Integrative Biology最新文献

Glioblastoma (GBM) is the most common primary malignant brain tumor. The tissue microenvironment adjacent to vasculature, termed the perivascular niche, has been implicated in promoting biological processes involved in glioblastoma progression such as invasion, proliferation, and therapeutic resistance. However, the exact nature of the cues that support tumor cell aggression in this niche is largely unknown. Soluble angiocrine factors secreted by tumor-associated vasculature have been shown to support such behaviors in other cancer types. Here, we exploit macroscopic and microfluidic gelatin hydrogel platforms to profile angiocrine factors secreted by self-assembled endothelial networks and evaluate their relevance to glioblastoma biology. Aggregate angiocrine factors support increases in U87-MG cell number, migration, and therapeutic resistance to temozolomide. We also identify a novel role for TIMP1 in facilitating glioblastoma tumor cell migration. Overall, this work highlights the use of multidimensional hydrogel models to evaluate the role of angiocrine signals in glioblastoma progression.

Soft tissue tumors, including breast cancer, become stiffer throughout disease progression. This increase in stiffness has been shown to correlate to malignant phenotype and epithelial-to-mesenchymal transition (EMT) in vitro. Unlike current models, utilizing static increases in matrix stiffness, our group has previously created a system that allows for dynamic stiffening of an alginate-matrigel composite hydrogel to mirror the native dynamic process. Here, we utilize this system to evaluate the role of matrix stiffness on EMT and metastasis both in vitro and in vivo. Epithelial cells were seen to lose normal morphology and become protrusive and migratory after stiffening. This shift corresponded to a loss of epithelial markers and gain of mesenchymal markers in both the cell clusters and migrated cells. Furthermore, stiffening in a murine model reduced tumor burden and increased migratory behavior prior to tumor formation. Inhibition of FAK and PI3K in vitro abrogated the morphologic and migratory transformation of epithelial cell clusters. This work demonstrates the key role extracellular matrix stiffening has in tumor progression through integrin signaling and, in particular, its ability to drive EMT-related changes and metastasis.

Type 2 diabetes is the most common metabolic disease, and insulin resistance plays a role in the pathogenesis of the disease. Because completely functional mitochondria are necessary to obtain glucose-stimulated insulin from pancreatic beta cells, dysfunction of mitochondrial oxidative pathway could be involved in the development of diabetes. As a simple animal model, Caenorhabditis elegans renders itself to investigate such metabolic mechanisms because it possesses insulin/insulin-like growth factor-1 signaling pathway similar to that in humans. Currently, the widely spread agarose pad-based immobilization technique for fluorescence imaging of the mitochondria in C. elegans is laborious, batchwise, and does not allow for facile handling of the worm. To overcome these technical challenges, we have developed a single-channel microfluidic device that can trap a C. elegans and allow to image the mitochondria in body wall muscles accurately and in higher throughput than the traditional approach. In specific, our microfluidic device took advantage of the proprioception of the worm to rotate its body in a microfluidic channel with an aspect ratio above one to gain more space for its undulation motion that was favorable for quantitative fluorescence imaging of mitochondria in the body wall muscles. Exploiting this unique feature of the microfluidic chip-based immobilization and fluorescence imaging, we observed a significant decrease in the mitochondrial fluorescence intensity under hyperglycemic conditions, whereas the agarose pad-based approach did not show any significant change under the same conditions. A machine learning model trained with these fluorescence images from the microfluidic device could classify healthy and hyperglycemic worms at high accuracy. Given this significant technological advantage, its easiness of use and low cost, our microfluidic imaging chip could become a useful immobilization tool for quantitative fluorescence imaging of the body wall muscles in C. elegans.

Characterization of decision-making in cells in response to received signals is of importance for understanding how cell fate is determined. The problem becomes multi-faceted and complex when we consider cellular heterogeneity and dynamics of biochemical processes. In this paper, we present a unified set of decision-theoretic, machine learning and statistical signal processing methods and metrics to model the precision of signaling decisions, in the presence of uncertainty, using single cell data. First, we introduce erroneous decisions that may result from signaling processes and identify false alarms and miss events associated with such decisions. Then, we present an optimal decision strategy which minimizes the total decision error probability. Additionally, we demonstrate how graphing receiver operating characteristic curves conveniently reveals the trade-off between false alarm and miss probabilities associated with different cell responses. Furthermore, we extend the introduced framework to incorporate the dynamics of biochemical processes and reactions in a cell, using multi-time point measurements and multi-dimensional outcome analysis and decision-making algorithms. The introduced multivariate signaling outcome modeling framework can be used to analyze several molecular species measured at the same or different time instants. We also show how the developed binary outcome analysis and decision-making approach can be extended to more than two possible outcomes. As an example and to show how the introduced methods can be used in practice, we apply them to single cell data of PTEN, an important intracellular regulatory molecule in a p53 system, in wild-type and abnormal cells. The unified signaling outcome modeling framework presented here can be applied to various organisms ranging from viruses, bacteria, yeast and lower metazoans to more complex organisms such as mammalian cells. Ultimately, this signaling outcome modeling approach can be utilized to better understand the transition from physiological to pathological conditions such as inflammation, various cancers and autoimmune diseases.

Natural killer (NK) cells are part of the innate immune system and are capable of killing diseased cells. As a result, NK cells are being used for adoptive cell therapies for cancer patients. The activation of NK cell stimulatory receptors leads to a cascade of intracellular phosphorylation reactions, which activates key signaling species that facilitate the secretion of cytolytic molecules required for cell killing. Strategies that maximize the activation of such intracellular species can increase the likelihood of NK cell killing upon contact with a cancer cell and thereby improve efficacy of NK cell-based therapies. However, due to the complexity of intracellular signaling, it is difficult to deduce a priori which strategies can enhance species activation. Therefore, we constructed a mechanistic model of the CD16, 2B4 and NKG2D signaling pathways in NK cells to simulate strategies that enhance signaling. The model predictions were fit to published data and validated with a separate dataset. Model simulations demonstrate strong network activation when the CD16 pathway is stimulated. The magnitude of species activation is most sensitive to the receptor's initial concentration and the rate at which the receptor is activated. Co-stimulation of CD16 and NKG2D in silico required fewer ligands to achieve half-maximal activation than other combinations, suggesting co-stimulating these pathways is most effective in activating the species. We applied the model to predict the effects of perturbing the signaling network and found two strategies that can potently enhance network activation. When the availability of ligands is low, it is more influential to engineer NK cell receptors that are resistant to proteolytic cleavage. In contrast, for high ligand concentrations, inhibiting phosphatase activity leads to sustained species activation. The work presented here establishes a framework for understanding the complex, nonlinear aspects of NK cell signaling and provides detailed strategies for enhancing NK cell activation.