SCExecute: custom cell barcode-stratified analyses of scRNA-seq data.

MOTIVATION: In single-cell RNA-sequencing (scRNA-seq) data, stratification of sequencing reads by cellular barcode is necessary to study cell-specific features. However, apart from gene expression, the analyses of cell-specific features are not sufficiently supported by available tools designed for high-throughput sequencing data. RESULTS: We introduce SCExecute, which executes a user-provided command on barcode-stratified, extracted on-the-fly, single-cell binary alignment map (scBAM) files. SCExecute extracts the alignments with each cell barcode from aligned, pooled single-cell sequencing data. Simple commands, monolithic programs, multi-command shell scripts or complex shell-based pipelines are then executed on each scBAM file. scBAM files can be restricted to specific barcodes and/or genomic regions of interest. We demonstrate SCExecute with two popular variant callers-GATK and Strelka2-executed in shell-scripts together with commands for BAM file manipulation and variant filtering, to detect single-cell-specific expressed single nucleotide variants from droplet scRNA-seq data (10X Genomics Chromium System).In conclusion, SCExecute facilitates custom cell-level analyses on barcoded scRNA-seq data using currently available tools and provides an effective solution for studying low (cellular) frequency transcriptome features. AVAILABILITY AND IMPLEMENTATION: SCExecute is implemented in Python3 using the Pysam package and distributed for Linux, MacOS and Python environments from https://horvathlab.github.io/NGS/SCExecute. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Extracellular vesicles carrying HIV-1 Nef induce long-term hyperreactivity of myeloid cells.

A possible explanation for chronic inflammation in HIV-infected individuals treated with anti-retroviral therapy is hyperreactivity of myeloid cells due to a phenomenon called "trained immunity." Here, we demonstrate that human monocyte-derived macrophages originating from monocytes initially treated with extracellular vesicles containing HIV-1 protein Nef (exNef), but differentiating in the absence of exNef, release increased levels of pro-inflammatory cytokines after lipopolysaccharide stimulation. This effect is associated with chromatin changes at the genes involved in inflammation and cholesterol metabolism pathways and upregulation of the lipid rafts and is blocked by methyl-ß-cyclodextrin, statin, and an inhibitor of the lipid raft-associated receptor IGF1R. Bone-marrow-derived macrophages from exNef-injected mice, as well as from mice transplanted with bone marrow from exNef-injected animals, produce elevated levels of tumor necrosis factor α (TNF-α) upon stimulation. These phenomena are consistent with exNef-induced trained immunity that may contribute to persistent inflammation and associated co-morbidities in HIV-infected individuals with undetectable HIV load.

SCReadCounts: estimation of cell-level SNVs expression from scRNA-seq data.

BACKGROUND: Recent studies have demonstrated the utility of scRNA-seq SNVs to distinguish tumor from normal cells, characterize intra-tumoral heterogeneity, and define mutation-associated expression signatures. In addition to cancer studies, SNVs from single cells have been useful in studies of transcriptional burst kinetics, allelic expression, chromosome X inactivation, ploidy estimations, and haplotype inference. RESULTS: To aid these types of studies, we have developed a tool, SCReadCounts, for cell-level tabulation of the sequencing read counts bearing SNV reference and variant alleles from barcoded scRNA-seq alignments. Provided genomic loci and expected alleles, SCReadCounts generates cell-SNV matrices with the absolute variant- and reference-harboring read counts, as well as cell-SNV matrices of expressed Variant Allele Fraction (VAFRNA) suitable for a variety of downstream applications. We demonstrate three different SCReadCounts applications on 59,884 cells from seven neuroblastoma samples: (1) estimation of cell-level expression of known somatic mutations and RNA-editing sites, (2) estimation of cell- level allele expression of biallelic SNVs, and (3) a discovery mode assessment of the reference and each of the three alternative nucleotides at genomic positions of interest that does not require prior SNV information. For the later, we applied SCReadCounts on the coding regions of KRAS, where it identified known and novel somatic mutations in a low-to-moderate proportion of cells. The SCReadCounts read counts module is benchmarked against the analogous modules of GATK and Samtools. SCReadCounts is freely available ( https://github.com/HorvathLab/NGS ) as 64-bit self-contained binary distributions for Linux and MacOS, in addition to Python source. CONCLUSIONS: SCReadCounts supplies a fast and efficient solution for estimation of cell-level SNV expression from scRNA-seq data. SCReadCounts enables distinguishing cells with monoallelic reference expression from those with no gene expression and is applicable to assess SNVs present in only a small proportion of the cells, such as somatic mutations in cancer.

scReQTL: an approach to correlate SNVs to gene expression from individual scRNA-seq datasets.

BACKGROUND: Recently, pioneering expression quantitative trait loci (eQTL) studies on single cell RNA sequencing (scRNA-seq) data have revealed new and cell-specific regulatory single nucleotide variants (SNVs). Here, we present an alternative QTL-related approach applicable to transcribed SNV loci from scRNA-seq data: scReQTL. ScReQTL uses Variant Allele Fraction (VAFRNA) at expressed biallelic loci, and corelates it to gene expression from the corresponding cell. RESULTS: Our approach employs the advantage that, when estimated from multiple cells, VAFRNA can be used to assess effects of SNVs in a single sample or individual. In this setting scReQTL operates in the context of identical genotypes, where it is likely to capture RNA-mediated genetic interactions with cell-specific and transient effects. Applying scReQTL on scRNA-seq data generated on the 10 × Genomics Chromium platform using 26,640 mesenchymal cells derived from adipose tissue obtained from three healthy female donors, we identified 1272 unique scReQTLs. ScReQTLs common between individuals or cell types were consistent in terms of the directionality of the relationship and the effect size. Comparative assessment with eQTLs from bulk sequencing data showed that scReQTL analysis identifies a distinct set of SNV-gene correlations, that are substantially enriched in known gene-gene interactions and significant genome-wide association studies (GWAS) loci. CONCLUSION: ScReQTL is relevant to the rapidly growing source of scRNA-seq data and can be applied to outline SNVs potentially contributing to cell type-specific and/or dynamic genetic interactions from an individual scRNA-seq dataset. AVAILABILITY: https://github.com/HorvathLab/NGS/tree/master/scReQTL.

Evaluation of an angiotensin Type 1 receptor blocker on the reconsolidation of fear memory.

Inhibition of the angiotensin type 1 receptor (AT1R) has been shown to decrease fear responses in both humans and rodents. These effects are attributed to modulation of extinction learning, however the contribution of AT1R to alternative memory processes remains unclear. Using classic Pavlovian conditioning combined with radiotelemetry and whole-genome RNA sequencing, we evaluated the effects of the AT1R antagonist losartan on fear memory reconsolidation. Following the retrieval of conditioned auditory fear memory, animals were given a single intraperitoneal injection of losartan or saline. In response to the conditioned stimulus (CS), losartan-treated animals exhibited significantly less freezing at 24 h and 1 week; an effect that was dependent upon memory reactivation and independent of conditioned cardiovascular reactivity. Using an unbiased whole-genome RNA sequencing approach, transcriptomic analysis of the basolateral amygdala (BLA) identified losartan-dependent differences in gene expression during the reconsolidation phase. These findings demonstrate that post-retrieval losartan modifies behavioral and transcriptomic markers of conditioned fear memory, supporting an important regulatory role for this receptor in reconsolidation and as a potential pharmacotherapeutic target for maladaptive fear disorders such as PTSD.

ReQTL: identifying correlations between expressed SNVs and gene expression using RNA-sequencing data.

MOTIVATION: By testing for associations between DNA genotypes and gene expression levels, expression quantitative trait locus (eQTL) analyses have been instrumental in understanding how thousands of single nucleotide variants (SNVs) may affect gene expression. As compared to DNA genotypes, RNA genetic variation represents a phenotypic trait that reflects the actual allele content of the studied system. RNA genetic variation at expressed SNV loci can be estimated using the proportion of alleles bearing the variant nucleotide (variant allele fraction, VAFRNA). VAFRNA is a continuous measure which allows for precise allele quantitation in loci where the RNA alleles do not scale with the genotype count. We describe a method to correlate VAFRNA with gene expression and assess its ability to identify genetically regulated expression solely from RNA-sequencing (RNA-seq) datasets. RESULTS: We introduce ReQTL, an eQTL modification which substitutes the DNA allele count for the variant allele fraction at expressed SNV loci in the transcriptome (VAFRNA). We exemplify the method on sets of RNA-seq data from human tissues obtained though the Genotype-Tissue Expression (GTEx) project and demonstrate that ReQTL analyses are computationally feasible and can identify a subset of expressed eQTL loci. AVAILABILITY AND IMPLEMENTATION: A toolkit to perform ReQTL analyses is available at https://github.com/HorvathLab/ReQTL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.