TCellAlign: Cross-study T-cell Populations Alignment with Nomenclature-Guided Multi-Agent Workflow
2026-07-27 • Databases
Databases
AI summaryⓘ
The authors address the problem that different scientific studies often name the same types of cells in different ways, making it hard to compare results. They created TCellAlign, a system that links study-specific cell labels to a common standard by using literature search, data extraction, and predefined naming rules. Their method keeps the original study terms and evidence but also produces unified labels that let researchers compare data across multiple studies. They tested TCellAlign on many studies about T-cells from healthy and diseased conditions, showing it works better than existing tools. This helps scientists better understand T-cell types by connecting diverse data and expert knowledge.
Cell type standardizationCell OntologyT-cellSingle-cell studiesLabel alignmentInformation extractionTranscriptomic coherenceLarge Language ModelsBiomedical nomenclatureData integration
Authors
Pengyu Xie, Rongjia Zhou, Zhilin Ou, Junyuan Zhang, Xiang Zhou, Xiaobo Sun, Jiaying Lu, Wenjing Ma
Abstract
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.