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A cell line is a population of cells grown in vitro that can be subcultured over several generations. Normal cells cease to divide after a certain number of cell divisions, a process known as replicative senescence. This number, called the Hayflick limit, was conceptualized by Leonard Hayflick in 1961 when he observed that fetal cells grown in culture could only divide 40-60 times. This limit is due to the shortening of the telomeres during each round of cell division, preventing cell division...

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LLM-based cell type annotation harmonization across single-cell studies using GCTHarmony.

Xingyuan Zhang^1,2, Zhicheng Ji^1,2

¹Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA.

Research Square

|August 20, 2025

Summary

This summary is machine-generated.

Integrating single-cell RNA sequencing studies is difficult due to inconsistent cell type labels. GCTHarmony, a new large language model (LLM) method, harmonizes these annotations, improving data integration and analysis consistency.

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Area of Science:

Computational Biology
Bioinformatics
Genomics

Background:

Single-cell RNA sequencing (scRNA-seq) enables high-resolution cellular analysis.
Integrating diverse scRNA-seq datasets is crucial for comprehensive biological insights.
Inconsistent cell type annotations present a significant barrier to data integration.

Purpose of the Study:

To develop a novel method for harmonizing cell type annotations across disparate single-cell RNA sequencing studies.
To improve the consistency and comparability of cell type labels for enhanced data integration.

Main Methods:

Development of GCTHarmony, a large language model (LLM)-based approach.
Utilizing OpenAI's text embedding model for semantic understanding of cell type labels.
Mapping arbitrary annotations to standardized cell ontology terms.
Reconciling hierarchical discrepancies in cell type annotations across studies.

Main Results:

GCTHarmony accurately maps diverse cell type annotations to standardized ontology terms.
The method effectively reconciles inconsistencies in annotation hierarchies.
Demonstrated substantial improvement in cell type annotation consistency in a real-world data example.

Conclusions:

GCTHarmony offers a robust solution for harmonizing cell type annotations in single-cell studies.
This method facilitates more reliable integration and meta-analysis of scRNA-seq data.
Improved annotation consistency enhances the power of cross-study comparisons.