bioRxivImmunology
Longitudinal immune cell profiling in early systemic lupus erythematosus
T. Sasaki, S. Bracero, J. Keegan, L. Chen, Y. Cao, E. Stevens, Y. Qu, G. Wang, J. Nguyen, S. E. Alves, J. A. Lederer, K. H. Costenbader, D. A. Rao
Abstract
ObjectiveTo investigate the immune cell profiling and their longitudinal changes in systemic lupus erythematosus (SLE). MethodsWe employed mass cytometry with three different 38-39 marker panels (Immunophenotyping, T cell/monocyte, and B cell) in cryopreserved peripheral blood mononuclear cells (PBMCs) from nine patients with early SLE, 15 patients with established SLE, and 14 non-inflammatory controls. We used machine learning-driven clustering, FlowSOM (Flow Self-Organizing Maps) and dimensional reduction with tSNE (t-distributed Stochastic Neighbor Embedding) to identify unique cell populations in early and established SLE. For the nine early SLE patients, longitudinal mass cytometry analysis was applied to PBMCs at three time points (at enrollment, six months post-enrollment, and one year post-enrollment). Serum samples were also assayed for 65 cytokines by Luminex multiplex assay, and associations between cell types and cytokines/chemokines assessed. ResultsT peripheral helper cells (Tph cells), T follicular helper cells (Tfh cells) and several Ki67+ proliferating subsets (ICOS+ Ki67+ CD8 T cells, Ki67+ regulatory T cells, CD19int Ki67hi plasmablasts, and Ki67hi PU.1hi monocytes) were increased in early SLE. Longitudinal mass cytometry and multiplex serum cytokine assays of samples from early SLE patients revealed that Tfh cells and CXCL10 decreased at one year post-enrollment. CXCL13 correlated positively with several of the expanded cell populations in early SLE. ConclusionsTwo major helper T cell subsets and unique Ki67+ proliferating immune cell subsets were expanded in the early phase of SLE, and the immunologic features characteristic of early SLE evolved over time.
- organism: human
- assay: flow cytometry
- research focus: cell type annotation
- research focus: differential expression
- approach: machine learning (classical)
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