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Abstract Parkinson’s Disease (PD) is a debilitating neurodegenerative disorder, characterized by motor and cognitive impairments, that affects >1% of the population over the age of 60. The pathogenesis of PD is complex and remains largely unknown. Due to the cellular heterogeneity of the human brain and changes in cell type composition with disease progression, this complexity cannot be fully captured with bulk tissue studies. To address this, we generated single-nucleus RNA sequencing and whole-genome sequencing data from 100 postmortem cases and controls, carefully selected to represent the entire spectrum of PD neuropathological severity and diverse clinical symptoms. The single nucleus data were generated from five brain regions, capturing the subcortical and cortical spread of PD pathology. Rigorous preprocessing and quality control were applied to ensure data reliability. Committed to collaborative research and open science, this dataset is available on the AMP PD Knowledge Platform, offering researchers a valuable tool to explore the molecular bases of PD and accelerate advances in understanding and treating the disease.

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. [ABSTRACT FROM AUTHOR]

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., (© The Author(s) 2022. Published by Oxford University Press.)

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-β-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., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)