Publications
64 results found
Kleijn IT, Marguerat S, Shahrezaei V, 2023, A coarse-grained resource allocation model of carbon and nitrogen metabolism in unicellular microbes, Journal of the Royal Society Interface, Vol: 20, ISSN: 1742-5662
Coarse-grained resource allocation models (C-GRAMs) are simple mathematical models of cell physiology, where large components of the macromolecular composition are abstracted into single entities. The dynamics and steady-state behaviour of such models provides insights on optimal allocation of cellular resources and have explained experimentally observed cellular growth laws, but current models do not account for the uptake of compound sources of carbon and nitrogen. Here, we formulate a C-GRAM with nitrogen and carbon pathways converging on biomass production, with parametrizations accounting for respirofermentative and purely respiratory growth. The model describes the effects of the uptake of sugars, ammonium and/or compound nutrients such as amino acids on the translational resource allocation towards proteome sectors that maximized the growth rate. It robustly recovers cellular growth laws including the Monod law and the ribosomal growth law. Furthermore, we show how the growth-maximizing balance between carbon uptake, recycling, and excretion depends on the nutrient environment. Lastly, we find a robust linear correlation between the ribosome fraction and the abundance of amino acid equivalents in the optimal cell, which supports the view that simple regulation of translational gene expression can enable cells to achieve an approximately optimal growth state.
Tang W, Jørgensen ACS, Marguerat S, et al., 2023, Modelling capture efficiency of single-cell RNA-sequencing data improves inference of transcriptome-wide burst kinetics, Bioinformatics, Vol: 39, Pages: 1-9, ISSN: 1367-4803
MOTIVATION: Gene expression is characterised by stochastic bursts of transcription that occur at brief and random periods of promoter activity. The kinetics of gene expression burstiness differs across the genome and is dependent on the promoter sequence, among other factors. Single-cell RNA sequencing (scRNA-seq) has made it possible to quantify the cell-to-cell variability in transcription at a global genome-wide level. However, scRNA-seq data is prone to technical variability, including low and variable capture efficiency of transcripts from individual cells. RESULTS: Here, we propose a novel mathematical theory for the observed variability in scRNA-seq data. Our method captures burst kinetics and variability in both the cell size and capture efficiency, which allows us to propose several likelihood-based and simulation-based methods for the inference of burst kinetics from scRNA-seq data. Using both synthetic and real data, we show that the simulation-based methods provide an accurate, robust and flexible tool for inferring burst kinetics from scRNA-seq data. In particular, in a supervised manner, a simulation-based inference method based on neural networks proves to be accurate and useful when applied to both allele and non-allele-specific scRNA-seq data. AVAILABILITY: The code for Neural Network and Approximate Bayesian Computation inference is available at https://github.com/WT215/nnRNA and https://github.com/WT215/Julia_ABC respectively. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Bertaux F, Kleijn I, Marguerat S, et al., 2023, Fission yeast obeys a linear size law under nutrient titration, microPublication Biology, Vol: 2023, ISSN: 2578-9430
Steady-state cell size and geometry depend on growth conditions. Here, we use an experimental setup based on continuous culture and single-cell imaging to study how cell volume, length, width and surface-to-volume ratio vary across a range of growth conditions including nitrogen and carbon titration, the choice of nitrogen source, and translation inhibition. Overall, we find cell geometry is not fully determined by growth rate and depends on the specific mode of growth rate modulation. However, under nitrogen and carbon titrations, we observe that the cell volume and the growth rate follow the same linear scaling.
Jorgensen ACS, Hill CS, Sturrock M, et al., 2023, Data-driven spatio-temporal modelling of glioblastoma, ROYAL SOCIETY OPEN SCIENCE, Vol: 10, ISSN: 2054-5703
Loukas I, Simeoni F, Milan M, et al., 2023, Selective advantage of epigenetically disrupted cancer cells via phenotypic inertia, Cancer Cell, Vol: 41, Pages: 70-87.e14, ISSN: 1535-6108
The evolution of established cancers is driven by selection of cells with enhanced fitness. Subclonal mutations in numerous epigenetic regulator genes are common across cancer types, yet their functional impact has been unclear. Here, we show that disruption of the epigenetic regulatory network increases the tolerance of cancer cells to unfavorable environments experienced within growing tumors by promoting the emergence of stress-resistant subpopulations. Disruption of epigenetic control does not promote selection of genetically defined subclones or favor a phenotypic switch in response to environmental changes. Instead, it prevents cells from mounting an efficient stress response via modulation of global transcriptional activity. This "transcriptional numbness" lowers the probability of cell death at early stages, increasing the chance of long-term adaptation at the population level. Our findings provide a mechanistic explanation for the widespread selection of subclonal epigenetic-related mutations in cancer and uncover phenotypic inertia as a cellular trait that drives subclone expansion.
Jorgensen ACS, Ghosh A, Sturrock M, et al., 2022, Efficient Bayesian inference for stochastic agent-based models, PLOS COMPUTATIONAL BIOLOGY, Vol: 18, ISSN: 1553-734X
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- Citations: 4
Pizzato J, Tang W, Bernabeu S, et al., 2022, Discrimination of Escherichia coli, Shigella flexneri, and Shigella sonnei using lipid profiling by MALDI-TOF mass spectrometry paired with machine learning, MicrobiologyOpen, Vol: 11, Pages: 1-14, ISSN: 2045-8827
Matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) has become a staple in clinical microbiology laboratories. Protein-profiling of bacteria using this technique has accelerated the identification of pathogens in diagnostic workflows. Recently, lipid profiling has emerged as a way to complement bacterial identification where protein-based methods fail to provide accurate results. This study aimed to address the challenge of rapid discrimination between Escherichia coli and Shigella spp. using MALDI-TOF MS in the negative ion mode for lipid profiling coupled with machine learning. Both E. coli and Shigella species are closely related; they share high sequence homology, reported for 16S rRNA gene sequence similarities between E. coli and Shigella spp. exceeding 99%, and a similar protein expression pattern but are epidemiologically distinct. A bacterial collection of 45 E. coli, 48 Shigella flexneri, and 62 Shigella sonnei clinical isolates were submitted to lipid profiling in negative ion mode using the MALDI Biotyper Sirius® system after treatment with mild-acid hydrolysis (acetic acid 1% v/v for 15 min at 98°C). Spectra were then analyzed using our in-house machine learning algorithm and top-ranked features used for the discrimination of the bacterial species. Here, as a proof-of-concept, we showed that lipid profiling might have the potential to differentiate E. coli from Shigella species using the analysis of the top five ranked features obtained by MALDI-TOF MS in the negative ion mode of the MALDI Biotyper Sirius® system. Based on this new approach, MALDI-TOF MS analysis of lipids might help pave the way toward these goals.
Ciechonska M, Sturrock M, Grob A, et al., 2022, Emergent expression of fitness-conferring genes by phenotypic selection, PNAS Nexus, Vol: 1, Pages: 1-13, ISSN: 2752-6542
Genotypic and phenotypic adaptation is the consequence of ongoing natural selection in populations and is key to predicting and preventing drug resistance. Whereas classic antibiotic persistence is all-or-nothing, here we demonstrate that an antibiotic resistance gene displays linear dose-responsive selection for increased expression in proportion to rising antibiotic concentration in growing E. coli populations. Furthermore, we report the potentially wide-spread nature of this form of emergent gene expression by instantaneous phenotypic selection process under bactericidal and bacteriostatic antxibiotic treatment, as well as an amino acid synthesis pathway enzyme under a range of auxotrophic conditions. We propose an analogy to Ohm’s law in electricity (V=IR) where selection pressure acts similarly to voltage (V), gene expression to current (I), and resistance (R) to cellular machinery constraints and costs. Lastly, mathematical modelling using agent-based models of stochastic gene expression in growing populations and Bayesian model selection reveal that the emergent gene expression mechanism requires variability in gene expression within an isogenic population, and a cellular ‘memory’ from positive feedbacks between growth and expression of any fitness-conferring gene. Finally, we discuss the connection of the observed phenomenon to a previously described general fluctuation-response relationship in biology.
Lasri A, Shahrezaei V, Sturrock M, 2022, Benchmarking imputation methods for network inference using a novel method of synthetic scRNA-seq data generation, BMC Bioinformatics, Vol: 23, ISSN: 1471-2105
Background:Single cell RNA-sequencing (scRNA-seq) has very rapidly become the new workhorse of modern biology providing an unprecedented global view on cellular diversity and heterogeneity. In particular, the structure of gene-gene expression correlation contains information on the underlying gene regulatory networks. However, interpretation of scRNA-seq data is challenging due to specific experimental error and biases that are unique to this kind of data including drop-out (or technical zeros).Methods:To deal with this problem several methods for imputation of zeros for scRNA-seq have been developed. However, it is not clear how these processing steps affect inference of genetic networks from single cell data. Here, we introduce Biomodelling.jl, a tool for generation of synthetic scRNA-seq data using multiscale modelling of stochastic gene regulatory networks in growing and dividing cells.Results:Our tool produces realistic transcription data with a known ground truth network topology that can be used to benchmark different approaches for gene regulatory network inference. Using this tool we investigate the impact of different imputation methods on the performance of several network inference algorithms.Conclusions:Biomodelling.jl provides a versatile and useful tool for future development and benchmarking of network inference approaches using scRNA-seq data.
Kleijn IT, Martínez-Segura A, Bertaux F, et al., 2022, Growth-rate dependent and nutrient-specific gene expression resource allocation in fission yeast, Life Science Alliance, Vol: 5, ISSN: 2575-1077
Cellular resources are limited and their relative allocation to gene expression programmes determines physiological states and global properties such as the growth rate. Here, we determinedtheimportanceof the growth rate in explaining relative changes in protein and mRNA levels in the simple eukaryote Schizosaccharomyces pombegrownon non-limiting nitrogen sources. Although expression of half of fission yeast genes was significantly correlated with the growth rate, this came alongside wide-spread nutrient-specific regulation. Proteome and transcriptome often showed coordinated regulation but with notable exceptions, such as metabolic enzymes. Genes positively correlated with growth rate participated in every level of protein production apart fromRNA polymerase II-dependent transcription.Negatively correlated genes belonged mainly to the environmental stress response programme. Critically, metabolic enzymes, which represent ~55-70% of the proteome by mass,showedmostly condition-specificregulation.In summary, we provide a rich account of resource allocation to gene expression in a simple eukaryote, advancing our 19basic understanding of the interplay between growth-rate dependent and nutrient-specific gene expression.
Evangelou M, Rodosthenous T, Shahrezaei V, 2021, Semi-Supervised Classification and Visualization of Multi-View Data, JSM 2021 - Section on Statistical Learning and Data Science
Rodosthenous T, Shahrezaei V, Evangelou M, 2021, S-multi-SNE: Semi-supervised classification and visualisation of multi-view data
An increasing number of multi-view data are being published by studies in several fields. This type of data corresponds to multiple data-views, each representing a different aspect of the same set of samples. We have recently proposed multi-SNE, an extension of t-SNE, that produces a single visualisation of multi-view data. The multi-SNE approach provides low-dimensional embeddings of the samples, produced by being updated iteratively through the different data-views. Here, we further extend multi-SNE to a semi-supervised approach, that classifies unlabelled samples by regarding the labelling information as an extra data-view. We look deeper into the performance, limitations and strengths of multi-SNE and its extension, S-multi-SNE, by applying the two methods on various multi-view datasets with different challenges. We show that by including the labelling information, the projection of the samples improves drastically and it is accompanied by a strong classification performance.
Rodosthenous T, Shahrezaei V, Evangelou M, 2021, S-multi-SNE: Semi-supervised classification and visualisation of multi-view data, Publisher: arXiv
An increasing number of multi-view data are being published by studies in several fields. This type of data corresponds to multiple data-views, each representing a different aspect of the same set of samples. We have recently proposed multi-SNE, an extension of t-SNE, that produces a single visualisation of multi-view data. The multi-SNE approach provides low-dimensional embeddings of the samples, produced by being updated iteratively through the different data-views. Here, we further extend multi-SNE to a semi-supervised approach, that classifies unlabelled samples by regarding the labelling information as an extra data-view. We look deeper into the performance, limitations and strengths of multi-SNE and its extension, S-multi-SNE, by applying the two methods on various multi-view datasets with different challenges. We show that by including the labelling information, the projection of the samples improves drastically and it is accompanied by a strong classification performance.
Rodosthenous T, Shahrezaei V, Evangelou M, 2021, Semi-supervised classification and visualisation of multi-view data, Joint Statistics Meeting (JSM) 2021, Publisher: American Statistical Association
An increasing number of multi-view data are being published by studies in several fields. This type of data corresponds to multiple data-views, each representing a different aspect of the same set of samples. We have recently proposed multi-SNE, an extension of t-SNE, that produces a single visualisation of multi-view data. The multi-SNE approach provides low-dimensional embeddings of the samples, produced by being updated iteratively through the different data-views. Here, we further extend multi-SNE to a semi-supervised approach, that classifies unlabelled samples byregarding the labelling information as an extra data-view. We look deeper into the performance, limitations and strengths of multi-SNE and its extension, S-multi-SNE, by applying the two methods on various multi-view datasets with different challenges. We show that by including the labelling information, the projection of the samples improves drastically and it is accompanied by a strong classification performance.
Thomas P, Shahrezaei V, 2021, Coordination of gene expression noise with cell size: analytical results for agent-based models of growing cell populations, Journal of the Royal Society Interface, Vol: 18, Pages: 1-16, ISSN: 1742-5662
The chemical master equation and the Gillespie algorithm are widely used to model the reaction kinetics inside living cells. It is thereby assumed that cell growth and division can be modelled through effective dilution reactions and extrinsic noise sources. We here re-examine these paradigms through developing an analytical agent-based framework of growing and dividing cells accompanied by an exact simulation algorithm, which allows us to quantify the dynamics of virtually any intracellular reaction network affected by stochastic cell size control and division noise. We find that the solution of the chemical master equation—including static extrinsic noise—exactly agrees with the agent-based formulation when the network under study exhibits stochastic concentration homeostasis, a novel condition that generalizes concentration homeostasis in deterministic systems to higher order moments and distributions. We illustrate stochastic concentration homeostasis for a range of common gene expression networks. When this condition is not met, we demonstrate by extending the linear noise approximation to agent-based models that the dependence of gene expression noise on cell size can qualitatively deviate from the chemical master equation. Surprisingly, the total noise of the agent-based approach can still be well approximated by extrinsic noise models.
Hintze M, Katsanos D, Shahrezaei V, et al., 2021, Phenotypic robustness of epidermal stem cell number in C. elegans Is modulated by the activity of the conserved N-acetyltransferase nath-10/NAT10, Frontiers in Cell and Developmental Biology, Vol: 9, Pages: 1-14, ISSN: 2296-634X
Individual cells and organisms experience perturbations from internal and external sources, yet manage to buffer these to produce consistent phenotypes, a property known as robustness. While phenotypic robustness has often been examined in unicellular organisms, it has not been sufficiently studied in multicellular animals. Here, we investigate phenotypic robustness in Caenorhabditis elegans seam cells. Seam cells are stem cell-like epithelial cells along the lateral edges of the animal, which go through asymmetric and symmetric divisions contributing cells to the hypodermis and neurons, while replenishing the stem cell reservoir. The terminal number of seam cells is almost invariant in the wild-type population, allowing the investigation of how developmental precision is achieved. We report here that a loss-of-function mutation in the highly conserved N-acetyltransferase nath-10/NAT10 increases seam cell number variance in the isogenic population. RNA-seq analysis revealed increased levels of mRNA transcript variability in nath-10 mutant populations, which may have an impact on the phenotypic variability observed. Furthermore, we found disruption of Wnt signaling upon perturbing nath-10 function, as evidenced by changes in POP-1/TCF nuclear distribution and ectopic activation of its GATA transcription factor target egl-18. These results highlight that NATH-10/NAT-10 can influence phenotypic variability partly through modulation of the Wnt signaling pathway.
Forouzannia F, Shahrezaei V, Kohandel M, 2021, The impact of random microenvironmental fluctuations on tumor control probability, JOURNAL OF THEORETICAL BIOLOGY, Vol: 509, ISSN: 0022-5193
Rodosthenous T, Shahrezaei V, Evangelou M, 2021, Multi-view Data Visualisation via Manifold Learning, Publisher: arXiv
Beltran T, Shahrezaei V, Katju V, et al., 2020, Epimutations driven by small RNAs arise frequently but most have limited duration in Caenorhabditis elegans, Nature Ecology and Evolution, Vol: 4, Pages: 1539-1548, ISSN: 2397-334X
Epigenetic regulation involves changes in gene expression independent of DNA sequence variation that are inherited through cell division. In addition to a fundamental role in cell differentiation, some epigenetic changes can also be transmitted transgenerationally through meiosis. Epigenetic alterations (epimutations) could thus contribute to heritable variation within populations and be subject to evolutionary processes such as natural selection and drift. However, the rate at which epimutations arise and their typical persistence are unknown, making it difficult to evaluate their potential for evolutionary adaptation. Here, we perform a genome-wide study of epimutations in a metazoan organism. We use experimental evolution to characterize the rate, spectrum and stability of epimutations driven by small silencing RNAs in the model nematode Caenorhabditis elegans. We show that epimutations arise spontaneously at a rate approximately 25 times greater than DNA sequence changes and typically have short half-lives of two to three generations. Nevertheless, some epimutations last at least ten generations. Epimutations mediated by small RNAs may thus contribute to evolutionary processes over a short timescale but are unlikely to bring about long-term divergence in the absence of selection.
Bertaux F, von Kugelgen J, Marguerat S, et al., 2020, A bacterial size law revealed by a coarse-grained model of cell physiology, PLOS COMPUTATIONAL BIOLOGY, Vol: 16, ISSN: 1553-734X
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- Citations: 16
Rodosthenous T, Shahrezaei V, Evangelou M, 2020, Integrating multi-OMICS data through sparse Canonical Correlation Analysis for the prediction of complex traits: A comparison study, Bioinformatics, Vol: 36, Pages: 4616-4625, ISSN: 1367-4803
MotivationRecent developments in technology have enabled researchers to collect multiple OMICS datasets for the same individuals. The conventional approach for understanding the relationships between the collected datasets and the complex trait of interest would be through the analysis of each OMIC dataset separately from the rest, or to test for associations between the OMICS datasets. In this work we show that integrating multiple OMICS datasets together, instead of analysing them separately, improves our understanding of their in-between relationships as well as the predictive accuracy for the tested trait. Several approaches have been proposed for the integration of heterogeneous and high-dimensional (p ≫ n) data, such as OMICS. The sparse variant of Canonical Correlation Analysis (CCA) approach is a promising one that seeks to penalise the canonical variables for producing sparse latent variables while achieving maximal correlation between the datasets. Over the last years, a number of approaches for implementing sparse CCA (sCCA) have been proposed, where they differ on their objective functions, iterative algorithm for obtaining the sparse latent variables and make different assumptions about the original datasets.ResultsThrough a comparative study we have explored the performance of the conventional CCA proposed by Parkhomenko et al. (2009), penalised matrix decomposition CCA proposed by Witten and Tibshirani (2009) and its extension proposed by Suo et al. (2017). The aforementioned methods were modified to allow for different penalty functions. Although sCCA is an unsupervised learning approach for understanding of the in-between relationships, we have twisted the problem as a supervised learning one and investigated how the computed latent variables can be used for predicting complex traits. The approaches were extended to allow for multiple (more than two) datasets where the trait was included as one of the input datasets. Both ways have shown improvement
Sun X-M, Bowman A, Priestman M, et al., 2020, Size-Dependent Increase in RNA Polymerase II Initiation Rates Mediates Gene Expression Scaling with Cell Size., Curr Biol, Vol: 30, Pages: 1217-1230.e7
Cell size varies during the cell cycle and in response to external stimuli. This requires the tight coordination, or "scaling," of mRNA and protein quantities with the cell volume in order to maintain biomolecule concentrations and cell density. Evidence in cell populations and single cells indicates that scaling relies on the coordination of mRNA transcription rates with cell size. Here, we use a combination of single-molecule fluorescence in situ hybridization (smFISH), time-lapse microscopy, and mathematical modeling in single fission yeast cells to uncover the precise molecular mechanisms that control transcription rates scaling with cell size. Linear scaling of mRNA quantities is apparent in single fission yeast cells during a normal cell cycle. Transcription of both constitutive and periodic genes is a Poisson process with transcription rates scaling with cell size and without evidence for transcriptional off states. Modeling and experimental data indicate that scaling relies on the coordination of RNA polymerase II (RNAPII) transcription initiation rates with cell size and that RNAPII is a limiting factor. We show using real-time quantitative imaging that size increase is accompanied by a rapid concentration-independent recruitment of RNAPII onto chromatin. Finally, we find that, in multinucleated cells, scaling is set at the level of single nuclei and not the entire cell, making the nucleus a determinant of scaling. Integrating our observations in a mechanistic model of RNAPII-mediated transcription, we propose that scaling of gene expression with cell size is the consequence of competition between genes for limiting RNAPII.
Sun X-M, Bowman A, Priestman M, et al., 2020, Size-dependent increase in RNA Polymerase II initiation rates mediates gene expression scaling with cell size, Current Biology, Vol: 30, Pages: 1217-1230.e7, ISSN: 0960-9822
Cell size varies during the cell cycle and in response to external stimuli. This requires the tight coordination, or “scaling”, of mRNA and protein quantities with the cell volume in order to maintain biomolecules concentrations and cell density. Evidence in cell populations and single cells indicates that scaling relies on the coordination of mRNA transcription rates with cell size. Here we use a combination of single-molecule fluorescence in situ hybridisation (smFISH), time-lapse microscopy and mathematical modelling in single fission yeast cells to uncover the precise molecular mechanisms that control transcription rates scaling with cell size. Linear scaling of mRNA quantities is apparent in single fission yeast cells during a normal cell cycle. Transcription rates of both constitutive and regulated genes scale with cell size without evidence for transcriptional bursting. Modelling and experimental data indicate that scaling relies on the coordination of RNAPII transcription initiation rates with cell size and that RNAPII is a limiting factor. We show using real-time quantitative imaging that size increase is accompanied by a rapid concentration independent recruitment of RNAPII onto chromatin. Finally, we find that in multinucleated cells, scaling is set at the level of single nuclei and not the entire cell, making the nucleus the transcriptional scaling unit. Integrating our observations in a mechanistic model of RNAPII mediated transcription, we propose that scaling of gene expression with cell size is the consequence of competition between genes for limiting RNAPII.
Larrouy-Maumus G, Shahrezaei V, tang W, et al., 2020, Discrimination of bovine milk from non-dairy milk by lipids fingerprinting using routine matrix-assisted laser desorption ionization mass spectrometry, Scientific Reports, Vol: 10, ISSN: 2045-2322
An important sustainable development goal for any country is to ensure food security by producing a sufficient and safe food supply. This is the case for bovine milk where addition of non-dairy milks such as vegetables (e.g., soya or coconut) has become a common source of adulteration and fraud. Conventionally, gas chromatography techniques are used to detect key lipids (e.g., triacylglycerols) has an effective read-out of assessing milks origins and to detect foreign milks in bovine milks. However, such approach requires several sample preparation steps and a dedicated laboratory environment, precluding a high throughput process. To cope with this need, here, we aimed to develop a novel and simple method without organic solvent extractions for the detection of bovine and non-dairy milks based on lipids fingerprint by routine MALDI-TOF mass spectrometry (MS). The optimized method relies on the simple dilution of milks in water followed by MALDI-TOF MS analyses in the positive linear ion mode and using a matrix consisting of a 9:1 mixture of 2,5-dihydroxybenzoic acid and 2-hydroxy-5-methoxybenzoic acid (super-DHB) solubilized at 10 mg/mL in 70% ethanol. This sensitive, inexpensive, and rapid method has potential for use in food authenticity applications.
Tang W, Bertaux F, Thomas P, et al., 2020, bayNorm: Bayesian gene expression recovery, imputation and normalisation for single cell RNA-sequencing data, Bioinformatics, Vol: 36, Pages: 1174-1181, ISSN: 1367-4803
Motivation:Normalisation of single cell RNA sequencing (scRNA-seq) data is a prerequisite to theirinterpretation. The marked technical variability, high amounts of missing observations and batch effecttypical of scRNA-seq datasets make this task particularly challenging. There is a need for an efficient andunified approach for normalisation, imputation and batch effect correction.Results:Here, we introduce bayNorm, a novel Bayesian approach for scaling and inference of scRNA-seq counts. The method’s likelihood function follows a binomial model of mRNA capture, while priorsare estimated from expression values across cells using an empirical Bayes approach. We first validateour assumptions by showing this model can reproduce different statistics observed in real scRNA-seqdata. We demonstrate using publicly-available scRNA-seq datasets and simulated expression data thatbayNorm allows robust imputation of missing values generating realistic transcript distributions that matchsingle molecule FISH measurements. Moreover, by using priors informed by dataset structures, bayNormimproves accuracy and sensitivity of differential expression analysis and reduces batch effect comparedto other existing methods. Altogether, bayNorm provides an efficient, integrated solution for global scalingnormalisation, imputation and true count recovery of gene expression measurements from scRNA-seqdata.Availability:The R package “bayNorm” is available at https://github.com/WT215/bayNorm. The code foranalysing data in this paper is available at https://github.com/WT215/bayNorm_papercode.Contact:samuel.marguerat@imperial.ac.uk or v.shahrezaei@imperial.ac.ukSupplementary information:Supplementary data are available atBioinformaticsonline.
Brugger SP, Angelescu I, Abi-Dargham A, et al., 2020, Heterogeneity of Striatal Dopamine Function in Schizophrenia: Meta-analysis of Variance, BIOLOGICAL PSYCHIATRY, Vol: 87, Pages: 215-224, ISSN: 0006-3223
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- Citations: 50
Ciechonska M, Sturrock M, Grob A, et al., 2019, Ohm’s Law for increasing fitness gene expression with selection pressure
<jats:title>Abstract</jats:title><jats:p>Natural selection relies on genotypic and phenotypic adaptation in response to fluctuating environmental conditions and is the key to predicting and preventing drug resistance. Whereas classic persistence is all-or-nothing, here we show for the first time that an antibiotic resistance gene displays linear dose-responsive selection for increased expression in proportion to rising antibiotic concentration in <jats:italic>E. coli</jats:italic>. Furthermore, we observe the general nature of an instantaneous phenotypic selection process upon bactericidal and bacteriostatic antibiotic treatment, as well as an amino acid synthesis pathway enzyme under a range of auxotrophic conditions. To explain this phenomenon, we propose an analogy to Ohm’s law in electricity (V=IR) where fitness pressure acts similarly to voltage (V), gene expression to current (I), and resistance (R) to cellular machinery constraints. Lastly, mathematical modelling approaches reveal that the emergent gene expression mechanism requires variation in mRNA and protein production within an isogenic population, and cell ‘memory’ from positive feedbacks between growth and expression of any fitness-inducing gene.</jats:p>
Tang W, Ranganathan N, Shahrezaei V, et al., 2019, MALDI-TOF mass spectrometry on intact bacteria combined with a refined analysis framework allows accurate classification of MSSA and MRSA., PLoS ONE, Vol: 14, Pages: 1-16, ISSN: 1932-6203
Fast and reliable detection coupled with accurate data-processing and analysis of antibiotic-resistant bacteria is essential in clinical settings. In this study, we use MALDI-TOF on intact cells combined with a refined analysis framework to demonstrate discrimination between methicillin-susceptible (MSSA) and methicillin-resistant (MRSA) Staphylococcus aureus. By combining supervised and unsupervised machine learning methods, we firstly show that the mass spectroscopy data contains strong signal for the clustering of MSSA and MRSA. Then we concentrate on applying supervised learning to extract and verify the important features. A new workflow is proposed that allows for extracting a fixed set of reference peaks so that any new data can be aligned to it and hence consistent feature matrices can be obtained. Also note that by doing so we are able to examine the robustness of the important features that have been found. We also show that appropriate size of the benchmark data, appropriate alignment of the testing data and use of an optimal set of features via feature selection results in prediction accuracy over 90%. In summary, as proof-of-principle, our integrated experimental and bioinformatics study suggests a novel intact cell MALDI-TOF to be of great promise for fast and reliable detection of MRSA strains.
Dyer NP, Shahrezaei V, Hebenstreit D, 2019, LiBiNorm: an htseq-count analogue with improved normalisation of Smart-seq2 data and library preparation diagnostics, PeerJ, Vol: 7, Pages: 1-13, ISSN: 2167-8359
Protocols for preparing RNA sequencing (RNA-seq) libraries, most prominently“Smart-seq” variations, introduce global biases that can have a significant impact on thequantification of gene expression levels. This global bias can lead to drastic over- orunder-representation of RNA in non-linear length-dependent fashion due to enzymaticreactions during cDNA production. It is currently not corrected by any RNA-seqsoftware, which mostly focus on local bias in coverage along RNAs. This paper describesLiBiNorm, a simple command line program that mimics the popular htseq-countsoftware and allows diagnostics, quantification, and global bias removal. LiBiNormoutputs gene expression data that has been normalized to correct for global biasintroduced by the Smart-seq2 protocol. In addition, it produces data and several plotsthat allow insights into the experimental history underlying library preparation. TheLiBiNorm package includes an R script that allows visualization of the main results.LiBiNorm is the first software application to correct for the global bias that is introducedby the Smart-seq2 protocol. It is freely downloadable at http://www2.warwick.ac.uk/fac/sci/lifesci/research/libinorm.
Saint M, Bertaux F, Tang W, et al., 2019, Single-cell imaging and RNA sequencing reveal patterns of gene expression heterogeneity during fission yeast growth and adaptation, Nature Microbiology, Vol: 4, Pages: 480-491, ISSN: 2058-5276
Phenotypic cell-to-cell variability is a fundamental determinant of microbial fitness that contributes to stress adaptation and drug resistance. Gene expression heterogeneity underpins this variability but is challenging to study genome-wide. Here we examine the transcriptomes of >2,000 single fission yeast cells exposed to various environmental conditions by combining imaging, single-cell RNA sequencing and Bayesian true count recovery. We identify sets of highly variable genes during rapid proliferation in constant culture conditions. By integrating single-cell RNA sequencing and cell-size data, we provide insights into genes that are regulated during cell growth and division, including genes whose expression does not scale with cell size. We further analyse the heterogeneity of gene expression during adaptive and acute responses to changing environments. Entry into the stationary phase is preceded by a gradual, synchronized adaptation in gene regulation that is followed by highly variable gene expression when growth decreases. Conversely, sudden and acute heat shock leads to a stronger, coordinated response and adaptation across cells. This analysis reveals that the magnitude of global gene expression heterogeneity is regulated in response to different physiological conditions within populations of a unicellular eukaryote.
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