Splicing in a single neuron is coordinately controlled by RNA binding proteins and transcription factors
Bibliographic record
Abstract
Single-cell transcriptomes are established by transcription factors (TFs), which determine a cell's gene-expression complement. Post-transcriptional regulation of single-cell transcriptomes, and the RNA binding proteins (RBPs) responsible, are more technically challenging to determine, and combinatorial TF-RBP coordination of single-cell transcriptomes remains unexplored. We used fluorescent reporters to visualize alternative splicing in single Caenorhabditis elegans neurons, identifying complex splicing patterns in the neuronal kinase sad-1. Most neurons express both isoforms, but the ALM mechanosensory neuron expresses only the exon-included isoform, while its developmental sister cell the BDU neuron expresses only the exon-skipped isoform. A cascade of three cell-specific TFs and two RBPs are combinatorially required for sad-1 exon inclusion. Mechanistically, TFs combinatorially ensure expression of RBPs, which interact with sad-1 pre-mRNA. Thus a combinatorial TF-RBP code controls single-neuron sad-1 splicing. Additionally, we find ‘phenotypic convergence,’ previously observed for TFs, also applies to RBPs: different RBP combinations generate similar splicing outcomes in different 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".