Interleukin-4-mediated Pro-Regenerative Cellular Reprogramming in 3-dimensional Liver Culture
Bibliographic record
Abstract
BACKGROUND & AIMS: Interleukin-4 (IL-4) is a key contributor to liver regeneration, but its effects remain poorly understood due to a lack of models that preserve the complex cellular interactions of the liver. Here, we use murine precision-cut liver slices (PCLS), a 3-dimensional tissue culture system that maintains both parenchymal and non-parenchymal cells, to investigate the role of IL-4 in hepatic cell reprogramming. Through longitudinal single-cell transcriptomics and protein-level validation, we demonstrate the proregenerative potential of IL-4. METHODS: We performed longitudinal single nucleus RNA sequencing on PCLS from 8- to 10-week-old C57BL/6 mice over 5 days of culture in the presence and absence of IL-4. We assessed intracellular ATP output to demonstrate slice viability. We further performed orthogonal evaluations of the impact of IL-4 treatment via immunhistochemical staining to confirm proliferation and cell identity within the slices. We then assessed the impact of IL-4 exposure in slices generated from the diseased livers (hepatonecrosis/fibrosis) of mice treated with thioacetamide. RESULTS: IL-4 induced transcriptional changes, including increased expression of tissue repair-associated markers in myeloid cells, expansion of hepatocyte and cholangiocyte progenitors, and inhibition of fibroblast activation. Additionally, IL-4 treatment significantly increased Ki67 protein expression and intracellular ATP production, indicating enhanced proliferation and viability. Notably, IL-4 also improved cellular viability in slices from thioacetamide-treated mice, highlighting its potential proregenerative effects in injured liver tissue. CONCLUSIONS: Our study highlights the potential of IL-4-driven modulation of the liver microenvironment, paving the way for cytokine-based therapeutic strategies to enhance immune-mediated hepatic regeneration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".