Enteric glial cell diversification is influenced by spatiotemporal factors and source of neural progenitors in mice
Bibliographic record
Abstract
Previously focused primarily on enteric neurons, studies of the enteric nervous system (ENS) in both health and disease are now broadening to recognize the equally significant role played by enteric glial cells (EGCs). Commensurate to the vast array of gastrointestinal functions they influence, EGCs exhibit considerable diversity in terms of location, morphology, molecular profiles, and functional attributes. However, the mechanisms underlying this diversification of EGCs remain largely unexplored. To begin unraveling the mechanistic complexities of EGC diversity, the current study aimed to examine its spatiotemporal aspects in greater detail, and to assess whether the various sources of enteric neural progenitors contribute differentially to this diversity. Based on established topo-morphological criteria for categorizing EGCs into four main subtypes, our detailed immunofluorescence analyses first revealed that these subtypes emerge sequentially during early postnatal development, in a coordinated manner with the structural changes that occur in the ENS. When combined with genetic cell lineage tracing experiments, our analyses then uncovered a strongly biased contribution by Schwann cell-derived enteric neural progenitors to particular topo-morphological subtypes of EGCs. Taken together, these findings provide a robust foundation for further investigations into the molecular and cellular mechanisms governing EGC diversity.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".