在此报告了一项概念验证演示，通过利用纳米孔技术和机器学习之间的协同作用，展示了固态孔洞传感器的形状传感能力。我们发现两种细菌的离子电流尖峰模式相似，反映了超低厚度直径纵横比孔隙中非常相似的形态和大小。我们研究了机器学习策略的可行性，以对每个离子电流波形中的亚纳安波纹进行模式分析，并识别表示微生物形状纳米级差异的特征电信号，从而证明对单个细菌细胞的识别准确率高达90%。这种数据分析驱动的显微微孔扫描能力开辟了阻性脉冲分析的新应用，用于在单颗粒水平上通过病毒和细菌的独特形态进行筛查。
Conventional concepts of resistive pulse analysis is to discriminate particles in liquid by the difference in their size through comparing the amount of ionic current blockage. In sharp contrast, we herein report a proof-of-concept demonstration of the shape sensing capability of solid-state pore sensors by leveraging the synergy between nanopore technology and machine learning. We found ionic current spikes of similar patterns for two bacteria reflecting the closely resembled morphology and size in an ultra-low thickness-to-diameter aspect-ratio pore. We examined the feasibility of a machine learning strategy to pattern-analyse the sub-nanoampere corrugations in each ionic current waveform and identify characteristic electrical signatures signifying nanoscopic differences in the microbial shape, thereby demonstrating discrimination of single-bacterial cells with accuracy up to 90%. This data-analytics-driven microporescopy capability opens new applications of resistive pulse analyses for screening viruses and bacteria by their unique morphologies at a single-particle level.