A novel system using standing surface acoustic waves may effectively and precisely separate circulating tumor cells from red blood cells, according to a novel study published by Kouhkord and Naserifar in the Physics of Fluids.
Background
Cancer accounted for nearly 10 million deaths in 2020—nearly 17% of all deaths globally—according to the World Health Organization. Because the detection of abnormal diseased cellular growth often occurs too late, timely cancer diagnosis remains one of the most pressing and elusive medical objectives.
Recent research has focused on the detection of circulating tumor cells in peripheral blood, which can serve as noninvasive markers to help inform diagnoses. Researchers have faced challenges separating controllable target cells to examine. Traditional methods typically require elaborate sample preparation, substantial equipment, and large sample volumes; however, even with these strategies, it can be challenging to efficiently separate the target cells.
Study Methods and Results
Researchers developed a novel platform designed to integrate advanced computational modeling, experimental analysis, and artificial intelligence (AI) algorithms to analyze complex acoustofluidic phenomena.
“We combined machine learning algorithms with data-driven modeling and computational data to fine-tune a system for optimal recovery rates and cell separation rates,” detailed co–study author Naser Naserifar, MS, PhD, of the K. N. Toosi University of Technology in Tehran, Iran. “Our system achieves 100% recovery at optimal conditions, with significant reductions in energy consumption through precise control of acoustic pressures and flow rates,” he highlighted.
Although various methods for enriching particles through microfluidics have emerged, those that employ acoustofluidics have shown potential because they are biocompatible, generate high-force magnitudes at MPa pressure ranges, and produce cell-scale wavelengths.
The researchers included the use of dualized pressure acoustic fields to develop their novel platform, which doubled the impact on target cells and strategically located them at critical channel geometry positions on a lithium niobate substrate. By means of acoustic pressure applied within the microchannel, the system design provided for the generation of reliable data sets that illustrate cell interaction times and trajectory patterns—which may help predict tumor cell migration.
Conclusions
“We have produced an advanced, lab-on-chip platform that enables real-time, energy-efficient, and highly accurate cell separation,” underscored co–study author Afshin Kouhkord, MSc, of the K. N. Toosi University of Technology. “The technology promises to improve [circulating tumor cell] separation efficiency and open new possibilities for earlier and more effective cancer diagnosis. It also paves the way for microengineering and applied AI in personalized medicine and cancer diagnostics.”
Disclosure: For full disclosures of the study authors, visit pubs.aip.org.
