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GG-06 Single cell gene expression studies in lupus patient monocytes reveal novel patterns reflecting disease activity, interferon, and medical treatment
  1. Zhongbo Jin1,
  2. Wei Fan1,
  3. Mark A Jensen 1,
  4. Jessica M Dorschner1,
  5. Danielle M Vsetecka1,
  6. Shreyasee Amin2,
  7. Ashima Makol2,
  8. Floranne Ernste2,
  9. Thomas Osborn2,
  10. Kevin Moder2,
  11. Vaidehi Chowdhary2 and
  12. Timothy B Niewold1
  1. 1Department of Immunology and Division of Rheumatology, Mayo Clinic, MN, USA
  2. 2Division of Rheumatology, Mayo Clinic, MN, USA

Abstract

Background Our previous studies have shown that different cell types from the same sample demonstrate diverse gene expression, and important findings can be masked in mixed cell populations. In this study, we examine single cell gene expression in SLE patient monocytes and determine correlations with clinical features.

Materials and methods CD14++CD16- classical monocytes (CLs) and CD14dimCD16+ non-classical monocytes (NCLs) from SLE patients were purified by magnetic separation. The Fluidigm single cell capture and Rt-PCR system was used to quantify expression of 87 monocyte-related genes.

Results Both CLs and NCLs demonstrated a wide range of expression of IFN-induced genes, and NCL monocytes had higher IFN scores than CL monocytes. Unsupervised hierarchical clustering of the entire data set demonstrated two unique clusters found only in SLE patients, one related to high disease activity and one related to prednisone use. Independent clusters in the SLE patients were related to disease activity (SLEDAI 10 or greater), interferon signature, and medication use, indicating that each of these factors exerted a different impact on monocyte gene expression that could be separately identified. A subset of anti-inflammatory gene set expressing NCLs was inversely correlated with anti-dsDNA titers (rho = −0.77, p = 0.0051) and positively correlated with C3 complement (rho = 0.68, p = 0.030) in the SLE patient group.

Conclusions Using single cell gene expression, we have identified a unique gene expression patterns that reflect the major clinical and immunologic characteristics of the SLE patients which are not evident in bulk cell data, supporting the critical importance of the single cell technique.

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