| name | tcell-exhaustion-analysis-agent |
| description | AI-powered analysis of T-cell exhaustion states, epigenetic scarring, stem-like T-cell populations, and checkpoint blockade response prediction in cancer immunotherapy. |
T-Cell Exhaustion Analysis Agent
The T-Cell Exhaustion Analysis Agent provides comprehensive profiling of T-cell dysfunction states in cancer and chronic infection. It analyzes exhaustion signatures, identifies stem-like progenitor populations, characterizes epigenetic scarring, and predicts checkpoint immunotherapy response.
When to Use This Skill
- When profiling tumor-infiltrating lymphocyte (TIL) exhaustion states from scRNA-seq data.
- To identify stem-like exhausted T-cells (Tex-prog) that predict checkpoint blockade response.
- For analyzing epigenetic exhaustion programs via ATAC-seq or CUT&Tag.
- To assess exhaustion reversal potential and re-exhaustion risk.
- When designing combination immunotherapy strategies.
Core Capabilities
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Exhaustion State Classification: Distinguishes progenitor exhausted (Tex-prog), intermediate, and terminally exhausted (Tex-term) populations using transcriptional signatures.
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Stem-like T-Cell Detection: Identifies TCF1+ stem-like exhausted cells that sustain anti-tumor immunity and respond to PD-1 blockade.
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Epigenetic Scarring Analysis: Characterizes chromatin accessibility patterns that maintain exhaustion programs despite checkpoint blockade.
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Checkpoint Expression Profiling: Quantifies inhibitory receptors (PD-1, TIM-3, LAG-3, TIGIT, CTLA-4) at single-cell resolution.
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Response Prediction: Machine learning models predict checkpoint blockade response based on exhaustion profiles.
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TME Interaction Analysis: Maps suppressive cell interactions (Tregs, MDSCs, TAMs) promoting exhaustion.
Exhaustion Signatures
Progenitor Exhausted (Tex-prog):
- TCF1+, SLAMF6+, PD-1+
- Self-renewal capacity
- Proliferative burst upon checkpoint blockade
- Good prognosis marker
Terminal Exhausted (Tex-term):
- TCF1-, TIM-3+, CD39+
- Effector-like but dysfunctional
- Limited proliferative potential
- Epigenetically fixed exhaustion
Workflow
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Input: scRNA-seq, CITE-seq, or scATAC-seq data from TILs or PBMCs.
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Preprocessing: Quality control, normalization, batch correction.
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Clustering: Identify T-cell subsets and exhaustion states.
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Signature Scoring: Apply exhaustion gene signatures (TOX, NR4A, NFAT targets).
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Epigenetic Analysis: Assess chromatin accessibility at exhaustion loci.
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Prediction: Model checkpoint response from exhaustion profiles.
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Output: Exhaustion state proportions, stem-like cell fractions, response predictions.
Example Usage
User: "Analyze T-cell exhaustion states in this TIL scRNA-seq dataset and predict anti-PD-1 response."
Agent Action:
python3 Skills/Immunology_Vaccines/TCell_Exhaustion_Analysis_Agent/exhaustion_analyzer.py \
--input til_scrnaseq.h5ad \
--tcells CD8A+CD3E+ \
--signatures exhaustion_signatures.gmt \
--epigenetic til_scatacseq.h5ad \
--predict_response true \
--output exhaustion_report/
Key Markers and Genes
| Category | Markers | Role |
|---|
| Exhaustion TFs | TOX, TOX2, NR4A1-3 | Exhaustion program drivers |
| Stem-like | TCF7 (TCF1), LEF1, SLAMF6 | Progenitor maintenance |
| Terminal | HAVCR2 (TIM-3), ENTPD1 (CD39), LAYN | Terminal exhaustion |
| Checkpoints | PDCD1, CTLA4, LAG3, TIGIT | Inhibitory receptors |
| Effector | GZMB, PRF1, IFNG | Cytotoxic function |
Epigenetic Exhaustion Program
The exhaustion epigenetic landscape is largely resistant to checkpoint blockade:
- Stable open chromatin at exhaustion-associated genes (TOX, NR4A, checkpoint loci)
- Epigenetic scars maintained even after PD-1 therapy
- Re-exhaustion occurs upon cessation of checkpoint blockade
- Therapeutic implications: Epigenetic modifiers may enhance durability
Prerequisites
- Python 3.10+
- Scanpy/Seurat for scRNA-seq
- ArchR/Signac for scATAC-seq
- CellTypist or custom classifiers
Related Skills
- CAR_T_Design - For engineering exhaustion-resistant CAR-T cells
- Immune_Repertoire_Analysis - For TCR clonotype tracking
- Tumor_Microenvironment - For TIL context analysis
Clinical Implications
- Patient Selection: High stem-like Tex predicts checkpoint response
- Combination Therapy: TIGIT + PD-1 for resistant tumors
- Epigenetic Therapy: DNMT/HDAC inhibitors to reprogram exhausted cells
- CAR-T Engineering: TOX knockout to prevent CAR-T exhaustion
Author
AI Group - Biomedical AI Platform
