Expression of the transcription factor PU.1 induces the generation of microglia-like cells in human cortical organoids
Bibliographic record
Abstract
Microglia play a role in the emergence and preservation of a healthy brain microenvironment. Dysfunction of microglia has been associated with neurodevelopmental and neurodegenerative disorders. Investigating the function of human microglia in health and disease has been challenging due to the limited models of the human brain available. Here, we develop a method to generate functional microglia in human cortical organoids (hCOs) from human embryonic stem cells (hESCs). We apply this system to study the role of microglia during inflammation induced by amyloid-β (Aβ). The overexpression of the myeloid-specific transcription factor PU.1 generates microglia-like cells in hCOs, producing mhCOs (microglia-containing hCOs), that we engraft in the mouse brain. Single-cell transcriptomics reveals that mhCOs acquire a microglia cell cluster with an intact complement and chemokine system. Functionally, microglia in mhCOs protect parenchyma from cellular and molecular damage caused by Aβ. Furthermore, in mhCOs, we observed reduced expression of Aβ-induced expression of genes associated with apoptosis, ferroptosis, and Alzheimer's disease (AD) stage III. Finally, we assess the function of AD-associated genes highly expressed in microglia in response to Aβ using pooled CRISPRi coupled with single-cell RNA sequencing in mhCOs. In summary, we provide a protocol to generate mhCOs that can be used in fundamental and translational studies as a model to investigate the role of microglia in neurodevelopmental and neurodegenerative disorders.
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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 | 0.000 | 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) | 0.001 | 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".