Technology Development Example: FRISCR

Quantitative predictive biological studies are facilitated by well defined cell culture environments; improvements in measuring cell state (inferred by gene and protein expression) in real-time, in situ, and heterogeneous populations; and versatile data analysis tools that help identify meaning from large and noisy datasets. The Doyle group develops experimental, engineering, and computational analysis methods on an as-needed basis to better address our long-term scientific questions about progenitor cell mechanobiology. FRISCR (Fixed and Recovered Intact Single Cell RNA) enables recovery of high quality RNA from fixed and permeabilized cells, allowing intracellular labeling and purification prior to RNA analysis. This method allows next generation sequencing to be applied to cells that lack live markers, a frequent challenge in heterogeneous mixtures of differentiating cells. Recently we have developed computational tools enabling automated identification of molecular networks from large literature repositories (millions of articles) or user-selected cell functions.

Transcriptome analysis of cell responses to applied forces

Shear stress, cyclic strain, and material stiffness are known to affect gene expression of differentiated cells and stem cells. Our work has identified gene expression signatures of mesenchymal stem cells (MSCs) that both overlap and are distinct from mature vascular cell types. Mechanosignaling responses were measured in response to both applied shear stress and applied cyclic strain. We identified a network of genes whose expression depends on applied shear stress in MSCs and endothelial cells, and demonstrated this network converges to regulate specific cell functions via conserved mechanosignaling responses.

Synapse morphology of primary neurons

Neurons enable sensation, motion, and thought by communicating to one another through a series of coupled ionic (electrical) and chemical reactions. Neural cells can be distinguished by their specialized ability to sense and respond to electrical cues. We study the development of this electrogenic signaling capacity as cells transition from pluripotent stem cells to mature neuron subtypes. Using systematic meta analysis of published data combined with novel computer code, we have established a set of homologous genes that are involved in electrical signaling and are sequentially activated during neural differentiation. We also identified sets of transcription factors that interact to help specify neurotransmitter expression, demonstrating that these factors have mutually inhibiting interactions that can act to reinforce cell fate choice.