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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 proteomics...

DNA Microarrays

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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Updated: May 14, 2026

Probing High-density Functional Protein Microarrays to Detect Protein-protein Interactions

Probing High-density Functional Protein Microarrays to Detect Protein-protein Interactions

Published on: August 2, 2015

Emerging protein array technologies for proteomics.

Jung-Rok Lee¹, Dewey Mitchell Magee, Richard Samuel Gaster

¹Department of Mechanical Engineering, Stanford University, 476 Lomita Mall, Room 208, Stanford, CA 94305, USA.

Expert Review of Proteomics

|February 19, 2013

Summary

This summary is machine-generated.

Protein microarrays offer a more reliable approach than gene expression for understanding disease onset and monitoring treatment. This review covers emerging magneto-nanosensor and nucleic acid programmable protein array technologies.

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Last Updated: May 14, 2026

Probing High-density Functional Protein Microarrays to Detect Protein-protein Interactions

Probing High-density Functional Protein Microarrays to Detect Protein-protein Interactions

Published on: August 2, 2015

Identifying Protein-protein Interaction Sites Using Peptide Arrays

Identifying Protein-protein Interaction Sites Using Peptide Arrays

Published on: November 18, 2014

Extracellular Protein Microarray Technology for High Throughput Detection of Low Affinity Receptor-Ligand Interactions

Extracellular Protein Microarray Technology for High Throughput Detection of Low Affinity Receptor-Ligand Interactions

Published on: January 7, 2019

Area of Science:

Biotechnology
Proteomics
Clinical Diagnostics

Background:

Gene expression studies have limitations in clarifying disease biology.
Protein profiles are considered more reliable biomarkers for disease onset and treatment monitoring than gene expression.
Protein array technologies are advancing rapidly for biological and medical research.

Purpose of the Study:

To review emerging protein array technologies.
To discuss key practical issues in protein array development, including protein immobilization and surface neutralization.
To explore the potential of novel protein array platforms in proteomics and diagnostics.

Main Methods:

Review of magneto-nanosensor arrays utilizing giant magnetoresistive sensors and magnetic nanoparticles for analyte detection.
Review of nucleic acid programmable protein arrays (NAPPAs) with in-situ protein expression.
Discussion of common principles and challenges in protein array fabrication.

Main Results:

Magneto-nanosensor arrays enable quantitative measurement of target biomolecules.
Nucleic acid programmable protein arrays allow for high-density, direct protein expression on array surfaces.
Both technologies present distinct advantages for high-throughput proteomic analysis.

Conclusions:

Emerging protein array platforms like magneto-nanosensor and nucleic acid programmable protein arrays show significant promise.
Combining these technologies could lead to synergistic benefits and novel applications.
These advancements are expected to enhance proteomics research and clinical diagnostics.