Fundamental Neurochemistry Review: Incorporating a greater diversity of cell types, including microglia, in brain organoid cultures improves clinical translation
Bibliographic record
Abstract
Brain organoids have the potential to improve clinical translation, with the added benefit of reducing any extraneous use of experimental animals. As brain organoids are three-dimensional in vitro constructs that emulate the human brain, they bridge in vitro and in vivo studies more appropriately than monocultures. Although many factors contribute to the failure of extrapolating monoculture-based information to animal-based experiments and clinical trials, for the purpose of this review, we will focus on glia (non-neuronal brain cells), whose functions and transcriptome are particularly abnormal in monocultures. As discussed herein, glia require signals from-and contact with-other cell types to exist in their homeostatic state, which likely contributes to some of the differences between data derived from monocultures and data derived from brain organoids and even two-dimensional co-cultures. Furthermore, we highlight transcriptomic differences between humans and mice in regard to aging and Alzheimer's disease, emphasizing need for a model using the human genome-again, a benefit of brain organoids-to complement data derived from animals. We also identify an urgency for guidelines to improve the reporting and transparency of research using organoids. The lack of reporting standards creates challenges for the comparison and discussion of data from different articles. Importantly, brain organoids mark the first human model enabling the study of brain cytoarchitecture and development.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".