LC-MS/MS for Ultra-Sensitive Quantification of Multiple Estrogens in the Blood and Brain
Bibliographic record
Abstract
Abstract Estrogens are steroid hormones that affect many aspects of brain function, including cognition, social behavior, and neuroprotection. It is well-known that estrogens are synthesized in the ovaries. Estrogens are also synthesized in the brain, where aromatase is expressed in specific regions. Importantly, estrogens play crucial roles in the brain, even at extremely low levels. Current assays lack the necessary sensitivity and/or specificity to measure brain-synthesized estrogens. Furthermore, current methods focus on only 17β-estradiol and generally disregard other estrogens that are synthesized in the brain. Here, we developed a method to measure several estrogens simultaneously, with high sensitivity and specificity. To improve sensitivity, we derivatized estrogens with 1,2-dimethylimidazole-5-sulfonyl-chloride (DMIS). We used liquid chromatography tandem mass spectrometry (LC-MS/MS) to examine a panel of eight estrogens: 17β-estradiol, 17α-estradiol, estrone, estriol, 2-hydroxyestradiol, 4-hydroxyestradiol, 2-methoxyestradiol, and 4-methoxyestradiol. After derivatization, we have improved sensitivity 20-fold, detecting as little as 0.01 pg per sample, demonstrating that our method is extremely sensitive. For each analyte, we have identified a distinct retention time as well as 2 scheduled multiple reaction monitoring (sMRM) transitions that were used as quality control criteria for clear identification. Therefore, we are able to distinguish each estrogen (even stereoisomers) by the chromatographic separation and the sMRM, demonstrating that our method is highly specific. This method has been applied to microdissected brain samples. Initially, we used a songbird model because songbirds have high levels of aromatase and 17β-estradiol in specific brain regions. We were able to simultaneously quantify multiple estrogens in small amounts of brain sample (1-2 mg). We examined seasonal changes of estrogens in the brain and blood. Future work will apply this method to mouse, rat, and human samples and expand the panel of estrogens examined. Our ultra-sensitive assay is essential for small animal models, where estrogen measurement is extremely challenging because of the limited amount of brain tissue. This novel technique will also have wide-ranging applications for basic research and clinical testing, including estrogen measurement in humans with low estrogen levels, such as men, pre-pubertal children, and post-menopausal women.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".