Characterization of a Knock-In Mouse Line Expressing a Fusion Protein of κ Opioid Receptor Conjugated with tdTomato: 3-Dimensional Brain Imaging via CLARITY
Bibliographic record
Abstract
Abstract Activation of κ opioid receptor (KOR) produces analgesia, antipruritic effect, sedation and dysphoria. To characterize neuroanatomy of KOR at high resolutions and circumvent issues of specificity of KOR antibodies, we generated a knock-in mouse line expressing KOR fused at the C terminus with the fluorescent protein tdTomato (KtdT). The selective KOR agonist U50,488H caused anti-scratch effect and hypolocomotion, indicating intact KOR neuronal circuitries. Clearing of brains with CLARITY revealed three-dimensional (3-D) images of distribution of KOR, and any G-protein-coupled receptors, for the first time. 3-D brain images of KtdT and immunohistochemistry (IHC) on brain sections with antibodies against tdTomato show similar distribution to that of autoradiography of [3H]U69,593 binding to KOR in wild-type mice. KtdT was observed in regions involved in reward and aversion, pain modulation, and neuroendocrine regulation. KOR is present in several areas with unknown roles, including the claustrum (CLA), dorsal endopiriform nucleus, paraventricular nucleus of the thalamus (PVT), lateral habenula (LHb), and substantia nigra pars reticulata (SNr), which are discussed. Prominent KtdT-containing fibers were observed to project from caudate putamen (CP) and nucleus accumbens (ACB) to substantia innominata (SI) and SNr. Double IHC revealed co-localization of KtdT with tyrosine hydroxylase (TH) in brain regions, including CP, ACB, and ventral tegmental area (VTA). KOR was visualized at the cellular level, such as co-localization with TH and agonist-induced KOR translocation into intracellular space in some VTA neurons. These mice thus represent a powerful and heretofore unparalleled tool for neuroanatomy of KOR at both the 3-D and cellular levels.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".