Biology subjects

McCarthy, D. J.

Publications and source records attributed to McCarthy, D. J..

At least 19 recordsLinked to original sources

bioRxiv2026

Power is a major confounder in the analysis of cross-ancestry 'portability' in human eQTLs

The phenotypic effects of germline variants are often mediated through gene regulation. Expression quantitative trait loci (eQTLs) are genetic variants associated with changes in gene expression. Understanding how eQTLs vary across populations is essential for characterising the genetic and regulatory drivers of trait diversity. Meta-analysing eQTL studies from multiple populations enables more robust detection of eQTLs and can reveal regulatory mechanisms shaped by population-specific environmental or ancestry-related factors. However, across the multi-ancestry eQTL literature, a wide range of methods have been used to quantify eQTL portability across ancestry groups. Because different studies employ different portability metrics, it is challenging to form a coherent view of the regulatory landscape across populations. In this work, we analyse eQTL summary statistics from ten datasets matched on tissue type and sequencing technology. We compare portability metrics used previously and show that they can yield markedly different patterns of apparent regulatory conservation or divergence. We then examine the statistical determinants of portability across metrics and demonstrate that sample size, minor allele frequency, and linkage disequilibrium are major drivers of the observed differences in eQTL portability across studies. These findings highlight that differences in statistical power stemming from factors such as population size and allele frequency must be accounted for when evaluating eQTL portability. To address this issue, we introduce a new approach designed to correct for these factors when calling eQTL portability. Finally, we show that empirical Bayes multivariate adaptive shrinkage provides a powerful framework for meta-analysing multiple eQTL studies, with the ability to pool signals across populations to produce more robust effect-size estimates within each population, and better powered detection of population specific eQTLs. Author SummaryGenetic variants often influence human traits and diseases by altering gene regulation. Expression quantitative trait loci (eQTLs) are variants associated with changes in gene expression. Many eQTLs are not portable -- they fail to reproduce across cohorts and ancestries -- yet studies use many different metrics to decide when an eQTL has reproduced, making it hard to form a coherent picture of gene regulation across populations. Here we compiled eQTL studies from ten cohorts spanning ancestries, tissues, and sequencing technologies, showing that commonly used portability metrics reveal distinct patterns of sharing. We find the main drivers of non-portability are differences in sample size, minor allele frequency, and linkage disequilibrium, and develop a method that separates these power-related confounders from biological differences. Finally, we show that analysing studies together boosts power, increasing portability. Our results provide a framework to interpret multiple eQTL studies for how genetic variation affects gene regulation across cohorts and ancestries.

genomics↗

bioRxiv2025

Denoising image-based spatial transcriptomics data with DenoIST

Image-based spatial transcriptomics (IST) technologies provide unprecedented resolution of gene expression in tissue sections, but suffer from contamination of cells gene expression profiles due to imperfect cell segmentation. We present DenoIST (Denoising Image-based Spatial Transcriptomics data), a new computational tool that accurately identifies and removes contaminating transcripts from IST datasets. DenoIST models the observed transcript counts using a Poisson mixture model that explicitly accounts for local neighbourhood contamination. Applied to multiple real IST datasets of varying cell densities, DenoIST restores gene expression specificity and clarifies local biological structures by identifying and filtering transcripts spilt over from neighbouring cells. The denoised data enable more consistent and interpretable cell type annotation by minimising conflicting gene expression profiles, reducing the prevalence of hybrid or ambiguous cell types, and enhancing the contrast between distinct functional compartments. Overall, we demonstrate that DenoIST can be integrated to existing IST analysis workflows to improve biological interpretability and robustness of IST data.

bioinformatics↗

bioRxiv2025

simPIC: flexible simulation of paired-insertion counts for single-cell ATAC sequencing data

Single-cell Assay for Transposase Accessible Chromatin (scATAC-seq) is increasingly used at population scale to study how genetic variation shapes chromatin accessibility. Method development is limited by the lack of flexible simulation tools with known ground truth. Here, we present simPIC, a fast, memoryefficient framework for simulating realistic single-cell ATAC-seq count data across individuals and populations. simPIC models cell groups, batch effects, and genotype-dependent accessibility variation, enabling controlled evaluation of population-scale methods, including chromatin accessibility quantitative traits locus (QTL) mapping. Across multiple datasets and cell types, simPIC closely matches real data distributions while scaling to cohort sizes impractical for current tools.

bioinformatics↗

bioRxiv2025

Frataxin deficiency drives cardiac dysfunction and transcriptional dysregulation in Friedreich ataxia iPSC model

Friedreich ataxia (FRDA) is a progressive neuromuscular degenerative disorder caused by GAA repeat expansions in the FXN gene, leading to frataxin deficiency and multisystem pathology. Cardiomyopathy is the leading cause of mortality in individuals with FRDA. To investigate the cellular and molecular mechanisms underlying FRDA-associated cardiac dysfunction, we employed induced pluripotent stem cell (iPSC) lines derived from three individuals with FRDA, each paired with an isogenic control line generated through CRISPR/Cas9-mediated excision of the pathogenic GAA repeat expansion. Correction of the mutation restored FXN expression to levels comparable to healthy donor iPSCs, and all lines differentiated efficiently into cardiomyocytes. Functional analysis revealed significant contractile abnormalities in FRDA cardiomyocytes and multicellular cardiac microtissues, including prolonged contraction and relaxation times and faster beating rates, consistent with clinical observations of cardiac contractile dysfunction. FRDA cardiomyocytes also exhibited pathological features such as increased cell size, irregular calcium transients, elevated mitochondrial reactive oxygen species levels, increased mitochondrial fission and increased cell death. These phenotypes were exacerbated by pathological levels of iron supplementation in culture media, highlighting the heightened sensitivity of frataxin-deficient cardiomyocytes to iron-induced metabolic stress. RNA sequencing revealed a distinct transcriptional profile associated with frataxin deficiency. MEG3 and PCDHGA10 were consistently dysregulated across all three FRDA-iPSC lines and may represent early molecular markers of FRDA cardiomyopathy. Together, these findings establish a robust human iPSC model of FRDA cardiomyopathy that captures early disease phenotypes and reveals novel molecular targets. This preclinical human model provides valuable insight into the pathogenesis of FRDA and provides a platform for developing early-stage therapeutic interventions.

pathology↗

bioRxiv2025

A spatial transcriptomic atlas of acute neonatal lung injury across development and disease severity

A molecular understanding of lung organogenesis requires delineation of the timing and regulation of the cellular transitions that ultimately form and support a surface capable of gas exchange. While the advent of single-cell transcriptomics has allowed for the discovery and identification of transcriptionally distinct cell populations present during lung development, the spatiotemporal dynamics of these transcriptional shifts remain undefined. With imaging-based spatial transcriptomics, we analyzed the gene expression patterns in 17 human infant lungs at varying stages of development and injury, creating a spatial transcriptomic atlas of [~]1.2 million cells. We applied computational clustering approaches to identify shared molecular patterns among this cohort, informing how tissue architecture and molecular spatial relationships are coordinated during development and disrupted in disease. Recognizing that all preterm birth represents an injury to the developing lung, we created a simplified classification scheme that relies upon the routinely collected objective measures of gestational age and life span. Within this framework, we have identified cell type patterns across gestational age and life span variables that would likely be overlooked when using the conventional "disease vs. control" binary comparison. Together, these data represent an open resource for the lung research community, supporting discovery-based inquiry and identification of targetable molecular mechanisms in both normal and arrested human lung development.

genomics↗

bioRxiv2025

Orchestrating multi-state QTL analysis with Bioconductor

MotivationThe scope of many Quantitative Trait Loci (QTL) mapping studies has increased to include different cellular and environmental states. However, drawing biologically relevant conclusions from the large, high-dimensional data that come from multi-state QTL mapping studies is not straightforward. ResultsTo address this problem, we introduce two R packages, QTLExperiment and multistateQTL. The QTLExperiment package provides a robust container for storing and manipulating QTL summary statistics and associated metadata. Building upon existing Bioconductor infrastructure and conventions, this object class is consistent, user-friendly, and well-documented. The multistateQTL package introduces tools to facilitate the analysis of multi-state QTL data stored in a QTLExperiment container. This package provides methods for statistical analysis, quantification of sharing, classification of multi-state QTL associations, visualization of the data, and more. It also provides flexible methods for simulating multi-state QTL summary statistics with user-defined properties. Availability and implementationThe packages QTLExperiment and multistateQTL are available on Bioconductor (https://www.bioconductor.org/packages/QTLExperiment and https://bioconductor.org/packages/multistateQTL).

bioinformatics↗

bioRxiv2025

SpatialRNA: a Python package for easy application of Graph Neural Network models on single-molecule spatial transcriptomicsdataset

Image-based spatial transcriptomics deliver gene expression measurements of RNA transcripts in tissue slices with single-molecule resolution and spatial context preserved. Modern Graph Neural Network (GNN) models are promising methods for capturing the complex molecular and cellular phenotypes in tissues at single-transcript and single-cell levels. A key application of GNNs is the detection of spatial domains or niches, that is, groups of molecules and/or cells that collaboratively work together to produce complex phenotypes. Due to the vast number of detected transcripts in image-based spatial transcriptomics, applying GNNs on RNA molecule graphs is not trivial. We present a Python package, SpatialRNA, for easy (sub)graph generation from tissue samples and provide comprehensive tutorials for convenient and efficient application of Graph Neural Network models under the PyG framework. Availability and implementation: The SpatialRNA package is freely accessible from GitLab https://gitlab.svi.edu.au/biocellgen-public/spatialrna and can be installed via pip. Python notebooks and scripts used in case studies are freely accessible at https://gitlab.svi.edu.au/biocellgen-public/case_study_ipf and https://gitlab.svi.edu.au/biocellgen-public/case_study_xenium_5k_panel.

bioinformatics↗

bioRxiv2024

A high-resolution meiotic crossover map from single-nucleus ATAC-seq reveals insights into the recombination landscape in mammalian sperm

Meiotic crossovers promote correct chromosome segregation and the shuffling of genetic diversity. However, the measurement of crossovers remains challenging, impeding our ability to decipher the molecular mechanisms that are necessary for their formation and regulation. Here we demonstrate a novel repurposing of the single-nucleus Assay for Transposase Accessible Chromatin with sequencing (snATAC-seq) as a simple and high-throughput method to identify and characterise meiotic crossovers from sperm nuclei. We first validate the feasibility of obtaining genome-wide coverage from snATAC-seq by using ATAC-seq on bulk haploid mouse sperm, ensuring adequate variant detection for haplotyping. Subsequently, we adapt droplet-based snATAC-seq for crossover detection, revealing over 25,000 crossovers in F1 hybrid mice. Comparison between wildtype and a hyper-recombinogenic Fancm-deficient mutant mouse model confirmed an increase in crossover rates in this genotype, however a distribution which was unchanged. We also find that regions with the highest rate of crossover formation are enriched for DMC1 and PRDM9, with a subset that is further enriched for DMC1 in Fancm-deficient mice. Our findings demonstrate the utility of snATAC-seq as a robust and scalable tool for high-throughput crossover detection, offering insights into meiotic crossover dynamics and elucidating the underlying molecular mechanisms. It is possible that the research presented here with snATAC-seq of haploid sperm could be extended into fertility-related diagnostics.

genetics↗

bioRxiv2024

Going beyond cell clustering and feature aggregation: Is there single cell level information in single-cell ATAC-seq data?

1Single-cell Assay for Transposase Accessible Chromatin with sequencing (scATAC-seq) has become a widely used method for investigating chromatin accessibility at single-cell resolution. However, the resulting data is highly sparse with most data entries being zeros. As such, currently available computational methods for scATAC-seq feature a range of transformation procedures to extract meaningful information from the sparse data. Most notably, these transformations can be categorized into: 1) feature aggregation with known biological associations, 2) pseudo-bulking cells of similar biology, and 3) binarisation of count data. These strategies beg the question of whether or not scATAC-seq data actually has usable single-cell and single-region information as intended from the assay. If we can go beyond aggregated features and pooled cells, it opens up the possibility of more complex statistical tasks that require that degree of granularity. To reach the finest possible resolution of single-cell, single-region information there are inevitably many computational challenges to overcome. Here, we review the major data analysis challenges lying between raw data readout and biological discovery, and discuss the limitations of current data analysis approaches. Lastly, we conclude that chromatin accessibility profiling at true single-cell resolution is not yet achieved with current technology, but that it may be achieved with promising developments in optimising the efficiency of scATAC-seq assays.

bioinformatics↗

bioRxiv2024

Profiling genetically driven alternative splicing across the Indonesian Archipelago

One of the regulatory mechanisms influencing the functional capacity of genes is alternative splicing (AS). Previous studies exploring the splicing landscape of human tissues have shown that AS has contributed to human biology, especially in disease progression and the immune response. Nonetheless, this phenomenon remains poorly characterised across human populations, and it is unclear how genetic and environmental variation contribute to alternative splicing. Here, we examine a set of 115 Indonesian samples from three traditional island populations spanning the genetic ancestry cline that characterizes Island Southeast Asia. We conduct a global AS analysis between islands to ascertain the degree of functionally significant AS events and their consequences. Using a hierarchical event-based statistical model, we detected over 1,000 significant differential AS events across all comparisons. Additionally, we identify over 6,000 genetic variants associated with changes in splicing (splicing quantitative trait loci; sQTLs), some of which are driven by Papuan-like genetic ancestry, and only show partial overlap with other publicly available sQTL datasets derived from other populations. Computational predictions of RNA binding activity revealed that a fraction of these sQTLs directly modulate the binding propensity of proteins involved in the splicing regulation of immune genes. Overall, these results contribute towards elucidating the role of genetic variation in shaping gene regulation in one of the most diverse regions in the world.

genomics↗

bioRxiv2024

MAssively-Parallel Flow cytometry Xplorer (MAPFX): A Toolbox for Analysing Data from the Massively-Parallel Cytometry Experiments

Massively-Parallel Cytometry (MPC) experiments allow cost-effective quantification of more than 200 surface proteins at single-cell resolution. The Infinity Flow (Inflow) analysis protocol was developed to measure highly informative protein backbone markers on all cells in all wells distributed across three 96-well plates, along with well-specific exploratory protein infinity markers. Backbone markers can be used to impute the infinity markers on cells in all other wells using machine learning methods. This protocol offers unprecedented opportunities for more comprehensive classification of cell types. However, some aspects of the protocol can be improved, including methods for background correction and removal of unwanted variation. Here, we propose MAPFX as an end-to-end toolbox that carefully pre-processes the raw data from MPC experiments, and further imputes the missing infinity markers in the wells without those measurements. Our pipeline starts by performing background correction on raw intensities to remove the noise from electronic baseline restoration and fluorescence compensation by adapting a normal-exponential convolution model. Unwanted technical variation, from sources such as well effects, is then removed using a log-normal model with plate, column, and row factors, after which infinity markers are imputed using the informative backbone markers as predictors. The completed dataset can then be used for clustering and other statistical analyses. Unique features of our approach include performing background correction prior to imputation and removing unwanted variation from the data at the cell-level, while explicitly accounting for the potential association between biology and unwanted factors. We benchmark our pipeline against alternative pipelines and demonstrate that our approach is better at preserving biological signals, removing unwanted variation, and imputing unmeasured infinity markers.

bioinformatics↗

bioRxiv2023

Image-based spatial transcriptomics identifies molecular niche dysregulation associated with distal lung remodeling in pulmonary fibrosis

The human lung is structurally complex, with a diversity of specialized epithelial, stromal and immune cells playing specific functional roles in anatomically distinct locations, and large-scale changes in the structure and cellular makeup of this distal lung is a hallmark of pulmonary fibrosis (PF) and other progressive chronic lung diseases. Single-cell transcriptomic studies have revealed numerous disease-emergent/enriched cell types/states in PF lungs, but the spatial contexts wherein these cells contribute to disease pathogenesis has remained uncertain. Using sub-cellular resolution image-based spatial transcriptomics, we analyzed the gene expression of more than 1 million cells from 19 unique lungs. Through complementary cell-based and innovative cell-agnostic analyses, we characterized the localization of PF-emergent cell-types, established the cellular and molecular basis of classical PF histopathologic disease features, and identified a diversity of distinct molecularly-defined spatial niches in control and PF lungs. Using machine-learning and trajectory analysis methods to segment and rank airspaces on a gradient from normal to most severely remodeled, we identified a sequence of compositional and molecular changes that associate with progressive distal lung pathology, beginning with alveolar epithelial dysregulation and culminating with changes in macrophage polarization. Together, these results provide a unique, spatially-resolved characterization of the cellular and molecular programs of PF and control lungs, provide new insights into the heterogeneous pathobiology of PF, and establish analytical approaches which should be broadly applicable to other imaging-based spatial transcriptomic studies.

genomics↗

bioRxiv2023

De novo transcriptome assembly and genome annotation of the fat-tailed dunnart (Sminthopsis crassicaudata)

Marsupials exhibit highly specialized patterns of reproduction and development, making them uniquely valuable for comparative genomics studies with their sister lineage, eutherian (also known as placental) mammals. However, marsupial genomic resources still lag far behind those of eutherian mammals, limiting our insight into mammalian diversity. Here, we present a series of novel genomic resources for the fat-tailed dunnart (Sminthopsis crassicaudata), a mouse-like marsupial that, due to its ease of husbandry and ex-utero development, is emerging as a laboratory model. To enable wider use, we have generated a multi-tissue de novo transcriptome assembly of dunnart RNA-seq reads spanning 12 tissues. This highly representative transcriptome is comprised of 2,093,982 assembled transcripts, with a mean transcript length of 830 bp. The transcriptome mammalian BUSCO completeness score of 93% is the highest amongst all other published marsupial transcriptomes. Additionally, we report an improved fat-tailed dunnart genome assembly which is 3.23 Gb long, organized into 1,848 scaffolds, with a scaffold N50 of 72.64 Mb. The genome annotation, supported by assembled transcripts and ab initio predictions, revealed 21,622 protein-coding genes. Altogether, these resources will contribute greatly towards characterizing marsupial biology and mammalian genome evolution.

genomics↗

bioRxiv2023

Bayesian inference for copy number intra-tumoral heterogeneity from single-cell RNA-sequencing data

High-resolution molecular characterization of intra-tumoral clonal structure defined by genomic and epigenomic alterations is crucial in understanding the natural history of tumors and advancing cancer treatment strategies. Copy number alterations (CNA) are of notable importance as both drivers and markers of clonal structure that can now be assayed at individual cell resolution. However, specific computational methods are needed for accurate inference of clonal profiles and cell states from sparse and noisy single-cell omics data. Here, we develop a new Bayesian model to utilize single-cell RNA sequencing (scRNA-seq) data for automatic analysis of intra-tumoral clonal structure with respect to CNAs, without reliance on prior knowledge. The model clusters cells into sub-tumoral clones while simultaneously identifying CNA events in each clone, jointly modelling input from gene expression and germline single-nucleotide polymorphisms. Unlike previous methods, our approach automatically infers the number of clones present in the tumor. In detailed simulation studies our model frequently achieves very high (>90%) cell clustering accuracy and high (>80%) CN state inference accuracy, even in settings of high variance and sparsity. Overall, our method compares strongly against existing software tools. Application to human metastatic melanoma tumor data demonstrates accurate clustering of tumor and non-tumor cells, and reveals clonal CNA profiles that highlight functional gene expression differences between clones from the same tumor. Our method is implemented in a publicly-available, open-source R package, Chloris.

bioinformatics↗

bioRxiv2023

Cell type-specific and disease-associated eQTL in the human lung

Common genetic variants confer substantial risk for chronic lung diseases, including pulmonary fibrosis (PF). Defining the genetic control of gene expression in a cell-type-specific and context-dependent manner is critical for understanding the mechanisms through which genetic variation influences complex traits and disease pathobiology. To this end, we performed single-cell RNA-sequencing of lung tissue from 67 PF and 49 unaffected donors. Employing a pseudo-bulk approach, we mapped expression quantitative trait loci (eQTL) across 38 cell types, observing both shared and cell type-specific regulatory effects. Further, we identified disease-interaction eQTL and demonstrated that this class of associations is more likely to be cell-type specific and linked to cellular dysregulation in PF. Finally, we connected PF risk variants to their regulatory targets in disease-relevant cell types. These results indicate that cellular context determines the impact of genetic variation on gene expression, and implicates context-specific eQTL as key regulators of lung homeostasis and disease.

genomics↗

bioRxiv2022

Trade-off between conservation of biological variation and batch effect removal in deep generative modeling for single-cell transcriptomics

Single-cell RNA sequencing (scRNA-seq) technology has contributed significantly to diverse research areas in biology, from cancer to development. Since scRNA-seq data is high-dimensional, a common strategy is to learn low-dimensional latent representations better to understand overall structure in the data. In this work, we build upon scVI, a powerful deep generative model which can learn biologically meaningful latent representations, but which has limited explicit control of batch effects. Rather than prioritizing batch effect removal over conservation of biological variation, or vice versa, our goal is to provide a birds eye view of the trade-offs between these two conflicting objectives. Specifically, using the well established concept of Pareto front from economics and engineering, we seek to learn the entire trade-off curve between conservation of biological variation and removal of batch effects. A multi-objective optimisation technique known as Pareto multi-task learning (Pareto MTL) is used to obtain the Pareto front between conservation of biological variation and batch effect removal. Our results indicate Pareto MTL can obtain a better Pareto front than the naive scalarization approach typically encountered in the literature. In addition, we propose to measure batch effect by applying a neural-network based estimator called Mutual Information Neural Estimation (MINE) and show benefits over the more standard Maximum Mean Discrepancy (MMD) measure. The Pareto front between conservation of biological variation and batch effect removal is a valuable tool for researchers in computational biology. Our results demonstrate the efficacy of applying Pareto MTL to estimate the Pareto front in conjunction with applying MINE to measure the batch effect.

bioinformatics↗

bioRxiv2022

Fancm regulates meiotic double-strand break repair pathway choice in mammals

Meiotic crossovers are required for accurate chromosome segregation and to produce new allelic combinations. Meiotic crossover numbers are tightly regulated within a narrow range, despite an excess of initiating DNA double-strand breaks. Here, we describe the tumour suppressor FANCM as a meiotic anti-crossover factor in mammals. Crossover analyses with single-gamete and pedigree datasets both reveal a genome-wide increase in crossover frequencies in Fancm-deficient mice. Gametogenesis is heavily perturbed in Fancm loss of function mice, which is consistent with the reproductive defects reported in humans with biallelic FANCM mutations. A portion of the gametogenesis defects can be attributed to the cGAS-STING pathway. Despite the gametogenesis phenotypes in Fancm mutants both sexes were capable of producing offspring. We propose that the anti-crossover function and role in gametogenesis of Fancm are separable and will inform diagnostic pathways for human genomic instability disorders.

genetics↗

bioRxiv2022

A comparison of marker gene selection methods for single-cell RNA sequencing data

The development of single-cell RNA sequencing (scRNA-seq) has enabled scientists to catalogue and probe the transcriptional heterogeneity of individual cells in unprecedented detail. A common step in the analysis of scRNA-seq data is the selection of so-called marker genes, most commonly to enable annotation of the biological cell types present in the sample. In this paper we benchmarked 56 computational methods for selecting marker genes in scRNA-seq data. The performance of the methods was compared using 10 real scRNA-seq datasets and over 170 additional simulated datasets. Methods were compared on their ability to recover simulated and expert-annotated marker genes, the predictive performance and characteristics of the gene sets they select, their memory usage and speed and their implementation quality. In addition, various case studies were used to scrutinise the most commonly used methods, highlighting issues and inconsistencies. Overall, we present a comprehensive evaluation of methods for selecting marker genes in scRNA-seq data. Our results highlight the efficacy of simple methods, especially the Wilcoxon rank-sum test, Students t-test and logistic regression. All code used in the evaluation, including an extensible Snakemake pipeline, is available at: https://gitlab.svi.edu.au/biocellgen-public/mage_2020_marker-gene-benchmarking.

bioinformatics↗