Capicua regulates the survival of Cajal-Retzius cells in the postnatal hippocampus
Bibliographic record
Abstract
Programmed cell death is crucial for organ morphogenesis and tissue homeostasis. Understanding programmed cell death in the developing brain is essential for comprehending both normal brain development and neurological disorders. In this study, we utilize Cajal-Retzius (CR) cells, transient neurons that populate the embryonic cortex and are predominantly eliminated in early postnatal stages, as a model to investigate the regulation of programmed cell death. While many CR cells typically undergo postnatal cell death, some persist into adulthood in the hippocampus, influencing local circuits and behaviors. Here, we show that the loss of capicua (CIC), a transcriptional repressor implicated in a rare neurodevelopmental syndrome and multiple cancers, results in aberrant survival of CR cells in the adult hippocampus. Altered cell survival is mediated by the cell-autonomous function of CIC in hippocampal CR cells. Surprisingly, the atypical persistence of CR cells following CIC loss does not impact hippocampal-dependent behaviors or susceptibility to kainic acid-induced seizures. Single-cell transcriptomic analysis unveils previously unrecognized heterogeneity among hippocampal CR cells and suggests a role of CIC in repressing Fgf1 expression. Additionally, we reveal that FGF1 and BCL2 serve as pivotal regulators enhancing CR cell survival in the postnatal hippocampus. Our findings shed light on a previously unacknowledged role of CIC upstream of FGF signaling and elucidate the apoptosis mechanism governing developmental programmed CR cell death.
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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".