Single-neuron diversity generated by Protocadherin-β cluster in mouse central and peripheral nervous systems
Bibliographic record
Abstract
The generation of complex neural circuits depends on the correct wiring of neurons with diverse individual characteristics. To understand the complexity of the nervous system, the molecular mechanisms for specifying the identity and diversity of individual neurons must be elucidated. The clustered protocadherins (Pcdh) in mammals consist of approximately 50 Pcdh genes (Pcdh-α, Pcdh-β, and Pcdh-γ) that encode cadherin-family cell surface adhesion proteins. Individual neurons express a random combination of Pcdh-α and Pcdh-γ, whereas the expression patterns for the Pcdh-β genes, 22 one-exon genes in mouse, are not fully understood. Here we show that the Pcdh-β genes are expressed in a 3'-polyadenylated form in mouse brain. In situ hybridization using a pan-Pcdh-β probe against a conserved Pcdh-β sequence showed widespread labeling in the brain, with prominent signals in the olfactory bulb, hippocampus, and cerebellum. In situ hybridization with specific probes for individual Pcdh-β genes showed their expression to be scattered in Purkinje cells from P10 to P150. The scattered expression patterns were confirmed by performing a newly developed single-cell 3'-RACE analysis of Purkinje cells, which clearly demonstrated that the Pcdh-β genes are expressed monoallelically and combinatorially in individual Purkinje cells. Scattered expression patterns of individual Pcdh-β genes were also observed in pyramidal neurons in the hippocampus and cerebral cortex, neurons in the trigeminal and dorsal root ganglion, GABAergic interneurons, and cholinergic neurons. Our results extend previous observations of diversity at the single-neuron level generated by Pcdh expression and suggest that the Pcdh-β cluster genes contribute to specifying the identity and diversity of individual neurons.
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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 | 0.001 | 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 | 0.001 |
| 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".