Label-free microfluidic chip and AI identify blood-borne tumor cells

Finding a few tumor cells hidden among billions of blood cells is one of the biggest challenges in liquid biopsy. Circulating tumor cells (CTCs), which break away from primary tumors and enter the bloodstream, can provide important clues about cancer progression and treatment response. However, their extremely low abundance-often only one to ten cells among a billion blood cells-makes them difficult to isolate and accurately identify.

A chip that separates tumor cells by size

AI helps identify tumor cells without fluorescent staining

Combining microfluidics and AI for next-generation CTC analysis

By bringing together physical cell sorting and artificial intelligence, the new platform addresses two major bottlenecks in CTC analysis: finding rare tumor cells in a complex blood environment and recognizing them accurately after enrichment.The label-free workflow may simplify sample processing and help maintain cell integrity for downstream applications, including single-cell sequencing and drug susceptibility testing. More broadly, the study demonstrates how combining microfluidic technology with deep learning can create new possibilities for analyzing rare biological cells.

The researchers note that the current work remains a proof-of-concept study. The platform was validated using MCF-7 cell lines and artificial blood samples rather than patient-derived clinical samples, and the enrichment and recognition modules have not yet been fully integrated into a single automated system. Future studies will focus on evaluating additional tumor types and patient samples, while improving system integration and automation.

Source:

Biomedical Analysis

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