hiPSC-derived GRN-deficient astrocytes delay spiking activity of developing neurons
Bibliographic record
Abstract
Frontotemporal dementia (FTD) refers to a group of neurodegenerative disorders that are characterized by pathology predominantly localized to the frontal and temporal lobes. Approximately 40% of FTD cases are familial, and up to 20% of these are caused by heterozygous loss of function mutations in the gene encoding for progranulin (PGRN), GRN. The mechanisms by which loss of PGRN leads to FTD remain incompletely understood. While astrocytes and microglia have long been linked to the neuropathology of FTD due to mutations in GRN (FTD-GRN), a primary mechanistic role of these supporting cells have not been thoroughly addressed. In contrast, mutations in MAPT, another leading cause of familial FTD, greatly alters astrocyte gene expression leading to subsequent non-cell autonomous effects on neurons, suggesting similar mechanisms may be present in FTD-GRN. Here, we utilized human induced pluripotent stem cell (hiPSC)-derived neural tissue carrying a homozygous GRN R493X−/− knock-in mutation to investigate in vitro whether GRN mutant astrocytes have a non-cell autonomous effect on neurons. Using microelectrode array (MEA) analysis, we demonstrate that the development of spiking activity of neurons cultured with GRN R493X−/− astrocytes was significantly delayed compared to cultures with WT astrocytes. Histological analysis of synaptic markers in these cultures showed an increase in GABAergic synaptic markers and a decrease in glutamatergic synaptic markers during this period when activity was delayed. We also demonstrate that this effect may be due in-part to soluble factors. Overall, this work represents one of the first studies investigating astrocyte-induced neuronal pathology in GRN mutant hiPSCs, and supports the hypothesis of astrocyte involvement in the early pathophysiology of FTD.
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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 | 0.001 |
| 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".