Advanced optical imaging has uncovered new details about how individual immune cells function within complex blood samples, according to a study published in Biophotonics Discovery. The research, led by Melissa Skala from the Morgridge Institute for Research and the University of Wisconsin–Madison, focuses on peripheral blood mononuclear cells (PBMCs), which are key components in diagnosing and studying infections, autoimmune diseases, cancer, and immune responses to treatments. Traditionally, scientists use fluorescent labels to identify immune cells by attaching them to specific surface markers. However, these methods only provide limited information about how the cells function and can sometimes change the cells during preparation. The new technique, called optical metabolic imaging (OMI), avoids these issues by measuring the metabolism of individual immune cells without disrupting them. OMI uses a method called two-photon microscopy to excite naturally occurring metabolic cofactors inside the cells and measures how long they emit light. These light-emission times, known as fluorescence lifetimes, reveal information about the cell’s metabolic activity and whether it is activated or resting. In the study, researchers applied OMI to PBMC samples from three healthy donors, analyzing thousands of individual cells in both resting and activated states. Using machine learning, they determined whether metabolic measurements alone could identify different types of immune cells and detect activation. The results showed that metabolism is a strong indicator of immune-cell identity and function. The team could distinguish activated from resting PBMCs with nearly 94% accuracy just two hours after stimulation. Monocytes, which are part of the body’s first-line immune defense, were identified with high accuracy in both resting and activated states. Natural killer (NK) cells, which also play a role in the immune response, were identified with about 74% accuracy in both states. The study highlights that certain immune cells, like monocytes and NK cells, have unique metabolic signatures, likely due to their role in quickly responding to threats. In contrast, T cells and B cells, which are involved in more specialized, long-term immune responses, showed more similar metabolic profiles. The single-cell approach used in the study revealed significant differences in metabolism within the immune-cell population. Some cells, like monocytes, were highly metabolically active, while others showed little activity. These differences would be hidden if researchers used traditional methods that average the activity of all cells in a sample. The nondestructive nature of OMI could be especially useful for cell therapy applications, where preserving the cells’ viability is crucial. Unlike traditional methods, which often require chemicals or processing steps that can alter cell behavior, OMI allows cells to remain intact after analysis. This opens up possibilities for assessing cell quality before using them in therapies such as CAR T-cell treatments, which use PBMCs as starting material. While the technology is still primarily used in research, its ability to measure single-cell metabolism without altering the sample makes it a valuable addition to existing tools. Researchers hope to further develop the method for broader use in clinical settings, combining advanced imaging with machine learning to better understand diseases and improve cell-based therapies.