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Related Experiment Video

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Statistical Analysis of Multiplex Immunofluorescence and Immunohistochemistry Imaging Data.

Julia Wrobel1, Coleman Harris2, Simon Vandekar2

  • 1Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA. julia.wrobel@cuanschutz.edu.

Methods in Molecular Biology (Clifton, N.J.)
|March 17, 2023
PubMed
Summary

Multiplexed imaging (mIF/mIHC) reveals cell interactions in tissues, advancing disease research. This chapter details image processing and analysis methods for these powerful techniques.

Keywords:
Bioimage analysisCell phenotypingImage normalizationMultiplex imagingSpatial statistics

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Area of Science:

  • Biomedical imaging
  • Computational pathology
  • Immunohistochemistry

Background:

  • Multiplexed immunofluorescence (mIF) and immunohistochemistry (mIHC) enable detailed analysis of cellular spatial relationships.
  • Understanding these interactions is crucial for diagnosing and treating tissue-based diseases and autoimmune disorders.

Purpose of the Study:

  • To discuss methods and software for processing and analyzing multiplexed imaging data.
  • To highlight the importance of spatial context in cellular-level tissue analysis.

Main Methods:

  • Image acquisition as multichannel TIFF files.
  • Image processing including denoising, segmentation, and normalization.
  • Cellular phenotyping and spatial context analysis.

Main Results:

  • Established a workflow for analyzing complex multiplexed imaging data.
  • Demonstrated the utility of mIF/mIHC in understanding tissue composition.
  • Provided insights into cell-to-cell relationships within diseased tissues.

Conclusions:

  • Advanced mIF/mIHC technologies offer revolutionary potential for disease research.
  • Robust image processing and statistical analysis are essential for extracting meaningful biological insights from multiplexed data.