Measuring and simulating the biophysical basis of the acoustic contrast factor of biological cells

Abstract

The acoustic contrast factor (ACF) is calculated from the relative density and compressibility differences between a fluid and an object in the fluid. To name but one application, knowing the ACF of a biological cell represents a crucial step in the design of acoustophoretic systems, for instance to isolate cancer cells from a liquid biopsy such as blood without labels or physical contact. For biological cells the static compressibility is different from the high frequency counterpart relevant for the ACF. In this study, we started by characterizing the ACF of low vs. high metastatic cell lines with known associated differences in phenotypic static E-modulus. The change in the static E-modulus, however, was not reflected in a change of the ACF, prompting a more in depth analysis of the influences on the ACF. We demonstrate that static E-modulus increased biological cells through formaldehyde fixation have an increased ACF. Furthermore, the static E-modulus decreased biological cells treated with actin polymerization inhibitor cytochalasin D have a decreased ACF. Complementing these mechanical tests, a numerical COMSOL model was implemented and used to parametrically explore the effects of cell density, cell density ratios, dynamic compressibility and therefore the dynamic bulk modulus. Collectively the combined laboratory and numerical experiments reveal that a change in the static E-modulus alone might, but does not automatically lead to a change of the dynamic ACF for biological cells. This highlights the need for a multiparametic view of the biophysical basis of the cellular ACF, as well as the challenges in harnessing acoustophoretic systems to isolate circulating cells based on their mechanical properties alone.

Ultrasound‐Induced Adsorption of Acousto‐Responsive Microgels at Water–Oil Interface

“…The contrast factor Φ for PNIPAM microgels can be estimated in the order of 0.1 according to some measurements for other polymers and soft particles. [38,39] By defining a characteristic length l ≈ 30 mm (the liquid height in the cuvette), the streaming characteristic time 𝜏 s = l/v s can be estimated to about 5 min. The choice of this specific length is driven by the collective behavior of particles as they navigate through the geometry to reach the oil-water interface.…”