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Related Experiment Video

Updated: Dec 26, 2025

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RESTORE: Robust intEnSiTy nORmalization mEthod for multiplexed imaging.

Young Hwan Chang1,2,3, Koei Chin4,5, Guillaume Thibault4

  • 1Computational Biology Program, Oregon Health & Science University, Portland, OR, 97201, USA. chanyo@ohsu.edu.

Communications Biology
|March 11, 2020
PubMed
Summary
This summary is machine-generated.

Multiplexed imaging normalization corrects for staining variations between samples. This method uses negative control cells to remove artifacts, improving data reliability for clinical applications.

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Area of Science:

  • Biomedical imaging
  • Cellular biology
  • Computational pathology

Background:

  • Multiplexed imaging advances understanding of cell states and tissue interactions.
  • Quantitative analysis across multiple samples is hindered by staining intensity and background fluorescence variations.
  • Artifacts obscure true biological variations, complicating downstream analysis and clinical utility.

Purpose of the Study:

  • To develop a method for normalizing multiplexed imaging data across samples.
  • To compensate for variations in staining intensity and background fluorescence.
  • To improve the reliability of multiplexed imaging for clinical applications.

Main Methods:

  • Automatic identification of negative control cells within each tissue sample.
  • Inference of background signal level using negative control cell expression.
  • Normalization of intensity profiles by inferred negative control levels to correct for between-sample variation.

Main Results:

  • Demonstrated effective removal of unwanted variations in multiplexed imaging data.
  • Showcased robust performance using tissue microarray and longitudinal biopsy samples.
  • Validated the normalization approach for improving quantitative data integration.

Conclusions:

  • The proposed method effectively removes between-sample variations in multiplexed imaging.
  • This normalization strategy enhances the accuracy and reliability of cellular and tissue analysis.
  • Improved data quality supports the clinical application of multiplexed imaging technologies.