A scalable SCENIC workflow for single-cell gene regulatory network analysis.

This protocol explains how to perform a fast SCENIC analysis alongside standard best practices steps on single-cell RNA-sequencing data using software containers and Nextflow pipelines. SCENIC reconstructs regulons (i.e., transcription factors and their target genes) assesses the activity of these discovered regulons in individual cells and uses these cellular activity patterns to find meaningful clusters of cells. Here we present an improved version of SCENIC with several advances. SCENIC has been refactored and reimplemented in Python (pySCENIC), resulting in a tenfold increase in speed, and has been packaged into containers for ease of use. It is now also possible to use epigenomic track databases, as well as motifs, to refine regulons. In this protocol, we explain the different steps of SCENIC: the workflow starts from the count matrix depicting the gene abundances for all cells and consists of three stages. First, coexpression modules are inferred using a regression per-target approach (GRNBoost2). Next, the indirect targets are pruned from these modules using cis-regulatory motif discovery (cisTarget). Lastly, the activity of these regulons is quantified via an enrichment score for the regulon's target genes (AUCell). Nonlinear projection methods can be used to display visual groupings of cells based on the cellular activity patterns of these regulons. The results can be exported as a loom file and visualized in the SCope web application. This protocol is illustrated on two use cases: a peripheral blood mononuclear cell data set and a panel of single-cell RNA-sequencing cancer experiments. For a data set of 10,000 genes and 50,000 cells, the pipeline runs in <2 h.

Contribution of rare and predicted pathogenic gene variants to childhood-onset lupus: a large, genetic panel analysis of British and French cohorts.

BACKGROUND: Systemic lupus erythematosus (SLE) is a rare immunological disorder and genetic factors are considered important in its causation. Monogenic lupus has been associated with around 30 genotypes in humans and 60 in mice, while genome-wide association studies have identified more than 90 risk loci. We aimed to analyse the contribution of rare and predicted pathogenic gene variants in a population of unselected cases of childhood-onset SLE. METHODS: For this genetic panel analysis we designed a next-generation sequencing panel comprising 147 genes, including all known lupus-causing genes in humans, and potentially lupus-causing genes identified through GWAS and animal models. We screened 117 probands fulfilling American College of Rheumatology (ACR) criteria for SLE, ascertained through British and French cohorts of childhood-onset SLE, and compared these data with those of 791 ethnically matched controls from the 1000 Genomes Project and 574 controls from the FREX Consortium. FINDINGS: After filtering, mendelian genotypes were confirmed in eight probands, involving variants in C1QA, C1QC, C2, DNASE1L3, and IKZF1. Seven additional patients carried heterozygous variants in complement or type I interferon-associated autosomal recessive genes, with decreased concentrations of the encoded proteins C3 and C9 recorded in two patients. Rare variants that were predicted to be damaging were significantly enriched in the childhood-onset SLE cohort compared with controls; 25% of SLE probands versus 5% of controls were identified to harbour at least one rare, predicted damaging variant (p=2·98 × 10-11). Inborn errors of immunity were estimated to account for 7% of cases of childhood-onset SLE, with defects in innate immunity representing the main monogenic contribution. INTERPRETATION: An accumulation of rare variants that are predicted to be damaging in SLE-associated genes might contribute to disease expression and clinical heterogeneity. FUNDING: European Research Council.