Estimating Dynamic Cellular Morphological Properties via the Combination of the RTCA System and a...

Area of Science:

• Cell Biology
• Biotechnology
• Image Analysis

Background:

• The xCELLigence real-time cell analysis (RTCA) system monitors cellular behavior but lacks detailed morphological insights.
• Transforming growth factor beta (TGF-β) induces epithelial-mesenchymal transition (EMT), significantly altering cell morphology.

Purpose of the Study:

• To develop and validate an algorithm for quantifying cell morphological changes.
• To integrate computer vision techniques with RTCA for enhanced cellular analysis.
• To compare the effects of TGF-β, lipopolysaccharide (LPS), and cigarette smoke extract (CSE) on A549 cell morphology.

Main Methods:

• Developed a novel algorithm involving image preprocessing, Hough transform (HT), and post-processing for morphological analysis.
• Utilized the RTCA system to record A549 cell index changes.
• Employed Western blot to confirm EMT markers and validate morphological findings.

Main Results:

• The developed algorithm quantified cell length changes, showing strong correlation with RTCA data for TGF-β treated cells.
• RTCA revealed distinct cell index curves for different stimulators (TGF-β, LPS, CSE).
• Western blot confirmed TGF-β induced EMT markers, while LPS and CSE did not.

Conclusions:

• Optics-based computer vision algorithms can provide crucial morphological data, complementing RTCA measurements.
• The developed algorithm offers a quantitative method to assess TGF-β-induced EMT and other cellular morphological changes.
• This integrated approach enhances the comprehensive analysis of cellular responses in real-time.