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Related Concept Videos

Proteomics01:33

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term proteomics...
DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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Related Experiment Video

Updated: May 27, 2026

The Use of Reverse Phase Protein Arrays (RPPA) to Explore Protein Expression Variation within Individual Renal Cell Cancers
12:22

The Use of Reverse Phase Protein Arrays (RPPA) to Explore Protein Expression Variation within Individual Renal Cell Cancers

Published on: January 22, 2013

Reverse-phase protein microarrays.

Mariaelena Pierobon1, Amy J Vanmeter, Noemi Moroni

  • 1Center for Applied Proteomics and Molecular Medicine, George Mason University, Manassas, VA, USA. mpierobo@gmu.edu

Methods in Molecular Biology (Clifton, N.J.)
|November 15, 2011
PubMed
Summary
This summary is machine-generated.

Understanding cancer requires analyzing cellular protein networks. Reverse-phase protein microarrays (RPMAs) offer a powerful method to map protein interactions and activations for personalized cancer therapies.

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Last Updated: May 27, 2026

The Use of Reverse Phase Protein Arrays (RPPA) to Explore Protein Expression Variation within Individual Renal Cell Cancers
12:22

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Published on: January 22, 2013

Profiling of Pre-micro RNAs and microRNAs using Quantitative Real-time PCR (qPCR) Arrays
10:58

Profiling of Pre-micro RNAs and microRNAs using Quantitative Real-time PCR (qPCR) Arrays

Published on: December 3, 2010

Extracellular Protein Microarray Technology for High Throughput Detection of Low Affinity Receptor-Ligand Interactions
06:01

Extracellular Protein Microarray Technology for High Throughput Detection of Low Affinity Receptor-Ligand Interactions

Published on: January 7, 2019

Area of Science:

  • Oncology
  • Molecular Biology
  • Biochemistry

Background:

  • Cancer arises from disrupted cellular signaling networks and homeostasis.
  • Proteins are key drivers of cellular functions, making protein network analysis crucial for understanding cancer.
  • Personalized medicine necessitates detailed molecular profiling of individual tumors.

Purpose of the Study:

  • To highlight the importance of protein analysis in understanding cancer.
  • To introduce Reverse-Phase Protein Microarrays (RPMAs) as an advanced technology for proteomic analysis.
  • To demonstrate the utility of RPMAs in mapping cellular signaling pathways for targeted cancer therapy.

Main Methods:

  • Utilizing Reverse-Phase Protein Microarrays (RPMAs) for high-throughput proteomic analysis.
  • Investigating protein activation and post-translational modifications.
  • Mapping intracellular and extracellular signaling cascades.

Main Results:

  • RPMAs enable the study of protein activation resulting from various biochemical reactions like phosphorylation and glycosylation.
  • This technology facilitates the analysis of protein interactions and conformational changes.
  • RPMAs can generate protein interaction and activation maps crucial for identifying therapeutic targets.

Conclusions:

  • RPMAs are a valuable tool for analyzing protein alterations in cancer.
  • Mapping cellular signaling networks using RPMAs aids in understanding tumor growth and treatment response.
  • This technology supports the development of personalized and combinatorial targeted therapies for cancer.