Biology subjects

Babtie, A. C.

Publications and source records attributed to Babtie, A. C..

5 recordsLinked to original sources

bioRxiv2026

A single-nuclei multiomics resource across four brain regions prioritises human neural cell types influencing brain-related traits

Genetic studies have identified thousands of variants associated with brain-related traits. However, the majority of these map to non-coding regions and their causal roles and functional consequences are unclear. In this study, we profiled gene expression and chromatin accessibility in ~140,000 individual nuclei from 40 post-mortem adult human brain samples from 11 donors spanning four brain regions (amygdala, hippocampus, hypothalamus and prefrontal cortex). Integrating these data with genome-wide association study statistics allowed us to prioritise specific neural cell populations relevant for complex traits. Concordant with epidemiological evidence, we prioritise similar neuronal cell populations for BMI, schizophrenia, bipolar disorder and age at menarche associated variants. Our paired multiomic data also provides support for putative enhancer-gene relationships relevant to Alzheimer's disease. These data provide a valuable resource to help interpret trait-associated genetic variation and nominate effector transcripts and cellular pathways relevant to brain-related phenotypes.

genomics↗

bioRxiv2026

Cell-type-resolved DNA methylation profiling in the cortex reveals female-specific X chromosome differences in autism

Sex differences are a prominent feature of many neurodevelopmental conditions, particularly autism for which approximately three males are diagnosed for every female. Although epigenetic dysregulation has been implicated in autism, the extent to which autism-associated epigenetic differences vary by sex and neural cell type remains poorly understood. Using fluorescence-activated nuclei sorting (FANS), we isolated neuron-enriched (NeuN+), oligodendrocyte-enriched (SOX10+), microglia-enriched (IRF8+) and astrocyte-enriched (NeuN-/SOX10-/IRF8-) nuclei from post-mortem prefrontal cortex tissue from 24 autistic (70.8% male) and 23 non-autistic (69.6% male) donors, and performed cell-type-resolved epigenome-wide association studies (EWASs) of DNA methylation. We identified cell-type-specific autism-associated differentially methylated positions (DMPs), with microglia harbouring the largest number of associations. Modelling the interaction between sex and diagnosis revealed a striking female-specific signature concentrated on the X chromosome in microglia. At the majority of these sex-by-autism DMPs, autistic females showed lower DNA methylation compared to control females consistent with an altered X chromosome inactivation (XCI) profile. Across the X chromosome, autism-associated DNA methylation differences were significantly larger in females than males and were greater at sites annotated to genes normally subject to XCI than at genes that escape XCI. Together, these data further support the importance of microglia in autism and suggest that altered X chromosome dosage regulation may contribute to sex-dependent molecular mechanisms in autism.

genomics↗

bioRxiv2022

Single-cell Mendelian randomisation identifies cell-type specific genetic effects on human brain disease and behaviour

Translating genome-wide association loci to therapies requires knowledge of the causal genes, their directionality of effect and the cell-types in which they act. To infer these relationships in the human brain, we implemented Mendelian randomisation using single cell-type expression quantitative trait loci (eQTLs) as genetic anchors. Expression QTLs were mapped across 8 major cell-types in brain tissue exclusively ascertained from donors with no history of brain disease. We report evidence for a causal association between the change in expression of 118 genes and one or more of 16 brain phenotypes, revealing candidate targets for risk mitigation and opportunities for shared preventative therapeutic strategies. We highlight key causal genes for neurodegenerative and neuropsychiatric disease and for each, we report its cellular context and the therapeutic directionality required for risk mitigation. Our use of control samples establishes a new resource for the causal interpretation of GWAS risk alleles for human brain phenotypes.

neuroscience↗

bioRxiv2018

Empirical Bayes Meets Information Theoretical Network Reconstruction from Single Cell Data

Gene expression is controlled by networks of transcription factors and regulators, but the structure of these networks is as yet poorly understood and is thus inferred from data. Recent work has shown the efficacy of information theoretical approaches for network reconstruction from single cell transcriptomic data. Such methods use information to estimate dependence between every pair of genes in the dataset, then edges are inferred between top-scoring pairs. Dependence, however, does not indicate significance, and the definition of \"top-scoring\" is often arbitrary and a priori related to expected network size. This makes comparing networks across datasets difficult, because networks of a similar size are not necessarily similarly accurate. We present a method for performing formal hypothesis tests on putative network edges derived from information theory, bringing together empirical Bayes and work on theoretical null distributions for information measures. Thresholding based on empirical Bayes allows us to control network accuracy according to how we intend to use the network. Using single cell data from mouse pluripotent stem cells, we recover known interactions and suggest several new interactions for experimental validation (using a stringent threshold) and discover high-level interactions between sub-networks (using a more relaxed threshold). Furthermore, our method allows for the inclusion of prior information. We use in-silico data to show that even relatively poor quality prior information can increase the accuracy of a network, and demonstrate that the accuracy of networks inferred from single cell data can sometimes be improved by priors from population-level ChIP-Seq and qPCR data.

systems biology↗

bioRxiv2016

Network inference and hypotheses-generation from single-cell transcriptomic data using multivariate information measures

While single-cell gene expression experiments present new challenges for data processing, the cell-to-cell variability observed also reveals statistical relationships that can be used by information theory. Here, we use multivariate information theory to explore the statistical dependencies between triplets of genes in single-cell gene expression datasets. We develop PIDC, a fast, efficient algorithm that uses partial information decomposition (PID) to identify regulatory relationships between genes. We thoroughly evaluate the performance of our algorithm and demonstrate that the higher order information captured by PIDC allows it to outperform pairwise mutual information-based algorithms when recovering true relationships present in simulated data. We also infer gene regulatory networks from three experimental single-cell data sets and illustrate how network context, choices made during analysis, and sources of variability affect network inference. PIDC tutorials and open-source software for estimating PID are available here: https://github.com/Tchanders/network_inference_tutorials. PIDC should facilitate the identification of putative functional relationships and mechanistic hypotheses from single-cell transcriptomic data.

systems biology↗