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Visualizing Lymph Node Structure and Cellular Localization using Ex-Vivo Confocal Microscopy
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Graph-based description of tertiary lymphoid organs at single-cell level.

Nadine S Schaadt1, Ralf Schönmeyer2, Germain Forestier3

  • 1Institute for Pathology, Hannover Medical School, Hannover, Germany.

Plos Computational Biology
|February 22, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a reproducible graph-based method to analyze immune cell spatial patterns in tissue images, aiding therapeutic decisions in cancer and transplantation. The approach quantifies lymphocytic infiltrates for improved diagnostic accuracy.

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Area of Science:

  • Computational pathology
  • Immunohistochemistry
  • Bioinformatics

Background:

  • Observer-dependent immune cell evaluation in microscopy is subjective.
  • Analyzing spatial patterns of lymphocytic infiltrates is crucial for therapeutic decisions in oncology and transplantation.
  • Reproducible quantitative measures are needed to complement existing methods.

Purpose of the Study:

  • To develop a reproducible, graph-based method for assessing the spatial composition of lymphocytic infiltrates in whole slide images.
  • To quantify and classify immune cell clustering patterns for improved diagnostic accuracy.
  • To address the need for scalable and adjustable frameworks in computational pathology.

Main Methods:

  • Developed a graph-based approach using Delaunay triangulation and distance criteria on cell coordinates from whole slide images.
  • Utilized neighborhood graphs, node/edge composition, and support vector machines for classification.
  • Incorporated automated cell detection, region identification, and classification of lymphocytic clusters by organization degree.

Main Results:

  • Demonstrated the method's ability to describe variability in lymphocytic infiltrates across different tissues (renal allografts, breast cancer, cystic fibrosis lung).
  • Introduced a neighborhood feature to distinguish phenotypically different immune infiltrates based on graph edge types.
  • Successfully classified lymphocytic clusters according to their degree of organization.

Conclusions:

  • The graph-based assessment provides reproducible measures for spatial immune cell composition, complementing subjective methods.
  • The scalable framework can be adapted for various research questions in immunology and pathology.
  • This approach offers a significant advancement for analyzing immune infiltrates in clinical and research settings.