Acute Pericentral Liver Injury Induces a Novel Transient Hepatocyte Population
Bibliographic record
Abstract
Abstract The liver exhibits robust regenerative capacity in response to injury, a property shaped by its essential metabolic and detoxification roles. While the mechanisms of regeneration following chronic liver injury have been extensively studied, the response during the early phases of acute injury remains poorly understood. Here, we employed a pericentral model of acute liver injury using carbon tetrachloride (CCl4) to investigate the initial hepatocyte response to damage. Through integration of single-nucleus multiome-sequencing, lineage tracing, spatial transcriptomics, and immunostaining, we identified a novel, transient population of damage-responsive SOX9+ pericentral hepatocytes. This population does not exhibit progenitor-like behavior but instead potentially mediates key aspects of early tissue repair. Specifically, SOX9+ pericentral hepatocytes engage in interactions with hepatic stellate cells and display migratory features associated with wound closure. Moreover, these cells exhibit an immune-related transcriptional signature and colocalize with liver macrophages at injury sites, suggesting a role in modulating immune responses. Our findings highlight a previously underappreciated function of SOX9 as a stress-responsive regulator of hepatocyte behavior and intercellular crosstalk during the early stages of acute liver injury.
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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".