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New Imaging Platform Maps Cell Type and Metabolic Activity Simultaneously
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Cells of the same type adopt different metabolic states depending on where exactly they sit in the tissue. Put another way, the metabolic activity of any one cell in a tissue sample cannot be inferred based only on what cell type it is.
This is one of the insights made possible thanks to a new imaging platform developed by a cross-disciplinary research team led by bioengineers at the University of California San Diego.
The new imaging platform, recently reported in Nature Methods, allows researchers to compare what a cell is genetically programmed to do versus what it was actually doing when the sample was taken.
The platform is called REDCAT (Raman Enhanced Delineation of Cell Atlases in Tissues). It was developed by a team led by Lingyan Shi, a faculty member in the Shu Chien-Gene Lay Department of Bioengineering at the UC San Diego Jacobs School of Engineering, along with collaborators from Columbia University, the University of Pennsylvania and Yale. Shi and collaborators Rong Fan, Mina L. Xu, Zongming Ma from Yale University are corresponding authors on the Nature Methods paper.
“Our work is both conceptual and technical,” said Shi. “Using REDCAT, we find again and again that metabolic states are not solely determined by cell identity. To understand tissue physiology, immune function and disease, we need to be able to identify cell types and at the same time measure what these very same cells are doing metabolically.”
REDCAT works on both fresh-frozen and formalin-fixed paraffin-embedded (FFPE) clinical specimens. The team found that lipid signals are remarkably well preserved in deparaffinized FFPE tissue — raising the prospect of retrospectively analyzing the metabolic state of the millions of archival tissue samples that contain valuable clinical correlates already stored in pathology departments worldwide.
“The number of archived tissue samples saved in collections around the world is very large,” said Shi. “The potential impact of technologies that can extract new molecular and metabolic information from these existing specimens is tremendous.”
The researchers describe REDCAT as a step toward a functional histopathology approach in which metabolic context sits alongside cellular and molecular information within tissue samples being analyzed.
First authors Yajuan Li (a UC San Diego bioengineering postdoctoral researcher), Zhaojun Zhang (affiliated with University of Pennsylvania and Yale University) and Archibald Enninful (Yale University) note that advances now underway in spectral coverage, metabolite annotation, and AI-assisted analysis, together with the commercial arrival of high-throughput multimodal Raman systems, could broaden the platform's reach in research and, eventually, the clinic.
REDCAT’s analysis code and supporting data are publicly available. Researchers who are interested in working with the complete imaging platform are encouraged to reach out to Lingyan Shi regarding collaboration possibilities.
Paper citation: “All-optical multimodal mapping of single-cell-type-specific metabolic activities via REDCAT” in Nature Methods.
Additional context is available in the associated Research Briefing from Nature Methods.
Lymph node tissue sample displaying metabolic information encoded in color, thanks to REDCAT the multimodal imaging platform developed by bioengineers at UC San Diego, Yale and Penn. The different colors within a lymph node tissue biopsy highlight different cell types and metabolic activity levels among cells of the same type. The red color highlights the general B cell marker (CD20) expressed by B cells starting from late pre-B lymphocytes. The white color highlights positive tumor cells (Ki67) that are dividing at an abnormal rate. The green color highlights double negative T cells (CD3e). Each cell type has its own metabolic activities — including lipid subtypes, redox ratios, chemical molecular compositions etc. Even among the same cell type, individual cells have different phenotypic expressions and they are metabolically heterogeneous. This metabolic heterogeneity can be detected at very early stages of disease development. Image credit: Multimodal label free image courtesy of Fan Lab and Shi Lab, based on Li et al., Nature Methods (2026).
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