An Efficient Method for Generating Murine Hypothalamic Neurospheres for the Study of Regional Neural Progenitor Biology
Bibliographic record
Abstract
The hypothalamus is a key homeostatic brain region and the primary effector of neuroendocrine signaling. Recent studies show that early embryonic developmental disruption of this region can lead to neuroendocrine conditions later in life, suggesting that hypothalamic progenitors might be sensitive to exogenous challenges. To study the behavior of hypothalamic neural progenitors, we developed a novel dissection methodology to isolate murine hypothalamic neural stem and progenitor cells at the early timepoint of embryonic day 12.5, which coincides with peak hypothalamic neurogenesis. Additionally, we established and optimized a culturing protocol to maintain multipotent hypothalamic neurospheres that are capable of sustained proliferation or differentiation into neurons, oligodendrocytes, and astrocytes. We characterized media requirements, appropriate cell seeding density, and the role of growth factors and sonic hedgehog (Shh) supplementation. Finally, we validated the use of fluorescence activated cell sorting of either Sox2GFPKI or Nkx2.1GFPKI transgenic mice as an alternate cellular isolation approach to enable enriched selection of hypothalamic progenitors for growth into neurospheres. Combined, we present a new technique that yields reliable culturing of hypothalamic neural stem and progenitor cells that can be used to study hypothalamic development in a controlled environment.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".