Letzkus, Martin, Luesink, Evert, Starck-Schwertz, Sandrine, Bigaud, Marc, Mirza, Fareed, Hartmann, Nicole, Gerstmayer, Bernhard, Janssen, Uwe, Scherer, Andreas, Schumacher, Martin, Verles, Aurelie, Vitaliti Garami, Alessandra, Nirmala, Nanguneri, Johnson, Keith J and Staedtler, Frank (2014) Gene expression profiling of immunomagnetically separated cells directly from stabilized whole blood for multicenter clinical trials. Journal of Clinical and Translational Medicine, 3. p. 36. ISSN 2001-1326

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. Minimizing the number of intermediate technical steps of cell sample preparation will increase reproducibility of results. 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: Fresh whole blood samples were obtained from consented healthy donors preserved either in PAXgene Blood RNA tubes or used for cell sorting. Target cells were enriched using magnetic microbeads and an autoMACS Pro Separator (Miltenyi). Cells were enumerated prior to magnetic cell sorting using a Siemens ADVIA® 120 Hematology System. Flow cytometric analysis for purity was performed before and after the 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 magnetic-activated cell separation (MACS) 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, CD45+ pan leucocytes. RNA quality from enriched cells was above 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.
Conclusion: The proposed workflow generates reproducible cell-type specific transcriptome data for CD14+ -, CD3+ -, CD4+ -, CD8+ -, CD15+ -, CD19+ -, CD56+ -, and CD45+- cells, which can be translated to clinical settings and used to generate clinically relevant gene expression biomarkers from whole blood samples. This procedure facilitates the integration of transcriptomics of relevant immune cell subsets sorted directly from whole blood in clinical trial protocols.

Item Type:	Article
Keywords:	Cell sorting, transcriptomics, clinical
Date Deposited:	27 Apr 2016 23:45
Last Modified:	04 Jul 2016 23:45
URI:	https://oak.novartis.com/id/eprint/20659