TCellAlign：使用命名法引导的多智能体工作流程进行跨研究 T 细胞群体对齐
TCellAlign: Cross-study T-cell Populations Alignment with Nomenclature-Guided Multi-Agent Workflow
AI总结：
该研究针对跨研究 T 细胞群体对齐难题，提出 TCellAlign 多智能体框架，含文献检索等模块，能保留原始术语与证据并生成标准化标签。构建基准数据集，实验表明其在语义一致性等方面表现出色，助力 T 细胞相关知识整合与模型发展。
AI中文摘要：
细胞类型标准化在整合单细胞研究的生物学知识中起着核心作用。尽管有标准化资源，但科学出版物和公共数据集仍使用不同的特定研究标签，导致跨研究比较困难。本研究首次将此挑战表述为基于证据的细胞群体对齐问题，并提出 TCellAlign 多智能体框架，包括文献检索、信息提取、命名法引导的标签对齐和基于证据的裁决。该框架能保留各研究的原始术语和证据，生成可跨研究比较的标准化标签。还构建了经过人工验证的基准数据集。在评估任务中，TCellAlign 比基于本体的基线有更强的语义一致性，与大语言模型保持转录组连贯性，有助于跨研究一致解释 T 细胞亚型和状态，促进生物学知识整合及未来基础模型发展。
英文摘要：
Cell type standardization plays a central role in integrating biological knowledge across single-cell studies. While standardized resources (e.g., Cell Ontology, Nomenclature Frameworks) provide unified vocabularies of cell populations, scientific publications and public datasets continue to use heterogeneous study-specific labels, making cross-study comparison difficult even when biologically equivalent cell populations are described. In this work, we are the first to formulate this challenge as an evidence-grounded cell population alignment problem and propose TCellAlign, a multi-agent framework that includes literature retrieval, information extraction, nomenclature-guided label alignment, and evidence-based adjudication. This modular design preserves the original terminology and supporting evidence reported by each study while producing standardized labels that can be compared across studies. We further construct a manually validated benchmark dataset linking study-specific labels, CZ CELLxGENE annotations, and standardized T-cell nomenclature across 44 manually curated, published studies (including over seven million cells) spanning four biological categories: healthy, cancer, infectious disease and inflammatory diseases. Across the evaluated tasks, TCellAlign achieves stronger semantic agreement than ontology-based baselines and maintains transcriptomic coherence with both open-source and closed-source large language models (LLM) backbones. By connecting literature, datasets, and expert's nomenclature, TCellAlign enables consistent interpretation of T-cell subtypes and states across studies, facilitating biological knowledge integration and the development of future foundation models built upon standardized cellular representations.
