iPSC-derived healthy human astrocytes selectively load miRNAs targeting neuronal genes into extracellular vesicles
Bibliographic record
Abstract
Abstract Astrocytes are in constant communication with neurons during the establishment and maturation of functional networks in the developing brain. Astrocytes release extracellular vesicles (EVs) containing microRNA (miRNA) cargo that regulates transcript stability in recipient cells. Astrocyte released factors are thought to be involved in neurodevelopmental disorders. Healthy astrocytes partially rescue Rett Syndrome (RTT) neuron function. EVs isolated from stem cell progeny also correct aspects of RTT. EVs cross the blood-brain barrier (BBB) and their cargo is found in peripheral blood which may allow non-invasive detection of EV cargo as biomarkers produced by healthy astrocytes. Here we characterize miRNA cargo and sequence motifs in healthy human astrocyte derived EVs (ADEVs). First, human induced Pluripotent Stem Cells (iPSC) were differentiated into Neural Progenitor Cells (NPCs) and subsequently into astrocytes using a rapid differentiation protocol. iPSC derived astrocytes expressed specific markers, displayed intracellular calcium transients and secreted EVs. miRNAs were identified by RNA-Seq on astrocytes and ADEVs and target gene pathway analysis detected brain related terms. The miRNA profile was consistent with astrocyte identity, and included approximately 80 miRNAs found in astrocytes that were relatively depleted in ADEVs suggestive of passive loading. About 120 miRNAs were relatively enriched in ADEVs and motif analysis discovered binding sites for RNA binding proteins FUS, SRSF7 and CELF5. miRNA-483-5p was the most significantly enriched in ADEVs. This miRNA regulates MECP2 expression in neurons and has been found differentially expressed in blood samples from RTT patients. Our results identify potential miRNA biomarkers selectively sorted into ADEVs and implicate RNA binding protein sequence dependent mechanisms for miRNA cargo loading.
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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 | 0.001 |
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".