Biology subjects

Barry, T.

Publications and source records attributed to Barry, T..

9 recordsLinked to original sources

bioRxiv2026

Enhancer-gene regulatory interactions in colorectal cancer revealed through genome-wide CRISPRi perturbations

BackgroundEstablishing functional relationships between distal enhancers and their target genes is a central challenge in genome biology and cancer genetics. CRISPR/dCas9-mediated perturbations followed by single-cell RNA sequencing (Perturb-seq) has emerged as an efficient means of establishing enhancer-target gene relationships. ResultsWe used genome-wide CRISPRi Perturb-seq with 35,139 guide RNAs targeting 12,117 enhancers to create a functional enhancer-gene map of colorectal cancer (CRC), identifying 238 significant regulatory associations (FDR < 0.1). Integration of chromatin accessibility (ATAC-seq), histone modification profiling (ChIP-seq), and ultra-high resolution chromatin conformation capture (Micro-C) data revealed that enhancer regulation in CRC is constrained by topological domains and often targets the nearest gene, findings corroborated by Activity-by-Contact modelling. ConclusionsWe present here a comprehensive, genome-scale functional atlas of enhancer-gene associations in CRC. This resource should provide a foundation for studying gene regulation in colorectal tumorigenesis and for prioritising candidate non-coding drivers in cancer sequencing studies. PLAIN LANGUAGE SUMMARYO_ST_ABSMapping the hidden DNA switches that control colon cancer genesC_ST_ABSThe total amount of DNA in humans is vast, but only a tiny proportion of it is directly related to genes. While people may know that genes act as blueprints for the body, there are also short stretches of DNA called "enhancers", that fine-tune how strongly genes are activated. When these enhancers get damaged or mutated, it can trigger disease, suggesting that changes to these "control switches" themselves, and not just the genes, can be a driver of cancer. Thus, understanding how these enhancers function is key to uncovering the mechanisms that drive colorectal cancer. In this study, we aimed to map the role of enhancers in colorectal (bowel) cancer development. We used Perturb-seq, a cutting-edge genetic tool that allowed us to systematically turn off more than 12,000 individual enhancers, to identify which ones control which genes. We successfully linked 238 enhancers to the genes they regulate. Combining these results with other biological information, such as the 3D-shape of the DNA, allowed us to build a detailed picture of how enhancers physically interact with their target genes inside cancer cells. This work provides a comprehensive "wiring diagram" of enhancer-gene connections in colorectal cancer, and a resource for researchers seeking to develop new treatment approaches.

genomics↗

bioRxiv2025

Analyzing long-read CRISPR experiments with CRISPRLungo

Long-read sequencing can characterize complex genome editing-induced DNA sequence changes such as large deletions, insertions, and inversions that are difficult to detect using short-read sequencing. However, PCR amplification and sequencing errors complicate accurate variant detection, and existing analysis tools are not optimized for gene editing specific allelic outcomes. Here we present CRISPRLungo, a computational pipeline specifically designed for long-read amplicon sequencing of gene edited samples. CRISPRLungo incorporates unique molecular identifier (UMI)-based error correction and statistical filtering to distinguish true editing events from background noise, enabling robust detection of small indels and structural variants. Through systematic benchmarking using simulated datasets, we demonstrate that CRISPRLungo outperforms existing approaches in both accuracy and read recovery. CRISPRLungo supports both Oxford Nanopore and PacBio platforms and identify previously undetected structural variant edits such as inversions in published CRISPR datasets. To demonstrate allele-specific edit quantification, we applied CRISPRLungo to analyze edited primary cells from a patient with harboring compound heterozygous SBDS mutations, accurately quantifying SBDS editing outcomes despite contaminating reads from the homologous SBDSP1 pseudogene. To maximize accessibility, we developed a fully client-side web application requiring no installation, making advanced long-read analysis accessible to researchers regardless of computational expertise. CRISPRLungo is freely available at https://github.com/pinellolab/CRISPRLungo with a user-friendly web interface available at https://pinellolab.github.io/CRISPRLungo.

bioinformatics↗

bioRxiv2025

An unbiased survey of distal element-gene regulatory interactions with direct-capture targeted Perturb-seq

Identifying the impact of distal regulatory elements on gene expression is a core challenge in human genetics. Large-scale CRISPR screens have not captured lower effect size element-gene interactions due to selection bias and limited statistical power. We developed a framework for highly powered CRISPR screens, consisting of Direct-Capture Targeted Perturb-seq (DC-TAP-seq), unbiased target selection, and a pipeline accounting for statistical power. Surveying 10,000 random distal element-gene pairs revealed most element-gene interactions have effect sizes <10%, which were virtually undetectable in prior studies. Most interactions occur within 100kb, many elements bind CTCF without classical enhancer chromatin, and housekeeping genes have similar frequencies of distal regulatory elements but with weaker effects. We also highlight limitations of predictive models and suggest that new models consider elements with smaller effect sizes. Our study provides an expanded view of distal regulatory elements and a framework for building more comprehensive maps of distal regulation.

genomics↗

bioRxiv2025

Computational modelling reveals slower safety learning and threat extinction are associated with higher anxiety severity in remote fear conditioning

Anxiety disorders are are chronic, pervasive, and debilitating; characterised by a persistent or exaggerated response to distal or abstract threats. Impaired threat discrimination (distinguishing safe from threatening stimuli) and impaired threat extinction (learning a once threatening stimulus is now safe), are known risk factors in the development and persistence of anxiety disorders. These effects can be experimentally elicited through fear conditioning. First, repeated trials of paired aversive and neutral stimuli are delivered during a fear acquisition phase, followed by repeated trials with no aversive stimuli in a fear extinction phase. The effects are typically measured through comparison of end-phase data points, or simple descriptive or statistical models. Computational modelling, by contrast, can offer a hypothesis-driven, trial-by-trial mechanistic account of fear conditioning. This unmasks within subject task variance by estimating the rate of threat learning, safety learning, and threat extinction, examining individual differences in the cognitive mechanisms behind anxiety. A normative sample (n = 145) underwent a differential fear conditioning task on a bespoke smartphone app, in addition to completing an anxiety severity measure (GAD-7). Computational models fitted to task data estimated learning rates. Whilst the threat learning rate showed no association, the threat extinction and safety learning rates showed small negative associations with anxiety severity (r = -0.218, p = 0.008 & r = -0.214, p = 0.01 respectively). These findings are in keeping with prior studies using traditional analytical approaches, and indicate that anxious individuals are not quicker to develop fear of a stimulus, but take more time than their non-anxious counterparts to learn that a stimulus is safe. This study strengthens the evidence for impairments in fear extinction in those with anxiety, and the importance of learning rates as an index of anxiety severity, a previously hidden cognitive mechanism underlying anxiety persistence.

neuroscience↗

bioRxiv2023

3D Nuclei Segmentation by Combining GAN Based Image Synthesis and Existing 3D Manual Annotations

Nuclei segmentation is an important task in cell biology analysis that requires accurate and reliable methods, especially within complex low signal to noise ratio images with crowded cells populations. In this context, deep learning-based methods such as Stardist have emerged as the best performing solutions for segmenting nucleus. Unfortunately, the performances of such methods rely on the availability of vast libraries of ground truth hand-annotated data-sets, which become especially tedious to create for 3D cell cultures in which nuclei tend to overlap. In this work, we present a workflow to segment nuclei in 3D in such conditions when no specific ground truth exists. It combines the use of a robust 2D segmentation method, Stardist 2D, which have been trained on thousands of already available ground truth datasets, with the generation of pair of 3D masks and synthetic fluorescence volumes through a conditional GAN. It allows to train a Stardist 3D model with 3D ground truth masks and synthetic volumes that mimic our fluorescence ones. This strategy allows to segment 3D data that have no available ground truth, alleviating the need to perform manual annotations, and improving the results obtained by training Stardist with the original ground truth data.

bioinformatics↗

bioRxiv2023

Robust differential expression testing for single-cell CRISPR screens at low multiplicity of infection

Single-cell CRISPR screens (perturb-seq) link genetic perturbations to phenotypic changes in individual cells. The most fundamental task in perturb-seq analysis is to test for association between a perturbation and a count outcome, such as gene expression. We conduct the first-ever comprehensive benchmarking study of association testing methods for low multiplicity-of-infection (MOI) perturb-seq data, finding that existing methods produce excess false positives. We conduct an extensive empirical investigation of the data, identifying three core analysis challenges: sparsity, confounding, and model misspecification. Finally, we develop an association testing method -- SCEPTRE low-MOI -- that resolves these analysis challenges and demonstrates improved calibration and power.

genomics↗

bioRxiv2022

SalienceNet: an unsupervised Image-to-Image translation method for nuclei saliency enhancement in microscopy images

Automatic segmentation of nuclei in low-light microscopy images remains a difficult task, especially for high-throughput experiments where need for automation is strong. Low saliency of nuclei with respect to the background, variability of their intensity together with low signal-to-noise ratio in these images constitute a major challenge for mainstream algorithms of nuclei segmentation. In this work we introduce SalienceNet, an unsupervised deep learning-based method that uses the style transfer properties of cycleGAN to transform low saliency images into high saliency images, thus enabling accurate segmentation by downstream analysis methods, and that without need for any parameter tuning. We have acquired a novel dataset of organoid images with soSPIM, a microscopy technique that enables the acquisition of images in low-light conditions. Our experiments show that SalienceNet increased the saliency of these images up to the desired level. Moreover, we evaluated the impact of SalienceNet on segmentation for both Otsu thresholding and StarDist and have shown that enhancing nuclei with SalienceNet improved segmentation results using Otsu thresholding by 30% and using StarDist by 26% in terms of IOU when compared to segmentation of non-enhanced images. Together these results show that SalienceNet can be used as a common preprocessing step to automate nuclei segmentation pipelines for low-light microscopy images.

bioinformatics↗

bioRxiv2022

Cellular Heterogeneity of Pluripotent Stem Cell Derived Cardiomyocyte Grafts is Mechanistically Linked to Treatable Arrhythmias

BackgroundExciting pre-clinical data have confirmed that human pluripotent stem cell derived cardiomyocytes (PSC-CMs) can remuscularise the injured or diseased heart, with several clinical trials now in planning or recruitment stages worldwide. However, ventricular arrhythmias are a predictable complication following engraftment of intramyocardially injected PSC-CMs. Therefore, there is an urgent unmet need to gain mechanistic insights and treatment strategies to control or prevent these engraftment arrhythmias (EAs). MethodsWe used a porcine model of myocardial infarction and PSC-CM transplantation to investigate efficacy of pharmacologic and catheter based anti-arrhythmic strategies in mitigating EAs. Furthermore, cell doses were robustly phenotyped using single cell ribonucleic acid sequencing and high parameter flow cytometry to identify cellular characteristics predictive of arrhythmogenesis. ResultsCombination therapy with amiodarone and ivabradine significantly reduced EA rate and burden following PSC-CM transplantation. Catheter ablation was also a feasible and effective treatment strategy which could be considered in the case of pharmacologically refractory arrhythmias. In addition, we show that EAs are mechanistically linked to cellular heterogeneity in the input PSC-CM and resultant graft. Specifically, we identify atrial and pacemaker-like cardiomyocytes as culprit arrhythmogenic subpopulations. We further describe two unique surface marker signatures, SIRPA+/CD90-/CD200+ and SIRPA+/CD90-/CD200-, which identify arrhythmogenic and non-arrhythmogenic cardiomyocytes respectively. ConclusionOur data deepens mechanistic understanding of EAs and suggests that modifications to current PSC-CM production and/or selection protocols could ameliorate this problem. We further show that current clinical pharmacologic and interventional anti-arrhythmic strategies can control and potentially abolish these arrhythmias, an important safety consideration given several impending clinical trials.

cell biology↗

bioRxiv2021

Discovery of target genes and pathways of blood trait loci using pooled CRISPR screens and single cell RNA sequencing

The majority of variants associated with complex traits and common diseases identified by genome-wide association studies (GWAS) map to noncoding regions of the genome with unknown regulatory effects in cis and trans. By leveraging biobank-scale GWAS data, massively parallel CRISPR screens and single cell transcriptome sequencing, we discovered target genes of noncoding variants for blood trait loci. The closest gene was often the target gene, but this was not always the case. We also identified trans-effects networks of noncoding variants when cis target genes encoded transcription factors, such as GFI1B and NFE2. We observed that GFI1B trans-target genes were enriched for GFI1B binding sites and fine-mapped GWAS variants, and expressed in human bone marrow progenitor cells, suggesting that GFI1B acts as a master regulator of blood traits. This platform will enable massively parallel assays to catalog the target genes of human noncoding variants in both cis and trans.

genomics↗