Morphological pseudotime ordering and fate mapping reveals diversification of cerebellar inhibitory interneurons
Bibliographic record
Abstract
Abstract Understanding how diverse neurons are assembled into circuits requires a framework for describing cell types and their developmental trajectories. Here, we combined genetic fate mapping and pseudo-temporal profiling to resolve the diversification of cerebellar inhibitory interneurons based on morphology. The molecular layer interneurons (MLIs) derive from a common progenitor but comprise a diverse population of dendritic-, somatic-, and axon initial segment-targeting interneurons. MLIs are classically divided into two types. However, their morphological heterogeneity suggests an alternate model of one continuously varying population. Through clustering and trajectory inference of 811 MLI reconstructions at maturity and during development, we show that MLIs divide into two discrete classes but also present significant within-class heterogeneity. Pseudotime trajectory mapping uncovered the emergence of distinct phenotypes during migration and axonogenesis, well before neurons reach their final positions. Our study illustrates the utility of quantitative single-cell methods to morphology for defining the diversification of neuronal subtypes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".