Protein arrays have become powerful tools to investigate the status of signaling pathways in cells or tissues. The ability to perform multiplexed assays on hundreds to thousands of samples enables time-resolved studies of cells stimulated or perturbed in different ways. The data from these studies can then be used to infer the structure of the underlying network. Protein array technology is well suited for these types of investigations because it provides a way to measure many different proteins in parallel while consuming very little material (1
). Over the past decade, two array platforms—bead-based arrays and lysate microarrays—have become well established in cell signaling research (). Both methods have been used to analyze signaling networks in a time-resolved fashion (3
), and both methods offer multiplexing capabilities. In the case of bead arrays, a mixture of microspheres is used to detect and quantify different analytes in a sample. The beads are typically coated with capture antibodies specific to different analytes, and captured analytes are detected and quantified by using a mixture of fluorescently labeled detection antibodies (). The identity of each bead is revealed by using an internal fluorescent color code. In the case of lysate microarrays, different samples are spotted onto a series of nitrocellulose-coated slides, and each slide is probed with a different antibody (). In this case, the identity of each slide specifies the analyte and the location of each spot in the array specifies the sample. In both assays, posttranslational modifications can be detected by using posttranslational modification–specific antibodies.
Monitoring β-catenin by bead array assay or lysate array
One application of the bead-based assay is the acquisition of detailed information on a single protein. Because critical, highly connected nodes in signaling networks are often pleiotropic, it is important not just to quantify the abundance of the protein, but to obtain quantitative information on its different forms and on its interaction with other proteins. The specific state of a central signaling protein is often influenced by the surrounding network and, in turn, dictates downstream signaling. Thus, to understand the role of such a protein requires detailed information on not only the protein, but on its surrounding network as well. Here, we describe how to obtain such information in a time-resolved fashion, using, as an example, the response of hepatocarcinoma (HepG2) cells to stimulation with either a canonical Wnt ligand, Wnt3a, or a noncanonical ligand, Wnt5a.
In the case of Wnt signaling, the intracellular protein β-catenin is multifunctional, playing critical roles in both signaling and cell-cell adhesion complexes. β-catenin is also a proto-oncogene, and activating mutations in the gene that encodes β-catenin contribute to the genesis of common cancers, such as colorectal cancer and hepatocellular carcinoma (7
). The different functions of β-catenin as a transcriptional coactivator and as a cell adhesion molecule are regulated by changes in protein abundance and phosphorylation state, both of which affect the ability of β-catenin to complex with other transcription factors or to interact with adhesion proteins, such as the cadherins (10
). Increases in the abundance of cytoplasmic β-catenin and accumulation of the uncomplexed, transcriptionally active form of β-catenin are hallmarks of active β-catenin–dependent “canonical” Wnt signaling (13
). Noncanonical signaling regulates cell polarity and cell movements and involves pathways, such as the planar cell polarity pathway, the Wnt to Jun N-terminal kinase pathway, or the Wnt to Ca2+
signaling pathway (14
The analytical methods described here are designed to provide a holistic view of the complex interactions mediated by β-catenin and how these interactions influence its function (15
). Data obtained with these methods can then be used to train computational models of Wnt signaling, which provide insight into the structure of the network and how best to intervene pharmacologically (17
). More generally, the dual approach described here could be used to gain insight into other complex pathways with central signaling proteins, such the DNA damage response network and p53, or the epidermal growth factor (EGF) signaling network and the EGF receptor.