Human hepatic stellate cells orchestrate the accumulation and function of CD103 <sup>+</sup> tissue-resident CD8 <sup>+</sup> T-cells in liver fibrosis
Bibliographic record
Abstract
Abstract Tissue-resident memory cells (T RM ) contribute to protective and pathogenic responses in the liver, yet precisely how hepatic T RM adapt and integrate cues from the underlying stroma and extracellular matrix (ECM) in chronic liver disease (CLD) has yet to be fully defined. Here we describe a role for activated myofibroblast-like hepatic stellate cells (HSCs) in the accumulation and in situ localisation of CD8 + T RM in the CLD liver. Activated HSCs drive a program of tissue residence in activated, tissue-infiltrating CD8 + T-cells in a TGFβ-dependent manner. We show upregulation of CD103, ECM-binding integrins and adhesion molecules driven by TGFβ which together contribute to the sequestration of T RM within the ECM-rich fibrotic niche. Ex vivo , hepatic CD103 + T RM correlate with the extent of ECM deposited, express an altered repertoire of co-stimulatory and co-inhibitory receptors, transcriptional regulators of cellular exhaustion and produce less proinflammatory mediators upon TCR engagement in CLD than in health. Through expression of several co-inhibitory ligands, we further demonstrate the potential for activated HSCs to acquire an immunomodulatory phenotype and limit the capacity of CD103 + T RM to produce anti-viral and anti-tumour mediators upon antigen encounter. Finally, we demonstrate that strategies to block such regulatory pathways, including the PD1:PD-L1/PD-L2 axis, have the potential to restore the antigen-specific effector function of tissue-compartmentalised CD103 + T RM and thus contribute to improving the effectiveness of local immunosurveillance in CLD. One Sentence Summary: Activated hepatic stellate cells characteristic of liver fibrosis orchestrate an accumulation of a CD103 + T RM population with a reduced capacity for antigen-specific effector function in human CLD.
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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) | 0.002 | 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".