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Proteomics

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...

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Related Experiment Video

Updated: Feb 23, 2026

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Published on: November 15, 2017

Using Public Data for Comparative Proteome Analysis in Precision Medicine Programs.

Christopher S Hughes¹, Gregg B Morin^1,2

¹Canada's Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, British Columbia, Canada.

Proteomics. Clinical Applications

|September 10, 2017

Summary

This summary is machine-generated.

Publicly available mass-spectrometry proteomics data enables robust comparisons for precision medicine. This approach allows for effective analysis of cell lines and tumor tissues, aiding disease phenotype identification.

Keywords:

isobaric tagging label free quantification mass spectrometry precision medicine

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Area of Science:

Proteomics
Precision Medicine
Bioinformatics

Background:

Longitudinal precision medicine programs require comparative analyses to maximize clinical utility.
Identifying disease-driving biological features necessitates robust comparisons with existing data.

Purpose of the Study:

To investigate the utility of public mass-spectrometry-based proteomics data for inter-study comparisons.
To assess the robustness of comparing proteomics studies using different methodologies.

Main Methods:

Utilized publicly deposited mass-spectrometry proteomics data.
Included data from label-free (MS1) and isobaric tagging (MS2 and MS3) quantification methods.
Analyzed ovarian cancer cell lines and tumor tissues.

Main Results:

Demonstrated robust recapitulation of gene expression dynamics across studies with different methodologies.
Enabled robust inter-study clustering of cell line samples.
Established the ability to classify and cluster tumor samples using single-patient data against repository data.

Conclusions:

State-of-the-art mass-spectrometry proteomics data can serve as a resource for robust comparative analyses in precision medicine.
This approach enhances the identification of underlying features driving disease phenotypes.