Transplantation of Human Cortically-Specified Neuroepithelial Progenitor Cells Leads to Improved Functional Outcomes in a Mouse Model of Stroke
Bibliographic record
Abstract
Stroke is a leading cause of death and long-term disability worldwide. Current therapeutic options are limited in terms of their time for implementation and efficacy in promoting recovery. Cell transplantation has been shown to have promise in several animal models however significant challenges remain, including the optimal source of cells to promote neural repair. Here, we report on the use of a population of human ESC derived, cortically specified, neuroepithelial precursor cells (cNEPs) that are neurally restricted in their lineage potential. CNEPs have the potential to give rise to mature neural cell types following transplantation, including neurons, astrocytes and oligodendrocytes. With a view towards translation, we sought to determine whether this human cell source was effective in promoting improved functional outcomes following stroke. Undifferentiated cNEPs were transplanted in a pre-clinical endothelin-1 (ET-1) model of ischemic motor cortical stroke in immunocompromised SCID-beige mice and cellular and functional outcomes were assessed. We demonstrate that cNEP transplantation in the acute phase (4 days post-stroke) improves motor function as early as 20 days post-stroke, compared to stroke-injured, non-transplanted mice. At the time of recovery, a small fraction (<6%) of the transplanted cNEPs are observed within the stroke injury site. The surviving cells expressed the immature neuronal marker, doublecortin, with no differentiation into mature neural phenotypes. At longer survival times (40 days), the majority of recovered, transplanted mice had a complete absence of surviving cNEPS. Hence, human cNEPs grafted at early times post-stroke support the observed functional recovery following ET-1 stroke but their persistence is not required, thereby supporting a by-stander effect rather than cell replacement.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".