Lysate Microarrays Enable High-throughput, Quantitative Investigations of Cellular Signaling*

Lysate microarrays (reverse-phase protein arrays) hold great promise as a tool for systems-level investigations of signaling and multiplexed analyses of disease biomarkers. To date, however, widespread use of this technology has been limited by questions concerning data quality and the specificity of detection reagents. To address these concerns, we developed a strategy to identify high-quality reagents for use with lysate microarrays. In total, we tested 383 antibodies for their ability to quantify changes in protein abundance or modification in 20 biological contexts across 17 cell lines. Antibodies yielding significant differences in signal were further evaluated by immunoblotting and 82 passed our rigorous criteria. The large-scale data set from our screen revealed that cell fate decisions are encoded not just by the identities of proteins that are activated, but by differences in their signaling dynamics as well. Overall, our list of validated antibodies and associated protocols establish lysate microarrays as a robust tool for systems biology.

[1]  O. Meyuhas,et al.  Ribosomal protein S6 phosphorylation: from protein synthesis to cell size. , 2006, Trends in biochemical sciences.

[2]  Johan Lindberg,et al.  Determination of Binding Specificities in Highly Multiplexed Bead-based Assays for Antibody Proteomics *S , 2007, Molecular & Cellular Proteomics.

[3]  Dean Brenner,et al.  Multiplexed analysis of glycan variation on native proteins captured by antibody microarrays , 2007, Nature Methods.

[4]  P. Sorger,et al.  Profiling receptor tyrosine kinase activation by using Ab microarrays , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[5]  Garry P Nolan,et al.  Phospho-specific flow cytometry: intersection of immunology and biochemistry at the single-cell level. , 2006, Current opinion in molecular therapeutics.

[6]  Jaakko Astola,et al.  Analysis and Visualization of Gene Expression Microarray Data in Human Cancer Using Self-Organizing Maps , 2003, Machine Learning.

[7]  J. Blenis,et al.  ERK and p38 MAPK-Activated Protein Kinases: a Family of Protein Kinases with Diverse Biological Functions , 2004, Microbiology and Molecular Biology Reviews.

[8]  Gavin MacBeath,et al.  Linear combinations of docking affinities explain quantitative differences in RTK signaling , 2009, Molecular systems biology.

[9]  Paul Tempst,et al.  A Prototype Antibody Microarray Platform to Monitor Changes in Protein Tyrosine Phosphorylation* , 2004, Molecular & Cellular Proteomics.

[10]  G. Thomas The S6 kinase signaling pathway in the control of development and growth. , 2002, Biological research.

[11]  Vince R. Boveia,et al.  Development of multiplexed protein profiling and detection using near infrared detection of reverse-phase protein microarrays , 2004, Clinical Proteomics.

[12]  M. V. Velzen,et al.  Self-organizing maps , 2007 .

[13]  Teiji Wada,et al.  Mitogen-activated protein kinases in apoptosis regulation , 2004, Oncogene.

[14]  Gavin MacBeath,et al.  State-based discovery: a multidimensional screen for small-molecule modulators of EGF signaling , 2006, Nature Methods.

[15]  Antibody screening database for protein kinetic modeling , 2007, Proteomics.

[16]  C. Marshall,et al.  Specificity of receptor tyrosine kinase signaling: Transient versus sustained extracellular signal-regulated kinase activation , 1995, Cell.

[17]  Yosef Yarden,et al.  Signal transduction and oncogenesis by ErbB/HER receptors. , 2004, International journal of radiation oncology, biology, physics.

[18]  K. Kellar,et al.  Multiplexed Microsphere‐Based Flow Cytometric Immunoassays , 2006, Current protocols in cytometry.

[19]  Paul J Utz,et al.  Protein microarrays for multiplex analysis of signal transduction pathways , 2004, Nature Medicine.

[20]  D. Lauffenburger,et al.  Systems Analysis of EGF Receptor Signaling Dynamics with Micro-Western Arrays , 2010, Nature Methods.

[21]  Esa Alhoniemi,et al.  Clustering of the self-organizing map , 2000, IEEE Trans. Neural Networks Learn. Syst..

[22]  E. Petricoin,et al.  Reverse phase protein microarrays which capture disease progression show activation of pro-survival pathways at the cancer invasion front , 2001, Oncogene.

[23]  S. Nishizuka,et al.  Reverse-phase protein lysate microarrays for cell signaling analysis , 2008, Nature Protocols.

[24]  Esa Alhoniemi,et al.  SOM Toolbox for Matlab 5 , 2000 .

[25]  Anne E Carpenter Image-based chemical screening. , 2007, Nature chemical biology.

[26]  Gavin MacBeath,et al.  An integrated approach to prognosis using protein microarrays and nonparametric methods , 2007, Molecular systems biology.

[27]  Sampsa Hautaniemi,et al.  Effects of HER2 overexpression on cell signaling networks governing proliferation and migration , 2006, Molecular systems biology.

[28]  Paul Lizardi,et al.  Two-color, rolling-circle amplification on antibody microarrays for sensitive, multiplexed serum-protein measurements , 2004, Genome Biology.

[29]  P. Brown,et al.  Protein microarrays for highly parallel detection and quantitation of specific proteins and antibodies in complex solutions , 2001, Genome Biology.

[30]  Fridtjof Lund-Johansen,et al.  Antibody Array Analysis with Label-based Detection and Resolution of Protein Size *S , 2009, Molecular & Cellular Proteomics.