Urbana Campus Research Calendar (OVCRI)

DNA Nanostructures for Next-Generation Theranostics

Abstract: DNA can function not only as the carrier of genetic information but also as a programmable material for building functional nanostructures. Focusing on engineering DNA nanotechnology that remains stable and active in complex biological environments, enabling its use in biomedical applications. These structures are applied to key challenges in cancer, viral infection, and immune signaling. Examples include a synthetic DNA scaffold that mimics the apoptosome to re activate programmed cell death in cancer cells, multivalent DNA architectures that inhibit viral entry, and DNA based nanoswitches that enable real time mapping of inflammatory signaling pathways. Together, these studies demonstrate how platform-oriented DNA nanotechnology can bridge fundamental molecular design with emerging therapeutic and diagnostic opportunities.

By Dhanush Gandavadi, Department of Bioengineering, Faculty Advisor: Xing Wang

CRISP: in vivo cell-type–resolved metabolic imaging using spectral-phenotype decomposition

Abstract: Many tissues contain different types of cells that behave differently and use energy in different ways. However, when we image metabolism in vivo, the signals from all these cells are mixed together, so we cannot tell which cells are contributing to what we see.  In this work, we introduce CRISP, a simple idea to separate these mixed metabolic signals. Instead of focusing on individual metabolites, CRISP uses whole spectral patterns that represent how a specific cell type or metabolic state looks in MRS. We first measure these patterns from isolated cells under controlled conditions. Then, we use them as references to break down in vivo MRSI data into contributions from different cell populations.  We apply this method to glioma and find that tumor cells mainly occupy two metabolic states: a proliferative state and a stressed state. Using CRISP, we can map where these states are located in the tumor and how they change over time. These maps better define tumor boundaries and help predict tumor growth compared to standard metabolite maps.  Overall, CRISP provides an intuitive way to connect in vivo metabolic imaging with underlying cellular behavior, making complex tissue metabolism easier to interpret.

By Yizun Wang, Beckman Institute for Advanced Science and Technology, Faculty Advisor: Fan Lam