Active Projects

Current Research Projects

Project 1: Neuroimaging Markers for Predicting the Outcome of Brain Tumor Surgery

Research Project Leader: Dr. Han Yuan. Mentors: Dr. Michael Wenger and Dr. Ian Dunn.

Illustration of multimodal imaging. (A) showing rsfMRI connectivity in two patients seeded at tumor, near tumor and contralateral to tumor, obtained from our pilot data at OUHSC. (B) nTMS map in a patient. Orange triangles indicate the points with motor response recorded in the nTMS mapping. Red circles indicate points of motor response in direct cortical electrical stimulation

The objective of this proposal is to develop an intelligent and multimodal strategy for identifying and predicting plasticity based on images of brain connectivity that relates to the neurological deficits due to brain tumor surgery in the patients with focal brain gliomas involving motor or language regions.
Success of this project will demonstrate feasibility and advantages of this new strategy, and provide important preliminary data to support applying for a more comprehensive research project (i.e., NIH R01) to further optimize and validate this new strategy and multimodality imaging markers of plasticity, which can be leveraged into surgery planning to improve overall survival of patients by increasing the extent of tumor resection without compromising patient safety or long-term functional outcome.

Project 2: High-throughput dynamic contrast optical coherence tomography for anti-cancer drug screening

Research Project Leader: Dr. Yuye Ling. Mentors: Dr. Danny N. Dhanasekaran and Dr. Qinggong Tang

Optical imaging-based drug screening platform using PDTOs to rapidly measure individualized therapeutic response and guide personalized cancer treatment.

Improving the success rate of oncology drug development requires pre-clinical models that better mimic in vivo tumor environments and advanced imaging tools capable of real-time monitoring of treatment responses. Tumor organoids represent a biologically relevant 3D cancer model, yet existing imaging platforms fall short in achieving both high temporal resolution and high throughput.
To address this critical gap, we propose to develop an ultrafast Dynamic Contrast Optical Coherence Tomography (DyC-OCT) system empowered by artificial intelligence (Al) for label-free, high-throughput 4D imaging of tumor organoids.

Project 3: High-throughput, bond-specific 3D chemical microscopy for lipid metabolism in cancer metastasis

Research Project Leader: Dr. Jian Zhao Mentors: Dr. Javier A. Jo and Dr. Priyabrata Mukherjee

Cancer metastasis relies heavily on altered lipid metabolism, yet the precise mechanisms linking fatty acid saturation states to metastatic progression remain poorly understood. Saturated and unsaturated fatty acids appear to differentially influence energy production, membrane composition, and signaling pathways, thereby shaping the tumor microenvironment and metastatic potential.

To advance our understanding, we will develop and apply a novel, high-throughput, bond-specific three-dimensional (3D) optical microscopy method—Optically Detected Mid-IR 3D (O-MIR) microscopy—that overcomes current limitations in live-cell metabolic imaging. Existing approaches, including fluorescence imaging and Raman-based techniques, are constrained by labeling requirements, photobleaching, and low signal throughput.
By integrating computational microscopy, mid-infrared (IR) spectroscopy, and click-free bio-orthogonal probes, our method will enable bond-specific, sub-micrometer resolution and rapid 3D live-cell imaging within both the mid-IR fingerprint and “cell-silent” spectral windows, facilitating direct, real-time tracking of fatty acids and their metabolic transformations.