Molecular Logic of Cell Diversity and Circuit Connectivity in the REM Sleep Hub
Bibliographic record
Abstract
ABSTRACT The complexity of the brain arises from the diversity of its circuits and the molecular heterogeneity of the cells that compose them. A mechanistic understanding therefore requires mapping cellular identity and connectivity at single-cell resolution. Here we define the cellular taxonomy of the murine sublaterodorsal tegmental nucleus (SLD), a critical hub for REM sleep, using single-nucleus RNA sequencing. We identified all major brain cell classes, with oligodendrocytes as the most abundant, and resolved seventeen transcriptionally distinct neuronal groups defined by neurotransmitters, neuromodulators, and neuropeptides, each with unique molecular signatures. Projection-specific analysis further revealed that glutamatergic subpopulations targeting the ventrolateral periaqueductal gray (vlPAG) and ventral medulla are molecularly distinct, marked by characteristic receptor motifs. Strikingly, we provide the first direct evidence that SLD GLUT neurons innervate the vlPAG. This newly uncovered SLD GLUT→vlPAG pathway represents a previously unrecognized circuit node for REM sleep regulation, with the potential to act as a REM-OFF population suppressing it. Together, these findings establish a transcriptionally resolved atlas of the SLD, reveal the molecular logic of its circuit connectivity, and nominate candidate molecular actuators of REM sleep control, opening new avenues for dissecting how brainstem circuits orchestrate REM state and its transitions.
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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".