Dr Ed Morrissey

Research Area: Bioinformatics & Stats (inc. Modelling and Computational Biology)
Technology Exchange: Computational biology
Scientific Themes: Bioinformatics, Statistics & Computational Biology and Developmental Biology & Stem Cells
Fig 1. Stochastic model of mutation acquisition and stem cell dynamics (from Kozar, Morrissey et al Cell Stem Cell 2013). The left most graph shows the model prediction, the middle graph shows the actual measured clone frequency and the far right images are examples of each of the types of clones measured.

Fig 1. Stochastic model of mutation acquisition and stem cell dynamics (from Kozar, Morrissey et al ...

Fig 2. Image processing of human biopsy sample. On the left you can see a whole scanned slide of from a block of colonic tissue. Two examples of the output of the image processing algorithm can be seen on the right. The method developed detects rare mutation events, seen approximately once every 10,000 crypts, as well as quantifying crypt numbers, sizes and shapes for tens of thousands of intestinal crypts.

Fig 2. Image processing of human biopsy sample. On the left you can see a whole scanned slide of ...

Mammalian tissues are highly dynamic systems, with tissue specific stem cells constantly making stochastic fate decisions, while committed cells migrate to specific locations to carry out the function of the organ. Despite the large number of stochastic events occurring simultaneously, this complex process is tightly regulated, maintaining balanced cell numbers and precise cell compartment organisation. The group is focused on using quantitative approaches to characterise such dynamics, determine how they are altered in disease and to use this information to understand how cell fate decisions are made. We work in close collaboration with experimental research groups working on adult or developing tissues.

In one example of this modelling approach we used a stochastic model of mutation acquisition and stem cell dynamics (Figure 1). This explained data patterns measured in intestinal tissue from a mouse that expresses a reporter protein activated by DNA mutations (Kozar, Morrissey et al Cell Stem Cell 2013). Using the model we were able to show that the number of stem cells contributing to the intestinal stem cell pool is considerably lower than previously thought. Additionally this quantitative description gave us a baseline to investigate what effect key oncogenic mutations have on stem cell dynamics (Vermeulen, Morrissey et al Science 2013).

Our main focus is to understand cell fate using mathematical modelling. This often requires the development of computational tools to derive information from imaging and high throughput data. For example, our lab has developed automatic image segmentation methods (Figure 2) and Bayesian inference models for high-throughput assays.

Name Department Institution Country
Dr Doug Winton FMedSci CRUK-Cambridge Institute United Kingdom
Dr Louis Vermeulen Academic Medical Centre Netherlands
Prof Claus Nerlov Nuffield Division of Clinical Laboratory Sciences Oxford University, Weatherall Institute of Molecular Medicine United Kingdom
Prof Alfonso Martinez Arias University of Cambridge United Kingdom
Morrissey ER, Vermeulen L. 2014. Stem cell competition: how speeding mutants beat the rest. EMBO J, 33 (20), pp. 2277-2278. | Show Abstract | Read more

© 2014 The Authors. How mutations lead to tumor formation is a central question in cancer research. Although cellular changes that follow the occurrence of common mutations are well characterized, much less is known about their effects on the population level. Now, two recent studies reveal in what way oncogenic aberrations alter stem cell dynamics to provide cells with an evolutionary advantage over their neighbors (Amoyel et al,; Baker et al,). A new study on clonal tracing in human tissue validates the concept of neutral competition, earlier revealed by genetic manipulation in various model organisms.

Kozar S, Morrissey E, Nicholson AM, van der Heijden M, Zecchini HI, Kemp R, Tavaré S, Vermeulen L, Winton DJ. 2013. Continuous clonal labeling reveals small numbers of functional stem cells in intestinal crypts and adenomas. Cell Stem Cell, 13 (5), pp. 626-633. | Show Abstract | Read more

Lineage-tracing approaches, widely used to characterize stem cell populations, rely on the specificity and stability of individual markers for accurate results. We present a method in which genetic labeling in the intestinal epithelium is acquired as a mutation-induced clonal mark during DNA replication. By determining the rate of mutation in vivo and combining this data with the known neutral-drift dynamics that describe intestinal stem cell replacement, we quantify the number of functional stem cells in crypts and adenomas. Contrary to previous reports, we find that significantly lower numbers of "working" stem cells are present in the intestinal epithelium (five to seven per crypt) and in adenomas (nine per gland), and that those stem cells are also replaced at a significantly lower rate. These findings suggest that the bulk of tumor stem cell divisions serve only to replace stem cell loss, with rare clonal victors driving gland repopulation and tumor growth.

Vermeulen L, Morrissey E, van der Heijden M, Nicholson AM, Sottoriva A, Buczacki S, Kemp R, Tavaré S, Winton DJ. 2013. Defining stem cell dynamics in models of intestinal tumor initiation. Science, 342 (6161), pp. 995-998. | Show Abstract | Read more

Cancer is a disease in which cells accumulate genetic aberrations that are believed to confer a clonal advantage over cells in the surrounding tissue. However, the quantitative benefit of frequently occurring mutations during tumor development remains unknown. We quantified the competitive advantage of Apc loss, Kras activation, and P53 mutations in the mouse intestine. Our findings indicate that the fate conferred by these mutations is not deterministic, and many mutated stem cells are replaced by wild-type stem cells after biased, but still stochastic events. Furthermore, P53 mutations display a condition-dependent advantage, and especially in colitis-affected intestines, clones harboring mutations in this gene are favored. Our work confirms the previously theoretical notion that the tissue architecture of the intestine suppresses the accumulation of mutated lineages.

Thomas L, Hodgson DA, Wentzel A, Nieselt K, Ellingsen TE, Moore J, Morrissey ER, Legaie R, STREAM Consortium, Wohlleben W et al. 2012. Metabolic switches and adaptations deduced from the proteomes of Streptomyces coelicolor wild type and phoP mutant grown in batch culture. Mol Cell Proteomics, 11 (2), pp. M111.013797. | Show Abstract | Read more

Bacteria in the genus Streptomyces are soil-dwelling oligotrophs and important producers of secondary metabolites. Previously, we showed that global messenger RNA expression was subject to a series of metabolic and regulatory switches during the lifetime of a fermentor batch culture of Streptomyces coelicolor M145. Here we analyze the proteome from eight time points from the same fermentor culture and, because phosphate availability is an important regulator of secondary metabolite production, compare this to the proteome of a similar time course from an S. coelicolor mutant, INB201 (ΔphoP), defective in the control of phosphate utilization. The proteomes provide a detailed view of enzymes involved in central carbon and nitrogen metabolism. Trends in protein expression over the time courses were deduced from a protein abundance index, which also revealed the importance of stress pathway proteins in both cultures. As expected, the ΔphoP mutant was deficient in expression of PhoP-dependent genes, and several putatively compensatory metabolic and regulatory pathways for phosphate scavenging were detected. Notably there is a succession of switches that coordinately induce the production of enzymes for five different secondary metabolite biosynthesis pathways over the course of the batch cultures.

Morrissey ER, Juárez MA, Denby KJ, Burroughs NJ. 2011. Inferring the time-invariant topology of a nonlinear sparse gene regulatory network using fully Bayesian spline autoregression. Biostatistics, 12 (4), pp. 682-694. | Show Abstract | Read more

We propose a semiparametric Bayesian model, based on penalized splines, for the recovery of the time-invariant topology of a causal interaction network from longitudinal data. Our motivation is inference of gene regulatory networks from low-resolution microarray time series, where existence of nonlinear interactions is well known. Parenthood relations are mapped by augmenting the model with kinship indicators and providing these with either an overall or gene-wise hierarchical structure. Appropriate specification of the prior is crucial to control the flexibility of the splines, especially under circumstances of scarce data; thus, we provide an informative, proper prior. Substantive improvement in network inference over a linear model is demonstrated using synthetic data drawn from ordinary differential equation models and gene expression from an experimental data set of the Arabidopsis thaliana circadian rhythm.

Nieselt K, Battke F, Herbig A, Bruheim P, Wentzel A, Jakobsen ØM, Sletta H, Alam MT, Merlo ME, Moore J et al. 2010. The dynamic architecture of the metabolic switch in Streptomyces coelicolor. BMC Genomics, 11 (1), pp. 10. | Show Abstract | Read more

BACKGROUND: During the lifetime of a fermenter culture, the soil bacterium S. coelicolor undergoes a major metabolic switch from exponential growth to antibiotic production. We have studied gene expression patterns during this switch, using a specifically designed Affymetrix genechip and a high-resolution time-series of fermenter-grown samples. RESULTS: Surprisingly, we find that the metabolic switch actually consists of multiple finely orchestrated switching events. Strongly coherent clusters of genes show drastic changes in gene expression already many hours before the classically defined transition phase where the switch from primary to secondary metabolism was expected. The main switch in gene expression takes only 2 hours, and changes in antibiotic biosynthesis genes are delayed relative to the metabolic rearrangements. Furthermore, global variation in morphogenesis genes indicates an involvement of cell differentiation pathways in the decision phase leading up to the commitment to antibiotic biosynthesis. CONCLUSIONS: Our study provides the first detailed insights into the complex sequence of early regulatory events during and preceding the major metabolic switch in S. coelicolor, which will form the starting point for future attempts at engineering antibiotic production in a biotechnological setting.

Morrissey ER, Juárez MA, Denby KJ, Burroughs NJ. 2010. On reverse engineering of gene interaction networks using time course data with repeated measurements. Bioinformatics, 26 (18), pp. 2305-2312. | Show Abstract | Read more

MOTIVATION: Gene expression measurements are the most common data source for reverse engineering gene interaction networks. When dealing with destructive sampling in time course experiments, it is common to average any available measurements for each time point and to treat this as the actual time series data for fitting the network, neglecting the variability contained in the repeated measurements. Proceeding in such a way can affect the retrieved network topology. RESULTS: We propose a fully Bayesian method for reverse engineering a gene interaction network, based on time course data with repeated measurements. The observations are treated as surrogate measurements of the underlying gene expression. As these measurements often contain outliers, we use a non-Gaussian specification for dealing with measurement error. The network interactions are assumed linear and an autoregressive model is specified, augmented with indicator variables that allow inference on the topology of the network. We analyse two in silico and one in vivo experiments, the latter dealing with the circadian clock in Arabidopsis thaliana. A systematic attenuation of the estimated regulation strengths and a concomitant overestimation of their precision is demonstrated when measurement error is disregarded. Thus, a clear improvement in the inferred topology for the synthetic datasets is demonstrated when this is included. Also, the influence of outliers in the retrieved network is demonstrated when using the in vivo data. AVAILABILITY: Matlab code and data used in the article are available from http://go.warwick.ac.uk/majuarez/home/materials.

Morrissey ER, Diaz-Uriarte R. 2009. Pomelo II: finding differentially expressed genes. Nucleic Acids Res, 37 (Web Server issue), pp. W581-W586. | Show Abstract | Read more

Pomelo II (http://pomelo2.bioinfo.cnio.es) is an open-source, web-based, freely available tool for the analysis of gene (and protein) expression and tissue array data. Pomelo II implements: permutation-based tests for class comparisons (t-test, ANOVA) and regression; survival analysis using Cox model; contingency table analysis with Fisher's exact test; linear models (of which t-test and ANOVA are especial cases) that allow additional covariates for complex experimental designs and use empirical Bayes moderated statistics. Permutation-based and Cox model analysis use parallel computing, which permits taking advantage of multicore CPUs and computing clusters. Access to, and further analysis of, additional biological information and annotations (PubMed references, Gene Ontology terms, KEGG and Reactome pathways) are available either for individual genes (from clickable links in tables and figures) or sets of genes. The source code is available, allowing for extending and reusing the software. A comprehensive test suite is also available, and covers both the user interface and the numerical results. The possibility of including additional covariates, parallelization of computation, open-source availability of the code and comprehensive testing suite make Pomelo II a unique tool.

Alibés A, Morrissey ER, Cañada A, Rueda OM, Casado D, Yankilevich P, Díaz-Uriarte R. 2007. Asterias: a parallelized web-based suite for the analysis of expression and aCGH data. Cancer Inform, 3 pp. 1-9. | Show Abstract

The analysis of expression and CGH arrays plays a central role in the study of complex diseases, especially cancer, including finding markers for early diagnosis and prognosis, choosing an optimal therapy, or increasing our understanding of cancer development and metastasis. Asterias (http://www.asterias.info) is an integrated collection of freely-accessible web tools for the analysis of gene expression and aCGH data. Most of the tools use parallel computing (via MPI) and run on a server with 60 CPUs for computation; compared to a desktop or server-based but not parallelized application, parallelization provides speed ups of factors up to 50. Most of our applications allow the user to obtain additional information for user-selected genes (chromosomal location, PubMed ids, Gene Ontology terms, etc.) by using clickable links in tables and/or figures. Our tools include: normalization of expression and aCGH data (DNMAD); converting between different types of gene/clone and protein identifiers (IDconverter/IDClight); filtering and imputation (preP); finding differentially expressed genes related to patient class and survival data (Pomelo II); searching for models of class prediction (Tnasas); using random forests to search for minimal models for class prediction or for large subsets of genes with predictive capacity (GeneSrF); searching for molecular signatures and predictive genes with survival data (SignS); detecting regions of genomic DNA gain or loss (ADaCGH). The capability to send results between different applications, access to additional functional information, and parallelized computation make our suite unique and exploit features only available to web-based applications.

Díaz-Uriarte R, Alibés A, Morrissey ER, Cañada A, Rueda OM, Neves ML. 2007. Asterias: integrated analysis of expression and aCGH data using an open-source, web-based, parallelized software suite. Nucleic Acids Res, 35 (Web Server issue), pp. W75-W80. | Show Abstract | Read more

Asterias (http://www.asterias.info) is an open-source, web-based, suite for the analysis of gene expression and aCGH data. Asterias implements validated statistical methods, and most of the applications use parallel computing, which permits taking advantage of multicore CPUs and computing clusters. Access to, and further analysis of, additional biological information and annotations (PubMed references, Gene Ontology terms, KEGG and Reactome pathways) are available either for individual genes (from clickable links in tables and figures) or sets of genes. These applications cover from array normalization to imputation and preprocessing, differential gene expression analysis, class and survival prediction and aCGH analysis. The source code is available, allowing for extention and reuse of the software. The links and analysis of additional functional information, parallelization of computation and open-source availability of the code make Asterias a unique suite that can exploit features specific to web-based environments.

Morrissey ER, Vermeulen L. 2014. Stem cell competition: how speeding mutants beat the rest. EMBO J, 33 (20), pp. 2277-2278. | Show Abstract | Read more

© 2014 The Authors. How mutations lead to tumor formation is a central question in cancer research. Although cellular changes that follow the occurrence of common mutations are well characterized, much less is known about their effects on the population level. Now, two recent studies reveal in what way oncogenic aberrations alter stem cell dynamics to provide cells with an evolutionary advantage over their neighbors (Amoyel et al,; Baker et al,). A new study on clonal tracing in human tissue validates the concept of neutral competition, earlier revealed by genetic manipulation in various model organisms.

Kozar S, Morrissey E, Nicholson AM, van der Heijden M, Zecchini HI, Kemp R, Tavaré S, Vermeulen L, Winton DJ. 2013. Continuous clonal labeling reveals small numbers of functional stem cells in intestinal crypts and adenomas. Cell Stem Cell, 13 (5), pp. 626-633. | Show Abstract | Read more

Lineage-tracing approaches, widely used to characterize stem cell populations, rely on the specificity and stability of individual markers for accurate results. We present a method in which genetic labeling in the intestinal epithelium is acquired as a mutation-induced clonal mark during DNA replication. By determining the rate of mutation in vivo and combining this data with the known neutral-drift dynamics that describe intestinal stem cell replacement, we quantify the number of functional stem cells in crypts and adenomas. Contrary to previous reports, we find that significantly lower numbers of "working" stem cells are present in the intestinal epithelium (five to seven per crypt) and in adenomas (nine per gland), and that those stem cells are also replaced at a significantly lower rate. These findings suggest that the bulk of tumor stem cell divisions serve only to replace stem cell loss, with rare clonal victors driving gland repopulation and tumor growth.

Vermeulen L, Morrissey E, van der Heijden M, Nicholson AM, Sottoriva A, Buczacki S, Kemp R, Tavaré S, Winton DJ. 2013. Defining stem cell dynamics in models of intestinal tumor initiation. Science, 342 (6161), pp. 995-998. | Show Abstract | Read more

Cancer is a disease in which cells accumulate genetic aberrations that are believed to confer a clonal advantage over cells in the surrounding tissue. However, the quantitative benefit of frequently occurring mutations during tumor development remains unknown. We quantified the competitive advantage of Apc loss, Kras activation, and P53 mutations in the mouse intestine. Our findings indicate that the fate conferred by these mutations is not deterministic, and many mutated stem cells are replaced by wild-type stem cells after biased, but still stochastic events. Furthermore, P53 mutations display a condition-dependent advantage, and especially in colitis-affected intestines, clones harboring mutations in this gene are favored. Our work confirms the previously theoretical notion that the tissue architecture of the intestine suppresses the accumulation of mutated lineages.

Morrissey ER, Juárez MA, Denby KJ, Burroughs NJ. 2011. Inferring the time-invariant topology of a nonlinear sparse gene regulatory network using fully Bayesian spline autoregression. Biostatistics, 12 (4), pp. 682-694. | Show Abstract | Read more

We propose a semiparametric Bayesian model, based on penalized splines, for the recovery of the time-invariant topology of a causal interaction network from longitudinal data. Our motivation is inference of gene regulatory networks from low-resolution microarray time series, where existence of nonlinear interactions is well known. Parenthood relations are mapped by augmenting the model with kinship indicators and providing these with either an overall or gene-wise hierarchical structure. Appropriate specification of the prior is crucial to control the flexibility of the splines, especially under circumstances of scarce data; thus, we provide an informative, proper prior. Substantive improvement in network inference over a linear model is demonstrated using synthetic data drawn from ordinary differential equation models and gene expression from an experimental data set of the Arabidopsis thaliana circadian rhythm.

Morrissey ER, Juárez MA, Denby KJ, Burroughs NJ. 2010. On reverse engineering of gene interaction networks using time course data with repeated measurements. Bioinformatics, 26 (18), pp. 2305-2312. | Show Abstract | Read more

MOTIVATION: Gene expression measurements are the most common data source for reverse engineering gene interaction networks. When dealing with destructive sampling in time course experiments, it is common to average any available measurements for each time point and to treat this as the actual time series data for fitting the network, neglecting the variability contained in the repeated measurements. Proceeding in such a way can affect the retrieved network topology. RESULTS: We propose a fully Bayesian method for reverse engineering a gene interaction network, based on time course data with repeated measurements. The observations are treated as surrogate measurements of the underlying gene expression. As these measurements often contain outliers, we use a non-Gaussian specification for dealing with measurement error. The network interactions are assumed linear and an autoregressive model is specified, augmented with indicator variables that allow inference on the topology of the network. We analyse two in silico and one in vivo experiments, the latter dealing with the circadian clock in Arabidopsis thaliana. A systematic attenuation of the estimated regulation strengths and a concomitant overestimation of their precision is demonstrated when measurement error is disregarded. Thus, a clear improvement in the inferred topology for the synthetic datasets is demonstrated when this is included. Also, the influence of outliers in the retrieved network is demonstrated when using the in vivo data. AVAILABILITY: Matlab code and data used in the article are available from http://go.warwick.ac.uk/majuarez/home/materials.

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