Source: Whitacre College of Engineering, Texas Tech University, by Shannon Kirkland (September 10, 2026). AI-generated summary by biochip.com, published . Not independently reviewed.
Key takeaways
- NIH awarded Texas Tech professor Wei Li a $607,488 R15 grant to study circulating tumor cell subtypes and metastasis.
- The team will pair a CTC-isolating microchip with multi-scale imaging and deep learning to profile cells from blood samples.
- The project is still in development and does not yet establish diagnostic accuracy, clinical utility or improved patient outcomes.
Texas Tech University chemical engineering professor Wei Li has received a $607,488 National Institutes of Health grant to investigate how cancer cells change as they spread through the body. The R15 award will support a project that combines a custom microchip, high-resolution imaging, deep learning and cancer biology to study circulating tumor cells, or CTCs, in blood. CTCs are tumor cells that have broken away from a primary cancer and entered the bloodstream, where some may eventually establish tumors elsewhere. Li and collaborators Jay Lu and Robert Bright will focus on a cellular shift called epithelial-mesenchymal transition, or EMT, which is closely tied to metastasis. Metastasis is the movement of cancer from its original site to distant parts of the body, and clinical data cited by the team links it to 60% to 90% of cancer-related deaths. The researchers aim to separate distinct CTC subtypes from blood, photograph them across multiple scales and use artificial intelligence to classify them. Alongside the scientific work, the grant will expand undergraduate research opportunities in biomedical engineering and cancer research at Texas Tech University and Texas Tech University Health Sciences Center. The project is an early effort to build a more detailed picture of the rare, difficult-to-capture cells that may help tumors travel.
Tracking the Cells That Travel
Most cancer cells remain within the original tumor, but a small number can enter circulation. These circulating tumor cells are especially important because they offer a possible window into the metastatic process through a blood sample, often called a liquid biopsy.
A liquid biopsy is much less invasive than surgically removing tissue from a tumor. But it comes with a hard technical problem: blood contains enormous numbers of normal cells, while CTCs are scarce and can differ substantially from one another. Isolating the relevant tumor cells accurately is therefore a central challenge.
Why Cell State Matters
Li's team will examine CTCs as they undergo epithelial-mesenchymal transition. An everyday comparison is a brick in a wall becoming a traveler with a backpack: epithelial cells tend to stay organized with neighboring cells in a tissue, while mesenchymal cells gain traits associated with movement.
In the cancer setting, epithelial CTC subtypes remain more local, while mesenchymal subtypes enter the blood and are described as more aggressive. For a tumor to spread, CTCs must make the transition from epithelial to mesenchymal types. After arriving at a distant location, the transition may sometimes reverse, allowing tumor cells to grow there.
Researchers suspect that this shifting identity contributes to metastasis, but the details remain unclear. Li said the difficulty of separating CTCs from blood has limited efforts to understand how EMT unfolds in these cells.
A Microchip for Sorting Rare Cells
The NIH-funded work will build on microfluidic technologies developed in Li's laboratory. Microfluidics means controlling tiny volumes of liquid through channels often built into a chip, much like directing water through an intricate set of miniature pipes.
Li plans to develop a specially designed microchip that can isolate and profile CTC subtypes from blood samples. The intended platform is a solid-state device, meaning the chip itself provides a fixed physical structure for handling and separating the cells.
Once isolated, the cells will be photographed under microscopes at different magnifications. That multi-scale approach is intended to reveal features visible at the micrometer scale, roughly the scale of whole cells, as well as at the nanometer scale, where details are thousands of times smaller.
Using Deep Learning to Read Cell Images
Jay Lu, an assistant professor of chemical engineering in Texas Tech's Edward E. Whitacre Jr. College of Engineering, will develop deep learning-based platforms to assess the images. Deep learning is a form of machine learning in which computer models identify patterns from large sets of examples, rather than relying only on rules specified one by one by a person.
For this project, the goal is to help distinguish CTC subtypes from their visual features. Lu expects the multi-scale strategy to support effective separation and accurate, robust identification of the cell types. The approach joins physical cell sorting with computational image analysis, so that the chip and the software address different parts of the same problem.
Robert Bright, a professor of immunology in Texas Tech University Health Sciences Center's Department of Immunology and Molecular Microbiology, will contribute expertise in metastasis models, CTC identification and EMT characterization. Bright said that definitive CTC subtype characterization using markers that differentiate metastatic behavior could support work on prognosis and earlier use of next-generation prevention regimens, including immuno-preventive vaccination.
Training Students Alongside Researchers
The R15 mechanism also has an educational mission. The project will expand undergraduate experience in biomedical engineering and cancer research across Texas Tech University and Texas Tech University Health Sciences Center.
Students will gain exposure to bioengineering, a field that applies engineering tools to biological and medical problems. The grant is intended to help prepare undergraduates for future roles in the pharmaceutical industry or government regulatory agencies, while placing them in a collaboration that spans engineering, machine learning and cancer biology.
Why This Matters
Cancer care increasingly depends on finding useful signals early and monitoring how disease changes over time. A blood-based system that can efficiently isolate CTCs and identify their subtypes could eventually give researchers a less invasive way to follow cancer progression and investigate which cells are most likely to contribute to metastasis.
That promise should not be confused with a finished clinical test. This award funds the development and study of an integrated system, not a reported demonstration that it can diagnose cancer or improve patient outcomes. Its value will depend on whether the microchip can reliably recover relevant cells and whether the image-analysis platform can correctly distinguish their changing states.
Li's team now plans to combine the user-friendly microchip with deep learning-based image analysis into a single platform. If the effort can connect detailed CTC profiles with the biology of metastasis, it may sharpen the tools available for studying cancer spread while training a new group of engineers to work at the intersection of chips, computation and medicine.