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

With the recent advances in single-cell RNA-sequencing (scRNA-seq) technologies, estimation of allele expression from single cells is becoming increasingly reliable. Allele expression is both quantitative and dynamic and is an essential component of the genomic interactome. Here, we systematically estimate allele expression from heterozygous single nucleotide variant (SNV) loci using scRNA-seq data generated on the 10x Genomics platform. We include in the analysis 26,640 human adipose-derived mesenchymal stem cells (from three healthy donors), with an average sequencing reads over 120K/cell (more than 4 billion scRNA-seq reads total). High quality SNV calls assessed in our study contained approximately 15% exonic and >50% intronic loci. To analyze the allele expression, we estimate the expressed Variant Allele Fraction (VAFRNA) from SNV-aware alignments and analyze its variance and distribution (mono-and bi-allelic) at different cutoffs for required minimal number of sequencing reads. Our analysis shows that when assessing SNV loci covered by a minimum of 3 unique sequencing reads, over 50% of the heterozygous SNVs show biallelic expression, while at minimum of 10 reads, nearly 90% of the SNVs are bi-allelic. Consistent with single cell studies on RNA velocity and models of transcriptional burst kinetics, we observe a substantially higher rate of monoallelic expression among intronic SNVs, signifying the usefulness of scVAFRNA to assess dynamic cellular processes. Our analysis demonstrates the feasibility of scVAFRNA estimation from current scRNA-seq datasets and shows that the 3'-based library generation protocol of 10x Genomics scRNA-seq data can be highly informative in SNV-based analyses.

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.

Imbalanced expression of somatic alleles in cancer can suggest functional and selective features, and can therefore indicate possible driving potential of the underlying genetic variants. To explore the correlation between allele frequency of somatic variants and total gene expression of their harboring gene, we used the unique data set of matched tumor and normal RNA and DNA sequencing data of 5523 distinct single nucleotide variants in 381 individuals across 10 cancer types obtained from The Cancer Genome Atlas (TCGA). We analyzed the allele frequency in the context of the variant and gene functional features and linked it with changes in the total gene expression. We documented higher allele frequency of somatic variants in cancer-implicated genes (Cancer Gene Census, CGC). Furthermore, somatic alleles bearing premature terminating variants (PTVs), when positioned in CGC genes, appeared to be less frequently degraded via nonsense-mediated mRNA decay, indicating possible favoring of truncated proteins by the tumor transcriptome. Among the genes with multiple PTVs with high allele frequency, ARID1, TP53 and NSD1 were known key cancer genes. All together, our analyses suggest that high allele frequency of tumor somatic variants can indicate driving functionality and can serve to identify potential cancer-implicated genes.

We introduce RNA2DNAlign, a computational framework for quantitative assessment of allele counts across paired RNA and DNA sequencing datasets. RNA2DNAlign is based on quantitation of the relative abundance of variant and reference read counts, followed by binomial tests for genotype and allelic status at SNV positions between compatible sequences. RNA2DNAlign detects positions with differential allele distribution, suggesting asymmetries due to regulatory/structural events. Based on the type of asymmetry, RNA2DNAlign outlines positions likely to be implicated in RNA editing, allele-specific expression or loss, somatic mutagenesis or loss-of-heterozygosity (the first three also in a tumor-specific setting). We applied RNA2DNAlign on 360 matching normal and tumor exomes and transcriptomes from 90 breast cancer patients from TCGA. Under high-confidence settings, RNA2DNAlign identified 2038 distinct SNV sites associated with one of the aforementioned asymetries, the majority of which have not been linked to functionality before. The performance assessment shows very high specificity and sensitivity, due to the corroboration of signals across multiple matching datasets. RNA2DNAlign is freely available from http://github.com/HorvathLab/NGS as a self-contained binary package for 64-bit Linux systems.

Purpose Anastrozole (ANS) is an aromatase inhibitor that is widely used as a treatment for breast cancer in postmenopausal women. Despite the wide use of ANS, it is associated with serious side effects due to uncontrolled delivery. In addition, ANS exhibits low solubility and short plasma half-life. Nanotechnology-based drug delivery has the potential to enhance the efficacy of drugs and overcome undesirable side effects. In this study, we aimed to prepare novel ANS-loaded PLA-PEG-PLA nanoparticles (ANS-NPs) and to compare the apoptotic response of MCF-7 cell line to both ANS and ANS-loaded NPs. Method ANS-NPs were synthesized using double emulsion method and characterized using different methods. The apoptotic response was evaluated by assessing cell viability, morphology, and studying changes in the expression of MAPK3 , MCL1 , and c-MYC apoptotic genes in MCF-7 cell lines. Results ANS was successfully encapsulated within PLA-PEG-PLA, forming monodisperse therapeutic NPs with an encapsulation efficiency of 67%, particle size of 186±27.13, and a polydispersity index of 0.26±0.11 with a sustained release profile extended over 144 hours. In addition, results for cell viability and for gene expression represent a similar apoptotic response between the free ANS and ANS-NPs. Conclusion The synthesized ANS-NPs showed a similar therapeutic effect as the free ANS, which provides a rationale to pursue pre-clinical evaluation of ANS-NPs on animal models.

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.

Recently, pioneering eQTLs studies on single cell RNA sequencing data have revealed new and cell specific regulatory SNVs. Because eQTLs correlate genotypes and gene expression across multiple individuals, they are confined to SNVs with sufficient population frequency. Here, we present an alternative single cell eQTL approach, scReQTL, wherein we substitute the genotypes with expressed Variant Allele Fraction (VAFRNA) at heterozygous SNV sites. Our approach employs the advantage that, when estimated from multiple cells, VAFRNA can be used to assess effects of rare SNVs in a single individual. ScReQTLs are enriched in known genetic interactions, therefore can be used to identify novel regulatory SNVs.