4.8 Article

Single-cell RNA-seq reveals cell type-specific molecular and genetic associations to lupus

期刊

SCIENCE
卷 376, 期 6589, 页码 153-+

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AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.1126/science.abf1970

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资金

  1. NIH [P30AR070155, R01AR071522, U01HG012192, R21AI133337, K25HL121295, U01HG009080, R01HG006399, R01CA227237, R03DE025665, R01CA194511, R01CA223484, T32HG002536]
  2. CDC [U01DP005120]
  3. Lupus Research Alliance
  4. DoD [W81XWH-16-2-0018]
  5. Manton Foundation
  6. Klarman Cell Observatory
  7. Howard Hughes Medical Institute
  8. NSF [GRFP 1650113]

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By using mux-seq technology, we conducted extensive single-cell RNA sequencing on peripheral blood cells from SLE patients and healthy individuals. We found increased expression of type 1 interferon-stimulated genes in monocytes, reduced naive CD4(+) T cells, and expanded cytotoxic CD8(+) T cells in SLE patients. The cell type-specific expression features could predict the case-control status and classify patients into two molecular subtypes. Furthermore, we linked SLE-associated genetic variants to cell type-specific expression using genotyping data.
Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease. Knowledge of circulating immune cell types and states associated with SLE remains incomplete. We profiled more than 1.2 million peripheral blood mononuclear cells (162 cases, 99 controls) with multiplexed single-cell RNA sequencing (mux-seq). Cases exhibited elevated expression of type 1 interferon-stimulated genes (ISGs) in monocytes, reduction of naive CD4(+) T cells that correlated with monocyte ISG expression, and expansion of repertoire-restricted cytotoxic GZMH(+) CD8(+) T cells. Cell type-specific expression features predicted case-control status and stratified patients into two molecular subtypes. We integrated dense genotyping data to map cell type-specific cis-expression quantitative trait loci and to link SLE-associated variants to cell type-specific expression. These results demonstrate mux-seq as a systematic approach to characterize cellular composition, identify transcriptional signatures, and annotate genetic variants associated with SLE.

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