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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: Feb 17, 2026

Saccharomyces cerevisiae Metabolic Labeling with 4-thiouracil and the Quantification of Newly Synthesized mRNA As a Proxy for RNA Polymerase II Activity

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Published on: October 22, 2018

A Comparative Transcriptomics Workflow for Analyzing Microarray Data From CHO Cell Cultures.

Chun Chen¹, Huong Le¹, Brian Follstad¹

¹Drug Substance Technologies, Process Development, Amgen Inc., 1 Amgen Center Drive, Thousand Oaks, CA 91320, USA.

Biotechnology Journal

|December 8, 2017

Summary

This summary is machine-generated.

This study introduces an automated R workflow for analyzing microarray data from Chinese hamster ovary (CHO) cells, crucial for biopharmaceutical production. The tool identifies gene expression changes, enabling better cell line optimization and bioprocess development.

Keywords:

CHO cells cell culture comparative transcriptomics microarray pathway analysis

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Area of Science:

Biotechnology
Molecular Biology
Bioinformatics

Background:

Mammalian cell lines, particularly Chinese hamster ovary (CHO) cells, are vital for producing therapeutic proteins.
Microarray analysis is essential for understanding gene expression in these cell lines.
A lack of integrated, end-to-end analysis workflows for CHO cell microarray data hinders optimization.

Purpose of the Study:

To develop an automated R-based data analysis workflow for comprehensive microarray data analysis in CHO cells.
To enable identification of differentially expressed genes and pathways with clear visualizations.
To facilitate mechanism-driven optimization of CHO cell lines for recombinant protein production.

Main Methods:

Development of an automated R workflow utilizing public domain analysis modules.
Application of the workflow to analyze gene expression data from CHO cells under varying conditions.
Comparative transcriptomic analysis of recombinant protein-expressing CHO cells with and without temperature shifts.

Main Results:

The workflow successfully identified global transcriptome differences in CHO cells.
Statistically significant differentially expressed genes and pathways were detected and visualized.
The analysis highlighted transcriptomic changes associated with temperature shifts in protein-producing CHO cells.

Conclusions:

The developed automated workflow provides an efficient method for analyzing CHO cell microarray data.
This tool facilitates the rapid translation of gene expression data into actionable insights for cell line optimization.
The workflow supports improved bioprocess development and recombinant protein production through data-driven strategies.