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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...

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Robust Comparison of Protein Levels Across Tissues and Throughout Development Using Standardized Quantitative Western Blotting
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The correlation between cellular size and protein expression levels--normalization for global protein profiling.

Emma Lundberg1, Marcus Gry, Per Oksvold

  • 1School of Biotechnology, AlbaNova University Center, Royal Institute of Technology (KTH), SE-106 91 Stockholm, Sweden.

Journal of Proteomics
|July 29, 2008
PubMed
Summary
This summary is machine-generated.

Automated image analysis quantified 1862 human proteins in cancer cells. Normalization is crucial for accurate protein quantification, revealing cell-specific expression patterns.

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Area of Science:

  • Proteomics
  • Cancer Biology
  • Biotechnology

Background:

  • Protein expression varies significantly across different cell types and conditions.
  • Accurate quantification of protein levels is essential for understanding cellular functions and disease mechanisms.
  • Immunohistochemistry (IHC) is a valuable technique, but requires careful normalization for reliable quantitative analysis.

Purpose of the Study:

  • To develop and validate an automated image analysis system for high-throughput protein quantification.
  • To investigate protein expression patterns across diverse cancer cell lines and clinical samples.
  • To establish robust normalization methods for comparative proteomic analysis using IHC.

Main Methods:

  • Utilized an automated image analysis system for protein quantification of 1862 human proteins.
  • Employed cell microarrays and immunohistochemistry on 47 cancer cell lines and 12 clinical samples.
  • Evaluated two reference standards for normalization and assessed cell size-dependent protein expression.

Main Results:

  • Identified cell size-dependent expression for most quantified proteins.
  • Demonstrated that normalization corrects for cell size bias and IHC ambiguities.
  • Revealed distinct protein expression profiles, including stable and cell-line specific patterns, across diverse cell populations.

Conclusions:

  • Automated proteome analysis via IHC is effective for large-scale studies.
  • Normalization is critical for accurate and comparative protein quantification in heterogeneous cell samples.
  • This approach facilitates the discovery of functional correlations and aids in mining proteomic data.