Biology subjects

Kirk, P. D.

Publications and source records attributed to Kirk, P. D..

15 recordsLinked to original sources

bioRxiv2026

TGF-β- and IL-1-dependent fibroblast states differentially shape pancreatic cancer microenvironments and metastasis

Pancreatic ductal adenocarcinoma (PDAC) contains heterogeneous cancer-associated fibroblast (CAF) populations, including interleukin 1 (IL-1)-dependent inflammatory CAFs (iCAFs) and transforming growth factor beta (TGF-{beta})-dependent myofibroblastic CAFs (myCAFs), whose functions at primary and metastatic sites remain incompletely defined. Here, we genetically disrupted IL-1 or TGF-{beta} signalling in fibroblast activation protein (FAP)-expressing CAFs arising from a shared cellular origin to determine how myCAF and iCAF states shape PDAC progression and microenvironments. Depletion of TGF-{beta}-dependent myCAFs, but not IL-1-dependent iCAFs, did not alter primary tumour growth or local metastatic dissemination but significantly reduced liver and lung metastases. Matched primary tumours and liver metastasis analyses also revealed site-specific stromal responses to TGF-{beta} signalling disruption. Experimental models of liver metastasis colonisation and outgrowth indicated that the reduced metastatic phenotype was linked primarily to alterations within pancreatic tumours rather than to impaired malignant cell outgrowth at metastatic sites. Single-cell RNA-sequencing of myCAF-depleted primary tumours revealed extensive transcriptional reprogramming across fibroblast, immune and malignant compartments, highlighting the complexity of assigning myCAF-to-malignant cell signalling mechanisms in vivo. To resolve this crosstalk, we co-cultured PDAC organoids with pancreatic stellate cells engineered to remain in myCAF or iCAF states. Cross-model transcriptomic analyses, together with genetic and pharmacological perturbation studies, showed that myCAF-derived TGF-{beta} directly promotes epithelial-to-mesenchymal transition signalling in PDAC malignant cells, providing a candidate mechanism for the reduced metastasis observed upon myCAF depletion in vivo. These findings reveal distinct, context-dependent roles for TGF-{beta}- and IL-1-dependent CAF states and identify myCAF-derived TGF-{beta} signalling as a stromal mechanism promoting malignant cell plasticity.

cancer biology↗

bioRxiv2026

scFLAME: a unified generative model for interpretable clustering, hierarchical structure discovery and marker-gene identification in single-cell RNA-seq data

Identifying cell types from single-cell RNA sequencing (scRNA-seq) data typically requires several separate and often uninterpretable steps: dimensionality reduction, batch-correction, clustering, marker-gene identification and the discovery of finer-grained structure. Here we introduce scFLAME (single-cell Factor Latent Analysis with Mixture Embeddings), a probabilistic generative model that unifies these tasks: a negative binomial factor analysis of the raw counts - which can be adjusted for batch - is coupled to a Gaussian mixture prior over the latent space, learning the embedding and clustering jointly, while a shared linear decoder provides cluster-specific marker genes directly from the fitted model, and a merging procedure recovers a probabilistic hierarchy of finer-grained partitions. On simulated and real data, scFLAME matches or exceeds state-of-the-art clustering accuracy, is robust across sequencing platforms, and scales near-linearly to hundreds of thousands of cells. scFLAME thus replaces a chain of separate tools with a single, interpretable model for single-cell analysis.

bioinformatics↗

bioRxiv2025

Multi-stromal organoid co-cultures model pancreatic cancer and pancreatitis epithelial cell-fibroblast heterogeneity

Malignant cell-fibroblast cross-talks modulate disease progression and therapy response of pancreatic ductal adenocarcinoma (PDAC). Our knowledge of the heterogeneous nature of PDAC cancer-associated fibroblasts (CAFs) has significantly increased over the last few years. Yet, whether CAFs in PDAC differ from fibroblasts in pancreatic inflammation remains poorly understood. Chronic pancreatitis - a prolonged inflammatory state of the pancreas - is a risk factor for PDAC and is characterised by abundant fibroblasts. Thus, dissecting pancreatic fibroblast and epithelial cell reprogramming in malignancy relative to inflammation could inform new preventative, diagnostic and therapeutic strategies for PDAC. Here, we studied how pancreatic malignancy and inflammation differently shape fibroblast heterogeneity and their crosstalk with epithelial cells. We analysed human samples and mouse models of pancreatitis and PDAC and leveraged new murine pancreatitis-derived epithelial organoids to establish pancreatitis and PDAC organoid/multi-stroma co-cultures comprising pancreatic stellate cells, fibroblasts and mesothelial cells. We demonstrate that a combination of in vitro and in vivo models better captures epithelial cell and fibroblast markers of human pancreatitis and PDAC compared to mouse models alone. Finally, we identify PDAC and pancreatitis epithelial cell-specific reprogramming of stromal cells of different origin, and we infer the contribution of these distinct stromal cell types to fibroblasts in PDAC and pancreatitis in vivo. Together, our study highlights different epithelial cell-fibroblast heterogeneity in PDAC and pancreatitis, and provides new platforms for the identification of markers and epithelial-stromal interdependencies of these diseases.

cancer biology↗

bioRxiv2022

A semi-supervised Bayesian mixture modelling approach for joint batch correction and classification

AO_SCPLOWBSTRACTC_SCPLOWSystematic differences between batches of samples present significant challenges when analysing biological data. Such batch effects are well-studied and are liable to occur in any setting where multiple batches are assayed. Many existing methods for accounting for these have focused on high-dimensional data such as RNA-seq and have assumptions that reflect this. Here we focus on batch-correction in low-dimensional classification problems. We propose a semi-supervised Bayesian generative classifier based on mixture models that jointly predicts class labels and models batch effects. Our model allows observations to be probabilistically assigned to classes in a way that incorporates uncertainty arising from batch effects. By simultaneously inferring the classification and the batch-correction our method is more robust to dependence between batch and class than pre-processing steps such as ComBat. We explore two choices for the within-class densities: the multivariate normal and the multivariate t. A simulation study demonstrates that our method performs well compared to popular off-the-shelf machine learning methods and is also quick; performing 15,000 iterations on a dataset of 750 samples with 2 measurements each in 11.7 seconds for the MVN mixture model and 14.7 seconds for the MVT mixture model. We further validate our model on gene expression data where cell type (class) is known and simulate batch effects. We apply our model to two datasets generated using the enzyme-linked immunosorbent assay (ELISA), a spectrophotometric assay often used to screen for antibodies. The examples we consider were collected in 2020 and measure seropositivity for SARS-CoV-2. We use our model to estimate seroprevalence in the populations studied. We implement the models in C++ using a Metropolis-within-Gibbs algorithm, available in the R package batchmix. Scripts to recreate our analysis are at https://github.com/stcolema/BatchClassifierPaper.

bioinformatics↗

bioRxiv2021

Noise-augmented directional clustering of genetic association data identifies distinct mechanisms underlying obesity

Clustering genetic variants based on their associations with different traits can provide insight into their underlying biological mechanisms. Existing clustering approaches typically group variants based on the similarity of their association estimates for various traits. We present a new procedure for clustering variants based on their proportional associations with different traits, which is more reflective of the underlying mechanisms to which they relate. The method is based on a mixture model approach for directional clustering and includes a noise cluster that provides robustness to outliers. The procedure performs well across a range of simulation scenarios. In an applied setting, clustering genetic variants associated with body mass index generates groups reflective of distinct biological pathways. Mendelian randomization analyses support that the clusters vary in their effect on coronary heart disease, including one cluster that represents elevated body mass index with a favourable metabolic profile and reduced coronary heart disease risk. Analysis of the biological pathways underlying this cluster identifies inflammation as playing a key role in mediating the effects of increased body mass index on coronary heart disease.

genetics↗

bioRxiv2021

Inferring differential subcellular localisation in comparative spatial proteomics using BANDLE

The steady-state localisation of proteins provides vital insight into their function. These localisations are context specific with proteins translocating between different sub-cellular niches upon perturbation of the subcellular environment. Differential localisation, that is a change in the steady-state subcellular location of a protein, provides a step towards mechanistic insight of subcellular protein dynamics. Aberrant localisation has been implicated in a number of pathologies, thus differential localisation may help characterise disease states and facilitate rational drug discovery by suggesting novel targets. High-accuracy high-throughput mass spectrometry-based methods now exist to map the steady-state localisation and re-localisation of proteins. Here, we propose a principled Bayesian approach, BANDLE, that uses these data to compute the probability that a protein differentially localises upon cellular perturbation, as well quantifying the uncertainty in these estimates. Furthermore, BANDLE allows information to be shared across spatial proteomics datasets to improve statistical power. Extensive simulation studies demonstrate that BANDLE reduces the number of both type I and type II errors compared to existing approaches. Application of BANDLE to datasets studying EGF stimulation and AP-4 dependent localisation recovers well studied translocations, using only two-thirds of the provided data. Moreover, we potentially implicate TMEM199 with AP-4 dependent localisation. In an application to cytomegalovirus infection, we obtain novel insights into the rewiring of the host proteome. Integration of high-throughput transcriptomic and proteomic data, along with degradation assays, acetylation experiments and a cytomegalovirus intcractome allows us to provide the functional context of these data.

systems biology↗

bioRxiv2020

Consensus clustering for Bayesian mixture models

AO_SCPLOWBSTRACTC_SCPLOWCluster analysis is an integral part of precision medicine and systems biology, used to define groups of patients or biomolecules. Consensus clustering is an ensemble approach that is widely used in these areas, which combines the output from multiple runs of a non-deterministic clustering algorithm. Here we consider the application of consensus clustering to a broad class of heuristic clustering algorithms that can be derived from Bayesian mixture models (and extensions thereof) by adopting an early stopping criterion when performing sampling-based inference for these models. While the resulting approach is non-Bayesian, it inherits the usual benefits of consensus clustering, particularly in terms of computational scalability and providing assessments of clustering stability/robustness. In simulation studies, we show that our approach can successfully uncover the target clustering structure, while also exploring different plausible clusterings of the data. We show that, when a parallel computation environment is available, our approach offers significant reductions in runtime compared to performing sampling-based Bayesian inference for the underlying model, while retaining many of the practical benefits of the Bayesian approach, such as exploring different numbers of clusters. We propose a heuristic to decide upon ensemble size and the early stopping criterion, and then apply consensus clustering to a clustering algorithm derived from a Bayesian integrative clustering method. We use the resulting approach to perform an integrative analysis of three omics datasets for budding yeast and find clusters of co-expressed genes with shared regulatory proteins. We validate these clusters using data external to the analysis. These clusters can help assign likely function to understudied genes, for example GAS3 clusters with histones active in S-phase, suggesting a role in DNA replication. Our approach can be used as a wrapper for essentially any existing sampling-based Bayesian clustering implementation, and enables meaningful clustering analyses to be performed using such implementations, even when computational Bayesian inference is not feasible, e.g. due to poor exploration of the target density (often as a result of increasing numbers of features) or a limited computational budget that does not along sufficient samples to drawn from a single chain. This enables researchers to straightforwardly extend the applicability of existing software to much larger datasets, including implementations of sophisticated models such as those that jointly model multiple datasets.

systems biology↗

bioRxiv2020

A Bayesian semi-parametric model for thermal proteome profiling

The thermal stability of proteins can be altered when they interact with small molecules, other biomolecules or are subject to post-translation modifications. Thus monitoring the thermal stability of proteins under various cellular perturbations can provide insights into protein function, as well as potentially determine drug targets and off-targets. Thermal proteome profiling is a highly multiplexed mass-spectrommetry method for monitoring the melting behaviour of thousands of proteins in a single experiment. In essence, thermal proteome profiling assumes that proteins denature upon heating and hence become insoluble. Thus, by tracking the relative solubility of proteins at sequentially increasing temperatures, one can report on the thermal stability of a protein. Standard thermodynamics predicts a sigmoidal relationship between temperature and relative solubility and this is the basis of current robust statistical procedures. However, current methods do not model deviations from this behaviour and they do not quantify uncertainty in the melting profiles. To overcome these challenges, we propose the application of Bayesian functional data analysis tools which allow complex temperature-solubility behaviours. Our methods have improved sensitivity over the state-of-the art, identify new drug-protein associations and have less restrictive assumptions than current approaches. Our methods allows for comprehensive analysis of proteins that deviate from the predicted sigmoid behaviour and we uncover potentially biphasic phenomena with a series of published datasets.

systems biology↗

bioRxiv2020

BaseQTL: a Bayesian method to detect eQTLs from RNA-seq data with or without genotypes

Available methods to detect molecular quantitative trait loci (QTL) require study individuals to be genotyped. Here, we describe BaseQTL, a Bayesian method that exploits allele-specific expression to map molecular QTL from sequencing reads even when no genotypes are available. When used with genotypes, BaseQTL has lower error rates and increased power compared with existing QTL mapping methods. Running without genotypes limits how many tests can be performed, but due to the proximity of QTL variants to gene bodies, the 2.8% of variants within a 100kB-window that could be tested, contained 26% of QTL variants detectable with genotypes. eQTL effect estimates were invariably consistent between analyses performed with and without genotypes. Often, sequencing data may be generated in absence of genotypes on patients and controls in differential expression studies, and we identified an apparent psoriasis-specific effect for GSTP1 in one such dataset, providing new insights into disease-dependent gene regulation.

genomics↗

bioRxiv2020

A semi-supervised Bayesian approach for simultaneous protein sub-cellular localisation assignment and novelty detection

The cell is compartmentalised into complex micro-environments allowing an array of specialised biological processes to be carried out in synchrony. Determining a proteins sub-cellular localisation to one or more of these compartments can therefore be a first step in determining its function. High-throughput and high-accuracy mass spectrometry-based sub-cellular proteomic methods can now shed light on the localisation of thousands of proteins at once. Machine learning algorithms are then typically employed to make protein-organelle assignments. However, these algorithms are limited by insufficient and incomplete annotation. We propose a semi-supervised Bayesian approach to novelty detection, allowing the discovery of additional, previously unannotated sub-cellular niches. Inference in our model is performed in a Bayesian framework, allowing us to quantify uncertainty in the allocation of proteins to new sub-cellular niches, as well as in the number of newly discovered compartments. We apply our approach across 10 mass spectrometry based spatial proteomic datasets, representing a diverse range of experimental protocols. Application of our approach to hyper LOPIT datasets validates its utility by recovering enrichment with chromatin-associated proteins without annotation and uncovers sub-nuclear compartmentalisation which was not identified in the original analysis. Moreover, using sub-cellular proteomics data from Saccharomyces cerevisiae, we uncover a novel group of proteins trafficking from the ER to the early Golgi apparatus. Overall, we demonstrate the potential for novelty detection to yield biologically relevant niches that are missed by current approaches.

systems biology↗

bioRxiv2020

Extreme phenotypes define epigenetic and metabolic signatures in cardiometabolic syndrome.

Improving the understanding of cardiometabolic syndrome pathophysiology and its relationship with thrombosis are ongoing healthcare challenges. Using plasma biomarkers analysis coupled with the transcriptional and epigenetic characterisation of cell types involved in thrombosis, obtained from two extreme phenotype groups (obese and lipodystrophy) and comparing these to lean individuals and blood donors, the present study identifies the molecular mechanisms at play, highlighting patterns of abnormal activation in innate immune phagocytic cells and shows that extreme phenotype groups could be distinguished from lean individuals, and from each other, across all data layers. The characterisation of the same obese group, six months after bariatric surgery shows the loss of the patterns of abnormal activation of innate immune cells previously observed. However, rather than reverting to the gene expression landscape of lean individuals, this occurs via the establishment of novel gene expression landscapes. Netosis and its control mechanisms emerge amongst the pathways that show an improvement after surgical intervention. Taken together, by integrating across data layers, the observed molecular and metabolic differences form a disease signature that is able to discriminate, amongst the blood donors, those individuals with a higher likelihood of having cardiometabolic syndrome, even when not presenting with the classic features.

genomics↗

bioRxiv2020

Characterisation of the genetic architecture of immune-mediated disease through informed dimension reduction

Integration of genome-wide association study (GWAS) data has been used to generate new hypotheses of biological mechanism, aetiological relationships between traits, or test causality of one factor for another. However, such approaches have typically been limited to pairwise comparisons of traits. We propose a generally applicable method, that exploits ideas from Bayesian genetic fine mapping to define a "lens" that focuses relevant variants before dimension reduction of a set of related GWAS summary statistics. We applied this technique to immune-mediated diseases, deriving 13 components which summarise the multidimensional patterns of genetic risk. Projection of independent datasets demonstrated the specificity and accuracy of our reduced dimension basis, enabled us to functionally characterise individual components, identify disease-discriminating components and suggest novel associations in rare diseases where classical GWAS approaches are challenging. Our approach summarises the genetic architectures underlying any range of aetiologically-related traits in fewer dimensions, facilitating more nuanced multidimensional comparative analyses.

genetics↗

bioRxiv2019

MR-Clust: Clustering of genetic variants in Mendelian randomization with similar causal estimates

MotivationMendelian randomization is an epidemiological technique that uses genetic variants as instrumental variables to estimate the causal effect of a risk factor on an outcome. We consider a scenario in which causal estimates based on each variant in turn differ more strongly than expected by chance alone, but the variants can be divided into distinct clusters, such that all variants in the cluster have similar causal estimates. This scenario is likely to occur when there are several distinct causal mechanisms by which a risk factor influences an outcome with different magnitudes of causal effect. We have developed an algorithm MR-Clust that finds such clusters of variants, and so can identify variants that reflect distinct causal mechanisms. Two features of our clustering algorithm are that it accounts for uncertainty in the causal estimates, and it includes null and junk clusters, to provide protection against the detection of spurious clusters. ResultsOur algorithm correctly detected the number of clusters in a simulation analysis, outperforming the popular Mclust method. In an applied example considering the effect of blood pressure on coronary artery disease risk, the method detected four clusters of genetic variants. A hypothesis-free search suggested that variants in the cluster with a negative effect of blood pressure on coronary artery disease risk were more strongly related to trunk fat percentage and other adiposity measures than variants not in this cluster. Availability and ImplementationMR-Clust can be downloaded from https://github.com/cnfoley/mrclust. Contactsb452@medschl.cam.ac.uk or christopher.foley@mrc-bsu.cam.ac.uk Supplementary InformationSupplementary Material is included in the submission.

bioinformatics↗

bioRxiv2019

A fast and efficient colocalization algorithm for identifying shared genetic risk factors across multiple traits

Genome-wide association studies (GWAS) have identified thousands of genomic regions affecting complex diseases. The next challenge is to elucidate the causal genes and mechanisms involved. One approach is to use statistical colocalization to assess shared genetic aetiology across multiple related traits (e.g. molecular traits, metabolic pathways and complex diseases) to identify causal pathways, prioritize causal variants and evaluate pleiotropy. We propose HyPrColoc (Hypothesis Prioritisation in multi-trait Colocalization), an efficient deterministic Bayesian algorithm using GWAS summary statistics that can detect colocalization across vast numbers of traits simultaneously (e.g. 100 traits can be jointly analysed in around 1 second). We performed a genome-wide multi-trait colocalization analysis of coronary heart disease (CHD) and fourteen related traits. HyPrColoc identified 43 regions in which CHD colocalized with [≥]1 trait, including 5 potentially new CHD loci. Across the 43 loci, we further integrated gene and protein expression quantitative trait loci to identify candidate causal genes.

genetics↗

bioRxiv2019

GPseudoClust: deconvolution of shared pseudo-trajectories at single-cell resolution

MotivationMany methods have been developed to cluster genes on the basis of their changes in mRNA expression over time, using bulk RNA-seq or microarray data. However, single-cell data may present a particular challenge for these algorithms, since the temporal ordering of cells is not directly observed. One way to address this is to first use pseudotime methods to order the cells, and then apply clustering techniques for time course data. However, pseudotime estimates are subject to high levels of uncertainty, and failing to account for this uncertainty is liable to lead to erroneous and/or over-confident gene clusters.\n\nResultsThe proposed method, GPseudoClust, is a novel approach that jointly infers pseudotem-poral ordering and gene clusters, and quantifies the uncertainty in both. GPseudoClust combines a recent method for pseudotime inference with nonparametric Bayesian clustering methods, efficient MCMC sampling, and novel subsampling strategies which aid computation. We consider a broad array of simulated and experimental datasets to demonstrate the effectiveness of GPseudoClust in a range of settings.\n\nAvailabilityAn implementation is available on GitHub: https://github.com/magStra/nonparametricSummaryPSM and https://github.com/magStra/GPseudoClust.\n\nContactms58@sanger.ac.uk\n\nSupplementary informationSupplementary materials are available.

bioinformatics↗