Molecular and functional dissection using CaMPARI-seq reveals the neuronal organization for dissociating optic flow-dependent behaviors
Bibliographic record
Abstract
Optic flow processing is critical for the visual control of body and eye movements in many animals. Rotational and translational binocular optic flow patterns need to be clearly distinguished to induce different behavior outputs. However, the specific neuron types and their connectivity involved in this computation remain unclear. Here, we developed a method to link the functional labeling using a photoconvertible calcium indicator called CaMPARI2 and single-cell RNA sequencing (CaMPARI-seq) to investigate the transcriptional profile of the pretectum, a center for processing optic flow in larval zebrafish. Using this technique, we identified a pretectal cluster expressing tcf7l2, which can be further classified into molecularly distinct subclusters. In vivo calcium imaging and cell ablation revealed that nkx1.2lb-positive pretectal neurons are commissural inhibitory neurons required for the optomotor response but not for the optokinetic response. Our genetic and functional dissection using CaMPARI-seq uncovered the neuronal organization essential for dissociating different optic flow-dependent behaviors.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".