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

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.