RPPA Process

RPPA analyzes cellular protein activity in signaling networks and can be applied to tissue and cultured cells.

Cell Lines

• Characterize cellular signaling networks under different culture conditions
• Determine drug selectivity
• Identify therapeutic targets
• Define regulatory mechanisms in signaling networks, including forward and feedback loops and cross talks
• Ability to analyze up to 1000 conditions concurrently and allows the detailed concentration and time courses required for systems biology approaches

Patient Samples

• Classify patient tumors
• Correlate DNA, RNA and Protein
• Determine prognosis
• Predict responses to targeted therapies
• Pharmacodynamics and biologically relevant dose
• Determine appropriate handling procedures for clinical samples (based on antigen stability analysis)

RPPA Experiment and Data Processing

• Frozen tumors/cell pellets were lysed and protein was extracted using RPPA lysis buffer (See Antibody Information & Protocols Page). Lysates were manually serial-diluted in 5 two-fold dilutions with lysis buffer and printed on nitrocellulose-coated slides using an Aushon Biosystems 2470 arrayer. Slides were probed with approximately 300 validated primary antibodies followed by detection with appropriate Biotinylated secondary antibodies (Goat anti-Rabbit IgG, Goat anti-Mouse IgG, or Rabbit anti-Goat IgG). The signal obtained was amplified using a Cytomation–catalyzed system of Avidin-Biotinylated Peroxidase (Vectastain Elite ABC kit from Vector Lab) binding to the secondary antibody and catalyzing Tyramide-Biotin conjugation to form insoluble biotinylated phenols. Signals were visualized by a secondary streptavidin-conjugated HRP and DAB colorimetric reaction. The slides were scanned, analyzed, and quantified using Array-Pro Analyzer software (MediaCybernetics) to generate spot intensity (Level 1 data). SuperCurve GUI (2), was used to estimate relative protein levels (in log2 scale). A fitted curve ("supercurve") was created with signal intensities on the Y-axis and relative log2 amounts of each protein on the X-axis using a non-parametric, monotone increasing B-spline model (1). Raw spot intensity data were adjusted to correct spatial bias before model fitting using “control spots” arrayed across the slides (3). A QC metric (4) was generated for each slide to determine slide quality and only slides with 0.8 on a 0-1 scale were used further. For replicate slides, the slide with the highest QC score was used for analysis (Level 2 data). Protein measurements were corrected for loading as described (2,5) using median-centering across antibodies (Level 3 data). Samples with low protein levels were excluded from further analysis. Antibodies were selected to represent the breadth of cell signaling and repair pathways (7) conditioned on a strict validation process as previously described (6). Antibodies are labeled as “validated” and “use with caution” based on degree of validation. 

1. Tibes, R. et al. Reverse phase protein array: validation of a novel proteomic technology and utility for analysis of primary leukemia specimens and hematopoietic stem cells. Mol. Cancer Ther. 5, 2512-2521 (2006)

2. Hu J, He X, Baggerly KA, Coombes KR, Hennessy BTJ, Mills GB. (2007) Non-parametric quantification of protein lysate arrays. Bioinformatics. 23, 1986-94.

3. Neeley ES, Baggerly KA, Kornblau SM. (2012) Surface Adjustment of Reverse Phase Protein Arrays using Positive Control Spots. Cancer Inform. 11, 77-86.

4. Ju, Z. et al. Development of a robust classifier for quality control of reverse-phase protein arrays. Bioinformatics 31, 912-918 (2015)

5. Gonzalez-Angulo, A.M. et al. Functional proteomics can define prognosis and predict pathologic complete response in patients with breast cancer. Clin. Proteomics 8-11 (2011)

6. Hennessy, B.T. et al. A technical assessment of the utility of reverse phase protein arrays for the study of the functional proteome in non-microdissected human breast cancers.  Clin. Proteomics 6, 129-151 (2010)

7. Akbani R., Ng KS., Werner HMJ., Shahmoradgoli M., Zhang F., Ju. Z., Liu W., Yang JY., Yoshihara K., Li J., Ling S.. Seviour EG., Ram PT., Minna JD., Diao L. Tong P. Heymach Jv., Hill SM., Dondelinger F., Stadler N., Byers LN., Meric-Bernstam F., Weinstein HN., Broom BM., Verhaak RGW., Liang H., Mukherjee S., Lu Y, and Mills GB., 2014 A pan-cancer proteomic perspective on the Cancer Genome Atlas Nature Comms: 5:3887 PMID:24871328 PMC4109726