Reactive Astrocytes Derived From Human Induced Pluripotent Stem Cells Suppress Oligodendrocyte Precursor Cell Differentiation
Bibliographic record
Abstract
Astrocytes are instrumental in maintaining central nervous system (CNS) homeostasis and responding to injury. A major limitation of studying neurodegenerative diseases like multiple sclerosis (MS) is lack of human pathological specimens obtained during the acute stages, thereby relegating research to post-mortem specimens obtained years after the initiation of pathology. Rodent reactive astrocytes have been shown to be cytotoxic to neurons and oligodendrocytes but may differ from human cells, especially in diseases with genetic susceptibility. Herein, we purified human CD49f+ astrocytes from induced pluripotent stem cells derived from individual patient and control peripheral leukocytes. We compared TNF and IL1α stimulated human reactive astrocytes from seven persons with MS and six non-MS controls and show their transcriptomes are remarkably similar to those described in rodents. The functional effect of astrocyte conditioned media (ACM) was examined in a human oligodendrocyte precursor cell (OPC) line differentiation assay. ACM was not cytotoxic to the OPCs but robustly inhibited the myelin basic protein (MBP) reporter. No differences were seen between MS and control stimulated astrocytes at either the transcript level or in ACM mediated OPC suppression assays. We next used RNAseq to interrogate differentially expressed genes in the OPC lines that had suppressed differentiation from the human ACM. Remarkably, not only was OPC differentiation and myelin gene expression suppressed, but we observed induction of several immune pathways in OPCs exposed to the ACM. These data support the notion that reactive astrocytes can inhibit OPC differentiation thereby limiting their remyelination capacity, and that OPCs take on an immune profile in the context of inflammatory cues.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".