Droplet single-cell RNA-sequencing (dscRNA-seq) has enabled rapid, massively parallel profiling of transcriptomes. However, assessing differential expression across multiple individuals has been hampered by inefficient sample processing and technical batch effects. Here we describe a computational tool, demuxlet, that harnesses natural genetic variation to determine the sample identity of each cell and detect droplets containing two cells. These capabilities enable multiplexed dscRNA-seq experiments in which cells from unrelated individuals are pooled and captured at higher throughput than in standard workflows. Using simulated data, we show that 50 SNPs per cell are sufficient to assign 97% of singlets and identify 92% of doublets in pools of up to 64 individuals. Given genotyping data for each of 8 pooled samples, demuxlet correctly recovers the sample identity of >99% of singlets and identifies doublets at rates consistent with previous estimates. We apply demuxlet to assess cell type-specific changes in gene expression in 8 pooled lupus patient samples treated with IFN-β and perform eQTL analysis on 23 pooled samples.

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 naïve 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.

SLE is a complex autoimmune disease that results from the interplay of genetics, epigenetics and environmental exposures. DNA methylation is an epigenetic mechanism that regulates gene expression and tissue differentiation. Among all the epigenetic modifications, DNA methylation perturbations have been the most widely studied in SLE. It mediates processes relevant to SLE, including lymphocyte development, X-chromosome inactivation and the suppression of endogenous retroviruses. The establishment of most DNA methylation marks occurs in utero; however, a small percentage of epigenetic marks are dynamic and can change throughout a person’s lifetime and in relation to exposures. In this review, we discuss the current understanding of the biology of DNA methylation and its regulators, the measurement and interpretation of methylation marks, the effects of genetics on DNA methylation and the role of environmental exposures with relevance to SLE. We also summarise research findings associated with SLE disease risk and heterogeneity. The robust finding of hypomethylation of interferon-responsive genes in patients with SLE and new associations beyond interferon-responsive genes such as cell-specific methylation abnormalities are described. We also discuss methylation changes associated with lupus nephritis, autoantibody status and disease activity. Lastly, we explore future research directions, emphasising the need for longitudinal studies, cell tissue and context-specific profiling, as well as integrative approaches. With new technologies, DNA methylation perturbations could be targeted and edited, offering novel therapeutic approaches.

Systemic lupus erythematous (SLE) is a heterogeneous autoimmune disease in which outcomes vary among different racial groups. Here, we aim to identify SLE subgroups within a multiethnic cohort using an unsupervised clustering approach based on the American College of Rheumatology (ACR) classification criteria. We identify three patient clusters that vary according to disease severity. Methylation association analysis identifies a set of 256 differentially methylated CpGs across clusters, including 101 CpGs in genes in the Type I Interferon pathway, and we validate these associations in an external cohort. A cis-methylation quantitative trait loci analysis identifies 744 significant CpG-SNP pairs. The methylation signature is enriched for ethnic-associated CpGs suggesting that genetic and non-genetic factors may drive outcomes and ethnic-associated methylation differences. Our computational approach highlights molecular differences associated with clusters rather than single outcome measures. This work demonstrates the utility of applying integrative methods to address clinical heterogeneity in multifactorial multi-ethnic disease settings.

No abstract