Biology subjects

Rattray, M.

Publications and source records attributed to Rattray, M..

At least 37 records · Page 2Linked to original sources

bioRxiv2023

A spatial map of human liver cirrhosis reveals the patho-architecture and gene signatures associated with cell state transitions during liver disease

Liver fibrosis is a major cause of death worldwide. As a progressive step in chronic liver disease, fibrosis is almost always diagnosed too late with limited treatment options. Here, we uncover the spatial transcriptional landscape driving human liver fibrosis using single nuclei RNA and Assay for Transposase-Accessible Chromatin (ATAC) sequencing to deconvolute multi-cell spatial transcriptomic profiling in human liver cirrhosis. Through multi-modal data integration, we define molecular signatures driving cell state transitions in liver disease and define an impaired cellular response and directional trajectory from hepatocytes to cholangiocytes associated with disease remodelling. We identify pro-fibrogenic signatures in non-parenchymal cell subpopulations co-localised within the fibrotic niche and localise transitional cell states at the scar interface. This combined approach provides a spatial atlas of gene regulation and defines molecular signatures associated liver disease for targeted therapeutics or as early diagnostic markers of progressive liver disease.

genomics↗

bioRxiv2023

Modulation of transcription burst amplitude underpins dosage compensation in the Drosophila embryo

Dosage compensation, the balancing of X linked gene expression between sexes and to the autosomes, is critical to an organisms fitness and survival. In Drosophila, dosage compensation involves hypertranscription of the male X chromosome. Here we use quantitative live imaging and modelling at single-cell resolution to determine the mechanism underlying X chromosome dosage compensation in Drosophila. We show that the four X chromosome genes studied undergo transcriptional bursting in male and female embryos. Mechanistically our data reveal that transcriptional upregulation of male X chromosome genes is primarily mediated by a higher RNA polymerase II initiation rate and burst amplitude across the expression domain. In contrast, burst frequency is spatially modulated in nuclei within the expression domain in response to different transcription factor concentrations to tune the transcriptional response. Together, these data show how the local and global regulation of distinct burst parameters establish the complex transcriptional outputs underpinning developmental patterning.

developmental biology↗

bioRxiv2023

SynBa: Improved estimation of drug combination synergies with uncertainty quantification

MotivationThere exists a range of different quantification frameworks to estimate the synergistic effect of drug combinations. The diversity and disagreement in estimates make it challenging to determine which combinations from a large drug screening should be proceeded with. Furthermore, the lack of accurate uncertainty quantification for those estimates precludes the choice of optimal drug combinations based on the most favourable synergistic effect. ResultsIn this work, we propose SynBa, a flexible Bayesian approach to estimate the uncertainty of the synergistic efficacy and potency of drug combinations, so that actionable decisions can be derived from the model outputs. The actionability is enabled by incorporating the Hill equation into SynBa, so that the parameters representing the potency and the efficacy can be preserved. Existing knowledge may be conveniently inserted due to the flexibility of the prior, as shown by the empirical Beta prior defined for the normalised maximal inhibition. Through experiments on large combination screenings and comparison against benchmark methods, we show that SynBa provides improved accuracy of dose-response predictions and better-calibrated uncertainty estimation for the parameters and the predictions. AvailabilityThe code for SynBa is available at https://github.com/HaotingZhang1/SynBa. The datasets are publicly available (DOI of DREAM: 10.7303/syn4231880; DOI of the NCI-ALMANAC subset: 10.5281/zenodo.4135059). Contacthz381@cam.ac.uk

pharmacology and toxicology↗

bioRxiv2022

Distinct cohorts of Aspergillus fumigatus transcription factors are required for epithelial damage occurring via contact- or soluble effector-mediated mechanisms

Damage to the lung epithelium is a unifying feature of disease caused by the saprophytic fungus Aspergillus fumigatus. However, the mechanistic basis and the regulatory control of such damage is poorly characterized. Previous studies have identified A. fumigatus mediated pathogenesis as occurring at early ([≤] 16 hours) or late (>16 hours) phases of the fungal interaction with epithelial cells, and respectively involve direct contact with the host cell or the action of soluble factors produced by mature fungal hyphae. Both early and late phases of epithelial damage have been shown to be subject to genetic regulation by the pH-responsive transcription factor PacC. This study sought to determine whether other transcriptional regulators play a role in modulating epithelial damage. In particular, whether the early and late phases of epithelial damage are governed by same or distinct regulators. Furthermore, whether processes such as spore uptake and hyphal adhesion, that have previously been documented to promote epithelial damage, are governed by the same cohorts of epithelial regulators. Using 479 strains from the recently constructed library of A. fumigatus transcription factor null mutants, two high-throughput screens assessing epithelial cell detachment and epithelial cell lysis were conducted. A total of 17 transcription factor mutants were found to exhibit reproducible deficits in epithelial damage causation. Of these, 10 mutants were defective in causing early phase damage via epithelial detachment and 8 mutants were defective in causing late phase damage via epithelial lysis. Remarkably only one transcription factor, PacC, was required for causation of both phases of epithelial damage. The 17 mutants exhibited varied and often unique phenotypic profiles with respect to fitness, epithelial adhesion, cell wall defects, and rates of spore uptake by epithelial cells. Strikingly, 9 out of 10 mutants deficient in causing early phase damage also exhibited reduced rates of hyphal extension, and culture supernatants of 7 out of 8 mutants deficient in late phase damage were significantly less cytotoxic. Our study delivers the first high-level overview of A. fumigatus regulatory genes governing lung epithelial damage, suggesting highly coordinated genetic orchestration of host-damaging activities that govern epithelial damage in both space and time. Contribution to the Field StatementAspergillus fumigatus is a soil dwelling fungus that can cause lethal lung infections in individuals with a compromised immune system. Disease initiates with inhalation of the fungal spores, followed by growth of the fungus leading to destruction of the lung. Our understanding of the A. fumigatus genes and mechanisms driving lung damage leading to establishment of disease is limited. This study has identified the genes moderating lung damage by assessing 479 regulatory mutants of A. fumigatus for their ability to cause epithelial damage using a lung cell line model. We observed that distinct cohorts of transcriptional regulators are required for driving early, and late phases of epithelial damage and that early- and late-occurring damage are associated respectively with hyphal growth rates and secreted fungal products. This study is the first to reveal that mechanistically distinct programs of host damage elicited during early and late stage of fungal interaction with epithelial cells are genetically regulated via distinct cohorts of A. fumigatus transcription factors.

microbiology↗

bioRxiv2022

Modelling global mRNA dynamics during Drosophila embryogenesis reveals a relationship between mRNA degradation and P-bodies

Regulation of mRNA degradation is critical for a diverse array of cellular processes and developmental cell fate decisions. Many methods for determining mRNA half-lives rely on transcriptional inhibition or metabolic labelling. Here we use a non-invasive method for estimating half-lives for hundreds of mRNAs in the early Drosophila embryo. This approach uses the intronic and exonic reads from a total RNA-seq time series and Gaussian process regression to model the dynamics of premature and mature mRNAs. We show how regulation of mRNA stability is used to establish a range of mature mRNA dynamics during embryogenesis, despite shared transcription profiles. Using single molecule imaging we provide evidence that, for the mRNAs tested, there is a correlation between short half-life and mRNA association with P-bodies. Moreover, we detect an enrichment of mRNA 3 ends in P-bodies in the early embryo, consistent with 5 to 3 degradation occurring in P-bodies for at least a subset of mRNAs. We discuss our findings in relation to recently published data suggesting that the primary function of P-bodies in other biological contexts is mRNA storage.

developmental biology↗

bioRxiv2021

Inferring kinetic parameters of oscillatory gene regulation from single cell time series data

Gene expression dynamics, such as stochastic oscillations and aperiodic fluctuations, have been associated with cell fate changes in multiple contexts, including development and cancer. Single cell live imaging of protein expression with endogenous reporters is widely used to observe such gene expression dynamics. However, the experimental investigation of regulatory mechanisms underlying the observed dynamics is challenging, since these mechanisms include complex interactions of multiple processes, including transcription, translation, and protein degradation. Here, we present a Bayesian method to infer kinetic parameters of oscillatory gene expression regulation using an auto-negative feedback motif with delay. Specifically, we use a delay-adapted nonlinear Kalman filter within a Metropolis-adjusted Langevin algorithm to identify posterior probability distributions. Our method can be applied to time series data on gene expression from single cells and is able to infer multiple parameters simultaneously. We apply it to published data on murine neural progenitor cells and show that it outperforms alternative methods. We further analyse how parameter uncertainty depends on the duration and time resolution of an imaging experiment, to make experimental design recommendations. This work demonstrates the utility of parameter inference on time course data from single cells and enables new studies on cell fate changes and population heterogeneity.

developmental biology↗

bioRxiv2020

Scalable inference of transcriptional kinetic parameters from MS2 time series data

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSMotivationC_ST_ABSThe MS2-McP (MS2 coat protein) live imaging system allows for visualisation of transcription dynamics through the introduction of hairpin stem-loop sequences into a gene. A fluorescent signal at the site of nascent transcription in the nucleus quantifies mRNA production. computational modelling can be used to infer the promoter states along with the kinetic parameters governing transcription, such as promoter switching frequency and polymerase loading rate. However, modelling of the fluorescent trace presents a challenge due its persistence; the observed fluorescence at a given time point depends on both current and previous promoter states. A memory-adjusted Hidden Markov Model (mHMM) was recently introduced to allow inference of promoter activity from MS2-McP data. However, the computational time for inference scales exponentially with gene length and the mHMM is therefore not currently practical for application to many eukaryotic genes. ResultsWe present a scalable implementation of the mHMM for fast inference of promoter activity and transcriptional kinetic parameters. This new method can model genes of arbitrary length through the use of a time-adaptive truncated compound state space. The truncated state space provides a good approximation to the full state space by retaining the most likely set of states at each time during the forward pass of the algorithm. Testing on MS2-MCP fluorescent data collected from early Drosophila melanogaster embryos indicates that the method provides accurate inference of kinetic parameters within a computationally feasible timeframe. The inferred promoter traces generated by the model can also be used to infer single-cell transcriptional parameters. AvailabilityPython implementation available at https://github.com/ManchesterBioinference/burstInfer, along with code to reproduce the examples presented here.

bioinformatics↗

bioRxiv2020

Non-parametric modelling of temporal and spatial counts data from RNA-seq experiments

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSMotivationC_ST_ABSThe negative binomial distribution has been shown to be a good model for counts data from both bulk and single-cell RNA-sequencing (RNA-seq). Gaussian process (GP) regression provides a useful non-parametric approach for modeling temporal or spatial changes in gene expression. However, currently available GP regression methods that implement negative binomial likelihood models do not scale to the increasingly large datasets being produced by single-cell and spatial transcriptomics. ResultsThe GPcounts package implements GP regression methods for modelling counts data using a negative binomial likelihood function. Computational efficiency is achieved through the use of variational Bayesian inference. The GP function models changes in the mean of the negative binomial likelihood through a logarithmic link function and the dispersion parameter is fitted by maximum likelihood. We validate the method on simulated time course data, showing that it is better able to identify changes in over-dispersed counts data than methods based on Gaussian or Poisson likelihoods. To demonstrate temporal inference, we apply GPcounts to single-cell RNA-seq datasets after pseudotime and branching inference. To demonstrate spatial inference, we apply GPcounts to data from the mouse olfactory bulb to identify spatially variable genes and compare to two published GP methods. We also provide the option of modelling additional dropout using a zero-inflated negative binomial. Our results show that GPcounts can be used to model temporal and spatial counts data in cases where simpler Gaussian and Poisson likelihoods are unrealistic. AvailabilityGPcounts is implemented using the GPflow library in Python and is available at https://github.com/ManchesterBioinference/GPcounts along with the data, code and notebooks required to reproduce the results presented here. Contactnuha.bintayyash@manchester.ac.uk or magnus.rattray@manchester.ac.uk

bioinformatics↗

bioRxiv2020

An active chromatin interactome elucidates the biological mechanisms underlying genetic risk factors of dermatological conditions in disease relevant cell lines

Chromatin looping between regulatory elements and gene promoters presents a potential mechanism whereby disease risk variants affect their target genes. Here we use H3K27ac HiChIP, a method for assaying the active chromatin interactome in two cell lines: keratinocytes and skin derived CD8+ T cells. We integrate public datasets for a lymphoblastoid cell line and primary CD4+ T cells and identify gene targets at risk loci for skin-related disorders. Interacting genes enrich for pathways of known importance in each trait, such as cytokine response (psoriatic arthritis, psoriasis) and replicative senescence (melanoma). We show examples of how our analysis can inform changes in the current understanding of multiple psoriasis associated risk loci. For example, the variant rs10794648, which is generally assigned to IFNLR1, was linked to GRHL3 in our dataset, a gene essential in skin repair and development. Our findings, therefore, indicate a renewed importance of skin related factors in the risk of disease. O_FIG O_LINKSMALLFIG WIDTH=158 HEIGHT=200 SRC="FIGDIR/small/973271v2_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@99cd40org.highwire.dtl.DTLVardef@1725b6dorg.highwire.dtl.DTLVardef@d05d21org.highwire.dtl.DTLVardef@1924d07_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract In this article we take disease associated variants from 5 dermatological conditions and use cell type specific datasets to map genes that could be affected by these variants, providing insight into disease mechanisms. C_FIG

genomics↗

bioRxiv2019

HOX paralogs selectively convert binding of ubiquitous transcription factors into tissue-specific patterns of enhancer activation

Gene expression programs determine cell fate in embryonic development and their dysregulation results in disease. Transcription factors (TFs) control gene expression by binding to enhancers, but how TFs select and activate their target enhancers is still unclear. HOX TFs share conserved homeodomains with highly similar sequence recognition properties, yet they impart the identity of different animal body parts. To understand how HOX TFs control their specific transcriptional programs in vivo, we compared HOXA2 and HOXA3 binding profiles in the mouse embryo. HOXA2 and HOXA3 directly cooperate with TALE TFs and selectively target different subsets of a broad TALE chromatin platform. Binding of HOX and tissue-specific TFs convert low affinity TALE binding into high confidence, tissue-specific binding events, which bear the mark of active enhancers. We propose that HOX paralogs, alone and in combination with tissue-specific TFs, generate tissue-specific transcriptional outputs by modulating the activity of TALE TFs at selected enhancers.

developmental biology↗

bioRxiv2019

Modulation of promoter occupancy dictates the transcriptional response to graded BMP signalling levels in the Drosophila embryo

SUMMARYMorphogen gradients specify cell fates during development, with a classic example being the BMP gradient’s conserved role in embryonic dorsal-ventral axis patterning. Here we elucidate how the BMP gradient is interpreted in the Drosophila embryo, by combining live imaging with computational modelling to infer transcriptional burst parameters at single cell resolution. By comparing burst kinetics in cells receiving different levels of BMP signaling, we show that BMP signaling controls burst frequency by regulating the promoter activation rate. We provide evidence that the promoter activation rate is influenced by both enhancer and promoter sequences, whereas Pol II loading rate is primarily modulated by the enhancer. Consistent with BMP-dependent regulation of burst frequency, the numbers of BMP target gene transcripts per cell are graded across their expression domains. We suggest that graded mRNA output is a general feature of morphogen gradient interpretation and discuss how this can impact on cell fate decisions.Competing Interest StatementThe authors have declared no competing interest.View Full Text

developmental biology↗

bioRxiv2019

Simultaneous analysis of open chromatin, promoter interactions and gene expression in stimulated T cells implicates causal genes for rheumatoid arthritis

Genome-wide association studies have identified genetic variation contributing to complex disease risk. However, assigning causal genes and mechanisms has been more challenging because disease-associated variants are often found in distal regulatory regions with cell-type specific behaviours. Here, we collect ATAC-seq, Hi-C, Capture Hi-C and nuclear RNA-seq data in stimulated CD4+ T-cells over 24 hours, to identify functional enhancers regulating gene expression. We characterise changes in DNA interaction and activity dynamics that correlate with changes gene expression, and find that the strongest correlations are observed within 200 kb of promoters. Using rheumatoid arthritis as an example of T-cell mediated disease, we demonstrate interactions of expression quantitative trait loci with target genes, and confirm assigned genes or show complex interactions for 20% of disease associated loci, including FOXO1, which we confirm using CRISPR/Cas9.

genomics↗

bioRxiv2019

HiChIP-Peaks: A HiChIP peak calling algorithm

MotivationHiChIP is a powerful tool to interrogate 3D chromatin organization. Current tools to analyse chromatin looping mechanisms using HiChIP data require the identification of loop anchors to work properly. However, current approaches to discover these anchors from HiChIP data are not satisfactory, having either a very high false discovery rate or strong dependence on sequencing depth. Moreover, these tools do not allow quantitative comparison of peaks across different samples, failing to fully exploit the information available from HiChIP datasets.\n\nResultsWe develop a new tool based on a representation of HiChIP data centred on the re-ligation sites to identify peaks from HiChIP datasets, which can subsequently be used in other tools for loop discovery. This increases the reliability of these tools and improves recall rate as sequencing depth is reduced. We also provide a method to count reads mapping to peaks across samples, which can be used for differential peak analysis using HiChIP data.\n\nAvailabilityHiChIP-Peaks is freely available at https://github.com/ChenfuShi/HiChIP_peaks\n\nContact: chenfu.shi@postgrad.manchester.ac.uk

bioinformatics↗

bioRxiv2019

Uncovering tissue-specific binding features from differential deep learning

MotivationTranscription factors (TFs) can bind DNA in a cooperative manner, enabling a mutual increase in occupancy. Through this type of interaction, alternative binding sites can be preferentially bound in different tissues to regulate tissue-specific expression programmes. Recently, deep learning models have become state-of-the-art in various pattern analysis tasks, including applications in the field of genomics. We therefore investigate the application of convolutional neural network (CNN) models to the discovery of sequence features determining cooperative and differential TF binding across tissues. ResultsWe analyse ChIP-seq data from MEIS, TFs which are broadly expressed across mouse branchial arches, and HOXA2, which is expressed in the second and more posterior branchial arches. By developing models predictive of MEIS differential binding in all three tissues we are able to accurately predict HOXA2 co-binding sites. We evaluate transfer-like and multitask approaches to regularising the high-dimensional classification task with a larger regression dataset, allowing for creation of deeper and more accurate models. We test the performance of perturbation and gradient-based attribution methods in identifying the HOXA2 sites from differential MEIS data. Our results show that deep regularised models significantly outperform shallow CNNs as well as k-mer methods in the discovery of tissue-specific sites bound in vivo. AvailabilityFor implementation and models please visit https://doi.org/10.5281/zenodo.2635463.

bioinformatics↗

bioRxiv2019

OscoNet: Inferring oscillatory gene networks

MotivationOscillatory genes, with periodic expression at the mRNA and/or protein level, have been shown to play a pivotal role in many biological contexts. However, with the exception of the circadian clock and cell cycle, only a few such genes are known. Detecting oscillatory genes from snapshot single-cell experiments is a challenging task due to the lack of time information. Oscope is a recently proposed method to identify co-oscillatory gene pairs using single-cell RNA-seq data. Although promising, the current implementation of Oscope does not provide a principled statistical criterion for selecting oscillatory genes.\n\nResultsWe improve the optimisation scheme underlying Oscope and provide a well-calibrated non-parametric hypothesis test to select oscillatory genes at a given FDR threshold. We evaluate performance on synthetic data and three real datasets and show that our approach is more sensitive than the original Oscope formulation, discovering larger sets of known oscillators while-avoiding the need for less interpretable thresholds. We also describe how our proposed pseudo-time estimation method is more accurate in recovering the true cell order for each gene cluster while-requiring substantially less computation time than the extended nearest insertion approach.\n\nConclusionOscoNet is a robust and versatile approach to detect oscillatory gene networks from snapshot single-cell data addressing many of the limitations of the original Oscope method.

bioinformatics↗

bioRxiv2018

Quantitative, real-time, single cell analysis in tissue reveals expression dynamics of neurogenesis

During embryogenesis cells make fate decisions within complex tissue environments. The levels and dynamics of transcription factor expression regulate these decisions. Here we use single cell live imaging of an endogenous HES5 reporter and absolute protein quantification to gain a dynamic view of neurogenesis in the embryonic mammalian spinal cord. We report that dividing neural progenitors show both aperiodic and periodic HES5 protein fluctuations. Mathematical modelling suggests that in progenitor cells the HES5 oscillator operates close to its bifurcation boundary where stochastic conversions between dynamics are possible. HES5 expression becomes more frequently periodic as cells transition to differentiation which, coupled with an overall decline in HES5 expression, creates a transient period of oscillations with higher fold expression change. This increases the decoding capacity of HES5 oscillations and correlates with interneuron versus motor neuron cell fate. Thus, HES5 undergoes complex changes in gene expression dynamics as cells differentiate.

developmental biology↗

bioRxiv2017

GrandPrix: Scaling up the Bayesian GPLVM for single-cell data

MotivationThe Gaussian Process Latent Variable Model (GPLVM) is a popular approach for dimensionality reduction of single-cell data and has been used for pseudotime estimation with capture time information. However current implementations are computationally intensive and will not scale up to modern droplet-based single-cell datasets which routinely profile many tens of thousands of cells.\n\nResultsWe provide an efficient implementation which allows scaling up this approach to modern single-cell datasets. We also generalize the application of pseudotime inference to cases where there are other sources of variation, such as branching dynamics. We apply our method on microarray, nCounter, RNA-seq, qPCR and droplet-based datasets from different organisms. The model converges an order of magnitude faster compared to existing methods whilst achieving similar levels of estimation accuracy. Further, we demonstrate the flexibility of our approach by extending the model to higher-dimensional latent spaces that can be used to simultaneously infer pseudotime and other structure such as branching. Thus, the model has the capability of producing meaningful biological insights about cell ordering as well as cell fate regulation.\n\nAvailabilitySoftware available at github.com/ManchesterBioinference/GrandPrix.

bioinformatics↗

bioRxiv2017

Classifying cells with Scasat - a tool to analyse single-cell ATAC-seq

MotivationThe assay for transposase-accessible chromatin using sequencing (ATAC-seq) reveals the landscape and principles of DNA regulatory mechanisms by identifying the accessible genome of mammalian cells. When done at single-cell resolution, it provides an insight into the cell-to-cell variability that emerges from identical DNA sequences by identifying the variability in the genomic location of open chromatin sites in each of the cells. Processing of single-cell ATAC-seq requires a number of steps and a simple pipeline to processes and analyse single-cell ATAC-seq is not yet available.\n\nResultsThis paper presents ScAsAT (single-cell ATAC-seq analysis tool), a complete pipeline to process scATAC-seq data with simple steps. The pipeline is developed in a Jupyter notebook environment that holds the executable code along with the necessary description and results. For the initial sequence processing steps, the pipeline uses a number of well-known tools which it executes from a python environment for each of the fastq files. While functions for the data analysis part are mostly written in R, it is robust, flexible, interactive and easy to extend. The pipeline was applied to a single-cell ATAC-seq dataset in order to identify different cell-types from a complex cell mixture. The results from Scasat showed that open chromatin location corresponding to potential regulatory elements can account for cellular heterogeneity and can identify regulatory regions that separates cells from a complex population.\n\nAvailabilityThe jupyter notebook with the complete pipeline applied to the dataset published with this paper are publicly available on the Github (https://github.com/ManchesterBioinference/Scasat). An additional notebook is also provided for analysis of a publicly available dataset. The fastq files are submitted at ArrayExpress database at EMBL-EBI (www.ebi.ac.uk/arrayexpress) under accession number E-MTAB-6116.\n\nContactsyed.murtuzabaker@manchester.ac.uk and magnus.rattray@manchester.ac.uk\n\nSupplementary informationSupplementary data are available at bioRxiv online.

bioinformatics↗