Ultra-multiplexed gene regulation and machine learning for next-generation cell therapy for stroke

Stroke leads to devastating long-term consequences because the brain is unable to replace lost neurons and restore tissue architecture.

Therapies that successfully treat stroke injuries must therefore both regenerate all lost neuronal subtypes and their circuitry.

A new generation of cell therapies is emerging where cells are programmed to perform complex tasks inside the body using custom-designed genetic circuits.

Because of cells’ natural ability to sense and respond to complex cues, such synthetic biology technology will be revolutionary for regenerative medicine.

We have developed new technology for ultra-multiplexed gene regulation guided by machine learning algorithms for high-precision cell engineering. With this technology, we can control cell identity with high precision.

We have also discovered that astrocytes, a very abundant brain cell, are dormant neural stem cells whose neuron-producing ability can be recruited on-demand. Now, I propose to combine these findings to develop the next generation of stroke therapy.

Using both in vivo experiments in healthy mice and in vitro culture systems, I will regenerate multiple neuronal subtypes simultaneously (Aim 1) and engineer their axonal projections to restore long-range circuitry (Aim 2).

If successful, this project will establish a foundational workflow for next-generation cell therapies for stroke based on data-driven precision cell engineering inside the living brain.