Protein expression profiles in brain organoids are more similar to those in human brain parenchyma than in mouse brain parenchyma
Bibliographic record
Abstract
Summary Human brain organoids are emerging as relevant models for the study of human brain health and disease. However, it has not been shown whether human brain organoids exhibit a proteoform profile similar to the human brain. Herein, we demonstrate that unguided brain organoids exhibit minimal batch-to-batch variability in cell composition and metabolism when generated from induced pluripotent stem cells (iPSCs) derived from male-female siblings. We then show that profiles of select proteins in these brain organoids are more similar to autopsied human cortical and cerebellar profiles than to those in mouse cortical samples. Brain organoids derived from sibling iPSCs do not exhibit any sex differences in protein proportions. By benchmarking human brain organoid proteoforms against human parenchymal tissue, we establish the foundation for future studies that could investigate, for example, how well brain organoids can model any of the known sex-dependent differences in cellular function, including responses of drug-receptor interactions. Highlights Brain organoids (BOs) display protein banding similar to human parenchymal lysates Protein banding differs between mouse and human brain parenchyma lysates Sibling-derived BOs have similar cell composition and metabolism at day 90 Sibling-derived BOs exhibit similar protein banding at day 90
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".