A resource for generating and manipulating human microglial states in vitro
Bibliographic record
Abstract
Abstract Microglia have emerged as key players in the pathogenesis of neurodegenerative conditions such as Alzheimer’s disease (AD). In response to CNS stimuli, these cells adopt distinct transcriptional and functional subtypes known as states. However, an understanding of the function of these states has been elusive, especially in human microglia, due to lack of tools to model and manipulate this cell-type. Here, we provide a platform for modeling human microglia transcriptional states in vitro . Using single-cell RNA sequencing, we found that exposure of human stem-cell differentiated microglia (iMGLs) to brain-related challenges generated extensive transcriptional diversity which mapped to gene signatures identified in human brain microglia. We identified two in vitro transcriptional clusters that were analogous to human and mouse disease-associated microglia (DAMs), a state enriched in neurodegenerative disease contexts. To facilitate scalable functional analyses, we established a lentiviral approach enabling broad and highly efficient genetic transduction of microglia in vitro . Using this new technology, we demonstrated that MITF (Melanocyte Inducing Transcription Factor), an AD-enriched transcription factor in microglia, drives both a disease-associated transcriptional signature and a highly phagocytic state. Finally, we confirmed these results across iMGLs differentiated from multiple iPSC lines demonstrating the broad utility of this platform. Together, these tools provide a comprehensive resource that enables the manipulation and functional interrogation of human microglial states in both homeostatic and disease-relevant contexts.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".