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Arising FromMathys, H.et al. Nature (2019).https://doi.org/10.1038/s41586-019-1195-2Mathyset al., conducted the first single-nucleus RNA-Seq study (snRNA-Seq) of Alzheimer’s disease (AD)1. The authors profiled the transcriptomes of approximately 80,000 cells from the prefrontal cortex, collected from 48 individuals – 24 of which presented with varying degrees of AD pathology. With bulk RNA-Seq, changes in gene expression across cell types can be lost, potentially masking the differentially expressed genes (DEGs) across different cell types. Through the use of single-cell techniques, the authors benefitted from increased resolution with the potential to uncover cell type-specific DEGs in AD for the first time2. However, there were limitations in both their data processing and quality control and their differential expression analysis. Here, we correct these issues with best-practice approaches to snRNA-Seq processing and differential expression, resulting 892 times fewer differentially expressed genes at a false discovery rate (FDR) of 0.05.

Reproducibility is essential to the progress of research, yet achieving it remains elusive even in computational fields. Continuous Integration (CI) platforms offer a powerful way to launch automated workflows to check and document code, but often require considerable time, effort, and technical expertise to setup. We therefore developed the rworkflows suite to make robust CI workflows easy and freely accessible to all R package developers (https://github.com/neurogenomics/rworkflows). rworkflows consists of 1) a CRAN/Bioconductor-compatible R package template, 2) an R package to quickly implement a standardised workflow, and 3) a centrally maintained GitHub Action. Each time it is triggered by a push to a GitHub repository, it automatically creates virtual machines across multiple OS, installs all dependencies, runs code checks, builds/deploys a documentation website, and builds/deploys version-controlled containers with a built-in RStudio interface. Additional analyses demonstrate that >50% of all R packages are only available via GitHub, highlighting the need for accessible solutions. Thus, rworkflows greatly reduces the barriers to implementing robust and reproducible best practices.

SummaryEpiCompare combines a variety of downstream analysis tools to compare, quality control and benchmark different epigenomic datasets. The package requires minimal input from users, can be run with just one line of code and provides all results of the analysis in a single interactive HTML report. EpiCompare thus enables downstream analysis of multiple epigenomic datasets in a simple, effective and user-friendly manner.Availability and ImplementationEpiCompare is available on Bioconductor (≥ v3.15):https://bioconductor.org/packages/release/bioc/html/EpiCompare.htmlAll source code is publically available via GitHub:https://github.com/neurogenomics/EpiCompareDocumentation websitehttps://neurogenomics.github.io/EpiCompareEpiCompare DockerHub repository:https://hub.docker.com/repository/docker/neurogenomicslab/epicompare

Leigh syndrome (subacute necrotising encephalomyelopathy) is a rare inherited, complex, and typically early onset mitochondrial disorder with clinical and genetic heterogeneity. It owes its heterogeneous nature to the complex nature of mitochondrial genetics and the significant interactions that occur between the mitochondrial and nuclear genomes. Stepwise developmental regression is a classical feature of patients with Leigh syndrome, but other neurological features include ataxia, tremor, seizures, and occasionally psychiatric manifestations. To date the involvement of more than 100 genes has been identified in patients with Leigh syndrome. Despite this, the pathophysiology of Leigh syndrome remains unknown, although it is thought to be due to primary mitochondrial dysfunction.Here, we sought to determine the cellular specificity of all Leigh syndrome associated genes within the brain and investigate a potential common genetic mechanism between Leigh syndrome and other disorders with overlapping clinical features. We utilized co-expression network analyses constructed using existing UK Brain Expression Consortium (UKBEC) data from 10 brain regions to identify regions of the brain where our genes of interest were enriched. Next, expression weighted cell type enrichment (EWCE) was employed to determine the cellular specificity of Leigh syndrome associated genes. Association with pre-or post-synaptic structures was identified using synaptic gene ontology (SynGO) analysis. Finally, heritability analysis was performed on the co-expression network modules demonstrating enrichment of Leigh syndrome associated genes to investigate potential genetic relationships with Parkinson disease, epilepsy, and schizophrenia.Co-expression network analyses reveal that genes encoding oxidative phosphorylation subunits and assembly factors exhibit the highest levels of expression in brain regions most affected on MRI and neuropathology in Leigh syndrome. Further, over two thirds of the genes were found to be enriched within the substantia nigra and 19 genes appeared to cluster, with selective enrichment in the putamen, substantia nigra, medulla and thalamus. EWCE analyses of single cell RNA-Seq data from mouse brain revealed significant enrichment of Leigh syndrome associated genes in hippocampal and somatosensory pyramidal neurones and interneurons of the brain. SynGO analysis demonstrated that expression of these genes is preferentially associated with pre-synaptic structures. Heritability studies suggested that some modules in which Leigh syndrome associated genes were enriched were also enriched for Parkinson disease and epilepsy.In conclusion, our findings suggest a primary mitochondrial dysfunction as the underlying basis of Leigh syndrome with causative genes primarily expressed in neuronal cells, explaining the relative sparing of glial cells observed in neuropathological studies.

Neurodegenerative diseases are characterized by the abnormal accumulation of aggregated proteins in the brain. Using in vivo pulse isotope labeling, we screened the proteome for changes in protein turnover and abundance in multiple mouse models of neurodegeneration. These data suggest that the disease state of pathologically affected tissue is characterized by a proteome-wide increase in protein turnover and repair. In contrast, in healthy wild-type mice, aging in the mammalian brain is associated with a global slowdown in protein turnover.

Advances in single-cell RNA-sequencing technology over the last decade have enabled exponential increases in throughput: datasets with over a million cells are becoming commonplace. The burgeoning scale of data generation, combined with the proliferation of alternative analysis methods, led us to develop the scFlow toolkit and the nf-core/scflow pipeline for reproducible, efficient, and scalable analyses of single-cell and single-nuclei RNA-sequencing data. The scFlow toolkit provides a higher level of abstraction on top of popular single-cell packages within an R ecosystem, while the nf-core/scflow Nextflow pipeline is built within the nf-core framework to enable compute infrastructure-independent deployment across all institutions and research facilities. Here we present our flexible pipeline, which leverages the advantages of containerization and the potential of Cloud computing for easy orchestration and scaling of the analysis of large case/control datasets by even non-expert users. We demonstrate the functionality of the analysis pipeline from sparse-matrix quality control through to insight discovery with examples of analysis of four recently published public datasets and describe the extensibility of scFlow as a modular, open-source tool for single-cell and single nuclei bioinformatic analyses.

SummaryGenome-wide association studies (GWAS) summary statistics have popularised and accelerated genetic research. However, a lack of standardisation of the file formats used has proven problematic when running secondary analysis tools or performing meta-analysis studies. To address this issue, we have developed MungeSumstats, a Bioconductor R package for the standardisation and quality control of GWAS summary statistics. MungeSumstats can handle the most common summary statistic formats, including variant call format (VCF) producing a reformatted, standardised, tabular summary statistic file, VCF or R native data object.ContactAlan Murphy:a.murphy@imperial.ac.uk, Nathan Skene:n.skene@imperial.ac.ukAvailability and implementationMungeSumstats is available on Bioconductor (v 3.13) and can also be found on Github at:https://neurogenomics.github.io/MungeSumstatsSupplementary informationThe analysis deriving the most common summary statistic formats is available at:https://al-murphy.github.io/SumstatFormats