Biology subjects

Thomas, P.

Publications and source records attributed to Thomas, P..

8 recordsLinked to original sources

bioRxiv2018

bayNorm: Bayesian gene expression recovery, imputation and normalisation for single cell RNA-sequencing data

Normalisation of single cell RNA sequencing (scRNA-seq) data is a prerequisite to their interpretation. The marked technical variability and high amounts of missing observations typical of scRNA-seq datasets make this task particularly challenging. Here, we introduce bayNorm, a novel Bayesian approach for scaling and inference of scRNA-seq counts. The methods likelihood function follows a binomial model of mRNA capture, while priors are estimated from expression values across cells using an empirical Bayes approach. We demonstrate using publicly-available scRNA-seq datasets and simulated expression data that bayNorm allows robust imputation of missing values generating realistic transcript distributions that match single molecule FISH measurements. Moreover, by using priors informed by dataset structures, bayNorm improves accuracy and sensitivity of differential expression analysis and reduces batch effect compared to other existing methods. Altogether, bayNorm provides an efficient, integrated solution for global scaling normalisation, imputation and true count recovery of gene expression measurements from scRNA-seq data.

bioinformatics↗

bioRxiv2018

Population growth affects intrinsic and extrinsic noise in gene expression

Clonal cells of exponentially growing populations vary substantially from cell to cell. The main drivers of this heterogeneity are the population dynamics and stochasticity in the intracellular reactions, which are commonly studied separately. Here we develop an agent-based framework that allows tracking of the biochemical dynamics in every single cell of a growing population that accounts for both of these factors. Apart from the common intrinsic variability of the biochemical reactions, the framework also predicts extrinsic noise arising from fluctuations in the histories of cells without the need to introduce fluctuating rate constants. Instead, these extrinsic fluctuations are explained by cell cycle fluctuations and differences in cell age, which are ubiquitously observed in growing populations. We give explicit formulas to quantify mean molecule numbers, intrinsic and extrinsic noise statistics as measured in two-colour experiments. We find that these statistics may differ significantly depending on the experimental setup used to observe the cells. We illustrate this fact using (i) averages over an isolated cell lineage tracked over many generations as observed in the mother machine, (ii) snapshots of a growing population with known cell ages as recorded in time-lapse microscopy, and (iii) snapshots of unknown cell ages as measured from static images. Our integrated approach applies to arbitrary biochemical networks and generation time distributions. By employing models of stochastic gene expression and feedback regulation, we elucidate that isolated lineages, as compared to snapshot data, can significantly overestimate the mean number of molecules, overestimate extrinsic noise but underestimate intrinsic noise and have qualitatively different sensitivities to cell cycle fluctuations.

systems biology↗

bioRxiv2018

Analysis of cell size homeostasis at the single-cell and population level

Growth pervades all areas of life from single cells to cell populations to tissues. However, cell size often fluctuates significantly from cell to cell and from generation to generation. Here we present a unified framework to predict the statistics of cell size variations within a lineage tree of a proliferating population. We analytically characterise (i) the distributions of cell size snapshots, (ii) the distribution within a population tree, and (iii) the distribution of lineages across the tree. Surprisingly, these size distributions differ significantly from observing single cells in isolation. In populations, cells seemingly grow to different sizes, typically exhibit less cell-to-cell variability and often display qualitatively different sensitivities to cell cycle noise and division errors. We demonstrate the key findings using recent single-cell data and elaborate on the implications for the ability of cells to maintain a narrow size distribution and the emergence of different power laws in these distributions.

systems biology↗

bioRxiv2018

Stochasticity of cellular growth: sources, propagation and consequences

Cellular growth impacts a range of phenotypic responses. Identifying the sources of fluctuations in growth and how they propagate across the cellular machinery can unravel mechanisms that underpin cell decisions. We present a stochastic cell model linking gene expression, metabolism and replication to predict growth dynamics in single bacterial cells. In addition to several population-averaged data, the model quantitatively recovers how growth fluctuations in single cells change across nutrient conditions. We develop a framework to analyse stochastic chemical reactions coupled with cell divisions and use it to identify sources of growth heterogeneity. By visualising cross-correlations we then determine how such initial fluctuations propagate to growth rate and affect other cell processes. We further study antibiotic responses and find that complex drug-nutrient interactions can both enhance and suppress heterogeneity. Our results provide a predictive framework to integrate single-cell and bulk data and draw testable predictions with implications for antibiotic tolerance, evolutionary biology and synthetic biology.

systems biology↗

bioRxiv2018

USP7 cooperates with NOTCH1 to drive the oncogenic transcriptionalprogram in T cell leukemia

T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive disease, affecting children and adults. Treatments1-6 show high response rates but have debilitating effects and carry risk of relapse5,7,8. Previous work implicated NOTCH1 and other oncogenes1,2,9-20. However, direct inhibition of these pathways affects healthy tissues and cancer alike. Here, we demonstrate that ubiquitin-specific protease 7 (USP7)21-32 controls leukemia growth by stabilizing the levels of the NOTCH1 and JMJD3 demethylase. USP7 is overexpressed T-ALL and is transcriptionally regulated by NOTCH1. In turn, USP7 controls NOTCH1 through deubiquitination. USP7 is bound to oncogenic targets and controls gene expression through H2B ubiquitination and H3K27me3 changes via stabilization of NOTCH1 and JMJD3. We also show that USP7 and NOTCH1 bind T-ALL superenhancers, and USP7 inhibition alters associated gene activity. These results provide a new model for deubiquitinase activity through recruitment to oncogenic chromatin loci and regulation of both oncogenic transcription factors and chromatin marks to promote leukemia. USP7 inhibition33 significantly blocked T-ALL cell growth in vitro and in vivo. Our studies also show that USP7 is upregulated in the aggressive high-risk cases of T-ALL and suggest that USP7 expression might be a prognostic marker in ALL and its inhibition could be a therapeutic tool against aggressive leukemia.

cancer biology↗

bioRxiv2017

Cell size control driven by the circadian clock and environment in cyanobacteria

How cells maintain their size has been extensively studied under constant conditions. In the wild, however, cells rarely experience constant environments. Here, we examine how the 24-hour circadian clock and environmental cycles modulate cell size control and division timings in the cyanobacterium Synechococcus elongatus using single-cell time-lapse microscopy. Under constant light, wild type cells follow an apparent sizer-like principle. Closer inspection reveals that the clock generates two subpopulations, with cells born in the subjective day following different division rules from cells born in subjective night. A stochastic model explains how this behaviour emerges from the interaction of cell size control with the clock. We demonstrate that the clock continuously modulates the probability of cell division throughout day and night, rather than solely applying an on-off gate to division as previously proposed. Iterating between modelling and experiments, we go on to show that the combined effects of the environment and the clock on cell division are explained by an effective coupling function. Under naturally graded light-dark cycles, this coupling shifts cell division away from dusk and dawn, when light levels are low and cell growth is reduced. Our analysis allows us to disentangle, and predict the effects of, the complex interactions between the environment, clock, and cell size control.

systems biology↗

bioRxiv2017

Abnormal cell sorting underlies the unique X-linked inheritance of PCDH19 Epilepsy

X-linked diseases typically exhibit more severe phenotypes in males than females. In contrast, Protocadherin 19 (PCDH19) mutations cause epilepsy in heterozygous females but spare hemizygous males. The cellular mechanism responsible for this unique pattern of X-linked inheritance is unknown. We show that PCDH19 contributes to highly specific combinatorial adhesion codes such that mosaic expression of Pcdh19 in heterozygous female mice leads to striking sorting between WT PCDH19- and null PCDH19-expressing cells in the developing cortex, correlating with altered network activity. Complete deletion of PCDH19 in heterozygous mice abolishes abnormal cell sorting and restores normal network activity. Furthermore, we identify variable cortical malformations in PCDH19 epilepsy patients. Our results highlight the role of PCDH19 in determining specific adhesion codes during cortical development and how disruption of these codes is associated with the unique X-linked inheritance of PCDH19 epilepsy.

developmental biology↗

bioRxiv2017

Single-cell histories in growing populations: relating physiological variability to population growth

Cell size and individual growth rates vary substantially across genetically identical cell populations. This variation cannot entirely be explained by asynchronous cell division cycles, but also needs to take into account the differences in the histories that cells experience during their lifespan. We describe a stochastic framework to characterise cell size histories in an exponentially growing population. We show that these histories differ from cells observed in isolation, such as observed in mother machines. Quantifying these historical fluctuations allows us to predict the population growth rate. We highlight that the maximum attainable population growth cannot exceed the rate at which an average cell grows, but the population doubles faster than an average cell doubles its size. We validate this prediction using recent single-cell data. The theory thus provides fundamental limits on population fitness in terms of individual cell properties.

systems biology↗