Applications of functional protein microarrays in basic and clinical research

Abstract

The protein microarray technology provides a versatile platform for characterization of hundreds of thousands of proteins in a highly parallel and high-throughput manner. It is viewed as a new tool that overcomes the limitation of DNA microarrays. On the basis of its application, protein microarrays fall into two major classes: analytical and functional protein microarrays. In addition, tissue or cell lysates can also be directly spotted on a slide to form the so-called "reverse-phase" protein microarray. In the last decade, applications of functional protein microarrays in particular have flourished in studying protein function and construction of networks and pathways. In this chapter, we will review the recent advancements in the protein microarray technology, followed by presenting a series of examples to illustrate the power and versatility of protein microarrays in both basic and clinical research. As a powerful technology platform, it would not be surprising if protein microarrays will become one of the leading technologies in proteomic and diagnostic fields in the next decade.

Figure 4.1

Classification of protein microarrays. Protein microarrays are of two types: analytical and functional protein microarrays. Left: Analytical protein microarrays are constructed using biomolecule with specific binding property, such as antibodies, antigens, and aptamers. Right: Functional protein microarrays are formed by immobilization of individually purified proteins or synthetic peptides. The major applications of both types are listed below. For color version of this figure, the reader is referred to the online version of this book.

Figure 4.2

Fabrication of high-content functional protein microarrays. Four major steps are involved to construct a functional protein microarray of high content. First, a high-quality ORF expression library is constructed to allow inducible overexpression of GST-His6 fused recombinant proteins in yeast. Second, a high-throughput protein purification protocol is applied to individually purify thousands of proteins from yeast. The purified proteins are stored in 384-well format. Third, silver stain and immunoblot analysis are employed to evaluate the quality and quantity of the purified proteins. Finally, when purified proteins pass the quality control, they are spotted in duplicate to glass slides using a robot microarrayer. The quality of printing is then tested by anti-GST probing. An image of a human protein microarray probed with anti-GST is shown in the lower panel. For color version of this figure, the reader is referred to the online version of this book.

Figure 4.3

Principle of the OIRD method. First, a p-polarized He-Ne laser beam (λ = 632.8 nM) passes through a photoelastic modulator to induce oscillation between p- and s-polarization at a frequency of 50 kHz. Second, after passing through a phase shifter, the resultant beam is incident on the microarray surface at an oblique angle theta (θ_inc). Finally, the first I(Ω) and second harmonics I(2Ω) of the reflected beam intensity are simultaneously monitored by two digital lock-in amplifiers.

Figure 4.4

Effects of surface chemistry on assay development. (A) A pilot experiment to optimize the reaction conditions for protein acetylation in a microarray format. In each reaction, the yeast NuA4 acetyltransferase complex was added to an acetylation reaction mixture containing ¹⁴C-Ac-CoA and incubated with histone H3 and H4 spotted on three different surfaces. Bovine serum albumin (BSA) was also included as a negative control. The acetylation signals were detected by long exposure to X-ray film. (B, C) Examples of newly identified non-histone substrates. For color version of this figure, the reader is referred to the online version of this book.

Figure 4.5

Similar consensus sites are recognized by both TFs and uDBPs. (A–D) Examples of proteins sharing similar DNA binding profiles. Each peak represents normalized signal intensity of a specific DNA motif probe, with individual motifs organized along the X-axis by sequence similarity. Binding peaks used to generate the major logos (outlined in red) are indicated by red triangles. For proteins that recognize more than one logo (outlined in blue), binding peaks for the second logo are indicated in blue. (E) Correlation network for proteins with highly similar DNA binding profiles (see Supplementary Methods for construction of the network). Protein class is indicated by colored dots. For interpretation of the references to color in this figure legend, the reader is referred to the online version of this book.

Figure 4.6

In vitro kinase assays on protein microarrays. Recombinant kinase proteins were overexpressed and purified from yeast. Each kinase was added to a kinase reaction mixture and incubated on a pre-blocked protein microarray in the presence of radiolabeled ATP. The reaction was terminated by 0.5% SDS washes, followed by PBS washes to assure complete removal of unincorporated ATP and the added kinase. The lower panel shows a portion of an image after exposing a phosphorylated protein microarray to X-ray film. For color version of this figure, the reader is referred to the online version of this book.

Figure 4.7

Biomarker identification using protein microarrays. Proteins spotted on a functional microarray can be viewed as potential autoantigens that may be associated with a particular disease (e.g., autoimmune diseases). (A) To identify such autoantigens, a protein microarray is blocked, incubated with diluted serum sample, washed, and a fluorescently labeled anti-human IgG is used to detect captured autoantibodies. Following statistic analyses (e.g., SAM) can be used to identify potential autoantigens associated with the disease of interest. (B). Examples of biomarker identification in inflammatory bowel diseases (Xie et al., 2010). UC, ulcerative colitis; CD, Crohn’s disease; normal, healthy subjects. For color version of this figure, the reader is referred to the online version of this book.