Expanding the neuroimmune research toolkit with <i>in vivo</i> brain organoid technologies
Bibliographic record
Abstract
Human pluripotent stem cell-derived microglia-like cells (MLCs) and brain organoid systems have revolutionized the study of neuroimmune interactions, providing new opportunities to model human-specific brain development and disease. Over the past decade, advances in protocol design have improved the fidelity, reproducibility and scalability of MLC and brain organoid generation. Co-culturing of MLCs and brain organoids have enabled direct investigations of human microglial interactions in vitro, although opportunities remain to improve microglial maturation and long-term survival. To address these limitations, innovative xenotransplantation approaches have introduced MLCs, organoids or neuroimmune organoids into the rodent brain, providing a vascularized environment that supports prolonged development and potential behavioral readouts. These expanding in vitro and in vivo toolkits offer complementary strategies to study neuroimmune interactions in health and disease. In this Perspective, we discuss the strengths, limitations and synergies of these models, highlighting important considerations for their future applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.003 |
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".