University of Michigan scientists have developed a method that uses laser technology to identify cancer cells circulating in blood samples, potentially offering a new approach to early cancer detection. The technique, which keeps cells viable for further study, shows particular promise for pancreatic and lung cancers, where early detection remains challenging.
The new approach addresses limitations in current detection methods, which rely on staining specific surface proteins and often destroy the cells in the process. “These existing techniques usually involve methods that end up killing the cancer cells, thus preventing us from utilizing these cells for further investigation,” says Sunitha Nagrath, professor of chemical engineering at the University of Michigan.
In their study, published in Biosensors and Bioelectronics, the researchers combined a cell-sorting device with laser technology and machine learning to identify cancer cells with 99% accuracy. The system first uses a circular maze called Labyrinth to separate larger cancer cells from smaller blood cells.
Nagrath explains the separation process with an analogy: “It’s like driving around a curve in a bicycle versus a truck—the forces you experience are very different. As a result, the larger tumor cells get focused into different location compared to smaller white blood cells.”
After separation, the cells are examined using a technique called biolaser emission. “The laser emission from a cell laser is much stronger than what we get from traditional fluorescent techniques,” explains Xudong (Sherman) Fan, professor of biomedical engineering. “The laser emission images are also different; in fluorescence emission the cells look like glowing spheres. However, with a laser you can see different shapes that provide information on how the DNA is organized inside cancer cells.”
While other research groups work with biolasers, Fan notes that they are “the first to use it for clinical studies on cancers and circulating tumor cells.” The technique’s effectiveness stems from its ability to examine the cell nucleus, a component present in all cells, rather than relying on surface proteins that may or may not be present.
The system’s machine learning component, called the Deep Cell-Laser Classifier, proved particularly versatile. Initially trained on pancreatic cancer cells, it successfully identified lung cancer cells without additional training.
The team is now working to enhance the technology. “We want to develop a system where cells move along one-by-one through the laser excitation spot and then go through a cell sorting device that helps us sort and collect cells for subsequent analysis,” Fan says.
The research has broader implications for cancer treatment. Nagrath points out that studying how cancer cells change during treatment could provide valuable insights: “All these circulating cells can be very different from each other. Identifying how aggressive cells change during treatment cycles would be helpful.”
This interdisciplinary effort involved collaboration with the Judith Tam ALK Lung Cancer Research Initiative and received funding from multiple sources including the National Institutes of Health and the Breast Cancer Research Foundation.
Both lead researchers have financial interests in companies commercializing related technologies. Fan’s laser emission technologies are licensed to LEMX Health Technology Co., LTD, while Nagrath’s Labyrinth technology is licensed to Bloodscan Biotech, where she serves as a scientific advisory board member.