Article

Angiogenesis

, Volume 7, Issue 2, pp 143-156

First online:

Bioinformatic analysis of primary endothelial cell gene array data illustrated by the analysis of transcriptome changes in endothelial cells exposed to VEGF-A and PlGF

  • Jonathan SchoenfeldAffiliated withDepartment of Pathology, Cambridge University
  • , Khashayar LessanAffiliated withDepartment of Pathology, Cambridge University
  • , Nicola JohnsonAffiliated withDepartment of Pathology, Cambridge University
  • , D. Charnock-jonesAffiliated withDepartment of Pathology, Cambridge University
  • , Amanda EvansAffiliated withDepartment of Pathology, Cambridge University
  • , Ekaterini VourvouhakiAffiliated withDepartment of Pathology, Cambridge University
  • , Laurie ScottAffiliated withUK MRC Hinxton Genome Mapping Project Resource Centre
  • , Richard StephensAffiliated withUK MRC Hinxton Genome Mapping Project Resource Centre
  • , Tom FreemanAffiliated withUK MRC Hinxton Genome Mapping Project Resource Centre
    • , Samir SaidiAffiliated withDepartment of Pathology, Cambridge University
    • , Brian TomAffiliated withMedical Research Council Biostatistics Unit
    • , Gareth WestonAffiliated withDepartment of Obstetrics and Gynaecology, Monash Medical Centre, Centre for Women's Health Research, Monash University
    • , Peter RogersAffiliated withDepartment of Obstetrics and Gynaecology, Monash Medical Centre, Centre for Women's Health Research, Monash University
    • , Stephen SmithAffiliated withDepartment of Pathology, Cambridge University
    • , Cristin PrintAffiliated withDepartment of Pathology, Cambridge University

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Abstract

We recently published a review in this journal describing the design, hybridisation and basic data processing required to use gene arrays to investigate vascular biology (Evans etal. Angiogenesis 2003; 6: 93--104). Here, we build on this review by describing a set of powerful and robust methods for the analysis and interpretation of gene array data derived from primary vascular cell cultures. First, we describe the evaluation of transcriptome heterogeneity between primary cultures derived from different individuals, and estimation of the false discovery rate introduced by this heterogeneity and by experimental noise. Then, we discuss the appropriate use of Bayesian t-tests, clustering and independent component analysis to mine the data. We illustrate these principles by analysis of a previously unpublished set of gene array data in which human umbilical vein endothelial cells (HUVEC) cultured in either rich or low-serum media were exposed to vascular endothelial growth factor (VEGF)-A165 or placental growth factor (PlGF)-1131. We have used Affymetrix U95A gene arrays to map the effects of these factors on the HUVEC transcriptome. These experiments followed a paired design and were biologically replicated three times. In addition, one experiment was repeated using serial analysis of gene expression (SAGE). In contrast to some previous studies, we found that VEGF-A and PlGF consistently regulated only small, non-overlapping and culture media-dependant sets of HUVEC transcripts, despite causing significant cell biological changes.

bioinformatics endothelial gene array PlGF VEGF-A