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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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Related Experiment Video

Updated: May 20, 2026

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

Published on: March 15, 2011

Spot identification and quality control in cell-based microarrays.

Michael Bauer¹, Keekyoung Kim, Yiling Qiu

¹Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, United States. mihi@tentacleriot.eu

ACS Combinatorial Science

|August 2, 2012

Summary

This summary is machine-generated.

This study presents an automated method for analyzing cell-based microarrays, improving speed and reducing bias in screening cellular microenvironments. The new approach accurately identifies cell spots and assesses their quality for high-throughput analysis.

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Last Updated: May 20, 2026

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

Published on: March 15, 2011

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

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A Guided Materials Screening Approach for Developing Quantitative Sol-gel Derived Protein Microarrays

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Published on: August 26, 2013

Area of Science:

Biotechnology and Biomedical Engineering
Cell Biology and Imaging
Bioinformatics and Data Analysis

Background:

Cell-based microarrays are crucial for high-throughput screening of cellular microenvironments.
Manual analysis of microarray data is time-consuming and prone to human bias.
Existing methods lack efficient postprocessing and quality control for combinatorial microenvironment studies.

Purpose of the Study:

To develop an automated approach for identifying cell spots and performing quality control on cell-based microarrays.
To enable fast, unbiased analysis of cellular adhesion on combinatorial extracellular matrix protein arrays.
To identify key extracellular matrix proteins influencing cell attachment.

Main Methods:

Automated fluorescence microscopy for microarray imaging.
Open-source CellProfiler software for cell identification.
OPTICS density-based clustering algorithm for cell spot identification.
Naïve Bayesian classifiers for spot quality control based on size, cell count, and location.

Main Results:

The automated approach reliably identified cell spots and assessed their quality.
The system achieved 78% accuracy for high-quality spots and 87% for poor-quality spots.
Collagen IV was identified as the extracellular matrix protein with the most significant positive effect on cell attachment.

Conclusions:

The developed automated data processing approach significantly enhances the speed and objectivity of cell-based microarray analysis.
This method facilitates efficient investigation of cellular responses in combinatorial microenvironments.
The findings provide insights into extracellular matrix protein interactions with murine cardiac side population cells.