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Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy
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DIMPLE: An R package to quantify, visualize, and model spatial cellular interactions from multiplex imaging with

Maria Masotti1, Nathaniel Osher1, Joel Eliason2

  • 1University of Michigan, Department of Biostatistics, Ann Arbor, MI 48109, USA.

Patterns (New York, N.Y.)
|December 18, 2023
PubMed
Summary

A new framework, DIMPLE, analyzes cell interactions in multiplex imaging data. It scales to large datasets and links spatial patterns to patient outcomes, offering a powerful tool for cancer research.

Keywords:
multiplex imagingpoint processspatial statistics

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Spatial Biology

Background:

  • Spatial analysis of multiplex imaging (MI) data presents challenges in quantifying cell-cell interactions and linking them to patient outcomes.
  • Current methods for quantifying cell-cell interactions struggle to scale with increasing numbers of cell types and images in complex datasets.

Purpose of the Study:

  • To introduce a scalable analytical framework, DIMPLE, for quantifying, visualizing, and modeling cell-cell interactions within the tumor microenvironment (TME).
  • To demonstrate the utility of DIMPLE in uncovering associations between spatial interaction patterns and patient-level covariates using publicly available MI data.

Main Methods:

  • Development of a novel R package, DIMPLE, designed for scalable spatial analysis of multiplex imaging data.
  • Application of the DIMPLE framework to analyze publicly available multiplex imaging datasets.

Main Results:

  • DIMPLE provides a scalable solution for analyzing complex multiplex imaging data.
  • Statistically significant associations were identified between image-level measures of cell-cell interactions and patient-level covariates.

Conclusions:

  • The DIMPLE framework offers an effective and scalable approach for spatial analysis of multiplex imaging data.
  • DIMPLE facilitates the discovery of meaningful relationships between cellular spatial organization and patient outcomes in the TME.