Neural Regeneration Research ›› 2026, Vol. 21 ›› Issue (9): 4051-4060.doi: 10.4103/NRR.NRR-D-25-00610

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Multi-omics technologies: Novel tools and methods for assessing nerve injury and regeneration

Qiang Zhou1, #, Zongren Zhao2, #, Da Tan1, Chenhao Fang1, Zhaoli Shen1, *, Shun Li3, *, Xianzhen Chen1, *   

  1. 1Department of Neurosurgery, Shanghai Tenth People’s Hospital, School of Medicine, Tongji University, Shanghai, China; 
    2Department of Neurology, Ulm University, Ulm, Germany; 
    3Department of Neurology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
  • Online:2026-09-15 Published:2026-05-16
  • Contact: Xianzhen Chen, PhD, chenxianzheny@126.com; Shun Li, MD, lis24@upmc.edu; Zhaoli Shen, MS, leelies@sina.com.

Abstract: Recently, with the rapid advancement of multi-omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, new tools and approaches have been introduced for studying nerve injury and regeneration. This review highlights the application and progress of multi-omics in uncovering the mechanisms of nerve injury, guiding the development of regenerative strategies, and promoting clinical translation. By integrating multi-omics datasets, researchers can comprehensively track dynamic molecular changes following nerve injury, including abnormal gene expression, disrupted protein signaling, altered metabolic programs, and shifts in the immune microenvironment. Single-cell multi-omics technologies resolve cellular heterogeneity, revealing the distinct functions of neurons, glial cells, and immune cell subpopulations during the injury response. Spatially resolved transcriptomics maintain the spatial context of lesion and regeneration sites, enabling precise localization for targeted interventions. Multi-omics technologies not only identify key molecular players involved in nerve regeneration but also create opportunities for personalized medicine. Nonetheless, integrating multi-omics data poses technical challenges, including high dimensionality, batch effects, and algorithmic constraints, while ethical concerns related to stem cell therapy and gene editing require stringent oversight. To transition from structural reconstruction to functional remodeling, future research should emphasize artificial intelligence–driven data integration, organ-on-a-chip modeling, and cross-disciplinary collaboration to overcome existing technical barriers and accelerate the clinical application of neuroregenerative therapies. 

Key words: artificial intelligence, biomarkers, gene expression regulation, genomics, metabolomics, nerve regeneration, neural pathways, proteomics, single-cell analysis, transcriptomics