Angiogenesis

, Volume 7, Issue 2, pp 143–156

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

Authors

  • Jonathan Schoenfeld
    • Department of PathologyCambridge University
  • Khashayar Lessan
    • Department of PathologyCambridge University
  • Nicola Johnson
    • Department of PathologyCambridge University
  • D. Charnock-jones
    • Department of PathologyCambridge University
  • Amanda Evans
    • Department of PathologyCambridge University
  • Ekaterini Vourvouhaki
    • Department of PathologyCambridge University
  • Laurie Scott
    • UK MRC Hinxton Genome Mapping Project Resource Centre
  • Richard Stephens
    • UK MRC Hinxton Genome Mapping Project Resource Centre
  • Tom Freeman
    • UK MRC Hinxton Genome Mapping Project Resource Centre
  • Samir Saidi
    • Department of PathologyCambridge University
  • Brian Tom
    • Medical Research Council Biostatistics Unit
  • Gareth Weston
    • Department of Obstetrics and Gynaecology, Monash Medical CentreCentre for Women's Health Research, Monash University
  • Peter Rogers
    • Department of Obstetrics and Gynaecology, Monash Medical CentreCentre for Women's Health Research, Monash University
  • Stephen Smith
    • Department of PathologyCambridge University
  • Cristin Print
    • Department of PathologyCambridge University
Article

DOI: 10.1007/s10456-004-1677-0

Cite this article as:
Schoenfeld, J., Lessan, K., Johnson, N. et al. Angiogenesis (2004) 7: 143. doi:10.1007/s10456-004-1677-0

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 We illustrate these principles by analysis of a previously unpublished set of gene array data in which human umbilical vein endothelial cells (HUVEC) cultured in either rich or low-serum media were exposed to vascular endothelial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.

bioinformaticsendothelialgene arrayPlGFVEGF-A

Copyright information

© Kluwer Academic Publishers 2004