TCellAlign: Cross-study T-cell Populations Alignment with Nomenclature-Guided Multi-Agent Workflow

2026-07-27

AI Summary

This study addresses the challenge of cross-study comparability in single-cell T cell populations, which is hindered by label heterogeneity. To resolve this, the authors propose TCellAlign—the first multi-agent workflow that frames population alignment as an evidence-based task. Integrating large language models, ontological resources, and literature mining, TCellAlign standardizes cell type labels while preserving original terminological evidence and adhering to expert naming conventions. Evaluated on a manually curated benchmark encompassing 44 studies and over seven million cells, TCellAlign demonstrates significant improvements over existing ontology-based methods in both semantic consistency and transcriptomic coherence.

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.

Problem

• cell type standardization
• cross-study comparison
• T-cell nomenclature
• heterogeneous labels
• single-cell studies

Innovation

• multi-agent workflow
• nomenclature-guided alignment
• evidence-grounded cell population alignment
• T-cell standardization
• cross-study integration