A Machine Learning-Assisted Nanoparticle-Printed Biochip for Real-Time Single Cancer Cell Analysis
A Machine Learning-Assisted Nanoparticle-Printed Biochip for Real-Time Single Cancer Cell Analysis
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DOI：
10.1002/adbi.202000160
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发表时间：
2020-10-07
影响因子：
4.1
通讯作者：
Esfandyarpour, Rahim
中科院分区：
文献类型：
--
作者：
Joshi, Kushal;Javani, Alireza;Esfandyarpour, Rahim
癌症是具有不同基因型和表型的异质细胞群体的复杂聚集体。同一肿瘤内的细胞间异质性和各种肿瘤内的肿瘤内异质性是对癌症疗法的抗性和不同患者的不同结果的主要原因。因此，进行单细胞分析对于识别和分类癌细胞类型以及研究细胞异质性至关重要。在这里，一个机器学习辅助纳米粒子打印的生物芯片的单细胞分析的发展报告。生物芯片通过将强大的机器学习技术与易于使用的喷墨打印和微流体技术相结合而集成。该生物芯片易于原型化、小型化和成本效益，可能能够以无标记的方式区分各种细胞类型。建立了N-特征分类器，并评估了它们的性能度量。证明了生物芯片在单细胞水平上区分非癌细胞和癌细胞的实用性。生物芯片的效用在分类癌症亚型细胞也被证明。据设想，这种芯片在单细胞研究，肿瘤异质性研究，也许在即时癌症诊断中具有潜在的应用，特别是在发展中国家，那里的成本，有限的基础设施和有限的医疗技术是最重要的。
Cancers are a complex conglomerate of heterogeneous cell populations with varying genotypes and phenotypes. The intercellular heterogeneity within the same tumor and intratumor heterogeneity within various tumors are the leading causes of resistance to cancer therapies and varied outcomes in different patients. Therefore, performing single-cell analysis is essential to identify and classify cancer cell types and study cellular heterogeneity. Here, the development of a machine learning-assisted nanoparticle-printed biochip for single-cell analysis is reported. The biochip is integrated by combining powerful machine learning techniques with easily accessible inkjet printing and microfluidics technology. The biochip is easily prototype-able, miniaturized, and cost-effective, potentially capable of differentiating a variety of cell types in a label-free manner. n-feature classifiers are established and their performance metrics are evaluated. The biochip's utility to discriminate noncancerous cells from cancerous cells at the single-cell level is demonstrated. The biochip's utility in classifying cancer sub-type cells is also demonstrated. It is envisioned that such a chip has potential applications in single-cell studies, tumor heterogeneity studies, and perhaps in point-of-care cancer diagnostics-especially in developing countries where the cost, limited infrastructures, and limited access to medical technologies are of the utmost importance.