A team at the Georgia Institute of Technology and Emory University announced on Apr. 3 the development of a deep-ultraviolet (UV) microscopy method that can quickly assess T cell viability, activation state, and subtype without using fluorescent labels or destroying cells. The findings were published in BME Frontiers.
This new approach is significant because it addresses limitations of current methods such as flow cytometry, which require labeling, expensive equipment, and often result in cell destruction. These constraints hinder real-time monitoring and long-term studies of live T cells, which are crucial for understanding immune responses and improving therapies like CAR-T.
The researchers used static deep-UV images at a wavelength of 255 nanometers to create high-contrast images of live T cells without external stains. By training a custom neural network on samples from five human donors, they classified T cells into activated, dead, or quiescent categories with high accuracy. The results closely matched those from traditional flow cytometry tests.
To further distinguish between CD4⁺ helper T cells and CD8⁺ cytotoxic T cells—a more complex task—the team used dynamic imaging over time and analyzed intracellular activity through frequency domain techniques. This allowed them to classify these subtypes with about 90% accuracy using another neural network model.
The study found that CD4⁺T cells showed greater intracellular activity than CD8⁺T cells, consistent with their higher metabolic demands. This difference was observed mainly in the cytoplasm rather than the nucleus.
According to the researchers, this technology could have wide applications in immunology research and therapy development due to its speed and ability to monitor live cells non-destructively.
