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Measuring systematic changes in invasive cancer cell shape using Zernike moments.
Elaheh Alizadeh1, Samanthe Merrick Lyons2, Jordan Marie Castle3
1Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, CO 80523, USA. ashokp@engr.colostate.edu.
Integrative Biology : Quantitative Biosciences From Nano to Macro
|October 14, 2016
Summary
Cancer cell shape analysis using Zernike moments reveals distinct patterns for invasive osteosarcoma cell lines. These shape differences can predict invasiveness and are reproducible, offering a new diagnostic approach.
Area of Science:
- Biophysics
- Cell Biology
- Computational Biology
Background:
- Osteosarcoma is a primary bone cancer with varying invasiveness.
- Understanding cell shape changes is crucial for predicting cancer progression.
- Hydrophobicity of surfaces can influence cell behavior and morphology.
Purpose of the Study:
- To investigate how surface hydrophobicity affects osteosarcoma cell line shapes.
- To determine if cell shape characteristics can differentiate between invasive and less-invasive phenotypes.
- To establish Zernike moments as a reliable method for quantitative cell shape analysis.
Main Methods:
- Utilized Zernike moments to quantify cell shape characteristics.
- Compared shape profiles of four invasive osteosarcoma cell lines against a less-invasive parental line.
- Employed principal component analysis to analyze high-dimensional shape data.
- Trained a neural network to classify cell invasiveness based on shape features.
Main Results:
- Cell shape differences were sufficient for a neural network to accurately classify invasive vs. less-invasive phenotypes.
- Shape change patterns were reproducible across experimental repetitions.
- While substrate hydrophobicity influenced cell shape, differences were not always sufficient for classification.
- Three out of four invasive/parental cell line pairs exhibited similar shape change patterns.
Conclusions:
- Cell shape analysis using Zernike moments can serve as a powerful tool to infer phenotypic states, such as invasiveness.
- Quantitative shape profiling offers a promising avenue for non-invasive cancer diagnostics.
- Reproducibility of shape changes supports the robustness of this analytical approach.
