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

Cell-based biosensors (CBBs) utilize whole cells to detect biologically act ive agents. Although CBBs have shown success in detecting the presence of b iological agents, efforts to classify the type of agent based on functional activity have proven difficult because multiple biochemical pathways can l ead to the same cellular response. However, a new approach using a genetica lly-engineered cell-based biosensor (GECBB) described in this paper transla tes this cross-talk noise into common-mode noise that can be rejected. The GECBB operates by assaying for an agent's ability to differentially activat e two populations of cells, wild-type (WT) cells and cells genetically engi neered to lack a specific receptor, knockout (KO) cells. Any biological age nt that targets the knocked out receptor will evoke a response in the WT bu t not in the KO. Thus, the GECBB is exquisitely sensitive to agents that ef fect the engineered pathway. This approach provides the benefits of an assa y for specific functional activity while simplifying signal analysis. The G ECBB implemented was designed to be sensitive to agents that activate the b eta1-adrenergic receptor (beta1-AR). This was achieved by using mouse cardi omyocytes in which the Pl-AR had been knocked out. The cellular signal used in the GECBB was the spontaneous beat rate of the two cardiomyocyte syncit ia as measured with microelectrode arrays. The GECBB was able to detect the beta -AR agonist isoproterenol (ISO) at a concentration of 10 muM (P < 0.0 05). (C) 2001 Elsevier Science B.V. All rights reserved.