July 17, 2024

New Biomarker Identified to Predict Neuron Regeneration

Researchers from the University of California San Diego School of Medicine have identified a new biomarker that could help predict whether or not neurons will regenerate after an injury. Neurons in the brain and spinal cord are known for being slow to regenerate, and many fail to regenerate completely. Understanding why some neurons regenerate while others do not has been a challenge for scientists.

Using single-cell RNA sequencing, a method that identifies which genes are activated in individual cells, the researchers analyzed neurons from mice with spinal cord injuries. They found that their newly discovered biomarker was consistently reliable in predicting whether or not neurons would regenerate across the nervous system and at different developmental stages. The study was published in the journal Neuron.

The researchers focused on neurons of the corticospinal tract, which is essential for controlling movement. After an injury, these neurons are least likely to regenerate axons, the structures neurons use to communicate with each other. This is why injuries to the brain and spinal cord can be so devastating.

By studying the gene expression in neurons from mice with spinal cord injuries, the researchers were able to identify a unique pattern of gene expression that can predict whether an individual neuron will regenerate after an injury. This pattern includes genes that had never before been implicated in neuronal regeneration.

To validate their findings, the researchers tested the molecular fingerprint they identified, which they named the Regeneration Classifier, on 26 published datasets of single-cell RNA sequencing. These datasets included neurons from different parts of the nervous system and at different developmental stages. In almost all cases, the Regeneration Classifier successfully predicted the regeneration potential of individual neurons and reproduced known trends from previous research.

While the results are promising, the researchers note that the Regeneration Classifier is currently a tool for neuroscience researchers in the lab and not a diagnostic test for patients. The use of single-cell sequencing in clinical contexts has barriers such as cost, analyzing large amounts of data, and accessibility to tissues of interest. However, the researchers believe that their findings could be used to predict the effectiveness of new regenerative therapies in preclinical contexts and help advance those treatments closer to clinical trials. More refinement and research are needed to fully explore the potential of the Regeneration Classifier.

The discovery of this new biomarker could have significant implications for the development of regenerative therapies for spinal cord injuries and other neurological conditions. Being able to predict whether or not neurons will regenerate after an injury could help guide treatment strategies and improve outcomes for patients.

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  1. Source: Coherent Market Insights, Public sources, Desk research
  2. We have leveraged AI tools to mine information and compile it