Deep scRNA sequencing reveals a broadly applicable Regeneration Classifier and implicates antioxidant response in corticospinal axon regeneration

Neuron, 2023 · DOI: 10.1016/j.neuron.2023.09.019 · Published: December 20, 2023

Neurology Bioinformatics Regenerative Medicine & Stem Cells

Simple Explanation

After spinal cord injury, the corticospinal tract (CST) axons do not regenerate to a significant extent. This study uses single-cell RNA sequencing to understand the regenerative potential within CST neurons. The researchers developed a 'Regeneration Classifier' that can predict the regenerative potential of different types of neurons based on their gene expression. The study identified NFE2L2 (NRF2), an antioxidant response gene, as a key regulator of axon regeneration, suggesting that antioxidant mechanisms play a role in the process.

Study Duration

10 weeks post-injury survival time

Participants

326 CST neurons (123 regenerating, 203 non-regenerating) from 29 PTENfl/fl;SOCS3fl/fl;tdTomatofl/fl mice

Evidence Level

Not specified

Key Findings

1
A Regeneration Classifier was developed that can predict the regenerative potential of diverse neuronal types based on their single-cell transcriptomes.
2
Network analyses highlighted the importance of antioxidant response and mitochondrial biogenesis in axon regeneration.
3
Conditional gene deletion validated a role for NFE2L2 (NRF2), a master regulator of antioxidant response, in CST regeneration.

Research Summary

Kim et al. identify antioxidant response gene NFE2L2 as a new regulator of axon regeneration and develop a Regeneration Classifier that can be broadly applied to predict the regenerative potential of diverse neuronal types based on their single cell transcriptomes. Our data demonstrate a universal transcriptomic signature underlying the regenerative potential of vastly different neuronal populations, and illustrate that deep sequencing of only hundreds of phenotypically identified neurons has the power to advance regenerative biology. Thus, deep sequencing of even hundreds of neurons may reveal new biological insights into neuronal regeneration following spinal cord injury.

Practical Implications

Predictive Biomarker

The Regeneration Classifier can be used as a biomarker to predict the likelihood of success for candidate regenerative therapies in preclinical studies.

Therapeutic Target Identification

Identifying NFE2L2 as a regulator of axon regeneration suggests that targeting the antioxidant response pathway could be a potential therapeutic strategy for promoting regeneration after spinal cord injury.

Developmental Timing

The Regeneration Classifier can be used to pinpoint the critical transition time in development when neurons rapidly lose their regeneration abilities, which could inform interventions to maintain regenerative potential.

Study Limitations

1
Variations exist between different published datasets, likely due to differences in experimental paradigms and sequencing technologies.
2
The Regeneration Classifier is less compatible with single nucleus RNA sequencing (snRNA-Seq) datasets due to the lack of cytoplasmic RNAs and older technologies.
3
The Regeneration Classifier, developed on a regeneration-resistant CNS neuronal type, may not work well for PNS neurons.

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