Single-Cell Suspension Methodology Favors Survival and Vascularization of Fetal Striatal Grafts in the YAC128 Mouse Model of Huntington's Disease
Bibliographic record
Abstract
Cell replacement therapies have yielded variable and short-lived benefits in Huntington's disease (HD) patients. This suboptimal outcome is likely due to the fact that graft survival is compromised long term because grafts are subjected to a host's microglial inflammatory response, to a lack of adequate trophic support, and possibly to cortical excitotoxicity. However, graft demise may also relate to more straightforward issues such as cell preparation methodology (solid grafts vs. cell suspension). Indeed, we recently reported that solid grafts are poorly revascularized in HD patients transplanted 9 and 12 years previously. To evaluate whether methodological issues relating to cell preparation may have an impact on graft viability, we implanted green fluorescent protein (GFP(+)) single-cell suspensions of fetal striatal neuronal cells into the striatum of YAC128 HD mice. Postmortem evaluation yielded comparable graft survival in YAC128 mice and their wild-type littermates (noncarrier) at 1 and 3 months posttransplantation. Additionally, the degrees of graft revascularization in the YAC128 and noncarrier mice were similar, with both capillaries and large-caliber vessels observable within the grafted tissue. Furthermore, GFP(+) cells interacted well with host blood vessels, indicating integration of the donor cells within the recipient brain. These observations, combined with our recent report of poor revascularization of solid grafts in the HD-transplanted patients, suggest that the success of cell transplantation can be improved by optimizing methodological aspects relating to cell preparation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".