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Reproducibility and Crossplatform Validation of Reverse-Phase Protein Array Data.

Adam Byron1

  • 1Cancer Research UK Edinburgh Centre, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK. adam.byron@igmm.ed.ac.uk.

Advances in Experimental Medicine and Biology
|December 11, 2019
PubMed
Summary
This summary is machine-generated.

Reverse-phase protein array (RPPA) technology offers high-throughput protein and posttranslational modification profiling for biomedical research. Validated RPPA data are crucial for advancing diagnostics and understanding disease mechanisms.

Keywords:
BioinformaticsBiomarkersData evaluationData integrationData normalizationData validationProtein microarrayProteomicsReproducibilityReverse-phase protein array

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Area of Science:

  • Biotechnology
  • Proteomics
  • Molecular Biology

Background:

  • Reverse-phase protein array (RPPA) is a high-throughput method for profiling protein levels and posttranslational modifications.
  • RPPA enables sensitive detection of low-abundance proteins and parallel analysis of multiple signaling pathways.
  • Its features support applications in disease mechanism elucidation, drug target profiling, and biomarker discovery.

Purpose of the Study:

  • To highlight the importance of robust and validated data in RPPA applications.
  • To discuss the complexities and opportunities in RPPA workflow and platform setups.
  • To emphasize the role of cross-platform validation for reliable biomarker identification.

Main Methods:

  • Utilizes antibody- and microarray-based techniques for protein quantification.
  • Analyzes small sample volumes with high sensitivity and reproducibility.
  • Involves diverse technical setups and analysis parameters across different platforms.

Main Results:

  • RPPA technology facilitates rapid profiling of protein and posttranslational modification levels.
  • The approach is suitable for large-scale sample analysis and interexperimental reproducibility.
  • Applications span biomedical, translational, and clinical research, including diagnostics and prognostics.

Conclusions:

  • Robust and validated RPPA data are essential for complex applications like multiplex assays and proteogenomics.
  • Variations in RPPA platform setups necessitate cross-platform validation.
  • Cross-platform validation enhances the identification of reliable, platform-independent disease and therapy response markers.