A Machine Learning‐Assisted Nanoparticle‐Printed Biochip for Real‐Time Single Cancer Cell Analysis. Issue 11 (7th October 2020)

• Record Type: Journal Article
• Authors: Joshi, Kushal; Javani, Alireza; Park, Joshua; Velasco, Vanessa; Xu, Binzhi; Razorenova, Olga; Esfandyarpour, Rahim
• Is Part Of: Advanced biosystems. Volume 4:Issue 11(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-10-07
• Subjects: cancer -- developing world -- lab‐on‐a‐chip -- machine learning -- single‐cell; Biological systems -- Periodicals; Biotechnology -- Periodicals; Bioengineering -- Periodicals; Biomedical engineering -- Periodicals; Biological Science Disciplines; Periodicals; Periodicals; 660.6
• DOI: 10.1002/adbi.202000160
• Languages: English
• ISSNs: 2366-7478

Abstract

Abstract: 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. Abstract : A machine learning‐assisted nanoparticle‐printed biochip for single‐cell analysis is presented. The biochip combines powerful machine learning techniques with easily accessible inkjet‐printing and microfluidics technology. Such a biochip is envisioned to have potential applications in single‐cell studies, tumor heterogeneity studies, and perhaps in point‐of‐care cancer diagnostics—especially in developing countries with limited infrastructures and limited access to medical technologies.