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机器学习辅助的近红外光谱指纹用于巨细胞表型化

Aceer Nadeem¹, Sarah Lyons¹, Aidan Kindopp¹

¹Department of Chemical Engineering, University of Rhode Island, Kingston, Rhode Island 02881, United States.

|August 16, 2024

概括

巨细胞中DNA单壁碳纳米管 (DNA-SWCNTs) 的近红外光谱指纹显示出不同的细胞表型. 机器学习可以根据它们的内内体环境和DNA-SWCNT相互作用准确地识别M1和M2巨细胞.

科学领域:

纳米材料科学科学纳米材料科学
细胞生物学细胞生物学
频谱学是一种光谱学.

背景情况:

光谱指纹识别可以识别化合物和细胞相互作用.
工程纳米材料为生物学研究提供了新的工具.

研究的目的:

为了研究DNA功能化单壁碳纳米管 (DNA-SWCNTs) 和活细胞之间的相互作用.
通过光谱指纹和机器学习使得巨细胞在现场的表型歧视成为可能.

主要方法:

接近红外 (NIR) 光光谱指纹.
拉曼显微镜用于吸收和缺陷比率分析.
支持向量机 (SVM) 模型用于表型分类.

主要成果:

在M1巨细胞中,DNA-SWCNT吸收率统计较高,缺陷比率较低.
不同的内内体环境在光学特征上产生了显著的差异.
SVM模型在识别M1和M2巨细胞方面实现了>95%的准确性.

结论:

DNA-SWCNTs的光谱指纹可以作为巨细胞表型的标记物.
DNA序列长度影响DNA-SWCNT复合体的稳定性和模型的准确性.
纳米传感器平台有望实现实时in vivo细胞分化监测.

关键词:

同焦点的拉曼显微镜.在细胞内进行细胞内处理.机器学习是机器学习.纳米材料的使用方法接近红外的光效应.单壁碳纳米管是一种单壁碳纳米管.用光谱指纹进行指纹识别.

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