Characterization of Unknown Adult Stem Cell Samples by Large Scale Data Integration and Artificial Neural Networks
G. Bidaut1,2 and C.J. Stoeckert Jr.1
1Center for Bioinformatics, Department of Genetics, University of Pennsylvania School of Medicine, 423 Guardian Drive, Philadelphia, PA 19104, USA; 2Centre de Recherche en Cancérologie de Marseille, INSERM U891 - Institut Paoli-Calmettes Université de la Méditerranée, 27 Boulevard Leï Roure, 13009 Marseille, France
Email: firstname.lastname@example.org, email@example.com
Pacific Symposium on Biocomputing 14:356-367(2009)
Stem cells represent not only a potential source of treatment for degenerative diseases but can also shed light on developmental biology and cancer. It is believed that stem cells differentiation and fate is triggered by a common genetic program that endows those cells with the ability to differentiate into specialized progenitors and fully differentiated cells. To extract the stemness signature of several cells types at the transcription level, we integrated heterogeneous datasets (microarray experiments) performed in different adult and embryonic tissues (liver, blood, bone, prostate and stomach in Homo sapiens and Mus musculus). Data were integrated by generalization of the hematopoietic stem cell hierarchy and by homology between mouse and human. The variation-filtered and integrated gene expression dataset was fed to a single-layered neural network to create a classifier to (i) extract the stemness signature and (ii) characterize unknown stem cell tissue samples by attribution of a stem cell differentiation stage. We were able to characterize mouse stomach progenitor and human prostate progenitor samples and isolate gene signatures playing a fundamental role for every level of the generalized stem cell hierarchy.
[Full-Text PDF] [PSB Home Page]