Clinical and Translational Medicine (Dec 2014)

Gene expression profiling of immunomagnetically separated cells directly from stabilized whole blood for multicenter clinical trials

  • Martin Letzkus,
  • Evert Luesink,
  • Sandrine Starck‐Schwertz,
  • Marc Bigaud,
  • Fareed Mirza,
  • Nicole Hartmann,
  • Bernhard Gerstmayer,
  • Uwe Janssen,
  • Andreas Scherer,
  • Martin M Schumacher,
  • Aurelie Verles,
  • Alessandra Vitaliti,
  • Nanguneri Nirmala,
  • Keith J Johnson,
  • Frank Staedtler

DOI
https://doi.org/10.1186/s40169-014-0036-z
Journal volume & issue
Vol. 3, no. 1
pp. n/a – n/a

Abstract

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Abstract Background Clinically useful biomarkers for patient stratification and monitoring of disease progression and drug response are in big demand in drug development and for addressing potential safety concerns. Many diseases influence the frequency and phenotype of cells found in the peripheral blood and the transcriptome of blood cells. Changes in cell type composition influence whole blood gene expression analysis results and thus the discovery of true transcript level changes remains a challenge. We propose a robust and reproducible procedure, which includes whole transcriptome gene expression profiling of major subsets of immune cell cells directly sorted from whole blood. Methods Target cells were enriched using magnetic microbeads and an autoMACS® Pro Separator (Miltenyi Biotec). Flow cytometric analysis for purity was performed before and after magnetic cell sorting. Total RNA was hybridized on HGU133 Plus 2.0 expression microarrays (Affymetrix, USA). CEL files signal intensity values were condensed using RMA and a custom CDF file (EntrezGene‐based). Results Positive selection by use of MACS® Technology coupled to transcriptomics was assessed for eight different peripheral blood cell types, CD14+ monocytes, CD3+, CD4+, or CD8+ T cells, CD15+ granulocytes, CD19+ B cells, CD56+ NK cells, and CD45+ pan leukocytes. RNA quality from enriched cells was above a RIN of eight. GeneChip analysis confirmed cell type specific transcriptome profiles. Storing whole blood collected in an EDTA Vacutainer® tube at 4°C followed by MACS does not activate sorted cells. Gene expression analysis supports cell enrichment measurements by MACS. Conclusions The proposed workflow generates reproducible cell‐type specific transcriptome data which can be translated to clinical settings and used to identify clinically relevant gene expression biomarkers from whole blood samples. This procedure enables the integration of transcriptomics of relevant immune cell subsets sorted directly from whole blood in clinical trial protocols.

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