Using single-cell RNA sequencing to generate predictive cell-type-specific split-GAL4 reagents throughout development
Bibliographic record
Abstract
Cell-type-specific tools facilitate the identification and functional characterization of the distinct cell types that form the complexity of neuronal circuits. A large collection of existing genetic tools in Drosophila relies on enhancer activity to label different subsets of cells and has been extremely useful in analyzing functional circuits in adults. However, these enhancer-based GAL4 lines often do not reflect the expression of nearby gene(s) as they only represent a small portion of the full gene regulatory elements. While genetic intersectional techniques such as the split-GAL4 system further improve cell-type-specificity, it requires significant time and resources to screen through combinations of enhancer expression patterns. Here, we use existing developmental single-cell RNA sequencing (scRNAseq) datasets to select gene pairs for split-GAL4 and provide a highly efficient and predictive pipeline (scMarco) to generate cell-type-specific split-GAL4 lines at any time during development, based on the native gene regulatory elements. These gene-specific split-GAL4 lines can be generated from a large collection of coding intronic MiMIC/CRIMIC lines or by CRISPR knock-in. We use the developing Drosophila visual system as a model to demonstrate the high predictive power of scRNAseq-guided gene-specific split-GAL4 lines in targeting known cell types, annotating clusters in scRNAseq datasets as well as in identifying novel cell types. Lastly, the gene-specific split-GAL4 lines are broadly applicable to any other Drosophila tissue. Our work opens new avenues for generating cell-type-specific tools for the targeted manipulation of distinct cell types throughout development and represents a valuable resource for the Drosophila community.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".