Impact of implementation choices on quantitative predictions of cell-based computational models

‘Cell-based’ models provide a powerful computational tool for studying the mechanisms underlying the growth and dynamics of biological tissues in health and disease. An increasing amount of quantitative data with cellular resolution has paved the way for the quantitative parameterisation and validation of such models. However, the numerical implementation of cell-based models remains challenging, and little work has been done to understand to what extent implementation choices may influence model predictions. Here, we consider the numerical implementation of a popular class of cell-based models called vertex models, which are often used to study epithelial tissues. In two-dimensional vertex models, a tissue is approximated as a tessellation of polygons and the vertices of these polygons move due to mechanical forces originating from the cells. Such models have been used extensively to study the mechanical regulation of tissue topology in the literature. Here, we analyse how the model predictions may be affected by numerical parameters, such as the size of the time step, and non-physical model parameters, such as length thresholds for cell rearrangement. We find that vertex positions and summary statistics are sensitive to several of these implementation parameters. For example, the predicted tissue size decreases with decreasing cell cycle durations, and cell rearrangement may be suppressed by large time steps. These findings are counter-intuitive and illustrate that model predictions need to be thoroughly analysed and implementation details carefully considered when applying cell-based computational models in a quantitative setting

Coupled differentiation and division of embryonic stem cells inferred from clonal snapshots

The deluge of single-cell data obtained by sequencing, imaging and epigenetic markers has led to an increasingly detailed description of cell state. However, it remains challenging to identify how cells transition between different states, in part because data are typically limited to snapshots in time. A prerequisite for inferring cell state transitions from such snapshots is to distinguish whether transitions are coupled to cell divisions. To address this, we present two minimal branching process models of cell division and differentiation in a well-mixed population. These models describe dynamics where differentiation and division are coupled or uncoupled. For each model, we derive analytic expressions for each subpopulation's mean and variance and for the likelihood, allowing exact Bayesian parameter inference and model selection in the idealised case of fully observed trajectories of differentiation and division events. In the case of snapshots, we present a sample path algorithm and use this to predict optimal temporal spacing of measurements for experimental design. We then apply this methodology to an \textit{in vitro} dataset assaying the clonal growth of epiblast stem cells in culture conditions promoting self-renewal or differentiation. Here, the larger number of cell states necessitates approximate Bayesian computation. For both culture conditions, our inference supports the model where cell state transitions are coupled to division. For culture conditions promoting differentiation, our analysis indicates a possible shift in dynamics, with these processes becoming more coupled over time

Transient aging in fractional Brownian and Langevin-equation motion

Stochastic processes driven by stationary fractional Gaussian noise, that is, fractional Brownian motion and fractional Langevin-equation motion, are usually considered to be ergodic in the sense that, after an algebraic relaxation, time and ensemble averages of physical observables coincide. Recently it was demonstrated that fractional Brownian motion and fractional Langevin-equation motion under external confinement are transiently nonergodic—time and ensemble averages behave differently—from the moment when the particle starts to sense the confinement. Here we show that these processes also exhibit transient aging, that is, physical observables such as the time-averaged mean-squared displacement depend on the time lag between the initiation of the system at time t=0 and the start of the measurement at the aging time ta. In particular, it turns out that for fractional Langevin-equation motion the aging dependence on ta is different between the cases of free and confined motion. We obtain explicit analytical expressions for the aged moments of the particle position as well as the time-averaged mean-squared displacement and present a numerical analysis of this transient aging phenomenon

Robust cell tracking in epithelial tissues through identification of maximum common subgraphs

Tracking of cells in live-imaging microscopy videos of epithelial sheets is a powerful tool for investigating fundamental processes in embryonic development. Characterizing cell growth, proliferation, intercalation and apoptosis in epithelia helps us to understand how morphogenetic processes such as tissue invagination and extension are locally regulated and controlled. Accurate cell tracking requires correctly resolving cells entering or leaving the field of view between frames, cell neighbour exchanges, cell removals and cell divisions. However, current tracking methods for epithelial sheets are not robust to large morphogenetic deformations and require significant manual interventions. Here, we present a novel algorithm for epithelial cell tracking, exploiting the graph-theoretic concept of a ‘maximum common subgraph’ to track cells between frames of a video. Our algorithm does not require the adjustment of tissue-specific parameters, and scales in sub-quadratic time with tissue size. It does not rely on precise positional information, permitting large cell movements between frames and enabling tracking in datasets acquired at low temporal resolution due to experimental constraints such as phototoxicity. To demonstrate the method, we perform tracking on the Drosophila embryonic epidermis and compare cell–cell rearrangements to previous studies in other tissues. Our implementation is open source and generally applicable to epithelial tissues

Dual signal transduction pathways activated by TSH receptors in rat primary tanycyte cultures

Tanycytes play multiple roles in hypothalamic functions, including sensing peripheral nutrients and metabolic hormones, regulating neurosecretion and mediating seasonal cycles of reproduction and metabolic physiology. This last function reflects the expression of TSH receptors in tanycytes, which detect photoperiod-regulated changes in TSH secretion from the neighbouring pars tuberalis. The present overall aim was to determine the signal transduction pathway by which TSH signals in tanycytes. Expression of the TSH receptor in tanycytes of 10-day-old Sprague Dawley rats was observed by in situ hybridisation. Primary ependymal cell cultures prepared from 10-day-old rats were found by immunohistochemistry to express vimentin but not GFAP and by PCR to express mRNA for Dio2, Gpr50, Darpp-32 and Tsh receptors that are characteristic of tanycytes. Treatment of primary tanycyte/ependymal cultures with TSH (100 IU/l) increased cAMP as assessed by ELISA and induced a cAMP-independent increase in the phosphorylation of ERK1/2 as assessed by western blot analysis. Furthermore, TSH (100 IU/l) stimulated a 2.17-fold increase in Dio2 mRNA expression. We conclude that TSH signal transduction in cultured tanycytes signals via Gαs to increase cAMP and via an alternative G protein to increase phosphorylation of ERK1/2

AMULET: a novel read count-based method for effective multiplet detection from single nucleus ATAC-seq data.

Detecting multiplets in single nucleus (sn)ATAC-seq data is challenging due to data sparsity and limited dynamic range. AMULET (ATAC-seq MULtiplet Estimation Tool) enumerates regions with greater than two uniquely aligned reads across the genome to effectively detect multiplets. We evaluate the method by generating snATAC-seq data in the human blood and pancreatic islet samples. AMULET has high precision, estimated via donor-based multiplexing, and high recall, estimated via simulated multiplets, compared to alternatives and identifies multiplets most effectively when a certain read depth of 25K median valid reads per nucleus is achieved

Variation in histone configurations correlates with gene expression across nine inbred strains of mice.

The diversity outbred (DO) mice and their inbred founders are widely used models of human disease. However, although the genetic diversity of these mice has been well documented, their epigenetic diversity has not. Epigenetic modifications, such as histone modifications and DNA methylation, are important regulators of gene expression, and as such are a critical mechanistic link between genotype and phenotype. Therefore, creating a map of epigenetic modifications in the DO mice and their founders is an important step toward understanding mechanisms of gene regulation and the link to disease in this widely used resource. To this end, we performed a strain survey of epigenetic modifications in hepatocytes of the DO founders. We surveyed four histone modifications (H3K4me1, H3K4me3, H3K27me3, and H3K27ac), and DNA methylation. We used ChromHMM to identify 14 chromatin states, each of which represented a distinct combination of the four histone modifications. We found that the epigenetic landscape was highly variable across the DO founders and was associated with variation in gene expression across strains. We found that epigenetic state imputed into a population of DO mice recapitulated the association with gene expression seen in the founders suggesting that both histone modifications and DNA methylation are highly heritable mechanisms of gene expression regulation. We illustrate how DO gene expression can be aligned with inbred epigenetic states to identify putative cis-regulatory regions. Finally, we provide a data resource that documents strain-specific variation in chromatin state and DNA methylation in hepatocytes across nine widely used strains of laboratory mice

MAFTIA Conceptual Model and Architecture

This document builds on the work reported in MAFTIA deliverable D1. It contains a refinement of the MAFTIA conceptual model and a discussion of the MAFTIA architecture. It also introduces the work done in WP6 on verification and assessment of security properties, which is reported on in more detail in MAFTIA deliverable D

Genetic regulatory signatures underlying islet gene expression and type 2 diabetes

The majority of genetic variants associated with type 2 diabetes (T2D) are located outside of genes in noncoding regions that may regulate gene expression in disease-relevant tissues, like pancreatic islets. Here, we present the largest integrated analysis to date of high-resolution, high-throughput human islet molecular profiling data to characterize the genome (DNA), epigenome (DNA packaging), and transcriptome (gene expression). We find that T2D genetic variants are enriched in regions of the genome where transcription Regulatory Factor X (RFX) is predicted to bind in an islet-specific manner. Genetic variants that increase T2D risk are predicted to disrupt RFX binding, providing a molecular mechanism to explain how the genome can influence the epigenome, modulating gene expression and ultimately T2D risk

Conceptual Model and Architecture of MAFTIA

This deliverable builds on the work reported in [MAFTIA 2000] and [Powell and Stroud 2001]. It contains a further refinement of the MAFTIA conceptual model and a revised discussion of the MAFTIA architecture. It also introduces the work done in MAFTIA on verification and assessment of security properties, which is reported on in more detail in [Adelsbach and Creese 2003