BUFFALO, N.Y. — Much like a satellite provides a full picture of Earth, a ground-breaking new technology can generate thousands of detailed “satellite maps” showing where individual proteins are located and distributed across an entire tissue in the body or brain.
A University at Buffalo researcher has developed this novel technology, called hexagonal micro-scaffold assisted spatial proteomics (hex-MASP), which has the potential to transform how researchers study and create new drugs.
By mapping a drug alongside thousands of proteins, researchers can examine whether it reaches the tissue regions where it needs to act and then investigate how those regions respond to treatment.
“These insights could help uncover new therapeutic targets and guide the development of safer, more effective medicines,” explains Jun Qu , PhD, professor of pharmaceutical sciences in the School of Pharmacy and Pharmaceutical Sciences, who developed the technology with his team.
“Each protein map tells part of the story,” he says. “Seeing thousands of these maps together lets us explore how disease and treatment reshape the biological landscape of a tissue and discover patterns we never knew to look for.”
An article detailing the technology was published in the Sept. 3 issue of the Proceedings of the National Academy of Sciences ( PNAS ), the flagship journal of the National Academy of Sciences and one of the world’s most frequently cited multidisciplinary scientific journals.
Game changer for drug accuracy
The hex-MASP was applied to a mouse brain treated with monoclonal antibodies, which are medicines designed to efficiently bind specific protein targets. The technology produced detailed, accurate maps showing the distribution of both the therapeutic antibodies and more than 6,000 brain proteins across entire tissue sections.
This technology stemmed from the frustration of pharmacy researchers that when a drug enters the organ, it usually doesn’t do so uniformly. This, it creates uneven responses across the tissue, Qu says. Until now, there has been no technology to reliably map the tissue proteins, which is critical to understand the therapeutic responses.
Earlier designs used square compartments, which could bend, break or collapse when pressed into tougher tissues such as the heart or kidney, he explains.
The team’s solution is a 3D-printed scaffold of tiny hexagonal wells. The stronger design reliably divides a tissue section into numerous microsamples while preserving each sample’s original location. Ultrasensitive mass spectrometry then measures thousands of proteins in those samples, and the measurements are reconstructed into detailed, accurate maps, one for each protein, showing their distribution across the entire tissue section.
“Each protein has its own distribution map,” Qu says. “The power comes from seeing thousands of maps together, connecting where a drug goes with where its targets are located and how the tissue responds in spatial details.”
Could lead to Alzheimer’s disease treatment
That unprecedented, detailed view could help researchers understand why a treatment works better in some cases than in others, identify signs of unwanted effects, and improve a drug’s design or delivery. One goal is to make better decisions about drug candidates before they reach costly clinical trials.
One example was a drug meant to treat Alzheimer’s disease.
“For decades, researchers have pursued treatments that clear amyloid plaques from the brain,” Qu says. “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. In clinical trials, however, the cognitive benefit never matched the plaque clearance, he says.
Even the antibodies that have since won approval only moderately slow the rate of decline rather than reversing it. Apparently, Qu says, targeting amyloid alone does not address enough of the biology driving disease progression.
“We need to understand which biological changes are most important to the disease, and which ones offer the best opportunities for treatment,” he says. “Mapping thousands of proteins and their modification status across brain tissue at different stages of Alzheimer’s could reveal disease processes we have overlooked and point us toward better drug targets.”
The same approach could then help researchers evaluate medicines developed against those targets.
“By mapping a drug candidate together with the brain’s protein responses, we could see whether it changes the biology we intended to change,” Qu says. “That information could help us improve a candidate or reconsider its target before committing to costly clinical trials.”
Using hex-MASP, Qu’s lab has already identified promising new candidates for drug targets of Alzheimer’s disease, with further research underway.
Qu and his pharmaceutical science colleagues, including Min Ma, PhD, research assistant professor; Ming Zhang, research scientist; Shihan Huo, PharmD, former postdoctoral researcher who is now a principal scientist at Regeneron; and Chi Zhou, PhD, a professor in the UB Department of Industrial and Systems Engineering, plan to further develop the technology and extend its use to more diseases and types of medicines.
“Our ambition is to help researchers discover better targets and develop treatments that make a greater difference for patients,” Qu says. “It’s definitely promising for diseases such as Alzheimer’s that have proven hard to treat.”
He adds that sustained institutional support has been essential to developing this technology.
“We are deeply grateful to UB’s Center of Excellence in Bioinformatics and Life Sciences,” he says, “and to the leadership of the Department of Pharmaceutical Sciences and the School of Pharmacy and Pharmaceutical Sciences for helping make this work possible.”
Computational simulation/modeling
Animals
Hex-MASP for mapping the whole-tissue spatial proteome and the intrabrain distribution of monoclonal antibodies
3-Sep-2026
This work was supported by NIH Grants U01 DK137113, and a Center of Protein Therapeutics consortium grant. Data presented in this work were obtained via the University at Buffalo Center for Proteomics and Bioanalysis.