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Imaging Biological Samples with Optical Microscopy01:18

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Creating Virtual Hematoxylin and Eosin Images using Samples Imaged on a Commercial CODEX Platform.

Paul D Simonson1, Xiaobing Ren2, Jonathan R Fromm2

  • 1Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, Cornell University, New York, USA.

Journal of Pathology Informatics
|January 24, 2022
PubMed
Summary

Researchers developed a "virtual H&E" staining method to integrate advanced CODEX fluorescence imaging with traditional pathology slides. This approach enhances cell and microenvironment characterization, aiding adoption into clinical workflows and machine learning applications.

Keywords:
4′6-diamidino-2-phenylindoleCODEXdigital imagingeosinfluorescencemulticolor imagingmultiparametric imagingvirtual hematoxylin and eosin staining

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Area of Science:

  • Biomedical Imaging
  • Computational Pathology
  • Molecular Pathology

Background:

  • Multiparametric fluorescence imaging, such as CODEX, enables simultaneous detection of numerous biomarkers in single tissue sections.
  • Digital fluorescence data offers detailed cell and microenvironment characterization but differs from standard pathology images (H&E, IHC).
  • Integrating fluorescence data with H&E-like images can accelerate the adoption of advanced imaging techniques into routine pathology workflows and facilitate machine learning model transfer.

Purpose of the Study:

  • To develop a staining protocol and image processing pipeline for generating "virtual H&E" images compatible with CODEX multiplex imaging.
  • To enable seamless integration of high-plex fluorescence data with conventional histopathology visualization.

Main Methods:

  • Developed a novel staining protocol combining fluorescent nuclear staining (4",6-diamidino-2-phenylindole) with traditional eosin staining.
  • Implemented image processing techniques to generate "virtual H&E" images from CODEX data.
  • Generated additional images from nuclear staining and tissue autofluorescence for further analysis.

Main Results:

  • Successfully created "virtual H&E" images that can be coregistered with CODEX multiparametric fluorescence data.
  • The developed protocol integrates seamlessly into existing CODEX workflows.
  • Generated complementary nuclear and autofluorescence images for enhanced data evaluation.

Conclusions:

  • The "virtual H&E" staining protocol bridges the gap between advanced multiplex imaging and standard histopathology practices.
  • This approach facilitates the clinical adoption of CODEX imaging by providing familiar visual context.
  • The method supports the development and transfer of machine learning algorithms trained on H&E data for use with complex fluorescence imaging datasets.