Imaging Technique Reveals Immune Cell Metabolism Without Destroying Samples

A single blood draw already tells clinicians how a cancer or immune condition, and its treatment, is progressing. Isolated from that draw, peripheral blood mononuclear cells (PBMCs) are counted by cell type in clinical labs, along with basic function measures. Melissa Skala, an investigator in biomedical engineering at the Morgridge Institute and senior author of a new study in Biophotonics Discovery, says PBMCs are already familiar in clinical practice. “PBMCs can be isolated clinically really easily, and they’re already used in the clinical workflow,” she says. “So, the question is, what can we get from them that we aren’t already getting?” 

Skala and her team are testing whether advanced imaging can pull more information from immune cells, aiming to improve diagnosis and treatment of blood cancers, lupus, sepsis, and cognitive decline. PBMCs also serve as the starting material for CAR T cell therapies, so the same imaging could help evaluate those cells too.

Measuring immune cell metabolism traditionally meant isolating individual cell types or tagging them with chemical labels. First author Jeremiah Riendeau says the new study is the first to nondestructively track the metabolic state of single immune cells within a complex, mixed PBMC sample, so the cells stay usable afterward. “You can continue using the samples after this analysis,” he says, noting that with older methods, “if you want to study metabolism you have to add different reagents to the sample, and that can be harmful to the cells.” Skala sees a use in cell therapy manufacturing: “a lot of starting material for cell therapies are PBMCs. If you could do something to assess the fitness of those cells before processing them for cell therapy, that would be nice.”

The team used optical metabolic imaging (OMI), a technique developed in the Skala Lab, on PBMCs from three volunteers. OMI aims long-wavelength photons at a sample through a sensitive microscope, exciting the fluorescence of metabolic byproducts to show whether a cell is active or quiescent. Two hours after stimulation, the method sorted cells into those states with 93% accuracy, distinguished quiescent from activated monocytes with 96% and 88% accuracy, respectively, and identified natural killer cells in either state with 74% accuracy.

The team hopes to partner with hospitals on clinical applications. “Our lab and collaborators are in the process of commercializing our technology so we can make this measurement more available,” Riendeau says.