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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...

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

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Related Experiment Video

Updated: Mar 14, 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

Blood proteomics: insights from public data.

Asier Larrea-Sebal^1,2,3, Chengxin Dai^4,5, Alejandro J Brenes⁶

¹European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.

|March 13, 2026

Summary

This summary is machine-generated.

This study reviews blood proteomics data, highlighting its complexity and potential for personalized medicine. It examines current methods and data sources while addressing challenges for future clinical applications.

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Area of Science:

Biochemistry
Proteomics
Molecular Biology

Background:

The blood proteome contains vital information on physiological and pathological conditions.
Publicly available blood proteomics datasets are growing due to technological advancements.

Purpose of the Study:

To review the complexity of blood proteomics data.
To assess current methodologies and data integration strategies.
To identify challenges in translating blood proteomics to personalized medicine.

Main Methods:

Literature review of public blood proteomics resources.
Analysis of mass spectrometry and affinity-based proteomics techniques.
Evaluation of data complementarity across different abundance levels.

Main Results:

Blood proteomics data exhibit significant cellular and molecular complexity.
Diverse data sources offer complementary insights across the abundance spectrum.
Existing datasets have limitations for comprehensive assessment.

Conclusions:

Blood proteomics holds promise for personalized medicine.
Further methodological development and data integration are crucial.
Addressing current challenges is key to clinical translation.