Cell Therapy for Brain Regeneration

Background

Every year 5 million people remain permanently disabled after a stroke due to the brain's limited ability to regenerate damaged tissue. The lack of an effective therapy that promotes long-term recovery after stroke represents a substantial clinical burden and unmet need for medical treatment outside the confines of conventional therapies. Recent advances in cell therapies have shown promising preclinical results in animal stroke models but have not been confirmed beyond doubt in a clinical setting. Major uncertainties remain — e.g., regarding the source of the transplanted cells, immune compatibility to the recipient, and cell survival and functional integration into the damaged neural circuitry.

Research Goals

Our primary goal is to overcome major limitations recognized by scientists in the field of cell therapies following stroke. We work with our excellent collaborators to push the boundaries of what is possible:

• Decoding the molecular crosstalk between cell grafts and the stroke-injured tissue at single cell and spatial resolution
• CRISPR screen for systemic delivery of gene-edited cell grafts to the stroke brain
• Safe and immune evasive cell grafts for neurological disorders

Main Techniques

We exploit our expertise in experimental stroke research combined with molecular and genetic tools to optimize the cell source. We validate stroke procedures using laser Doppler flowmetry, utilize bioluminescence imaging for longitudinal cell tracking in vivo, and perform deep learning-based gait analysis. We further characterize cellular fate of transplants using single-cell and spatially resolved transcriptomics.

Graft–Host Interactions

To better understand how transplanted cells integrate with the host brain, we visualize hybrid graft–host vascular interactions in real time. Here, transplanted human iPSC-derived pericytes (green) home to and engraft along mouse blood vessels (magenta) — illuminating the dynamic cellular mechanisms that underpin successful graft integration after brain injury.

Gait changes following stroke are often subtle and difficult to detect by eye alone. We use AI-powered 3D pose estimation to track specific body landmarks frame by frame — uncovering fine-grained motor deficits that would otherwise go unnoticed. The colored markers show deep learning–based labeling of individual limbs and body parts, enabling precise, quantitative gait analysis after experimental stroke.