Concurrent temporal patterning of neural stem cells in the fly visual system
Bibliographic record
Abstract
Abstract The temporal and spatial patterning of neural stem cells is a powerful mechanism by which to generate neural diversity in both vertebrate and invertebrate brains. In the Drosophila optic lobe, the neuroblasts (NBs) that generate the ∼120 neuronal cell types of the medulla are patterned by independent temporal and spatial inputs. In the temporal axis, a cascade of twelve transcription factors (TFs) are expressed in medulla NBs as they age. In the spatial axis, the neuroepithelium from which these NBs are generated is sub-divided into eight compartments by the expression of five additional TFs. Distinct neuronal types are generated by NBs based on their spatio-temporal address. Here, we describe a third major patterning axis that further diversifies neuronal fates in the medulla. We show that the symmetrically dividing neuroepithelial cells from which the medulla NBs are generated are temporally patterned by opposing gradients of the Imp and Syp RNA-binding proteins. Imp and Syp regulate the expression of a set of TFs in the neuroepithelium to confer NBs from the same spatio-temporal address with unique identities based on the developmental stage they are generated. We show that Imp and Syp differentially pattern NBs in the Vsx1-Hth spatio-temporal birth window to generate seven distinct neuronal cell types (Li2, TmY17, TmY15, Tm23, Pm3a, Pm3b and TmY12) in successive developmental windows. We further demonstrate that the birthdate of these neurons correlates with their final position in the adult cortex, resulting in unanticipated specializations of the retinotopic circuit in the anterior-posterior axis of the visual system. The concurrent temporal patterning of symmetrically and asymmetrically dividing neural stem cells thus acts as a powerful mechanism to couple the generation of neural diversity with circuit patterning.
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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".