The leading edge: Emerging neuroprotective and neuroregenerative cell-based therapies for spinal cord injury
Bibliographic record
Abstract
Abstract Spinal cord injuries (SCIs) are associated with tremendous physical, social, and financial costs for millions of individuals and families worldwide. Rapid delivery of specialized medical and surgical care has reduced mortality; however, long-term functional recovery remains limited. Cell-based therapies represent an exciting neuroprotective and neuroregenerative strategy for SCI. This article summarizes the most promising preclinical and clinical cell approaches to date including transplantation of mesenchymal stem cells, neural stem cells, oligodendrocyte progenitor cells, Schwann cells, and olfactory ensheathing cells, as well as strategies to activate endogenous multipotent cell pools. Throughout, we emphasize the fundamental biology of cell-based therapies, critical features in the pathophysiology of spinal cord injury, and the strengths and limitations of each approach. We also highlight salient completed and ongoing clinical trials worldwide and the bidirectional translation of their findings. We then provide an overview of key adjunct strategies such as trophic factor support to optimize graft survival and differentiation, engineered biomaterials to provide a support scaffold, electrical fields to stimulate migration, and novel approaches to degrade the glial scar. We also discuss important considerations when initiating a clinical trial for a cell therapy such as the logistics of clinical-grade cell line scale-up, cell storage and transportation, and the delivery of cells into humans. We conclude with an outlook on the future of cell-based treatments for SCI and opportunities for interdisciplinary collaboration in the field. Significance statement Traumatic spinal cord injuries (SCIs) result in tremendous lifelong disability and financial burden for millions of patients and caregivers worldwide. Cell-based therapies have emerged as an exciting neuroprotective and neuroregenerative strategy for SCI. This review highlights key preclinical and clinical data in cell therapy with an emphasis on the pathobiology and mechanisms of recovery. Also discussed are adjunct treatments to maximize the efficacy of the grafts. Finally, important translational considerations such as clinical-grade scale-up and delivery techniques are discussed. The article succinctly provides readers with a working knowledge of SCI and cell therapies at the leading edge of research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".