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A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei
Jude M Phillip1,2, Kyu-Sang Han1, Wei-Chiang Chen1
1Department of Chemical and Biomolecular Engineering, Johns Hopkins Physical Sciences Oncology Center, Johns Hopkins Institute for Nanobiotechnology (INBT), Johns Hopkins University, Baltimore, MD, USA.
Nature Protocols
|January 11, 2021
Summary
This study introduces the VAMPIRE algorithm for analyzing cell and nuclear morphology from images. It quantifies cell shapes and heterogeneity, aiding in disease diagnosis and research.
Area of Science:
- Cell Biology
- Bioimaging
- Computational Biology
Background:
- Cell morphology provides critical information for disease diagnosis and research.
- Quantifying cell morphology has advanced, but defining shapes and heterogeneity remains difficult.
Purpose of the Study:
- To present a protocol and software (VAMPIRE algorithm) for analyzing cell and nuclear morphology.
- To enable profiling, classification, and quantitative measurement of morphological heterogeneity.
Main Methods:
- Utilizing the VAMPIRE algorithm on fluorescence or bright-field images.
- Classifying cells into shape modes using equidistant points along contours.
- Analyzing 2D projections of cells in 2D substrates or 3D microenvironments.
Main Results:
- The VAMPIRE algorithm profiles and classifies cells based on shape modes.
- Shape mode distributions visualize relationships between cell shapes and subtypes.
- Quantitative measurement of morphological heterogeneity within cell populations is achieved.
Conclusions:
- The VAMPIRE algorithm offers a fast, automated method for cell morphology analysis.
- It effectively quantifies morphological heterogeneity and relates cell shapes to cellular conditions.
- The protocol is applicable to various cell culture and tissue environments.
