Biology subjects

Jones, W.

Publications and source records attributed to Jones, W..

12 recordsLinked to original sources

bioRxiv2026

Prenatal e-cigarette exposure induces region- and cell type-specific metabolic and developmental reprogramming

Prenatal e-cigarette exposure (PeCE) is increasingly prevalent and has been associated with adverse neurodevelopmental outcomes, yet how maternal vaping perturbs early brain development remains poorly understood. We integrated spatial transcriptomics, snRNA-seq and lipidomics to define neonatal rat brain responses to PeCE in rats at regional and cellular resolution. PeCE induced pronounced spatial heterogeneity in vulnerability, with the striatum exhibiting the strongest developmental transcriptional disruption. PeCE disrupts lipid metabolic homeostasis and induced molecular signatures suggestive of enhance Ca2+ signaling, dopaminergic responsiveness and region-specific synaptic stress programs. In the striatum, PeCE induced transcriptional programs consistent with altered lipid utilization, silent synapse-like molecular features, suppressed dendritic spine development and delayed D1-medium spiny neuron maturation. PeCE-sensitive genes were enriched in human autism spectrum disorders and neurodevelopment risk loci. Together, these findings identify disrupted metabolic and developmental reprogramming as a central feature of neonatal brain vulnerability to maternal vaping and provide a mechanistic framework linking PeCE to neurodevelopmental disease risk.

neuroscience↗

bioRxiv2026

Prenatal THC exposure disrupts mitochondrial respiratory gene programs and delays medium spiny neuron maturation in the nucleus accumbens

Prenatal cannabis exposure (PCE) is increasingly prevalent and has been associated with adverse neurodevelopmental outcomes, yet its molecular impact on brain reward circuitry remains poorly defined. Here, we investigated transcriptional and epigenomic alterations in the nucleus accumbens (NAc) following prenatal {Delta}9-tetrahydrocannabinol exposure in a rat model using snRNA-seq and snATAC-seq analyses. PCE markedly suppressed the expression of genes involved in mitochondrial oxidative phosphorylation (OXPHOS) in the NAc on postnatal day 24 (P24), indicating reduced mitochondrial respiration capacity. This disrupted mitochondrial respiratory gene programming was accompanied by coordinated alterations in ribosomal and proteasomal pathways regulating protein homeostasis in NAc medium spiny neurons (MSNs), suggesting coupled disruption of cellular metabolism and neuronal maturation. snATAC-seq analysis revealed altered chromatin accessibility at promoter regions enriched for Nrf1 and Yy2 binding motifs, implicating Hrf1- and Yy2-associated transcriptional regulation of mitochondrial genes in MSNs following PCE. Moreover, an acute THC challenge in PCE offspring at P24 further exacerbated the suppression of genes involved in mitochondrial OXPHOS and MSN maturation. Together, these findings define a transcriptional and epigenetic framework through which PCE may perturb mitochondrial function and impair MSN maturation trajectories in the NAc, providing mechanistic insights into how PCE may alter the development of reward circuitry.

neuroscience↗

bioRxiv2026

Cross-species architecture of the cardiac transverse-axial tubular system in mammals

Cardiac excitation-contraction coupling relies on a pancellular network of regular cardiomyocyte surface membrane invaginations, termed the transverse-axial tubular system (TATS). The TATS is ubiquitously present in adult mammalian cardiomyocytes, enabling efficient structural and functional coupling of sarcolemma and intracellular Ca2+ stores. However, TATS ultrastructural characteristics across species, and their relation to cardiomyocyte morphology and physiological parameters such as heart rate, remain largely unexplored. Here, we quantified TATS and cardiomyocyte features in a large confocal microscopy dataset (78 3D volumes) obtained from tissue slices across eight species (mouse, rat, rabbit, pig, horse, elephant, whale, and human). We developed and applied a semi-automated image analysis pipeline to quantify mean cytosolic distances to the nearest TATS (Cyto-TATSmin, a measure inversely related to TATS density), transverse-to-axial tubule ratio, and cardiomyocyte dimensions. Cyto-TATSmin and transverse tubule fraction differed substantially between species, with the lowest Cyto-TATSmin in mouse and highest in human. Within species, except mouse, rat, and horse, Cyto-TATSmin was positively correlated with cardiomyocyte cross-sectional area. Across all species, Cyto-TATSmin correlated with species life span and body weight, and was inversely correlated with average resting heart rate. Our findings reveal structural scaling principles within species differences in cardiac cellular ultrastructure and provide a resource for studying TATS organisation in health and disease. As TATS remodelling is a common hallmark of cardiac pathology, awareness of species-differences is needed to guide the design and interpretation of translational research.

cell biology↗

bioRxiv2025

Dynamics of infant white matter maturation from birth to 6 months

The first months after a babys birth encompass the most rapid period of postnatal change in the human lifespan, but longitudinal trajectories of white matter maturation in this period remain uncharted. Using densely sampled diffusion tensor images collected longitudinally at a mean rate of 1 scan per 1.55 days, we measured non-linear growth and growth rate trajectories of major white matter tracts from birth to 6 months. Growth rates at birth were 6 to 11 times faster than at 6 months, with tracts less mature at birth developing fastest. When matched on chronological age, shorter gestation infants had less mature white matter at birth but faster growth rates than their longer gestation peers; however, growth trajectories were highly similar when corrected for gestational age. This is the first study to estimate white matter trajectories using dense sampling in the first 6 post-natal months, which can inform the study of neurodevelopmental disorders beginning in infancy.

neuroscience↗

bioRxiv2024

A benchmarking study of copy number variation inference methods using single-cell RNA-sequencing data

Single-cell RNA-sequencing (scRNA-seq) has emerged as a powerful tool for cancer research, enabling in-depth characterization of tumor heterogeneity at the single-cell level. Recently, scRNA-seq copy number variation (scCNV) inference methods have been developed, expanding the application of scRNA-seq to study genetic heterogeneity in cancer using transcriptomic data. However, the fidelity of these methods has not been investigated systematically. In this study, we benchmarked five commonly used scCNV inference methods, HoneyBADGER, CopyKAT, CaSpER, inferCNV, and sciCNV. We evaluated their performance across four different scRNA-seq platforms derived data from a multicenter study. We further evaluated the scCNV performance using scRNA-seq datasets derived from mixed samples consisting of five human lung adenocarcinoma cell lines and generated a clinical scRNA-seq dataset from a human small cell lung cancer patient to validate our findings. Our evaluation criteria included sensitivity and specificity of CNV detection, and subclone identification from mixed cancer samples. We found that the sensitivity and specificity of the five scCNV inference methods varied, depending on the selection of reference data, sequencing depths, and read lengths. Overall, CopyKAT and CaSpER exhibited superior performance to other methods, while inferCNV, sciCNV, and CopyKAT outperformed other methods in subclone identification accuracy. Remarkably, inferCNV achieved high accuracy in subclone identification when using data from a "single scRNA-seq protocol", however, when applying these methods to a dataset derived from multiple scRNA-seq platforms from the mixed samples, we found that batch effects significantly affected the performance of subclone identification for most methods, except for HoneyBADGER. Our benchmarking study revealed the strengths and weaknesses of each of the five scCNV inference methods and provided guidance for selecting the optimal CNV inference method using scRNA-seq data.

bioinformatics↗

bioRxiv2024

Epigenomic, transcriptomic and proteomic characterizations of reference samples

A variety of newly developed next-generation sequencing technologies are making their way rapidly into the research and clinical applications, for which accuracy and cross-lab reproducibility are critical, and reference standards are much needed. Our previous multicenter studies under the SEQC-2 umbrella using a breast cancer cell line with paired B-cell line have produced a large amount of different genomic data including whole genome sequencing (Illumina, PacBio, Nanopore), HiC, and scRNA-seq with detailed analyses on somatic mutations, single-nucleotide variations (SNVs), and structural variations (SVs). However, there is still a lack of well-characterized reference materials which include epigenomic and proteomic data. Here we further performed ATAC-seq, Methyl-seq, RNA-seq, and proteomic analyses and provided a comprehensive catalog of the epigenomic landscape, which overlapped with the transcriptomes and proteomes for the two cell lines. We identified >7,700 peptide isoforms, where the majority (95%) of the genes had a single peptide isoform. Protein expression of the transcripts overlapping CGIs were much higher than the protein expression of the non-CGI transcripts in both cell lines. We further demonstrated the evidence that certain SNVs were incorporated into mutated peptides. We observed that open chromatin regions had low methylation which were largely regulated by CG density, where CG-rich regions had more accessible chromatin, low methylation, and higher gene and protein expression. The CG-poor regions had higher repressive epigenetic regulations (higher DNA methylation) and less open chromatin, resulting in a cell line specific methylation and gene expression patterns. Our studies provide well-defined reference materials consisting of two cell lines with genomic, epigenomic, transcriptomic, scRNA-seq and proteomic characterizations which can serve as standards for validating and benchmarking not only on various omics assays, but also on bioinformatics methods. It will be a valuable resource for both research and clinical communities.

genomics↗

bioRxiv2022

Semi-automated protocol to quantify and characterize fluorescent, 3D vascular images

The microvasculature facilitates gas exchange, provides nutrients to cells, and regulates blood flow in response to stimuli. Vascular abnormalities are an indicator of pathology for various conditions, such as compromised vessel integrity in small vessel disease and angiogenesis in tumors. Traditional immunohistochemistry enables visualization of tissue cross-sections containing exogenously labeled vasculature. Although this approach can be utilized to quantify vascular changes within small fields-of-view, it is not a practical way to study the vasculature on the scale of whole organs. Three-dimensional (3D) imaging presents a more appropriate method to visualize the vascular architecture in tissue. Here we describe the complete protocol that we use to characterize the vasculature of different organs in mice encompassing the methods to fluorescently label vessels, optically clear tissue, collect 3D vascular images, and quantify these vascular images with a semi-automated approach. To validate the automated segmentation of vascular images, one user manually segmented fifty random regions of interest across different vascular images. The automated segmentation results had an average sensitivity of 80{+/-}8% and an average specificity of 90{+/-}5% when compared to manual segmentation. Applying this procedure of image analysis presents a method to reliably quantify and characterize vascular networks in a timely fashion. This procedure is also applicable to other methods of tissue clearing and vascular labels that generate 3D images of microvasculature.

bioengineering↗

bioRxiv2021

Deep oncopanel sequencing reveals fixation time- and within block position-dependent quality degradation in FFPE processed samples

Clinical laboratories routinely use formalin-fixed paraffin-embedded (FFPE) tissue or cell block cytology samples in oncology panel sequencing to identify mutations that can predict patient response to targeted therapy. To understand the technical error due to FFPE processing, a robustly characterized normal cell line was used to create FFPE samples with four different pre-tissue processing formalin fixation times. A total of 96 FFPE sections were then distributed to different laboratories for targeted sequencing analysis by four oncopanels, and variants resulting from technical error were identified. Tissue sections that failed more frequently showed low cellularity, lower than recommended library preparation DNA input, or target sequencing depth. Importantly, sections from block surfaces were more likely to show FFPE-specific errors, akin to "edge effects" seen in histology, and the depth of formalin damage was related to fixation time. To assure reliable results, we recommend avoiding the block surface portion and restricting mutation detection to genomic regions of high confidence.

cancer biology↗

bioRxiv2020

A benchmarking study of SARS-CoV-2 whole-genome sequencing protocols using COVID-19 patient samples

The COVID-19 pandemic is a once-in-a-lifetime event, exceeding mortality rates of the flu pandemics from the 1950s and 1960s. Whole-genome sequencing (WGS) of SARS-CoV-2 plays a critical role in understanding the disease. Performance variation exists across SARS-CoV-2 viral WGS technologies, but there is currently no benchmarking study comparing different WGS sequencing protocols. We compared seven different SARS-CoV-2 WGS library protocols using RNA from patient nasopharyngeal swab samples under two storage conditions. We constructed multiple WGS libraries encompassing three different viral inputs: 1,000,000, 250,000 and 1,000 copies. Libraries were sequenced using two distinct platforms with varying sequencing depths and read lengths. We found large differences in mappability and genome coverage, and variations in sensitivity, reproducibility and precision of single-nucleotide variant calling across different protocols. We ranked the performance of protocols based on six different metrics. Our results indicated that the most appropriate protocol depended on viral input amount and sequencing depth. Our findings offer guidance in choosing appropriate WGS protocols to characterize SARS-CoV-2 and its evolution.

genomics↗

bioRxiv2019

The breast cancer oncogene IKKε coordinates mitochondrial function and serine metabolism

The I{kappa}B kinase {varepsilon} (IKK{varepsilon}) is a key molecule at the crossroads of inflammation and cancer. Known for its role as an activator of NF{kappa}B and IRF3 signalling leading to cytokine secretion, the kinase is also a breast cancer oncogene, overexpressed in a variety of tumours. However, to what extent IKK{varepsilon} remodels cellular metabolism is currently unknown. Here we used a combination of metabolomics and phosphoproteomics to show that IKK{varepsilon} orchestrates a complex metabolic reprogramming that affects mitochondrial metabolism and serine biosynthesis. Acting independently of its canonical signalling role, IKK{varepsilon} upregulates the serine biosynthesis pathway (SBP) mainly by limiting glucose and pyruvate derived anaplerosis of the TCA cycle. In turn, this elicits activation of the transcription factor ATF4 and upregulation of the SBP genes. Importantly, pharmacological inhibition of the IKK{varepsilon}-induced metabolic phenotype reduces proliferation of breast cancer cells. Finally, we show that in a set of basal ER negative and highly proliferative human breast cancer tumours, IKK{varepsilon} and PSAT1 expression levels are positively correlated corroborating the link between IKK{varepsilon} and the SBP in the clinical context.

cancer biology↗

bioRxiv2018

Rapid Paediatric Sequencing (RaPS): Comprehensive real-life workflow for rapid diagnosis of critically ill children

BackgroundRare genetic conditions are frequent risk factors for, or direct causes of, organ failure requiring paediatric intensive care unit (PICU) support. Such conditions are frequently suspected but unidentified at PICU admission. Compassionate and effective care is greatly assisted by definitive diagnostic information. There is therefore a need to provide a rapid genetic diagnosis to inform clinical management.\n\nTo date, Whole Genome Sequencing (WGS) approaches have proved successful in diagnosing a proportion of children with rare diseases, but results may take months to report or require the use of equipment and practices not compatible with a clinical diagnostic setting. We describe an end-to-end workflow for the use of rapid WGS for diagnosis in critically ill children in a UK National Health Service (NHS) diagnostic setting.\n\nMethodsWe sought to establish a multidisciplinary Rapid Paediatric Sequencing (RaPS) team for case selection, trio WGS, a rapid bioinformatics pipeline for sequence analysis and a phased analysis and reporting system to prioritise genes with a high likelihood of being causal. Our workflow was iteratively developed prospectively during the analysis of the first 10 children and applied to the following 14 to assess its utility.\n\nFindingsTrio WGS in 24 critically ill children led to a molecular diagnosis in ten (42%) through the identification of causative genetic variants. In three of these ten individuals (30%) the diagnostic result had an immediate impact on the individuals clinical management. For the last 14 trios, the shortest time taken to reach a provisional diagnosis was four days (median 7 days).\n\nInterpretationRapid WGS can be used to diagnose and inform management of critically ill children using widely available off the shelf products within the constraints of an NHS clinical diagnostic setting. We provide a robust workflow that will inform and facilitate the rollout of rapid genome sequencing in the NHS and other healthcare systems globally.\n\nFundingThe study was funded by NIHR GOSH/UCL BRC: ormbrc-2012-1

genomics↗

bioRxiv2017

Sequence variation aware genome references and read mapping with the variation graph toolkit

Reference genomes guide our interpretation of DNA sequence data. However, conventional linear references are fundamentally limited in that they represent only one version of each locus, whereas the population may contain multiple variants. When the reference represents an individuals genome poorly, it can impact read mapping and introduce bias. Variation graphs are bidirected DNA sequence graphs that compactly represent genetic variation, including large scale structural variation such as inversions and duplications.1 Equivalent structures are produced by de novo genome assemblers.2,3 Here we present vg, a toolkit of computational methods for creating, manipulating, and utilizing these structures as references at the scale of the human genome. vg provides an efficient approach to mapping reads onto arbitrary variation graphs using generalized compressed suffix arrays,4 with improved accuracy over alignment to a linear reference, creating data structures to support downstream variant calling and genotyping. These capabilities make using variation graphs as reference structures for DNA sequencing practical at the scale of vertebrate genomes, or at the topological complexity of new species assemblies.

genomics↗