Transcriptional and Cellular Response of hiPSC-derived Microglia-Neural Progenitor Co-Cultures Exposed to IL-6
Bibliographic record
Abstract
Abstract Elevated interleukin (IL-)6 levels during prenatal development have been linked to increased risk for neurodevelopmental disorders (NDD) in the offspring, but the mechanism remains unclear. Human-induced pluripotent stem cell (hiPSC) models offer a valuable tool to study the effects of IL-6 on features relevant for human neurodevelopment in vitro . We previously reported that hiPSC-derived microglia-like cells (MGLs) respond to IL-6, but neural progenitor cells (NPCs) in monoculture do not. Therefore, we investigated whether co-culturing hiPSC-derived MGLs with NPCs would trigger a cellular response to IL-6 stimulation via secreted factors from the MGLs. Using N=4 donor lines without psychiatric diagnosis, we first confirmed that NPCs can respond to IL-6 through trans-signalling when recombinant IL-6Ra is present, and that this response is dose-dependent. MGLs secreted soluble IL-6R, but at lower levels than found in vivo and below that needed to activate trans-signalling in NPCs. Whilst transcriptomic and secretome analysis confirmed that MGLs undergo substantial transcriptomic changes after IL-6 exposure and subsequently secrete a cytokine milieu, NPCs in co-culture with MGLs exhibited a minimal transcriptional response. Furthermore, there were no significant cell fate-acquisition changes when differentiated into post-mitotic cultures, nor alterations in synaptic densities in mature neurons. These findings highlight the need to investigate if trans-IL-6 signalling to NPCs is a relevant disease mechanism linking prenatal IL-6 exposure to increased risk for psychiatric disorders. Moreover, our findings underscore the importance of establishing more complex in vitro human models with diverse cell types, which may show cell-specific responses to microglia-released cytokines to fully understand how IL-6 exposure may influence human neurodevelopment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".