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tcell-exhaustion-analysis-agent

Maintainer FreedomIntelligence · Last updated April 1, 2026

Analyze T cell exhaustion from scRNA-seq and ATAC-seq data.

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Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/tcell-exhaustion-analysis-agent

Skill Snapshot

2 min

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 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.

Source Doc

Exhaustion State Classification: Distinguishes progenitor exhausted (Tex-prog), intermediate, and terminally exhausted (Tex-term) populations using transcriptional signatures.
Stem-like T-Cell Detection: Identifies TCF1+ stem-like exhausted cells that sustain anti-tumor immunity and respond to PD-1 blockade.
Epigenetic Scarring Analysis: Characterizes chromatin accessibility patterns that maintain exhaustion programs despite checkpoint blockade.
Checkpoint Expression Profiling: Quantifies inhibitory receptors (PD-1, TIM-3, LAG-3, TIGIT, CTLA-4) at single-cell resolution.
Response Prediction: Machine learning models predict checkpoint blockade response based on exhaustion profiles.
TME Interaction Analysis: Maps suppressive cell interactions (Tregs, MDSCs, TAMs) promoting exhaustion.

Progenitor Exhausted (Tex-prog):

Terminal Exhausted (Tex-term):

Input: scRNA-seq, CITE-seq, or scATAC-seq data from TILs or PBMCs.
Preprocessing: Quality control, normalization, batch correction.
Clustering: Identify T-cell subsets and exhaustion states.
Signature Scoring: Apply exhaustion gene signatures (TOX, NR4A, NFAT targets).
Epigenetic Analysis: Assess chromatin accessibility at exhaustion loci.
Prediction: Model checkpoint response from exhaustion profiles.
Output: Exhaustion state proportions, stem-like cell fractions, response predictions.

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.