Zebrabow: multispectral cell labeling for cell tracing and lineage analysis in zebrafish
Bibliographic record
Abstract
Advances in imaging and cell-labeling techniques have greatly enhanced our understanding of developmental and neurobiological processes. Among vertebrates, zebrafish is uniquely suited for in vivo imaging owing to its small size and optical translucency. However, distinguishing and following cells over extended time periods remains difficult. Previous studies have demonstrated that Cre recombinase-mediated recombination can lead to combinatorial expression of spectrally distinct fluorescent proteins (RFP, YFP and CFP) in neighboring cells, creating a 'Brainbow' of colors. The random combination of fluorescent proteins provides a way to distinguish adjacent cells, visualize cellular interactions and perform lineage analyses. Here, we describe Zebrabow (Zebrafish Brainbow) tools for in vivo multicolor imaging in zebrafish. First, we show that the broadly expressed ubi:Zebrabow line provides diverse color profiles that can be optimized by modulating Cre activity. Second, we find that colors are inherited equally among daughter cells and remain stable throughout embryonic and larval stages. Third, we show that UAS:Zebrabow lines can be used in combination with Gal4 to generate broad or tissue-specific expression patterns and facilitate tracing of axonal processes. Fourth, we demonstrate that Zebrabow can be used for long-term lineage analysis. Using the cornea as a model system, we provide evidence that embryonic corneal epithelial clones are replaced by large, wedge-shaped clones formed by centripetal expansion of cells from the peripheral cornea. The Zebrabow tool set presented here provides a resource for next-generation color-based anatomical and lineage analyses in zebrafish.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".