The current knowledge of bone marrow mechanics is limited to its viscous properties, neglecting the elastic contribution of the extracellular matrix. To get a more complete view of the mechanics of marrow, we characterized intact yellow porcine bone marrow using three different, but complementary techniques: rheology, indentation, and cavitation. Our analysis shows that bone marrow is elastic, and has a large amount of intra- and inter-sample heterogeneity, with an effective Young’s modulus ranging from 0.25–24.7 kPa at physiological temperature. Each testing method was consistent across matched tissue samples, and each provided unique benefits depending on user needs. We recommend bulk rheology to capture the effects of temperature on tissue elasticity and moduli, indentation for quantifying local tissue heterogeneity, and cavitation rheology for mitigating destructive sample preparation. We anticipate the knowledge of bone marrow elastic properties for building in vitro models will elucidate mechanisms involved in disease progression and regenerative medicine.

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The polymer network of thiol-maleimide hydrogels assembles faster than individual components can be uniformly mixed due to their fast gelation kinetics. The lack of homogeneity can result in variable cell-based assay results, resulting in batch-to-batch variability and limiting their use in predictive screening assays. Although these hydrogels are incredibly useful in tissue engineering, this network heterogeneity is a known problem in the field. We screened a variety of possible techniques to slow down the reaction speed and improve the homogeneity of these hydrogels, without sacrificing the viability and distribution of encapsulated cells. As others have reported, an electronegative crosslinker was the most effective technique to slow the reaction, but the chemical modification required is technically challenging. Of interest to a broad community, we screened buffer type, strength, and crosslinker electronegativity to find an optimal reaction speed that allows for high cell viability and small molecule diffusion, without allowing cells to settle during gelation, allowing application of these materials to the drug screening industry and tissue engineering community.

Breast cancer preferentially spreads to the bone, brain, liver, and lung. The clinical patterns of this tissue-specific spread (tropism) cannot be explained by blood flow alone, yet our understanding of what mediates tropism to these physically and chemically diverse tissues is limited. While the microenvironment has been recognized as a critical factor in governing metastatic colonization, the role of the extracellular matrix (ECM) in mediating tropism has not been thoroughly explored. We created a simple biomaterial platform with systematic control over the ECM protein density and composition to determine if integrin binding governs how metastatic cells differentiate between secondary tissue sites. Instead of examining individual behaviors, we compiled large patterns of phenotypes associated with adhesion to and migration on these controlled ECMs. In combining this novel analysis with a simple biomaterial platform, we created an in vitro fingerprint that is predictive of in vivo metastasis. This rapid biomaterial screen also provided information on how β1, α2, and α6 integrins might mediate metastasis in patients, providing insights beyond a purely genetic analysis. We propose that this approach of screening many cell–ECM interactions, across many different heterogeneous cell lines, is predictive of in vivo behavior, and is much simpler, faster, and more economical than complex 3D environments or mouse models. We also propose that when specifically applied toward the question of tissue tropism in breast cancer, it can be used to provide insight into certain integrin subunits as therapeutic targets. Insight, innovation, integration We developed a high-throughput method to rapidly screen cell adhesion, motility, and growth factor responses on biomaterial surfaces. This approach is analogous to systems biology, relying on cell phenotypes in lieu of genetics. We used this technique to reveal patterns of phenotypes associated with breast cancer metastasis to possible tissue sites (bone, brain, lung). By comparing the phenotypic patterns between cell lines that metastasize to only one tissue site with heterogeneous cell lines, we provide the first method to connect in vitro phenotype to in vivo fate. This method is successful without genetic analysis, yet it also predicts outcomes related to integrin gene expression, potentially identifying new targets for tissue-specific metastasis.

Reducing the foreign body response (FBR) to implanted biomaterials will enhance their performance in tissue engineering. Poly(ethylene glycol) (PEG) hydrogels are increasingly popular for this application due to their low cost, ease of use, and the ability to tune their compliance via molecular weight and cross-linking densities. PEG hydrogels can elicit chronic inflammation in vivo, but recent evidence has suggested that extremely hydrophilic, zwitterionic materials and particles can evade the immune system. To combine the advantages of PEG-based hydrogels with the hydrophilicity of zwitterions, we synthesized hydrogels with comonomers PEG and the zwitterion phosphorylcholine (PC). Recent evidence suggests that stiff hydrogels elicit increased immune cell adhesion to hydrogels, which we attempted to reduce by increasing hydrogel hydrophilicity. Surprisingly, hydrogels with the highest amount of zwitterionic comonomer elicited the highest FBR. Lowering the hydrogel modulus (165 to 3 kPa), or PC content (20 to 0 wt %), mitigated this effect. A high density of macrophages was found at the surface of implants associated with a high FBR, and mass spectrometry analysis of the proteins adsorbed to these gels implicated extracellular matrix, immune response, and cell adhesion protein categories as drivers of macrophage recruitment. Overall, we show that modulus regulates macrophage adhesion to zwitterionic-PEG hydrogels, and demonstrate that chemical modifications to hydrogels should be studied in parallel with their physical properties to optimize implant design.

Traditional drug screening methods lack features of the tumor microenvironment that contribute to resistance. Most studies examine cell response in a single biomaterial platform in depth, leaving a gap in understanding how extracellular signals such as stiffness, dimensionality, and cell-cell contacts act independently or are integrated within a cell to affect either drug sensitivity or resistance. This is critically important, as adaptive resistance is mediated, at least in part, by the extracellular matrix (ECM) of the tumor microenvironment. We developed an approach to screen drug responses in cells cultured on 2D and in 3D biomaterial environments to explore how key features of ECM mediate drug response. This approach uncovered that cells on 2D hydrogels and spheroids encapsulated in 3D hydrogels were less responsive to receptor tyrosine kinase (RTK)-targeting drugs sorafenib and lapatinib, but not cytotoxic drugs, compared to single cells in hydrogels and cells on plastic. We found that transcriptomic differences between these in vitro models and tumor xenografts did not reveal mechanisms of ECM-mediated resistance to sorafenib. However, a systems biology analysis of phospho-kinome data uncovered that variation in MEK phosphorylation was associated with RTK-targeted drug resistance. Using sorafenib as a model drug, we found that co-administration with a MEK inhibitor decreased ECM-mediated resistance in vitro and reduced in vivo tumor burden compared to sorafenib alone. In sum, we provide a novel strategy for identifying and overcoming ECM-mediated resistance mechanisms by performing drug screening, phospho-kinome analysis, and systems biology across multiple biomaterial environments.

Cancer spread (metastasis) is responsible for 90% of cancer-related fatalities. Informing patient treatment to prevent metastasis, or kill all cancer cells in a patient’s body before it becomes metastatic is extremely powerful. However, aggressive treatment for all non-metastatic patients is detrimental, both for quality of life concerns, and the risk of kidney or liver-related toxicity. Knowing when and where a patient has metastatic risk could revolutionize patient treatment and care. In this review, we attempt to summarize the key work of engineers and quantitative biologists in developing strategies and model systems to predict metastasis, with a particular focus on cell interactions with the extracellular matrix (ECM), as a tool to predict metastatic risk and tropism.

Appropriately chosen descriptive models of cell migration in biomaterials will allow researchers to characterize and ultimately predict the movement of cells in engineered systems for a variety of applications in tissue engineering. The persistent random walk (PRW) model accurately describes cell migration on two-dimensional (2D) substrates. However, this model inherently cannot describe subdiffusive cell movement, i.e., migration paths in which the root mean square displacement increases more slowly than the square root of the time interval. Subdiffusivity is a common characteristic of cells moving in confined environments, such as three-dimensional (3D) porous scaffolds, hydrogel networks, and in vivo tissues. We demonstrate that a generalized anomalous diffusion (AD) model, which uses a simple power law to relate the mean square displacement to time, more accurately captures individual cell migration paths across a range of engineered 2D and 3D environments than does the more commonly used PRW model. We used the AD model parameters to distinguish cell movement profiles on substrates with different chemokinetic factors, geometries (2D vs 3D), substrate adhesivities, and compliances. Although the two models performed with equal precision for superdiffusive cells, we suggest a simple AD model, in lieu of PRW, to describe cell trajectories in populations with a significant subdiffusive fraction, such as cells in confined, 3D environments.