LiverZap: a chemoptogenetic tool for global and locally restricted hepatocyte ablation to study cellular behaviours in liver regeneration
Bibliographic record
Abstract
The liver restores its mass and architecture after injury. Yet, investigating morphogenetic cell behaviours and signals that repair tissue architecture at high spatiotemporal resolution remains challenging. We developed LiverZap, a tuneable chemoptogenetic liver injury model in zebrafish. LiverZap employs the formation of a binary FAP-TAP photosensitiser followed by brief near-infrared illumination inducing hepatocyte-specific death and recapitulating mammalian liver injury types. The tool enables local hepatocyte ablation and extended live imaging capturing regenerative cell behaviours, which is crucial for studying cellular interactions at the interface of healthy and damaged tissue. Applying LiverZap, we show that targeted hepatocyte ablation in a small region of interest is sufficient to trigger local liver progenitor-like cell (LPC)-mediated regeneration, challenging the current understanding of liver regeneration. Surprisingly, the LPC response is also elicited in adjacent uninjured tissue, at up to 100 µm distance to the injury. Moreover, dynamic biliary network rearrangement suggests active cell movements from uninjured tissue in response to substantial hepatocyte loss as an integral step of LPC-mediated liver regeneration. This precisely targetable liver cell ablation tool will enable the discovery of key molecular and morphogenetic regeneration paradigms.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".