Central nervous system (CNS) diseases are a leading cause of death and disability worldwide, and their incidence is expected to increase in tandem with global life expectancies. Many available CNS therapies only ameliorate disease symptoms rather than halting or reversing the actual disease burden, and potential CNS drugs also have lower success rates at all stages of the clinical development pipeline compared to non-CNS candidates. To combat these issues, we currently focus our efforts in the following general areas:

  • building more representative neurovascular models using stem cells

  • understanding disease mechanisms using genetic engineering approaches

  • developing improved methods for assessing drug transport, distribution, and efficacy

  • developing improved methods to precisely manipulate cell behavior for therapeutic intervention


Building CNS tissue models using human pluripotent stem cells (hPSCs)

Animal models of CNS disease can provide excellent insights into causative mechanisms, but therapies that are successful in rodents too often fail in human trials. Moreover, animal models are inherently low throughput for identifying effective therapeutic candidates. Human pluripotent stem cells (hPSCs), which grow indefinitely in culture and can be coaxed to form any cell type found in the human body, have been hailed as a potential alternative to animal models. For our purposes, hPSCs can produce an unlimited supply of human cells found in the vascularized CNS, including brain microvascular endothelial cells (BMECs), pericytes, neurons, astrocytes, oligodendrocytes, and microglia. We are interested in using a number of engineering techniques and classical differentiation methods to effectively build representative CNS models from hPSCs. Ultimately, these models will be used for applications in drug screening and disease modeling.


Modeling disease onset and progression via genetic engineering

Human induced pluripotent stem cells (iPSCs) derived from patients harboring causative genetic mutations have been used to study disease phenotypes outside the body. Yet, tissue models created from these cells offer little insight into how a disease progresses, particularly in the context of cell-cell communication. Therefore, we are interested in building iPSC lines amenable to on-demand genetic modifications. By incorporating wild-type and modifiable cells into our tissue models, we can assess changes to system behavior in response to cell-specific alterations from a defined time point. Then, using our approaches detailed below, we can monitor specific biomolecules of interest and assess therapeutic intervention strategies.


Improving the specificity of molecular targeting

Signaling pathways are incredibly complex, yet have surprising redundancy. For example, the >30 members of the transforming growth factor beta (TGFβ) superfamily signal through only 7 type I and 5 type II receptors that form homomeric or heteromeric complexes which dictate how the signal is processed. So, for example, if we want to use a TGFβ protein for a therapeutic purpose, how do we ensure that it only signals through the appropriate receptor complex that generates the desired response? To address this problem, we are developing advanced in vitro selection techniques to isolate molecular modulators of specific soluble proteins and cell surface receptors. While our immediate interests apply to the CNS, we anticipate these technologies could be applied to numerous diseases or used to intimately probe biological function.


Conditionally-fluorescent molecular biosensors

Substantial limitations exist when trying to detect and measure small molecule drug concentrations in engineered tissue constructs in order to model efficacious doses. Similar issues arise when trying to analyze hormone and protein spatial gradients to assess paracrine signaling patterns between multiple cell types. To address these issues, we are using in vitro selection and directed evolution techniques to develop molecular scaffolds that fluoresce only in the presence of specific analytes.