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Published on: November 7, 2025
NanoCellAnnotator: Formalizing Expert Cell Type Annotation with Large Language Models
Md Ishtyaq Mahmud1, Veena Kochat2, Humaira Anzum1
1Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77004, USA.
Biorxiv : the Preprint Server for Biology
|July 3, 2026
Summary
NanoCellAnnotator offers biologically constrained cell-type annotation for spatial transcriptomics, overcoming LLM limitations. It accurately identifies cell populations and flags ambiguous regions, enhancing reproducibility.
Area of Science:
- Computational Biology
- Genomics
- Bioinformatics
Background:
- Cell-type annotation in spatial transcriptomics is hindered by sparse gene panels, spatial heterogeneity, and lack of reference atlases.
- Current large language models (LLMs) for annotation can produce unsupported predictions and rely on cloud-hosted models, limiting reproducibility and privacy.
- Unconstrained LLM inference poses challenges for biologically accurate and deployable spatial transcriptomics analysis.
Purpose of the Study:
- To introduce NanoCellAnnotator, a novel framework for biologically constrained and confidence-aware automated cell-type annotation in spatial transcriptomics.
- To address limitations of existing LLM-based approaches by enabling local execution and incorporating biological constraints.
- To improve the accuracy, reproducibility, and interpretability of cell-type annotation in complex spatial transcriptomic data.
Main Methods:
- Spatial clusters identified using hybrid spatially regularized non-negative matrix factorization (hSNMF).
- Cluster markers mapped to ontology-derived functional programs via Gene Ontology enrichment and GO-slim projection.
- Lightweight, locally executable LLM performs constrained label selection using curated databases (PanglaoDB, CellMarker); confidence estimated via marker support and lineage separation.
Main Results:
- NanoCellAnnotator accurately recovers canonical cell populations in intrahepatic cholangiocarcinoma and breast cancer datasets with high confidence.
- The framework successfully identifies heterogeneous or transitional spatial domains as ambiguous, providing nuanced annotations.
- Annotation confidence metrics enable explicit flagging of ambiguous or heterogeneous clusters, enhancing interpretability.
Conclusions:
- NanoCellAnnotator provides a robust, reproducible, and privacy-preserving solution for spatial transcriptomics cell-type annotation.
- The biologically constrained and confidence-aware approach improves the reliability of automated annotations.
- This framework facilitates deeper understanding of cellular architecture in complex tissues.
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