Biology subjects

Michael PH Stumpf

Publications and source records attributed to Michael PH Stumpf.

5 recordsLinked to original sources

bioRxiv2016

Single Cell Phenotyping Reveals Heterogeneity among Haematopoietic Stem Cells Following Infection

The haematopoietic stem cell (HSC) niche provides essential micro-environmental cues for the production and maintenance of HSCs within the bone marrow. During inflammation, haematopoietic dynamics are perturbed, but it is not known whether changes to the HSC-niche interaction occur as a result. We visualise HSCs directly in vivo, enabling detailed analysis of the 3D niche dynamics and migration patterns in murine bone marrow following Trichinella spiralis infection. Spatial statistical analysis of these HSC trajectories reveals two distinct modes of HSC behaviour: (i) a pattern of revisiting previously explored space, and (ii) a pattern of exploring new space. Whereas HSCs from control donors predominantly follow pattern (i), those from infected mice adopt both strategies. Using detailed computational analyses of cell migration tracks and life-history theory, we show that the increased motility of HSCs following infection can, perhaps counterintuitively, enable mice to cope better in deteriorating HSC-niche micro-environments following infection.\n\nAuthor SummaryHaematopoietic stem cells reside in the bone marrow where they are crucially maintained by an incompletely-determined set of niche factors. Recently it has been shown that chronic infection profoundly affects haematopoiesis by exhausting stem cell function, but these changes have not yet been resolved at the single cell level. Here we show that the stem cell-niche interactions triggered by infection are heterogeneous whereby cells exhibit different behavioural patterns: for some, movement is highly restricted, while others explore much larger regions of space over time. Overall, cells from infected mice display higher levels of persistence. This can be thought of as a search strategy: during infection the signals passed between stem cells and the niche may be blocked or inhibited. Resultantly, stem cells must choose to either cling on, or to leave in search of a better environment. The heterogeneity that these cells display has immediate consequences for translational therapies involving bone marrow transplant, and the effects that infection might have on these procedures.

Systems Biology↗

bioRxiv2015

Inferring extrinsic noise from single-cell gene expression data using Approximate Bayesian Computation.

BackgroundGene expression is known to be an intrinsically stochastic process which can involve single-digit numbers of mRNA molecules in a cell at any given time. The modelling of such processes calls for the use of exact stochastic simulation methods, most notably the Gillespie algorithm. However, this stochasticity, also termed \"intrinsic noise\", does not account for all the variability between genetically identical cells growing in a homogeneous environment. Despite substantial experimental efforts, determining appropriate model parameters continues to be a challenge. Methods based on approximate Bayesian computation can be used to obtain posterior parameter distributions given the observed data. However, such inference procedures require large numbers of simulations of the model and exact stochastic simulation is computationally costly. In this work we focus on the specific case of trying to infer model parameters describing reaction rates and extrinsic noise on the basis of measurements of molecule numbers in individual cells at a given time point.\n\nResultsTo make the problem computationally tractable we develop an exact, model-specific, stochastic simulation algorithm for the commonly used two-state model of gene expression. This algorithm relies on certain assumptions and favourable properties of the model to forgo the simulation of the whole temporal trajectory of protein numbers in the system, instead returning only the number of protein and mRNA molecules present in the system at a specified time point. The computational gain is proportional to the number of protein molecules created in the system and becomes significant for systems involving hundreds or thousands of protein molecules. We employ this algorithm, approximate Bayesian computation, and published gene expression data for Escherichia coli to simultaneously infer the models rate parameters and parameters describing extrinsic noise for 86 genes.

Systems Biology↗

bioRxiv2015

A Graph Theoretical Approach to Data Fusion

The rapid development of high throughput experimental techniques has resulted in a growing diversity of genomic datasets being produced and requiring analysis. A variety of computational techniques allow us to analyse such data and to model the biological processes behind them. However, it is increasingly being recognised that we can gain deeper understanding by combining the insights obtained from multiple, diverse datasets. We therefore require scalable computational approaches for data fusion.\n\nWe propose a novel methodology for scalable unsupervised data fusion. Our technique exploits network representations of the data in order to identify (and quantify) similarities among the datasets. We may work within the Bayesian formalism, using Bayesian nonparametric approaches to model each dataset; or (for fast, approximate, and massive scale data fusion) can naturally switch to more heuristic modelling techniques. An advantage of the proposed approach is that each dataset can initially be modelled independently (and therefore in parallel), before applying a fast post-processing step in order to perform data fusion. This allows us to incorporate new experimental data in an online fashion, without having to rerun all of the analysis. The methodology can be applied to genomic scale datasets and we demonstrate its applicability on examples from the literature, using a broad range of genomic datasets, and also on a recent gene expression dataset from Sporadic inclusion body myositis Availability. Example R code and instructions are available from https://sites.google.com/site/gtadatafusion/.

Bioinformatics↗

bioRxiv2015

Cellular Population Dynamics Control the Robustness of the Stem Cell Niche

Within populations of cells, fate decisions are controlled by an indeterminate combination of cell-intrinsic and cell-extrinsic factors. In the case of stem cells, the stem cell niche is believed to maintain \"stemness\" through communication and interactions between the stem cells and one or more other cell-types that contribute to the niche conditions. To investigate the robustness of cell fate decisions in the stem cell hierarchy and the role that the niche plays, we introduce simple mathematical models of stem and progenitor cells, their progeny and their interplay in the niche. These models capture the fundamental processes of proliferation and differentiation and allow us to consider alternative possibilities regarding how niche-mediated signalling feedback regulates the niche dynamics. Generalised stability analysis of these stem cell niche systems enables us to describe the stability properties of each model. We find that although the number of feasible states depends on the model, their probabilities of stability in general do not: stem cell-niche models are stable across a wide range of parameters. We demonstrate that niche-mediated feedback increases the number of stable steady states, and show how distinct cell states have distinct branching characteristics. The ecological feedback and interactions mediated by the stem cell niche thus lend (surprisingly) high levels of robustness to the stem and progenitor cell population dynamics. Furthermore, cell-cell interactions are sufficient for populations of stem cells and their progeny to achieve stability and maintain homeostasis. We show that the robustness of the niche -- and hence of the stem cell pool in the niche -- depends only weakly, if at all, on the complexity of the niche make-up: simple as well as complicated niche systems are capable of supporting robust and stable stem cell dynamics.

Systems Biology↗

bioRxiv2015

Analog nitrogen sensing in Escherichia coli enables high fidelity information processing

The molecular reaction networks that coordinate the response of an organism to changing environmental conditions are central for survival and reproduction. Escherchia coli employs an accurate and flexible signalling system that is capable of processing ambient nitrogen availability rapidly and with high accuracy. Carefully orchestrated post-translational modifications of PII and the glutamine synthetase allow E. coli to trace nitrogen availability in a continuous, decidedly non-digital fashion. We measure the dynamic proteomic and metabolomic responses to trace the analog computations, and use an information theoretical framework to characterize the information capacity of E. colis nitrogen sensing network: we find that this system can transmit up to 9bits of information about the nitrogen state. This allows cells to respond rapidly and accurately even to small differences in metabolite concentrations.

Systems Biology↗