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Flexible and robust cell-type annotation for highly multiplexed tissue images.

Huangqingbo Sun1, Shiqiu Yu2, Anna Martinez Casals3

  • 1Ray and Stephanie Lane Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA; Department of Bioengineering, Stanford University, Stanford, CA, USA.

Cell Systems
|September 9, 2025
PubMed
Summary

We developed the Robust Image-Based Cell Annotator (RIBCA) for automated cell-type annotation in multiplexed images. This open-source tool accurately maps cell locations across diverse human tissues without manual input.

Keywords:
bioimage analysiscell-type annotationhighly multiplexed imagingmachine learningmarker imputationspatial proteomicsvision transformer

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Area of Science:

  • Spatial biology
  • Computational pathology
  • Bioinformatics

Background:

  • Accurate cell-type identification is crucial for understanding tissue architecture and disease mechanisms.
  • Existing cell annotation methods often require extensive manual effort and specialized reference datasets.

Purpose of the Study:

  • To introduce the Robust Image-Based Cell Annotator (RIBCA), an automated tool for precise cell-type annotation in multiplexed imaging.
  • To enable unbiased and fine-grained cell annotation across various antibody panels without retraining.

Main Methods:

  • Development of the Robust Image-Based Cell Annotator (RIBCA) software.
  • Application of RIBCA to annotate over 3 million cells across more than 40 human tissue types.
  • Utilizing a modular design for adaptability and future expansion.

Main Results:

  • RIBCA achieved accurate, automated, and unbiased cell-type annotation.
  • The tool successfully mapped spatial organization of diverse cell types in numerous human tissues.
  • Demonstrated utility across a wide range of antibody panels without requiring additional model training.

Conclusions:

  • RIBCA offers a powerful, open-source solution for high-throughput cell annotation in spatial biology.
  • The tool facilitates deeper insights into tissue organization and cellular interactions.
  • Its modular design ensures scalability and broad applicability in biomedical research.