Human-specific NOTCH2NL promotes astrogenesis by expanding proliferative glial progenitor states
Bibliographic record
Abstract
Abstract The human cerebral cortex contains an unusually large number of glial cells, particularly astrocytes, yet the developmental and genetic mechanisms underlying their expansion remain poorly understood. While human-specific genes have been shown to promote neuronal production during cortical development, whether such genes also regulate gliogenesis has remained unclear. Here, we identify a previously unrecognized role for the human-specific gene family NOTCH2NL in promoting astrocyte-lineage expansion. Reanalysis of human fetal single-cell transcriptomic datasets revealed that NOTCH2NL is robustly expressed along the gliogenic trajectory, from glial intermediate progenitor cells to astrocytes. Functional perturbations in a human astrocyte culture system demonstrated that NOTCH2NL is both required and sufficient for astrocyte proliferation. In vivo overexpression of NOTCH2NLB in the developing mouse cortex shifted progenitor output toward the astrocyte lineage, increasing the astrocyte-to-neuron ratio from the early postnatal period through adulthood. This phenotype was associated with an expansion of proliferative glial progenitors around birth. Single-nucleus transcriptomic profiling further showed that NOTCH2NLB suppresses neuronal gene programs while activating transcriptional modules related to cell proliferation and cellular homeostasis during gliogenesis. Together, these findings indicate that human-specific NOTCH2NL acts at a conserved developmental decision point to amplify astrocyte production. Our study extends the function of NOTCH2NL beyond neurogenesis and suggests that human lineage–specific gene duplications can modulate gliogenesis, providing a developmental mechanism that may have contributed to the coordinated expansion of neuronal and glial populations in the human cortex.
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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".