Single-cell mechanical analysis reveals viscoelastic similarities between normal and neoplastic brain cells.

Understanding cancer cell mechanics allows for the identification of novel disease mechanisms, diagnostic biomarkers, and targeted therapies. In this study, we utilized our previously established fluid shear stress assay to investigate and compare the viscoelastic properties of normal immortalized human astrocytes (IHAs) and invasive human glioblastoma (GBM) cells when subjected to physiological levels of shear stress that are present in the brain microenvironment. We used a parallel-flow microfluidic shear system and a camera-coupled optical microscope to expose single cells to fluid shear stress and monitor the resulting deformation in real-time, respectively. From the video-rate imaging, we fed cell deformation information from digital image correlation into a three-parameter generalized Maxwell model to quantify the nuclear and cytoplasmic viscoelastic properties of single cells. We further quantified actin cytoskeleton density and alignment in IHAs and GBM cells via immunofluorescence microscopy and image analysis techniques. Results from our study show that contrary to the behavior of many extracranial cells, normal and cancerous brain cells do not exhibit significant differences in their viscoelastic behavior. Moreover, we also found that the viscoelastic properties of the nucleus and cytoplasm as well as the actin cytoskeletal densities of both brain cell types are similar. Our work suggests that malignant GBM cells exhibit unique mechanical behaviors not seen in other cancer cell types. These results warrant future study to elucidate the distinct biophysical characteristics of the brain and reveal novel mechanical attributes of GBM and other primary brain tumors.</p

Granuloma IFP estimates and comparisons to tumor data.

(A) Predicted dimensionless IFP profiles within granulomas from the uniform perfusion case for varying values of dimensionless granuloma size, α0 = 1–15. (B) Fitting the theoretical IFP estimates (Eq III with a fitted modulus [9], see S1 Text) to experimentally measured tumor IFP data (from human neuroblastoma tumor models grown in immunosuppressed rats, ~2 cm in diameter [9]) demonstrates the applicability of the uniform perfusion model to physiological IFP levels with a single fitted parameter.</p

Granuloma IFP estimate comparisons from non-uniform, shell-core, and uniform perfusion models.

Predicted dimensionless IFP rise within granulomas for the uniform (Eq III), shell-core (Eq S25, see S1 Text), and non-uniform perfusion (Eq S35) models for case of α0 = 6, with an additional fitted uniform perfusion case for α0 = 4.1. (TIFF)</p

Granuloma oxygen and glucose profile estimates.

Dimensionless concentration, f, of oxygen (A) and glucose (B) as a function of dimensionless granuloma radius, ξ, for increasing values of dimensionless granuloma size (the modulus α0).</p

Estimates of drug delivery and comparisons to rifampicin and clofazimine distribution data in experimental granulomas.

Dimensionless concentration (lines), f, of rifampicin (A; RIF) and clofazimine (B; CFZ) as a function of dimensionless granuloma radius, ξ, for increasing values of dimensionless granuloma size (the modulus α0) in comparison to experimental data from mouse TB granulomas [17] (dots). The mean squared error (MSE, see Eq S44) between the theoretical and experimental results for rifampicin and clofazimine are 0.012 and 0.010, respectively (see S2 Table for all raw data, predicted data, and MSE values).</p

Granuloma IFP estimates from the shell-core model.

Predicted dimensionless (A) IFP and (B) IFP profiles within granulomas for different moduli α0 and for the shell-core perfusion model, with ξD = 0.5. (TIFF)</p

Supporting Information.

S1. Species mass balance in granulomas and tumors. S2. Interstitial fluid pressure and velocity profiles. S2.1. Shell-core model of non-uniform perfusion in granulomas. S2.2. Uniform perfusion case. S2.3. Non-uniform perfusion case. S3. Comparison of interstitial perfusion for varying vascular distribution. S3.1. Predictions with the shell-core model. S3.2. Predictions with the uniform vasculature model. S3.3 Comparisons with the non-uniform vessel distribution. S4. Overcoming transport barriers. S5. Convective zone thickness. S6. Mean squared error. Abbreviations, symbols, and terminology. (DOCX)</p

Physiological basis for compartmentalized transport models in TB granulomas.

Schematic depicting regions and consequences of compromised oxygen transport in idealized spherical granulomas, including 1) a vascularized region where convection dominates and plasma filtration from blood vessels occurs, and 2) an inner region lacking blood vessels where diffusion dominates, and hypoxia and necrosis result. (Adapted from [15]).</p

Experimental (Exp.), modeled (Mod.), and differences (Diff) between experimental and modeled data for drug delivery in TB granulomas for CFZ and RIF delivery, with MSE calculated on a per granuloma basis.

Experimental (Exp.), modeled (Mod.), and differences (Diff) between experimental and modeled data for drug delivery in TB granulomas for CFZ and RIF delivery, with MSE calculated on a per granuloma basis.</p

Effect of tissue hydraulic conductivity on oxygen and glucose delivery.

(A) Oxygen and (B) glucose concentration profiles for base case parameter values (Eq S39, see S1 Text) of tissue hydraulic conductivity Kv increased by a factor of 10 for small (α0 = 3.5) and large (α0 = 20) granulomas.</p