Back

Single-cell multi-omics analysis reveals IFN-driven alterations in T lymphocytes and Natural Killer cells in systemic lupus erythematosus

Trzupek, D.; Lee, M.; Hamey, F.; Wicker, L. S.; Todd, J. A.; Ferreira, R. C.

2021-06-09 genetic and genomic medicine
10.1101/2021.04.27.21256106 medRxiv
Show abstract

BackgroundThe characterisation of the peripheral immune system in the autoimmune disease systemic lupus erythematosus (SLE) at the single-cell level has been limited by the reduced sensitivity of current whole-transcriptomic technologies. Here we employ a targeted single-cell multi-omics approach, combining protein and mRNA quantification, to generate a high-resolution map of the T lymphocyte and natural killer (NK) cell populations in blood from SLE patients. MethodsWe designed a custom panel to quantify the transcription of 534 genes in parallel with the expression of 51 surface protein targets using the BD Rhapsody AbSeq single-cell system. We applied this technology to profile 20,656 T and NK cells isolated from peripheral blood from an SLE patient with a type I interferon (IFN)-induced gene expression signature (IFNhi), and an age- and sex- matched IFNlow SLE patient and healthy donor. ResultsWe confirmed the presence of a rare cytotoxic CD4+ T cell (CTL) subset, which was exclusively present in the IFNhi patient. Furthermore, we identified additional alterations consistent with increased immune activation in this patient, most notably a shift towards terminally differentiated CD57+ CD8+ T cell and CD16+ NKdim phenotypes, and the presence of a subset of recently-activated naive CD4+ T cells. ConclusionsOur results identify IFN-driven changes in the composition and phenotype of T and NK cells that are consistent with a systemic immune activation within the IFNhi patient, and underscore the added resolving power of this multi-omics approach to identify rare immune subsets. Consequently, we were able to find evidence for novel cellular peripheral biomarkers of SLE disease activity, including a subpopulation of CD57+ CD4+ CTLs.

Matching journals

The top 10 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.