title: Characterization of cytokine interactions by flow cytometry and factorial analysis.
abstract: BACKGROUND
  Multiple cytokines are required for the growth and development of hematopoietic cells. The effect of many cytokines depends on the activity of other signaling pathways. These interactions are quantified using factorial experimental design and analysis.
  
  
  METHODS
  Human umbilical cord blood (HUCB) CD34+ cells were cultured in fully defined media containing various combinations of recombinant cytokines as defined by resolution IV factorial (2(7-3)(IV)) or full factorial (2(4)) design experiments. The cytokines studied were stem cell factor (SCF), interleukin (IL)-3, megakaryocyte growth and development factor (MGDF), granulocyte-colony stimulating factor (G-CSF), Flt-3 ligand, IL-6, IL-11, and erythropoietin (EPO). In vitro cell divisions were tracked by staining CD34+ cells with 5-(and-6)-carboxyfluorescein diacetate, succinimidyl ester, followed by flow cytometric analysis at 4 days of culture. In separate experiments, lineage commitment and differentiation were determined at 7 days by immunophenotype.
  
  
  RESULTS
  In addition to the main effects of single cytokines, cytokine interactions were identified. There was a negative interaction between IL-3 and MGDF that resulted in a less than additive effect of these factors on erythroid and megakaryocytic development. The effect of Flt-3 ligand and SCF factor on CD34+ cell production was also less than additive, although the response to both cytokines was greater than single cytokines. The only positive interaction that was identified was between EPO and SCF, which resulted in the synergistic production of erythroid cells.
  
  
  CONCLUSIONS
  Factorial analysis provides a powerful methodology to study the integration of multiple signals at the cellular and molecular level.
authors: Children's Cancer Institute Australia for Medical Research, Sydney Children's Hospital, Randwick, NSW, Australia.
authors: J
authors: J
authors: Case
authors: A
authors: A
authors: Rice
authors: J
authors: J
authors: Wood
authors: L
authors: L
authors: Gaudry
authors: M
authors: M
authors: Vowels
authors: R E
authors: RE
authors: Nordon
mesh_terms: D000704:Analysis of Variance; D018952:Antigens, CD34 / Q000032:analysis; D002454:Cell Differentiation / Q000187:drug effects; D002455:Cell Division / Q000187:drug effects; D019070:Cell Lineage / Q000187:drug effects; D002469:Cell Separation; D002470:Cell Survival / Q000187:drug effects; D016207:Cytokines / Q000494:pharmacology*; D004347:Drug Interactions; D004912:Erythrocytes / Q000166:cytology / Q000187:drug effects / Q000378:metabolism; D005312:Fetal Blood / Q000166:cytology / Q000187:drug effects / Q000378:metabolism; D005434:Flow Cytometry / Q000379:methods* / Q000706:statistics & numerical data; D006098:Granulocytes / Q000166:cytology / Q000187:drug effects / Q000378:metabolism; D006410:Hematopoiesis / Q000187:drug effects; D016298:Hematopoietic Cell Growth Factors / Q000378:metabolism / Q000494:pharmacology; D006801:Humans; D016130:Immunophenotyping; D008533:Megakaryocytes / Q000166:cytology / Q000187:drug effects / Q000378:metabolism; D015398:Signal Transduction / Q000187:drug effects*; D013194:Staining and Labeling
chemical_list: D018952:Antigens, CD34; D016207:Cytokines; D016298:Hematopoietic Cell Growth Factors