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Related Concept Videos

Proteomics01:33

Proteomics

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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...
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SubCellBarCode: integrated workflow for robust spatial proteomics by mass spectrometry.

Taner Arslan1, Yanbo Pan1, Georgios Mermelekas1

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Summary
This summary is machine-generated.

This study presents a mass spectrometry (MS) and bioinformatics pipeline for mapping protein subcellular localization in human cancer cells. The method enables accurate classification of proteins to cellular compartments and neighborhoods, aiding functional understanding.

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Area of Science:

  • Proteomics
  • Cell Biology
  • Bioinformatics

Background:

  • Protein molecular functions are dictated by their subcellular location and interaction networks.
  • Understanding protein localization is critical for deciphering cellular functions.
  • A proteome-wide resource for protein subcellular localization in human cancer cell lines was previously developed (www.subcellbarcode.org).

Purpose of the Study:

  • To present a detailed wet-lab protocol for generating a proteome-wide resource of protein subcellular localization.
  • To describe the associated quantitative mass spectrometry (MS) data analysis and machine learning-based classification pipeline.
  • To provide a robust and broadly applicable method for protein localization studies.

Main Methods:

  • A comprehensive protocol for subcellular fractionation, MS sample preparation, and analysis, utilizing all cell fractions.
  • Quantitative MS data analysis, including machine learning classification and differential localization analysis.
  • Evaluation of the pipeline using diverse MS data acquisition strategies (isoelectric focusing, high-pH reverse-phase, and direct LC-MS).

Main Results:

  • A straightforward and robust protocol enabling protein classification into 15 compartments and 4 neighborhoods.
  • The entire workflow, from cell harvest to classification, is achievable within 1-2 weeks.
  • Development and availability of an R package (SubCellBarCode) for the dry-lab analysis.

Conclusions:

  • The presented protocol facilitates accurate protein subcellular localization determination.
  • The method supports visualization of localization data and differential localization analysis, including treatment-induced or condition-dependent changes.
  • The SubCellBarCode resource and package offer a valuable tool for cell biology and cancer research.