# Defining and exploiting the plasticity transcriptome to repair the damaged spinal cord

> **NIH NIH R01** · YALE UNIVERSITY · 2023 · $446,905

## Abstract

SUMMARY
A century of research has shown that the adult central nervous system is incapable of self-repair after injury or
disease. Indeed, adults with traumatic spinal cord injuries maintain chronic functional deficits that impact all
aspects of their lives. However, increasing evidence suggests that the adult CNS retains some ability to initiate
a growth program and functionally re-organize in response to activity, experience and mild trauma and
particularly after intensive rehabilitative therapy. In this proposal we have used in vivo viral tracing in
combination with FACS and single cell RNA sequencing to develop a comprehensive anatomical and
molecular atlas of the adult corticospinal tract (CST). We plan to leverage this atlas to define the molecular
mechanisms that drive axon growth in specific subsets of corticospinal tract neurons during rehab in the
presence and absence of the axon growth inhibitors nogo receptor-1. We believe that a comprehensive
understanding of the intrinsic molecular mechanism that initiates and sustains rehab-mediated axon growth
within defined subsets of CST neurons can then be exploited to design novel therapies to repair the acutely
and chronically damaged spinal cord.

## Key facts

- **NIH application ID:** 10536686
- **Project number:** 5R01NS121026-02
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** William B. Cafferty
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $446,905
- **Award type:** 5
- **Project period:** 2021-12-15 → 2026-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10536686

## Citation

> US National Institutes of Health, RePORTER application 10536686, Defining and exploiting the plasticity transcriptome to repair the damaged spinal cord (5R01NS121026-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10536686. Licensed CC0.

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