A dynamic and spatially periodic micro-pattern of HES5 expression underlies the probability of neuronal differentiation in the mouse spinal cord
Bibliographic record
Abstract
Some regulatory transcription factors (TFs), such as the Helix-loop-Helix TF, HES5, show dynamic expression including ultradian oscillations, when imaged in real time at the single cell level. Such dynamic expression is key for enabling cell state transitions in a tissue environment. In somitogenesis, such expression is highly synchronised in blocks of tissue (somites), however, in neurogenesis it is not known how single cell dynamics are coordinated, the multiscale pattern that emerges and the significance for differentiation. In this study, we monitor the expression of HES5 protein ex-vivo in the developing spinal cord and identify the existence of microclusters of HES5 expressing progenitors that are spatially periodic along the dorso-ventral (D-V) axis and that in addition are temporally dynamic. We use multiscale computational modelling to show that such microclusters arise at least in part from local synchronisation in HES5 levels between single cells mediated by Notch-Delta interactions. We find that the HES5 microclusters are less dynamic in the presence of a Notch inhibitor showing that Notch mediated cell-cell communication is required for temporal characteristics. Moreover, predictions from the computational modelling and experimental data show that the strength of interaction between neighbouring cells is a key factor controlling the rate of differentiation in a domain specific manner. Our work provides evidence of co-ordination between single cells in the tissue environment during the progenitor to neural transition and shows the functional role of complexity arising from simple interactions between cells.
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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".