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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: Jun 19, 2026

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

Published on: May 21, 2019

Improved reproducibility of reverse-phase protein microarrays using array microenvironment normalization.

Troy Anderson¹, Julia Wulfkuhle, Lance Liotta

¹Institute for Computational Medicine, Center for Cardiovascular Bioinformatics and Modeling, Johns Hopkins University, Baltimore, MD 21218, USA. troy@jhu.edu

|October 17, 2009

Summary

This summary is machine-generated.

A new method called array microenvironment normalization significantly improves protein microarray reproducibility. This technique enhances statistical power, enabling detection of small prostate specific antigen changes with fewer samples, aiding clinical use.

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A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces

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Published on: March 1, 2017

Area of Science:

Biotechnology
Analytical Chemistry
Biomarker Discovery

Background:

Protein microarrays are crucial for biomarker discovery but limited by measurement variability.
High coefficient of variation (CV) in protein microarrays hinders detection of subtle biological changes.
Current methods require numerous replicates, increasing costs and time.

Purpose of the Study:

To introduce a novel normalization technique for reverse-phase protein microarrays.
To enhance the reproducibility and statistical power of protein microarray measurements.
To reduce the number of technical replicates needed for biomarker detection.

Main Methods:

Developed and applied 'array microenvironment normalization' to reverse-phase protein microarrays.
Quantified measurement variability using coefficient of variation (CV).
Assessed the platform's ability to detect changes in prostate specific antigen (PSA) concentration.

Main Results:

Achieved up to a 70% reduction in the typical measurement CV.
Demonstrated detection of a 1.1-fold PSA shift with only six replicates, compared to 37 previously.
Significantly increased the statistical power of the protein microarray platform.

Conclusions:

Array microenvironment normalization substantially improves protein microarray performance.
The enhanced reproducibility and statistical power facilitate clinical implementation.
This methodology offers a more efficient and powerful tool for biomarker analysis.