title: Using digital RNA counting and flow cytometry to compare mRNA with protein expression in acute leukemias.
abstract: BACKGROUND
  The diagnosis of malignant hematologic diseases has become increasingly complex during the last decade. It is based on the interpretation of results from different laboratory analyses, which range from microscopy to gene expression profiling. Recently, a method for the analysis of RNA phenotypes has been developed, the nCounter technology (Nanostring® Technologies), which allows for simultaneous quantification of hundreds of RNA molecules in biological samples. We evaluated this technique in a Swiss multi-center study on eighty-six samples from acute leukemia patients.
  
  
  METHODS
  mRNA and protein profiles were established for normal peripheral blood and bone marrow samples. Signal intensities of the various tested antigens with surface expression were similar to those found in previously performed Affymetrix microarray analyses. Acute leukemia samples were analyzed for a set of twenty-two validated antigens and the Pearson Correlation Coefficient for nCounter and flow cytometry results was calculated.
  
  
  RESULTS
  Highly significant values between 0.40 and 0.97 were found for the twenty-two antigens tested. A second correlation analysis performed on a per sample basis resulted in concordant results between flow cytometry and nCounter in 44-100% of the antigens tested (mean = 76%), depending on the number of blasts present in a sample, the homogeneity of the blast population, and the type of leukemia (AML or ALL).
  
  
  CONCLUSIONS
  The nCounter technology allows for fast and easy depiction of a mRNA profile from hematologic samples. This technology has the potential to become a valuable tool for the diagnosis of acute leukemias, in addition to multi-color flow cytometry.
authors.affiliation: Kantonsspital Aarau, Aarau, Switzerland.
authors.forename: Paula
authors.initials: P
authors.lastname: Fernandez
authors.forename: Max
authors.initials: M
authors.lastname: Solenthaler
authors.forename: Olivier
authors.initials: O
authors.lastname: Spertini
authors.forename: Stephane
authors.initials: S
authors.lastname: Quarroz
authors.forename: Alicia
authors.initials: A
authors.lastname: Rovo
authors.forename: Pierre-Yves
authors.initials: PY
authors.lastname: Lovey
authors.forename: Leda
authors.initials: L
authors.lastname: Leoncini
authors.forename: Sylvie
authors.initials: S
authors.lastname: Ruault-Jungblut
authors.forename: Mathilde
authors.initials: M
authors.lastname: D'Asaro
authors.forename: Olivier
authors.initials: O
authors.lastname: Schaad
authors.forename: Mylène
authors.initials: M
authors.lastname: Docquier
authors.forename: Patrick
authors.initials: P
authors.lastname: Descombes
authors.forename: Thomas
authors.initials: T
authors.lastname: Matthes
mesh_terms: D015703:Antigens, CD / Q000235:genetics / Q000378:metabolism; D001773:Blood Cells / Q000378:metabolism; D001854:Bone Marrow Cells / Q000378:metabolism; D005434:Flow Cytometry / Q000379:methods; D023281:Genomics / Q000379:methods; D006801:Humans; D016130:Immunophenotyping; D007938:Leukemia / Q000175:diagnosis / Q000235:genetics* / Q000378:metabolism*; D020543:Proteome; D040901:Proteomics / Q000379:methods; D059467:Transcriptome
chemical_list: D015703:Antigens, CD; D020543:Proteome