New Technology Offers Deeper Understanding of Cancer Progression

Newswise — A Yale study published Jan. 15, 2026, in Nature Methods introduces new technology that allows researchers to examine where gene activity is happening in a tissue and what proteins are present and where, all on a single sample.

By combining these techniques, which have previously had to occur in separate tissue samples, researchers can get a more accurate view a tissue’s cellular and molecular makeup. And the technology could help researchers better understand how tissue changes in cancer and other diseases, informing detection, diagnosis, and treatment.

Typically, researchers sequence RNA to determine what genes are active in a given sample, while protein identification can be done through imaging.

“For years, imaging-based and sequencing-based approaches have offered complementary but fundamentally disconnected views of tissue biology,” says senior author Rong Fan, PhD, Harold Hodgkinson Professor of Biomedical Engineering and professor of pathology. “Our new approach overcomes this long-standing barrier by preserving tissue integrity while enabling both analyses on the same tissue section. This integration opens many new opportunities for this type of research.”

The study was led by Archibald Enninful, a Yale Biomedical Engineering PhD student in Fan’s laboratory. Enninful worked in close collaboration with Zongming Ma, PhD, professor in the Department of Statistics and Data Science in Yale’s Faculty of Arts and Sciences, and Mina L. Xu, MD, professor of pathology and laboratory medicine and director of hematopathology at Yale School of Medicine.

The technology—called Deterministic Barcoding in Tissue sequencing plus (DBiTplus)—addresses one of the most persistent challenges in spatial biology: determining how tissue sections differ from each other. When having to perform genetic and protein assessments separately, researchers must use different sections of tissue for each method. But those tissue sections could differ from each other; gene activity and protein makeup in one section may not be the same in another, deeper level of the sample. By combining both analyses on the same section, DBiTplus removes the need to match tissue samples for the different methods.

DBiTplus introduces several technical innovations. It leverages an enzyme that works at high temperatures to more efficiently retrieve the genetic material, which helps preserve the tissue for the protein imaging.

“Making this work required not only new chemistry but also extensive coordination across experimental platforms and institutions,” says Enninful, co-first author of the study. “We worked closely with Akoya Biosciences and Bruker to ensure broad compatibility with commercially available imaging systems, and with collaborators at the University of Pennsylvania to advance the computational framework. It was a team-driven effort to make this approach practical and scalable.”

At the core of DBiTplus is a novel step-by-step computer process that integrates the two types of data and maps them out at the single-cell level.

“Integrating sequencing and imaging data at single-cell resolution is statistically and computationally nontrivial,” says Ma. “We developed new algorithms that jointly analyze both. This unified framework enables a much more accurate and interpretable view of tissue organization and the underlying molecular mechanisms than either does alone.”

The researchers demonstrated how DBiTplus can be applied using human lymphoma tissue. The technology provided insight into how the cancer cells interacted with and reshaped the area around them over time.

Xu says the approach fundamentally changes the study of disease. “DBiTplus allows us to study lymphoma progression and transformation directly in patient samples with clarity.” She says it offers a new paradigm for understanding cancer progression and may inform treatment and patient care in the future.

DBiTplus can be applied to other complex diseases as well, say the researchers, and could advance precision medicine.

University of Pennsylvania statistics graduate student Jane Zhang is co-first author. Collaborators include Nancy Zhang, PhD, also of the University of Pennsylvania, and life science tools companies Akoya Biosciences and Bruker Spatial Biology.

The research in this news article was supported by the National Institutes of Health (awards U01CA294514, U54CA274509, UH3CA257393, RF1MH128876, U54AG076043, R01CA245313, RM1MH132648) as well as National Science Foundation (awards 2345215, 2245575).