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Shape Factor Analysis as a Quantitative Framework for Assessing Spheroid and Organoid Morphology and Invasiveness.

Brittany E Schutrum1, Jenny Deng1, Ju Hee Kim1

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Summary

Researchers developed a new MATLAB algorithm to quantify spheroid and organoid morphology. This radial length analysis offers more comprehensive insights into tissue function and disease for in vitro studies.

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Area of Science:

  • Biomedical Engineering
  • Cell Biology
  • Quantitative Imaging

Background:

  • Spheroid and organoid morphology are key in vitro indicators of tissue health and disease.
  • Quantitative methods for classifying spheroid and organoid morphology are currently limited.
  • Tumor shape in clinical breast imaging is a prognostic marker, with irregular margins indicating invasiveness.

Purpose of the Study:

  • To develop a quantitative method for analyzing spheroid and organoid morphology.
  • To adapt clinical imaging approaches for translational research in vitro.
  • To improve the comprehensive quantification of spheroid and organoid morphologies.

Main Methods:

  • Developed a custom MATLAB algorithm to quantify radial length variance in invasive protrusions.
  • Analyzed digital phantoms using ImageJ/FIJI shape descriptors and the new radial length analysis.
  • Compared the new method with standard shape descriptors on experimental spheroid and organoid datasets.

Main Results:

  • The radial length analysis effectively quantified morphological variations in digital phantoms.
  • Multivariate shape factor analysis, including radial length, provided more reliable quantification than standard descriptors alone.
  • The method demonstrated capabilities in analyzing experimental spheroid and organoid images.

Conclusions:

  • Multivariate shape factor analysis enhances the comprehensive quantification of spheroid and organoid morphologies.
  • This approach enables numerical morphological readouts for improved phenotypic profiling.
  • The developed method offers valuable metrics for in vitro studies, including high-throughput and drug screening.