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Novel expression signatures identified by transcriptional analysis of separated leukocyte subsets in SLE and vasculitis
  1. Paul A Lyons1,*,
  2. Eoin F McKinney1,
  3. Tim F Rayner1,
  4. Alexander Hatton1,
  5. Hayley B Woffendin1,
  6. Maria Koukoulaki1,
  7. Thomas C Freeman2,
  8. David RW Jayne3,
  9. Afzal N Chaudhry1,
  10. Kenneth GC Smith1
  1. 1 University of Cambridge, United Kingdom;
  2. 2 Roslin Institute, United Kingdom;
  3. 3 Addenbrooke's Hospital, United Kingdom
  1. Correspondence to: Paul Lyons, University of Cambridge, Addenbrookes Hospital, Hills Road, Cambridge, CB2 0XY, United Kingdom; pal34{at}


Objectives: To optimize a strategy for identifying gene expression signatures differentiating SLE and anti-neutrophil cytoplasmic antibody-associated vasculitis that provide insight into the pathogenesis and identify biomarkers.

Methods: Forty four vasculitis patients, 13 SLE patients and 25 age and sex-matched controls were enrolled. CD4 and CD8 T cells, B cells, monocytes and neutrophils were isolated from each patient and, together with unseparated peripheral blood mononuclear cells (PBMC), were hybridised to spotted oligonucleotide microarrays.

Results: Using expression data obtained from purified cells we identified a substantial number of differentially expressed genes that were not detectable in the analysis of PBMC. Analysis of purified T cells identified an SLE-associated, CD4 T cell signature consistent with type 1 interferon signalling driving the generation and survival of tissue homing T cells and thereby contributing to disease pathogenesis. Moreover, hierarchical clustering using expression data from purified monocytes provided significantly improved discrimination between the patient groups than that obtained using PBMC data, presumably because the differentially expressed genes reflect genuine differences in processes underlying disease pathogenesis.

Conclusion: The analysis of leucocyte subsets enabled the identification of gene signatures of both pathogenic relevance and with better disease discrimination than those identified in PBMCs. Thus, this approach provides substantial advantages in the search for diagnostic and prognostic biomarkers in autoimmune disease.

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