A Human Cerebral Organoid Model of Neural Cell Transplantation
Bibliographic record
Abstract
The advancement of cell transplantation approaches requires model systems that allow an accurate assessment of transplanted cell functional potency. For the central nervous system, although xenotransplantation remains state-of-the-art, such models are technically challenging, limited in throughput, and expensive. Moreover, the environmental signals present do not perfectly cross-react with human cells. This paper presents an inexpensive, accessible, and high-throughput-compatible model for the transplantation and tracking of human neural cells into human cerebral organoids. These organoids can be easily generated from human induced pluripotent stem cells using commercial kits and contain the key cell types of the cerebrum. We first demonstrate this transplant protocol with the injection of EGFP-labeled human iPSC-derived neural progenitor cells (NPCs) into these organoids. We next discuss considerations for tracking the growth of these cells in the organoid by live-cell fluorescence microscopy and demonstrate the tracking of transplanted EGFP-labeled NPCs in an organoid over a 4 month period. Finally, we present a protocol for the sectioning, cyclic immunofluorescent staining, and imaging of the transplanted cells in their local context. The organoid transplantation model presented here allows the long-term (at least 4 months) tracking of transplanted human cells directly in a human microenvironment with an inexpensive and simple-to-perform protocol. It, thus, represents a useful model both for neural cell therapies (transplants) and, likely, for modeling central nervous system (CNS) tumors in a more microenvironmentally accurate manner.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".