Detection of astrocyte epigenetic memory in <i>in vitro</i> systems, experimental autoimmune encephalomyelitis and multiple sclerosis samples
Bibliographic record
Abstract
Abstract We recently described astrocyte pro-inflammatory epigenetic memory based on multiple complementary in vivo and in vitro studies, and the analysis of multiple sclerosis samples. Based on bioinformatic analyses, O’Dea and Liddelow argued that the astrocyte epigenetic memory we described is the result of contamination with immune cells, particularly myeloid cells. We rebut O’Dea and Liddelow arguments as follows: (1) We show substantial purity of astrocytes analyzed in in vivo and in vitro systems; (2) We recapitulate astrocyte memory responses using five independent pure astrocyte in vitro systems, and show its dependency on the histone acetyl transferase p300; and (3) Using the Liddelow lab bioinformatic pipeline to implement purity and cell-quality criteria, we detect astrocyte epigenetic memory in five independent scRNA-seq experimental autoimmune encephalomyelitis (EAE) and multiple sclerosis (MS) astrocyte datasets. These additional analyses and studies provide further support for the existence of astrocyte pro-inflammatory epigenetic memory.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".