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Unveiling gene trait relationship by cross‐platform meta‐analysis on Chinese hamster ovary cell transcriptome
Author(s) -
Zhao Liang,
Fu HsuYuan,
Raju Ravali,
Vishwanathan Nandita,
Hu WeiShou
Publication year - 2017
Publication title -
biotechnology and bioengineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.136
H-Index - 189
eISSN - 1097-0290
pISSN - 0006-3592
DOI - 10.1002/bit.26272
Subject(s) - transcriptome , biology , chinese hamster ovary cell , kegg , computational biology , microarray analysis techniques , gene expression profiling , gene , genetics , gene expression , cell culture
In the past few years, transcriptome analysis has been increasingly employed to better understand the physiology of Chinese hamster ovary (CHO) cells at a global level. As more transcriptome data accumulated, meta‐analysis on data sets collected from various sources can potentially provide better insights on common properties of those cells. Here, we performed meta‐analysis on transcriptome data of different CHO cell lines obtained using NimbleGen or Affymetrix microarray platforms. Hierarchical clustering, non‐negative matrix factorization (NMF) analysis, and principal component analysis (PCA) accordantly showed the samples were clustered into two groups: one consists of adherent cells in serum‐containing medium, and the other suspension cells in serum‐free medium. Genes that were differentially expressed between the two clusters were enriched in a few functional classes by Database for Annotation, Visualization, and Integrated Discovery (DAVID) of which many were common with the enriched gene sets identified by Gene Set Enrichment Analysis (GSEA), including extracellular matrix (ECM) receptor interaction, cell adhesion molecules (CAMs), and lipid related metabolism pathways. Despite the heterogeneous sources of the cell samples, the adherent and suspension growth characteristics and serum‐supplementation appear to be a dominant feature in the transcriptome. The results demonstrated that meta‐analysis of transcriptome could uncover features in combined data sets that individual data set might not reveal. As transcriptome data sets accumulate over time, meta‐analysis will become even more revealing. Biotechnol. Bioeng. 2017;114: 1583–1592. © 2017 Wiley Periodicals, Inc.