8302 A Novel Tool to Map Estrogen Responsiveness Within Estrogen Sensitive Hypothalamic Neurons in Mice
Bibliographic record
Abstract
Abstract Disclosure: A.L. Cara: None. A.L. Hall: None. K.K. Soma: None. J. van Veen: None. S.M. Correa: None. Identification of hormone sensitive cells has been traditionally evaluated by hormone receptor expression. Receptor positive cells can be labeled via expression of mRNA transcript via in situ hybridization, presence of protein via immunohistology, or via conditionally-dependent Cre recombinase driven reporter expression. However, these methods used to label and manipulate cells only indicate which cells are capable of sensing hormones, and not necessarily which cells are responding to a hormonal signal in a specific physiologic or behavioral context. Furthermore, broad labeling of all receptor positive cells may not accurately reflect the heterogeneous nature of certain cell populations, particularly in the brain. The hypothalamus is a brain region with well characterized hormone receptor expression, including estrogen receptors, such as ERα (encoded by the Esr1 gene). Estrogens have a broad array of physiologic and behavioral functions which are mediated through hypothalamic nuclei, including thermoregulation, metabolism, and reproduction. Neuronal populations mediating these functions have been characterized, targeted, and manipulated based on ERα expression, but the dynamic response to estrogens in these highly heterogeneous populations has yet to be uncovered. We developed a custom reporter virus that fluorescently labels estrogen-sensitive and responsive cells. The virus contains a flip-excision switch (FLEX) to drive Cre-dependent expression of a FusionRed fluorescent reporter, eight tandem estrogen response elements (ERE), and a destabilized enhanced green fluorescent protein (EGFP) with a 2-hour half-life. The Cre-dependent expression of FusionRed identifies all Esr1-Cre cells at the time of transduction, while the ERE-EGFP identifies a subset of Esr1-Cre+ cells that are actively responding to estrogens via nuclear receptor signaling. We stereotactically injected the ERE-EGFP virus to distinct hypothalamic nuclei of Esr1-Cre female mice. Following transduction, mice were sacrificed during different stages of the estrous cycle. Spatial analysis of ERE-EGFP expression revealed dynamic changes across the estrous cycle, with responses differing based on hypothalamic nucleus. Reporter expression dynamics were compared to estrogen levels measured by LC-MS/MS in ovary, serum, and microdissected brain tissue. A separate group of mice were ovariectomized and given estradiol benzoate (EB) or vehicle control, and neurons were flow sorted for bulk RNA sequencing. We discovered transcriptomic differences in FusionRed+ cells with high or low ERE-EGFP expression, including upregulation of estrogen target genes (e.g., Pgr, encoding progesterone receptor) with EB treatment in ERE-EGFP+ cells. In summary, this new viral reporter reveals divergent responses to estrogens across subsets of estrogen sensitive neurons, brain regions, and hormonal states. Presentation: 6/2/2024
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".