Studying NF-κB signaling during neuroinflammation in Alzheimer’s Diseases <i>in vitro</i> using hiPSC-derived brain models
Bibliographic record
Abstract
Abstract Neuroinflammation is a key pathological driver of various neurological diseases, including Alzheimer’s disease (AD). NF-κB signaling pathway is widely recognized as a hallmark of inflammation and cellular senescence in which aged microglia may undergo cellular senescence partly influenced by NF-κB. Our previous data indicated that ex vivo microglia comprised of two distinct subpopulations distinguished by morphology and motility. Most microglia cells formed a tight cell cluster, termed “clustered microglia”. The rest was a subpopulation of microglia with smaller cell size and high motility, termed “free-roaming microglia”. In microglia from aged animals, the composition of clustered versus free-roaming subsets was shifted toward a higher prevalence of free-roaming microglia where c-Rel is expressed and the canonical NF-κB signaling is more sustained. To study NF-κB signaling in the context of neuroinflammation, we are developing 3D models, using hiPSC neurons derived from healthy donors of various age groups and co-culturing them with primary microglia from knock-in mice in which their endogenous c-Rel was labeled with a fluorescent protein. Moreover, individuals with APOE-e4 genotype have an increased risk of developing AD. To develop 3D brain models for AD, we will use co-culture of microglia and neurons derived from hiPSCs of APOE-e4 carriers. Live cell imaging and biomarker assessment of microglia and neurons activated by Amyloid β (1–42) will uncover important molecular mechanisms associated with age-dependent interactions between neurons from healthy versus AD-risk individuals, and resident macrophages of the young and aged brains.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".