Entries by Academic Web Pages

Dissecting cell-to-cell regulatory heterogeneity by stochastic profiling

There is tremendous enthusiasm for using the power of transcriptomics to unravel the state of single cells in tissues and tumors. If a single-cell profiling method is to be meaningful, however, it must be i) technically reproducible, ii) sensitive to low-abundance molecules, and iii) compatible with cells isolated in situ. There are many exciting techniques under development to profile the transcriptomes of single cells; unfortunately, none of them meet any of these three criteria.

Methods engineering of biomolecular and cellular assays

Problem solving subject to constraints is the hallmark of engineering design (1). We adopt a design approach to invent new methods for interrogating cells and biomolecules. Our goal is to develop bioassays that are sensitive, quantitative, and as high-throughput and multiplex as possible. Most importantly, they should be reliable, generalizable, and shareable.

Systems virology of coxsackievirus B3 pathogenesis

Coxsackievirus B3 (CVB3) is a cardiotropic positive-strand RNA virus that is a leading cause of viral myocarditis and heart failure in infants and young children. Nearly all work on CVB3 virology has focused on the docking interactions and gene products of the virus. Sparked by an early collaboration with the McManus laboratory (University of British Columbia), my group has taken a fundamentally different view of CVB3 pathogenesis as a systems-level perturbation of the host.

Predictive modeling of biomolecular networks

Thousands of biomolecular measurements mean little without a way to interpret them. As engineers, we believe that predictive modeling of biomolecular data is an important tool for interpretation and understanding. Using partial least squares regression (PLSR), we built a predictive model of host-cell responses to virus infection, which uncovered new connections between MAP kinase signaling pathways. We have also structured such models as mathematical tensors that retain how the data were collected. Application of tensor PLSR to a joint signaling–transcriptomic dataset revealed a function for a novel phosphorylation site on an understudied transcription factor. These powerful approaches now inform ongoing and future data-collection efforts in the lab.