title: Analysis of differentiation trees using transcriptome data : application to hematopoiesis
creator: Roels, Frederik
subject: 570
subject: 570 Life sciences
description: Cellular differentiation is a complicated and highly important system in all multicellular organisms. The remarkable aspect about differentiation is that the multitude of different and highly specialised cell types are all descendant from one cell, the zygote. Not surprisingly differentiation is a highly regulated process. A complicated interplay of environmental signals and intracellular regulation defines the ultimate mature state of all cell types. In this work a method was developed that can analyse differentiation trees computationally. The development of the method was guided by three questions. Do microarrays contain enough information to retrace steps in differentiation? Can this information be used to validate proposed differentiation paths? Can this information be used to compare differentiation in different contexts? The method starts from microarray data and uses a combination of methods to identify the most likely differentiation tree out of all possibilities. The method has two components, one component identifies the most likely conformation using a scoring system. The other component identifies the most likely root node using a comparison system. The conformation scoring system relies on transcriptional changes in previously defined subnetworks, all possible differentiation conformations are tested in a manner similar to maximum parsimony. Maximum parsimony is used in molecular phylogeny to score possible evolutionary trees, a problem similar to the one tackled in this work. Root node identification is done using a value calculated based on within cell type gene expression correlations, high values indicate the cell is less mature. The method was tested on microarray data from the myeloid lineage of hematopoiesis. The datasets are comprised of expression data taken from four different cell types: Hematopoietic Stem Cells, Common Myeloid Progenitors, Granulocyte Monocyte Progenitors and Megakaryocyte Erythrocyte Progenitors. Data was gathered from healthy donors and patients suffering Chronic Myeloid Leukemia and Multiple Myeloma respectively. The method performed well, in most cases the correct differentiation tree could be identified. This indicates that there is indeed enough information present in microarray data to retrace differentiation. Interesting results where seen for the root node identification component. When analysing the dataset taken from patients with CML, the method predicted known differences in stemness in that particular cancer.
date: 2010
type: Dissertation
type: info:eu-repo/semantics/doctoralThesis
type: NonPeerReviewed
format: application/pdf
identifier: https://archiv.ub.uni-heidelberg.de/volltextserverhttps://archiv.ub.uni-heidelberg.de/volltextserver/11080/1/PhDthesis_Roels.pdf
identifier: DOI:10.11588/heidok.00011080
identifier: urn:nbn:de:bsz:16-opus-110807
identifier:   Roels, Frederik  (2010) Analysis of differentiation trees using transcriptome data : application to hematopoiesis.  [Dissertation]     
relation: https://archiv.ub.uni-heidelberg.de/volltextserver/11080/
rights: info:eu-repo/semantics/openAccess
rights: http://archiv.ub.uni-heidelberg.de/volltextserver/help/license_urhg.html
language: eng