Researchers and engineers at UCI recently created a machine learning-assisted biochip that can both examine and differentiate between cancers and healthy tissues at the single cell level. The data produced by the device can be used to study tumor heterogeneity, which can help reduce resistance to cancer therapies.

Single-cell analysis is critical in understanding cancer because intercellular homogeneity within the same tumor and intratumor homogeneity within various tumors are the leading causes of resistance to cancer therapies. The instruments and techniques frequently used to perform single-cell analysis are large, expensive, require human specialists to operate, and take a long time to prepare. The team at UCI detailed their solution to this problem in a paper describing a new machine learning-assisted nanoparticle-printed biochip for single-cell analysis and precise characterization of a variety of cancer cells.

“The World Health Organization says that nearly 60 percent of deaths from breast cancer happen because of a lack of early detection programs in countries with meager resources,” said senior author Rahim Esfandyarpour; “Our work has potential applications in single-cell studies, in tumor heterogeneity studies and, perhaps, in point-of-care cancer diagnostics – especially in developing nations where cost, constrained infrastructure and limited access to medical technologies are of the utmost importance.”

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ML-Assisted Biochip Used for Real-Time Single Cancer Cell Analysis
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