Protein Maps Could Change How New Drugs Are Found

A team at the University at Buffalo has built a way to chart where individual proteins sit across an entire slice of tissue, producing detailed protein maps that show how a drug moves through an organ and how the tissue reacts to it. The technique, called hexagonal micro-scaffold assisted spatial proteomics, or hex-MASP, is described in a paper published on October 24, 2026, in the Proceedings of the National Academy of Sciences.

The work addresses a practical problem in drug development. When a medicine enters an organ, it rarely spreads evenly, according to Jun Qu, professor of pharmaceutical sciences at the university, who developed the technique with his team. Some regions receive more of the drug than others, and until now researchers had no reliable way to chart proteins across a whole tissue section to see why responses differ from place to place. "These insights could help uncover new therapeutic targets and guide the development of safer, more effective medicines," Qu said.

How the technique works

Earlier versions of the approach divided tissue into square compartments, but those compartments could bend, break, or collapse when pressed into tougher tissue such as heart or kidney, Qu explained. The team's answer is a 3D-printed scaffold of tiny hexagonal wells. The stronger shape divides a tissue section into numerous microsamples while preserving the original location of each sample.

Ultrasensitive mass spectrometry then measures the proteins in those samples, and the measurements are reconstructed into a separate distribution chart for every protein, showing where each one appears across the entire tissue section. In a test on a mouse brain treated with monoclonal antibodies, medicines designed to bind specific protein targets, the method charted both the therapeutic antibodies and more than 6,000 brain proteins across whole tissue sections.

Qu described the value of the approach in plain terms: "Each protein has its own distribution map." The strength of the method, he said, lies in viewing those charts together, connecting where a drug goes with where its targets sit and how the tissue responds in spatial detail. The study's authors span pharmaceutical sciences and industrial and systems engineering at the university, reflecting the mix of biology and manufacturing behind the scaffold.

What it could mean for Alzheimer's research

That detailed view could help researchers understand why a treatment works better in some cases than in others, spot signs of unwanted effects, and improve a drug's design or delivery before it reaches expensive clinical trials. One field where that matters is Alzheimer's disease research, where scientists have spent decades pursuing treatments that clear amyloid plaques from the brain.

According to Qu, the hope was that clearing those plaques would substantially improve patients' outcomes. Billions of dollars later, one pharmaceutical company developed a series of antibodies that cleared most of the plaque, yet the cognitive benefit in clinical trials never matched the plaque clearance. Even the antibodies that have since won approval only moderately slow the rate of decline rather than reversing it, apparently because targeting amyloid alone does not address enough of the biology driving the disease.

Qu argued that researchers need to identify which biological changes matter most to the disease and which offer the best opportunities for treatment. Charting proteins and their modification status across brain tissue at different stages of Alzheimer's could reveal disease processes that have been overlooked and point toward better drug targets, he said. The same approach could then test medicines developed against those targets by showing whether a candidate changes the biology it was meant to change.

Using hex-MASP, Qu's laboratory has already identified promising new candidates for drug targets of Alzheimer's disease, with further research underway. Qu and his colleagues, including Min Ma, Ming Zhang, Shihan Huo, and Chi Zhou, plan to develop the technology further and extend its use to more diseases and types of medicines. "It's definitely promising for diseases such as Alzheimer's that have proven hard to treat," Qu said.

The paper, by Shihan Huo and colleagues, is published in the Proceedings of the National Academy of Sciences, and the journal listing is available via DOI: 10.1073/pnas.2532946123.

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