数据与复现临床医学与医药FreedomIntelligence/OpenClaw-Medical-Skills数据与复现

tcell-exhaustion-analysis-agent

维护者 FreedomIntelligence · 最近更新 2026年4月1日

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

OpenClawNanoClaw分析处理复现实验tcell-exhaustion-analysis-agent🧠 bioos extended suiteimmunology & cell therapyanalyze

原始来源

FreedomIntelligence/OpenClaw-Medical-Skills

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

技能摘要

2 min

T-Cell Exhaustion Analysis Agent provides comprehensive profiling of T-cell dysfunction states in cancer 、 chronic infection. It analyzes exhaustion signatures，identifies stem-like progenitor populations，characterizes epigenetic scarring，、 predicts checkpoint immunotherapy response。
When profiling tumor-infiltrating lymphocyte (TIL) exhaustion states ，面向 scRNA-seq data。
To identify stem-like exhausted T-cells (Tex-prog) that predict checkpoint blockade response。
用于 analyzing epigenetic exhaustion programs ，通过 ATAC-seq 或 CUT&Tag。
To assess exhaustion reversal potential 、 re-exhaustion risk。

原始文档

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 ，面向 scRNA-seq data。
To identify stem-like exhausted T-cells (Tex-prog) that predict checkpoint blockade response。