Parametric Deconvolution for Cancer Cells Viscoelasticity Measurements from Quantitative Phase Images

Area of Science:

• Biophysics
• Cell Mechanics
• Quantitative Imaging

Background:

• Accurate measurement of cell viscoelastic properties (stiffness and viscosity) is crucial for understanding cell behavior.
• Dynamic flow systems are used for cell rheology, but system component properties can distort shear stress waveforms, affecting measurements.
• Existing models may not fully account for these distortions, limiting measurement reliability.

Purpose of the Study:

• To optimize a dynamic flow-based shear stress system for reliable cell shear modulus and viscosity assessment.
• To investigate the influence of flow system components on the estimation of cell viscoelastic properties.
• To develop and validate a novel correction method for improving measurement accuracy.

Main Methods:

• Optimization of a dynamic flow-based shear stress system.
• Utilizing quantitative phase imaging for cell property assessment.
• Application of a parametric deconvolution method to correct for flow system distortions.
• Comparison of the novel method with Kelvin-Voigt and steady-state models.

Main Results:

• Flow system components significantly influence measured cell viscoelastic characteristics.
• The parametric deconvolution method effectively corrects for waveform distortion.
• The novel approach demonstrates greater robustness and reliability in viscosity estimation compared to the Kelvin-Voigt model, especially concerning syringe compliance variations.

Conclusions:

• The optimized dynamic flow system with parametric deconvolution provides a reliable platform for cell viscoelastic property assessment.
• The developed correction method enhances the accuracy and robustness of viscosity measurements.
• This approach offers a significant improvement over existing methods for cell rheology studies.