title: Hematopoietic differentiation: a coordinated dynamical process towards attractor stable states.
abstract: BACKGROUND
  The differentiation process, proceeding from stem cells towards the different committed cell types, can be considered as a trajectory towards an attractor of a dynamical process. This view, taking into consideration the transcriptome and miRNome dynamics considered as a whole, instead of looking at few 'master genes' driving the system, offers a novel perspective on this phenomenon. We investigated the 'differentiation trajectories' of the hematopoietic system considering a genome-wide scenario.
  
  
  RESULTS
  We developed serum-free liquid suspension unilineage cultures of cord blood (CB) CD34+ hematopoietic progenitor cells through erythroid (E), megakaryocytic (MK), granulocytic (G) and monocytic (Mo) pathways. These cultures recapitulate physiological hematopoiesis, allowing the analysis of almost pure unilineage precursors starting from initial differentiation of HPCs until terminal maturation. By analyzing the expression profile of protein coding genes and microRNAs in unilineage CB E, MK, G and Mo cultures, at sequential stages of differentiation and maturation, we observed a coordinated, fully interconnected and scalable character of cell population behaviour in both transcriptome and miRNome spaces reminiscent of an attractor-like dynamics. MiRNome and transcriptome space differed for a still not terminally committed behaviour of microRNAs.
  
  
  CONCLUSIONS
  Consistent with their roles, the transcriptome system can be considered as the state space of a cell population, while the continuously evolving miRNA space corresponds to the tuning system necessary to reach the attractor. The behaviour of miRNA machinery could be of great relevance not only for the promise of reversing the differentiated state but even for tumor biology.
authors.affiliation: Department of Hematology, Oncology and Molecular Medicine Istituto Superiore di Sanità, Rome, Italy.
authors.forename: Nadia
authors.initials: N
authors.lastname: Felli
authors.forename: Luciano
authors.initials: L
authors.lastname: Cianetti
authors.forename: Elvira
authors.initials: E
authors.lastname: Pelosi
authors.forename: Alessandra
authors.initials: A
authors.lastname: Carè
authors.forename: Chang Gong
authors.initials: CG
authors.lastname: Liu
authors.forename: George A
authors.initials: GA
authors.lastname: Calin
authors.forename: Simona
authors.initials: S
authors.lastname: Rossi
authors.forename: Cesare
authors.initials: C
authors.lastname: Peschle
authors.forename: Giovanna
authors.initials: G
authors.lastname: Marziali
authors.forename: Alessandro
authors.initials: A
authors.lastname: Giuliani
mesh_terms: D018952:Antigens, CD34 / Q000378:metabolism; D018929:Cell Culture Techniques; D002454:Cell Differentiation / Q000235:genetics / Q000502:physiology*; D019070:Cell Lineage; D019295:Computational Biology / Q000379:methods; D005312:Fetal Blood / Q000166:cytology; D005434:Flow Cytometry; D020869:Gene Expression Profiling; D016678:Genome / Q000235:genetics*; D006412:Hematopoietic Stem Cells / Q000502:physiology*; D008954:Models, Biological; D020411:Oligonucleotide Array Sequence Analysis; D015398:Signal Transduction / Q000235:genetics / Q000502:physiology*
chemical_list: D018952:Antigens, CD34