Rheological characterization of poly-dimethyl siloxane formulations with tunable viscoelastic properties

Abstract

Studies from the past two decades have demonstrated convincingly that cells are able to sense the mechanical properties of their surroundings. Cells make major decisions in response to this mechanosensation, including decisions regarding cell migration, proliferation, survival, and differentiation. The vast majority of these studies have focused on the cellular mechanoresponse to changing substrate stiffness (or elastic modulus) and have been conducted on purely elastic substrates. In contrast, most soft tissues in the human body exhibit viscoelastic behavior; that is, they generate responsive force proportional to both the magnitude and rate of strain. While several recent studies have demonstrated that viscous effects of an underlying substrate affect cellular mechanoresponse, there is not a straightforward experimental method to probe this, particularly for investigators with little background in biomaterial fabrication. In the current work, we demonstrate that polymers comprised of differing polydimethylsiloxane (PDMS) formulations can be generated that allow for control over both the strain-dependent storage modulus and the strain rate-dependent loss modulus. These substrates requires no background in biomaterial fabrication to fabricate, are shelf-stable, and exhibit repeatable mechanical properties. Here we demonstrate that these substrates are biocompatible and exhibit similar protein adsorption characteristics regardless of mechanical properties. Finally, we develop a set of empirical equations that predicts the storage and loss modulus for a given blend of PDMS formulations, allowing users to tailor substrate mechanical properties to their specific needs.

Fig. 1. Changing the ratios of Sylgard 184 base : crosslinker and Sylgard 184 : 527 creates surfaces with varying storage and loss moduli. (A–D) Storage modulus (G′) versus decreasing Sylgard 184 : Sylgard 527 ratio for (A) 5 : 1 B : C Sylgard 184, (B) 10 : 1 B : C Sylgard 184, (C) 20 : 1 B : C Sylgard 184, and (D) 30 : 1 B : C Sylgard 184. (E–H) Loss modulus (G′′) versus decreasing Sylgard 184 : Sylgard 527 ratio for (E) 5 : 1 B : C Sylgard 184, (F) 10 : 1 B : C Sylgard 184, (G) 20 : 1 B : C Sylgard 184, and (H) 30 : 1 B : C Sylgard 184. (I) Compiled values for storage modulus for all formulations. (J) Compiled values for loss modulus for all formulations. Error bars represent standard error. Formulations are labeled on the X-axis according to the notation (184B : 184C) : 527.

Fig. 2. Protein adsorption to PDMS surfaces. Fluorescently-labeled protein was microcontact printed onto 4 representative PDMS formulations, and was then removed via trypsin digestion and quantified by spectrophotometry. Results indicate that there is no significant difference between protein adsorption across the formulations. Error bars represent standard error.

Fig. 3. Cell viability on PDMS formulations. Human adipose-derived mesenchymal stem cells were plated onto representative surfaces, cultured for 72 hours, and labeled with a green/red live/dead assay. (A–F) Representative images of labeled cells on (A–F). (G) Quantification of dead cells per mm². Results indicate no significant difference between cell viability on the various substrates. Error bars represent standard error.

Fig. 4. Data used to construct empirical relationships. Empirical relationships were first determined by performing linear regression analysis on (A) the storage modulus G′ versus the ratio of Sylgard 184 to Sylgard 527 (p₁₈₄), and (B) the loss modulus G′′ versus the ratio of Sylgard 184 to Sylgard 527 (p₁₈₄). Finally, the relationship of the slopes determined from A and B to the ratio of 184 crosslinker to 184 base (p_cl) were fit using linear regression. All regression analyses were performed using GraphPad Prism software.

Fig. 5. Predicted versus measured values of G′ and G′′. Empirical equations were developed and used to predict (A) G′ and (B) G′′ for various PDMS formulations. Solid line represents the unity equation, y = x. Results indicate strong agreement between predicted and measured values.