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

Huangqingbo Sun1,2, Shiqiu Yu1, Anna Martinez Casals2

  • 1Ray and Stephanie Lane Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA.

Biorxiv : the Preprint Server for Biology
|September 30, 2024
PubMed
Summary

Robust Image-Based Cell Annotator (RIBCA) automates cell type identification in multiplexed images. This open-source tool accurately annotates millions of cells across diverse human tissues without manual input or retraining.

Keywords:
Cell type annotationMachine learningMultiplexed imagingSpatial proteomics

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Digital pathology

Background:

  • Accurate cell type identification is crucial for understanding tissue architecture.
  • Existing methods for cell annotation often require extensive manual effort and reference datasets.
  • Limitations in current tools hinder large-scale analysis of multiplexed imaging data.

Purpose of the Study:

  • To develop an automated, unbiased, and accurate tool for cell type annotation in highly multiplexed images.
  • To enable fine-grained cell classification across diverse antibody panels without retraining.
  • To facilitate the analysis of spatial organization in human tissues.

Main Methods:

  • Development of the Robust Image-Based Cell Annotator (RIBCA) tool.
  • Application of RIBCA to annotate cell types in multiplexed imaging data.
  • Utilizing a modular design for extensibility to new cell types and antibody panels.

Main Results:

  • Successful annotation of over 3 million cells across more than 40 human tissue types.
  • Demonstrated accuracy, automation, and unbiased performance of the RIBCA tool.
  • Revealed intricate spatial organization patterns of various cell types within tissues.

Conclusions:

  • RIBCA provides a robust solution for automated cell type annotation in complex imaging datasets.
  • The tool's open-source nature and modular design promote wider adoption and further development.
  • RIBCA significantly advances the study of tissue spatial organization and cellular heterogeneity.