A routine blood sample contains a diverse collection of immune cells that can reveal important clues about health and disease. These cells, known as peripheral blood mononuclear cells (PBMCs), are widely used to study infections, autoimmune disorders, cancer, and the immune system's response to treatment. Scientists typically identify these cells using fluorescent labels that attach to specific surface markers. While effective, those methods provide limited information about how the cells are functioning and can alter the cells during preparation. As reported in Biophotonics Discovery , a recent study shows how advanced optical imaging can reveal a previously inaccessible layer of information: the metabolic activity of individual immune cells within a complex blood sample.
"PBMCs can be isolated clinically really easily, and they're already used in the clinical workflow," says Melissa Skala of the Morgridge Institute for Research and the University of Wisconsin–Madison , senior author of the study. "So, the question is, what can we get from them that we aren't already getting?"
That question has growing clinical relevance. PBMCs are routinely studied in conditions ranging from blood cancers and sepsis to lupus and cognitive decline. They also serve as the starting material for cell therapies such as CAR T-cell treatments, which engineer a patient's own immune cells to attack cancer. Understanding not just how many immune cells are present, but how active and metabolically fit they are, could provide new insights into disease progression and treatment response.
Traditionally, measuring immune-cell metabolism requires either isolating specific cell populations or adding fluorescent labels and chemical probes. These approaches can alter cells, consume samples, or fail to capture how different cell types behave together. In contrast, the new study demonstrates a nondestructive method that measures metabolism in individual immune cells while they remain part of a heterogeneous PBMC sample.
To accomplish this, the researchers used optical metabolic imaging (OMI), a technique developed and refined by the Skala Lab . OMI relies on the natural fluorescence of molecules involved in cellular energy production. Rather than introducing external dyes, the method uses two-photon microscopy to excite naturally occurring metabolic cofactors inside cells and measure how long they emit light. These fluorescence lifetimes provide information about cellular metabolism and activation state. Because the approach uses endogenous signals, the cells remain intact and available for additional analyses or therapeutic use.
The team applied OMI to PBMC samples isolated from the blood of three healthy donors. They then analyzed thousands of individual cells in both resting and activated states. Machine-learning algorithms were used to determine whether metabolic measurements alone could identify different immune-cell populations and detect immune activation.
The results showed that metabolism provides a powerful indicator of immune-cell identity and function. The researchers distinguished activated from resting PBMCs with nearly 94 percent accuracy only two hours after stimulation. They also identified monocytes, key cells of the innate immune system, with 96 percent accuracy in resting samples and 88 percent accuracy in activated samples. Natural killer (NK) cells were identified with approximately 74 percent accuracy in both states.
The findings highlight the importance of immune-cell metabolism. Although immune-cell counts are already used as diagnostic markers, metabolism provides additional information about whether cells are active, quiescent, or responding to a threat. The study found that monocytes and NK cells have particularly distinct metabolic signatures, likely reflecting their role as rapid responders in the immune system. By contrast, T cells and B cells, which drive more specialized adaptive immune responses, displayed more similar metabolic profiles under the experimental conditions.
Importantly, the single-cell approach revealed substantial heterogeneity within the immune-cell population. Some cells, like monocytes, showed high levels of metabolic activity while others remained metabolically quiet. These differences would be obscured by bulk measurements that average signals across an entire sample. By measuring metabolism at the level of individual cells, OMI provides a more detailed picture of how the immune system is functioning.
The nondestructive nature of the technique could be especially valuable for cell therapy applications. Current methods for assessing cellular metabolism often require reagents or processing steps that can alter cell behavior. OMI, by contrast, leaves cells viable after analysis, creating opportunities to evaluate cell quality before manufacturing therapeutic products. PBMCs are commonly used as starting material for CAR T-cell therapies and other immune-cell treatments, making pre-treatment assessment of cellular fitness an attractive possibility.
The researchers emphasize that the technology remains primarily a research tool and does not yet match the accuracy of established labeling methods for identifying every immune-cell subtype. However, its unique ability to measure single-cell metabolism without altering samples makes it a valuable complement to existing approaches.
Looking ahead, the team hopes to further develop the technology for broader use and clinical translation. By combining advanced biophotonic imaging with machine learning, they aim to make metabolic information more accessible for studies of cancer, immune disorders, and cell therapies. The work demonstrates how optical imaging can move beyond visualization alone to reveal functional information about cells, offering a richer understanding of immune-system dynamics in health and disease.
For details, see the original Gold Open Access article by J. M. Riendeau et al., " Autofluorescence lifetime imaging resolves cell heterogeneity within peripheral blood mononuclear cells ," Biophoton. Discovery 3(3), 035003 (2026), doi: 10.1117/1.BIOS.3.3.035003
