A genetically engineered cell-based biosensor for functional classification of agents.

Abstract

Cell-based biosensors (CBBs) utilize whole cells to detect biologically active agents. Although CBBs have shown success in detecting the presence of biological agents, efforts to classify the type of agent based on functional activity have proven difficult because multiple biochemical pathways can lead to the same cellular response. However, a new approach using a genetically-engineered cell-based biosensor (GECBB) described in this paper translates this cross-talk noise into common-mode noise that can be rejected. The GECBB operates by assaying for an agent's ability to differentially activate two populations of cells, wild-type (WT) cells and cells genetically engineered to lack a specific receptor, knockout (KO) cells. Any biological agent that targets the knocked out receptor will evoke a response in the WT but not in the KO. Thus, the GECBB is exquisitely sensitive to agents that effect the engineered pathway. This approach provides the benefits of an assay for specific functional activity while simplifying signal analysis. The GECBB implemented was designed to be sensitive to agents that activate the beta 1-adrenergic receptor (beta 1-AR). This was achieved by using mouse cardiomyocytes in which the beta 1-AR had been knocked out. The cellular signal used in the GECBB was the spontaneous beat rate of the two cardiomyocyte syncitia as measured with microelectrode arrays. The GECBB was able to detect the beta-AR agonist isoproterenol (ISO) at a concentration of 10 microM (P<0.005).