Distinct spatial distribution and roles of Kupffer cells and monocyte-derived macrophages in mouse acute liver injury
Bibliographic record
Abstract
Macrophages are key regulators of inflammation and repair, but their heterogeneity and multiple roles in the liver are not fully understood. We aimed herein to map the intrahepatic macrophage populations and their function(s) during acute liver injury. We used flow cytometry, gene expression analysis, multiplex-immunofluorescence, 3D-reconstruction, and spatial image analysis to characterize the intrahepatic immune landscape in mice post-CCl4-induced acute liver injury during three distinct phases: necroinflammation, and early and late repair. We observed hepatocellular necrosis and a reduction in liver resident lymphocytes during necroinflammation accompanied by the infiltration of circulating myeloid cells and upregulation of inflammatory cytokines. These parameters returned to baseline levels during the repair phase while pro-repair chemokines were upregulated. We identified resident CLEC4F+ Kupffer cells (KCs) and infiltrating IBA1+CLEC4F- monocyte-derived macrophages (MoMFs) as the main hepatic macrophage populations during this response to injury. While occupying most of the necrotic area, KCs and MoMFs exhibited distinctive kinetics, distribution and morphology at the site of injury. The necroinflammation phase was characterized by low levels of KCs and a remarkable invasion of MoMFs suggesting their potential role in phagoctosing necrotic hepatocytes, while opposite kinetics/distribution were observed during repair. During the early repair phase, yolksac - derived KCs were restored, whereas MoMFs diminished gradually then dissipated during late repair. MoMFs interacted with hepatic stellate cells during the necroinflammatory and early repair phases, potentially modulating their activation state and influencing their fibrogenic and pro-repair functions that are critical for wound healing. Altogether, our study reveals novel and distinct spatial and temporal distribution of KCs and MoMFs and provides insights into their complementary roles during 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 | 0.001 | 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 | 0.001 |
| 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".