Imaging Tool Links Cell Identity to Metabolism in Intact Tissue

Yale researchers have unveiled a new imaging platform that can measure the metabolic activity of individual cells and identify their specific cell type, all within a single slice of intact human tissue. The method, called REDCAT (Raman Enhanced Delineation of Cell Atlases in Tissues), bridges a long-standing gap in biology by connecting what a cell is genetically programmed to do with its actual physical behavior.

Reported in the journal Nature Methods, the technology was developed by a collaborative team led by professors from Yale University, the University of California, San Diego, Columbia University, and the University of Pennsylvania.

"By combining technologies, REDCAT provides an exciting and powerful advance that allows users to connect cell type or state to metabolic function," said Allison Doerr, chief editor of Nature Methods.

Yale senior co-authors of the study include Rong Fan, PhD, Harold Hodgkinson Professor of Biomedical Engineering and of pathology; Mina Xu, MD, professor of pathology; and Zongming Ma, PhD, professor of statistics and data science. The study was led by co-first authors Archibald Enninful, PhD, a postdoctoral associate in the Fan Lab at Yale, and Yajuan Li, PhD, currently a postdoctoral associate in the lab of senior author Lingyan Shi, PhD, at the University of California, San Diego, who will be joining the Yale School of Medicine faculty this fall. 

Existing spatial biology tools usually force scientists to choose between reading a cell's genetic markers or destroying the tissue to look at its metabolism. REDCAT solves this by layering five different optical imaging techniques onto a single tissue slide without destroying the sample.

The system uses advanced lasers and a computational framework to match metabolic measurements to a protein-defined cell identity, cell by cell. The team discovered that cells of the exact same type frequently adopt different metabolic states depending entirely on where they sit within the tissue.

Crucially, the platform works on both fresh-frozen and standard archival clinical tissue samples. This opens the door for pathologists to analyze retrospectively the metabolism of millions of patient biopsy samples currently stored in hospitals worldwide.

When tested on human tissues, the platform successfully mapped distinct biological processes: 

• Lymph nodes: It mapped metabolic differences across maturing immune cells.

• Liver: It tracked distinct metabolic gradients among identical liver cells based on their distance from key blood vessels.

• Cancer transformation: In a lymphoma biopsy, it captured a rare event known as Richter's transformation, showing the exact moment slow-growing leukemia cells shifted into an aggressive form of lymphoma. It even identified an intermediate "mid-transformation" cellular state.

Independent experts say the development will significantly aid the analysis of metabolic diseases and how patients respond to treatments. The researchers view REDCAT as a foundational step toward "functional histopathology"—a future form of medical diagnosis where a tissue's metabolic context is read right alongside traditional cellular and molecular data to better diagnose and treat diseases like cancer.