Biology subjects

Skene, N. G.

Publications and source records attributed to Skene, N. G..

At least 19 recordsLinked to original sources

bioRxiv2026

A roadmap for enriching rare cell populations from human post-mortem brain, demonstrated by 100% microglial purity.

Understanding brain disease requires studying the specific cell types that drive each condition: microglia in Alzheimers, dopaminergic neurons in Parkinsons, motor neurons in ALS, and many others. For most of these rare populations, protocols to isolate them from human post-mortem brain at the required purity have never been developed. The shared hurdles are reliable nuclear markers, compatible fluorescent dyes, antibody host-species cross-reactivity, and achieving pure rather than merely enriched populations. Here we present a generalisable roadmap for developing fluorescence-activated nuclear sorting (FANS) protocols for rare cell populations in human brain. We demonstrate it on microglia, reaching 100% purity in cortex and 98% in cerebellum, with the cortex sort simultaneously yielding astrocytes at 93% purity. The roadmap tackles each hurdle including an on-bench antibody labelling step that expands fluorescence channels without cross-reactivity problem and an affordable single-nucleus RNA-seq validation workflow, giving labs a pathway for enriching any rare cell type reliably.

neuroscience↗

bioRxiv2026

Decades of Parkinson's disease neuropathology yield a sparse and underpowered map of neuronal vulnerability: a systematic review and meta-analysis

Selective vulnerability is widely assumed in Parkinson's disease (PD), but whether dopaminergic neurons of the substantia nigra are uniquely vulnerable has not been established. Explanations of neuronal degeneration in Parkinson's disease often centre on the distinctive properties of substantia nigra dopaminergic neurons. Whether these properties are necessary for substantial neuronal loss remains unclear. We preregistered a systematic review of 166 post-mortem case-control studies published between 1963 and 2025 and synthesised neuronal counts and densities from 152 studies using a multilevel meta-analysis. After six decades, only 18% of countable brain atlas labels had been examined, and most populations were represented by a single study. Substantial loss beyond dopaminergic and classically pigmented populations shows that neither dopaminergic identity nor neuromelanin is necessary for marked degeneration, challenging key tenets of selective vulnerability in PD. Comparisons across other anatomical and physiological features remain limited by sparse sampling and uncertainty. We identify less-studied populations with substantial estimated loss and estimate the additional sampling needed to improve precision under specified assumptions. These findings direct replication and comparative counting towards uncertainties that limit explanations of neuronal loss across affected and potentially spared populations.

neuroscience↗

bioRxiv2026

orthogene: a Bioconductor package to easily map genes within and across hundreds of species

MotivationMapping genes across identifier systems and species is a routine but critical step in bioinformatics workflows. Despite its ubiquity, gene mapping is frequently handled using bespoke, ad hoc solutions, increasing duplicated effort and introducing opportunities for error. These issues are exacerbated by the prevalence of non-one-to-one homolog relationships and inconsistent handling of gene identifiers across species and databases, which can compromise downstream analyses and reproducibility. ResultsWe present orthogene, an R/Bioconductor package that simplifies gene mapping within and across hundreds of species. orthogene provides a unified, workflow-oriented framework that integrates automated species and identifier standardization, homolog inference across multiple databases, flexible handling of ambiguous homolog relationships, and transformation of gene lists, tables, and high-dimensional matrices into analysis-ready formats. By abstracting common sources of technical complexity while retaining user control, orthogene enables transparent, reproducible, and scalable gene mapping across a wide range of biological contexts. Availabilityhttps://bioconductor.org/packages/orthogene Contactbrian_schilder@alumni.brown.edu

bioinformatics↗

bioRxiv2024

Predicting gene expression from histone marks using chromatin deep learning models depends on histone mark function, regulatory distance and cellular states

To understand the complex relationship between histone mark activity and gene expression, recent advances have used in silico predictions based on large-scale machine learning models. However, these approaches have omitted key contributing factors like cell state, histone mark function or distal effects, that impact the relationship, limiting their findings. Moreover, downstream use of these models for new biological insight is lacking. Here, we present the most comprehensive study of this relationship to date - investigating seven histone marks, in eleven cell types, across a diverse range of cell states. We used convolutional and attention-based models to predict transcription from histone mark activity at promoters and distal regulatory elements. Our work shows that histone mark function, genomic distance and cellular states collectively influence a histone marks relationship with transcription. We found that no individual histone mark is consistently the strongest predictor of gene expression across all genomic and cellular contexts. This highlights the need to consider all three factors when determining the effect of histone mark activity on transcriptional state. Furthermore, we conducted in silico histone mark perturbation assays, uncovering functional and disease related loci and highlighting frameworks for the use of chromatin deep learning models to uncover new biological insight. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=179 SRC="FIGDIR/small/587323v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@18dfcbcorg.highwire.dtl.DTLVardef@cba6b8org.highwire.dtl.DTLVardef@2eb0ceorg.highwire.dtl.DTLVardef@159d5b7_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2024

Predicting cell type-specific epigenomic profiles accounting for distal genetic effects

Understanding genetic variants effects on the epigenome is crucial for interpreting genome-wide association studies (GWAS) results, yet profiling these effects across the non-coding genome remains challenging due to the scalability limits of experimental methods. This necessitates accurate computational models. Existing machine learning approaches, while progressively improving, are confined to the cell types they were trained on, limiting their applicability. Here, we propose the development of a deep learning model, Enformer Celltyping, which can both incorporate distal effects of DNA interactions, up to 100,000 base-pairs away, and predict epigenetic signals in a cell type-agnostic fashion. We demonstrate that Enformer Celltypings predictions of the epigenome out-perform the current best-in-class approach, have strong performance in a range of different cell types and biological regions and generalise to cell types assayed independently of the original training set. Finally, we propose an approach to test epigenetic models performance on genetic variant effect predictions using regulatory quantitative trait loci mapping studies, highlighting Enformer Celltypings and other genomic deep learning models limitations for this task. Our work introduces a new, accurate model for cell type-agnostic histone mark predictions and highlights the benefit of transfer learning for more efficient model development for deep learning in genomics. We make our customisable and efficient transfer learning approach for Enformer available to the community.

genomics↗

bioRxiv2023

Avoiding false discoveries: Revisiting an Alzheimer's disease snRNA-Seq dataset

Mathys et al., conducted the first single-nucleus RNA-Seq study (snRNA-Seq) of Alzheimers 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 and use best-practice approaches to snRNA-Seq differential expression, resulting 549 times fewer differentially expressed genes at a false discovery rate (FDR) of 0.05.

neuroscience↗

bioRxiv2023

Single-cell mRNA-regulation analysis reveals cell type-specific mechanisms of type 2 diabetes

Perturbed secretion of insulin and other pancreatic islet hormones is the main cause of type 2 diabetes (T2D). The islets harbor five cell types that are potentially altered differently by T2D. Whole-islet transcriptomics and single-cell RNA-sequencing (scRNAseq) studies have revealed differentially expressed genes without reaching consensus. Here, we demonstrate that unprecedented insights into disease mechanisms can be obtained by network-based analysis of scRNAseq data. We developed differential gene coordination network analysis (dGCNA) and analyzed islet scRNAseq data from 16 T2D and 16 non-T2D individuals. dGCNA revealed T2D-induced cell type-specific networks of dysregulated genes with remarkable ontological specificity, thus allowing for a comprehensive and unbiased functional classification of genes involved in T2D. In beta cells eleven networks of genes were detected, revealing that mitochondrial electron transport chain, glycolysis, cytoskeleton organization, cell proliferation, unfolded protein response and three networks of beta cell transcription factors are perturbed, whereas exocytosis, lysosomal regulation and insulin translation programs are instead enhanced in T2D. Furthermore, we validated the ability of dGCNA to reveal disease mechanisms and predict the functional context of genes by showing that TMEM176A/B regulates the beta cell cytoskeleton and that CEPBG is a key regulator of the unfolded protein response. In addition, comparing beta- and alpha and cells, we found substantial differences, reproduced across independent datasets, confirming cell type-specific alterations in T2D. We conclude that analysis of networks of differentially coordinated genes provides outstanding insight into cell type-specific gene function and T2D pathophysiology.

cell biology↗

bioRxiv2022

Transcriptomic analyses reveal neuronal specificity of Leigh syndrome associated genes

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.

genetics↗

bioRxiv2022

EpiCompare: R package for the comparison and quality control of epigenomic peak files

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.html All source code is publically available via GitHub: https://github.com/neurogenomics/EpiCompare Documentation website https://neurogenomics.github.io/EpiCompare EpiCompare DockerHub repository: https://hub.docker.com/repository/docker/neurogenomicslab/epicompare

bioinformatics↗

bioRxiv2022

CUT&Tag recovers up to half of ENCODE ChIP-seq peaks

Techniques for genome-wide epigenetic profiling have been undergoing accelerated development toward recovery of high-quality data from bulk and single cell samples. DNA-protein interactions have traditionally been profiled via chromatin immunoprecipitation followed by next generation sequencing (ChIP-seq), which has become the gold standard for studying histone modifications or transcription factor binding. Cleavage Under Targets & Tagmentation (CUT&Tag) is a rapidly expanding new technique that enables profiling of such interactions in situ at high sensitivity and is adaptable to single cell applications. However, thorough evaluation and benchmarking against established ChIP-seq datasets are lacking. Here, we comprehensively benchmarked CUT&Tag for H3K27ac and H3K27me3 against published ChIP-seq profiles from ENCODE in K562 cells. Combining multiple new and published CUT&Tag datasets, there was an average recall of 54% known ENCODE peaks for both histone modifications. To optimize data analysis steps, we tested peak callers MACS2 and SEACR and identified optimal peak calling parameters. Considering both precision and recall of known ENCODE peaks, the peak callers were comparable in their performance, although peaks produced by MACS2 match ENCODE peak width distributions more closely. We found that reducing PCR cycles during library preparation lowered duplication rates at the expense of ENCODE peak recovery. Despite the moderate ENCODE peak recovery, peaks identified by CUT&Tag represent the strongest ENCODE peaks and show the same functional and biological enrichments as ChIP-seq peaks identified by ENCODE. Our workflow systematically evaluates the merits of methodological adjustments, providing a benchmarking framework for the experimental design and analysis of CUT&Tag studies, and will facilitate future efforts to apply CUT&Tag in human tissues and single cells.

genomics↗

bioRxiv2022

A balanced measure shows superior performance of pseudobulk methods over mixed models and pseudoreplication approaches in single-cell RNA-sequencing analysis

Recently, Zimmerman et al.,1 highlighted the importance of accounting for the dependence between cells from the same individual when conducting differential expression analysis on single-cell RNA-sequencing data. Their work proved the inadequacy of pseudoreplication approaches for such analysis - This was an important step forward that was conclusively proven by them. A hierarchical single-cell expression simulation approach (hierarchicell) was developed by Zimmerman et al.,1 to generate non-differentially expressed genes upon which performance was evaluated using the type 1 error rate; the proportion of non-differentially expressed genes indicated as differentially expressed by a model. However, evaluating such models on their type 1 or type 2 error rate in isolation is insufficient to determine their true performance - for example, a method with low type 1 error may have a high type 2 error rate. Moreover, because no seed was set for the pseudo-random number generator used in hierarchicell, the different methods evaluated by Zimmerman et al. were done so on different simulated datasets. Here, we corrected these issues, reran the authors analysis and found pseudobulk methods outperformed mixed models. ContactAlan Murphy: a.murphy@imperial.ac.uk, Nathan Skene: n.skene@imperial.ac.uk Code availabilityThe modified version of hierarchicell which returns all error metrics, uses the same simulated data across approaches and has checkpointing capabilities (if runs are aborted or crashed) is available at: https://github.com/neurogenomics/hierarchicell. The benchmarking script along with the results are available at: https://github.com/Al-Murphy/reanalysis_scRNA_seq_benchmark.

bioinformatics↗

bioRxiv2021

MungeSumstats: A Bioconductor package for the standardisation and quality control of many GWAS summary statistics

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.uk Availability and implementationMungeSumstats is available on Bioconductor (v 3.13) and can also be found on Github at: https://neurogenomics.github.io/MungeSumstats Supplementary informationThe analysis deriving the most common summary statistic formats is available at: https://al-murphy.github.io/SumstatFormats

genomics↗

bioRxiv2020

Single nucleus sequencing fails to detect microglial activation

Single nucleus RNA-Seq (snRNA-Seq) methods are used as an alternative to single cell RNA-Seq methods, as they allow transcriptomic profiling of frozen tissue. However, it is unclear whether snRNA-Seq is able to detect cellular state in human tissue. Indeed, snRNA-Seq analyses of human brain samples have failed to detect a consistent microglial activation signature in Alzheimers Disease. A comparison of microglia from single cells and single nuclei of four human subjects reveals that ~1% of genes is depleted in nuclei compared to whole cells. This small population contains 18% of genes previously implicated in microglial activation, including APOE, CST3, FTL, SPP1, and CD74. We confirm our findings across multiple previous single nucleus and single cell studies. Given the low sensitivity of snRNA-Seq to this population of activation genes, we conclude that snRNA-Seq is not suited to detecting cellular activation in microglia in human disease.

neuroscience↗

bioRxiv2019

Genetic analysis identifies molecular systems and biological pathways associated with household income

Socio-economic position (SEP) is a multi-dimensional construct reflecting (and influencing) multiple socio-cultural, physical, and environmental factors. Previous genome-wide association studies (GWAS) using household income as a marker of SEP have shown that common genetic variants account for 11% of its variation. Here, in a sample of 286,301 participants from UK Biobank, we identified 30 independent genome-wide significant loci, 29 novel, that are associated with household income. Using a recently-developed method to meta-analyze data that leverages power from genetically-correlated traits, we identified an additional 120 income-associated loci. These loci showed clear evidence of functional enrichment, with transcriptional differences identified across multiple cortical tissues, in addition to links with GABAergic and serotonergic neurotransmission. We identified neurogenesis and the components of the synapse as candidate biological systems that are linked with income. By combining our GWAS on income with data from eQTL studies and chromatin interactions, 24 genes were prioritized for follow up, 18 of which were previously associated with cognitive ability. Using Mendelian Randomization, we identified cognitive ability as one of the causal, partly-heritable phenotypes that bridges the gap between molecular genetic inheritance and phenotypic consequence in terms of income differences. Significant differences between genetic correlations indicated that, the genetic variants associated with income are related to better mental health than those linked to educational attainment (another commonly-used marker of SEP). Finally, we were able to predict 2.5% of income differences using genetic data alone in an independent sample. These results are important for understanding the observed socioeconomic inequalities in Great Britain today.

genetics↗

bioRxiv2019

Genetic Identification of Cell Types Underlying Brain Complex Traits Yields Novel Insights Into the Etiology of Parkinson's Disease

Genome-wide association studies (GWAS) have discovered hundreds of loci associated with complex brain disorders, and provide the best current insights into the etiology of these idiopathic traits. However, it remains unclear in which cell types these variants may be active, which is essential for understanding disease etiology and for disease modelling. Here we integrate GWAS results with single-cell transcriptomic data from the entire nervous system to systematically identify cell types underlying psychiatric disorders, neurological conditions, and other brain complex traits. We show that psychiatric disorders are predominantly associated with excitatory neurons from the cortex/hippocampus, medium spiny neurons from the striatum, diverse sets of midbrain neurons, and inhibitory neurons from the cortex/hippocampus. Cognitive traits were generally associated with similar cell types but their associations were driven by different genes. Neurological disorders were associated with different cell types, consistent with other lines of evidence. Notably, we found that Parkinsons disease is not only genetically associated with dopaminergic neurons but also with serotonergic neurons and cells from the oligodendrocyte lineage. Using post-mortem brain transcriptomic data, we confirmed alterations in these cells, even at the earliest stages of disease progression. Altogether, our study provides a solid framework for understanding the cellular basis of complex brain disorders and reveals a new unexpected role of oligodendrocytes in Parkinsons disease.

genomics↗

bioRxiv2018

Synaptic combinatorial molecular mechanisms generate repertoires of innate and learned behavior

Although molecular mechanisms underpinning specific behaviors have been described, whether there are mechanisms that orchestrate a behavioral repertoire is unknown. To test if the postsynaptic proteome of excitatory synapses could impart such a mechanism we conducted the largest genetic study of mammalian synapses yet undertaken. A repertoire of sixteen innate and learned behaviors was assessed from 290,850 measures in 55 lines of mutant mice carrying targeted mutations in the principal classes of postsynaptic proteins. Each innate and learned behavior used a different combination of proteins. These combinations were comprised of proteins that amplified or attenuated the magnitude of each behavioral response. All behaviors required proteins found in PSD95 supercomplexes. We show the vertebrate increase in proteome complexity drove an expansion in behavioral repertoires and generated susceptibility to a wide range of diseases. Our results reveal a molecular mechanism that generates a versatile and complex behavioral repertoire that is central to human behavioral disorders.

neuroscience↗

bioRxiv2018

Prioritizing risk genes for neurodevelopmental disorders using pathway information

Trio family and case-control studies of next-generation sequencing data have proven integral to understanding the contribution of rare inherited and de novo single-nucleotide variants to the genetic architecture of complex disease. Ideally, such studies should identify individual risk genes of moderate to large effect size to generate novel treatment hypotheses for further follow-up. However, due to insufficient power, gene set enrichment analyses have come to be relied upon for detecting differences between cases and controls, implicating sets of hundreds of genes rather than specific targets for further investigation. Here, we present a Bayesian statistical framework, termed gTADA, that integrates gene-set membership information with gene-level de novo and rare inherited case-control counts, to prioritize risk genes with excess rare variant burden within enriched gene sets. Applying gTADA to available whole-exome sequencing datasets for several neuropsychiatric conditions, we replicated previously reported gene set enrichments and identified novel risk genes. For epilepsy, gTADA prioritized 40 risk genes (posterior probabilities > 0.95), 6 of which replicate in an independent whole-genome sequencing study. In addition, 30/40 genes are novel genes. We found that epilepsy genes had high protein-protein interaction (PPI) network connectivity, and show specific expression during human brain development. Some of the top prioritized EPI genes were connected to a PPI subnetwork of immune genes and show specific expression in prenatal microglia. We also identified multiple enriched drug-target gene sets for EPI which included immunostimulants as well as known antiepileptics. Immune biology was supported specifically by case-control variants from familial epilepsies rather than do novo mutations in generalized encephalitic epilepsy.

genomics↗

bioRxiv2017

Genetic Identification Of Brain Cell Types Underlying Schizophrenia

With few exceptions, the marked advances in knowledge about the genetic basis for schizophrenia have not converged on findings that can be confidently used for precise experimental modeling. Applying knowledge of the cellular taxonomy of the brain from single-cell RNA-sequencing, we evaluated whether the genomic loci implicated in schizophrenia map onto specific brain cell types. The common variant genomic results consistently mapped to pyramidal cells, medium spiny neurons, and certain interneurons but far less consistently to embryonic, progenitor, or glial cells. These enrichments were due to distinct sets of genes specifically expressed in each of these cell types. Many of the diverse gene sets associated with schizophrenia (including antipsychotic targets) implicate the same brain cell types. Our results provide a parsimonious explanation: the common-variant genetic results for schizophrenia point at a limited set of neurons, and the gene sets point to the same cells. While some of the genetic risk is associated with GABAergic interneurons, this risk largely does not overlap with that from projecting cells.

genomics↗