1,720,989 research outputs found
Single-cell gene expression data of mESC differentiation time-course toward the neuronal lineage.
No full textSingle-cell gene expression data generated using 96x96 fluidigm dynamic arrays. Experimental details including steps to reproduce data are included in the associated manuscript. Briefly, mouse embryonic stem cells kept in 2i+LIF conditions for four passages differentiated towards the neuronal lineage in N2B27 medium as described in the literature (Ying et al. 2003 - DOI: 10.1038/nbt780). At 0h, 24h, 48h, 72h, 96h, 120h and 168h individual cells were sorted by FACS based on light-scatter properties and deposited into 96-well plates containing lysis buffer and RT reagents before processing for qPCR using the Biomark HD.</span
Investigating the origins and consequences of cell-to-cell variability in stem cell populations
No full textIt is becoming increasingly recognised that stem cell populations from both the embryo and the adult are highly heterogeneous in their molecular expression patterns. However, the underlying causes and consequences are not well understood. This thesis examines cell-to-cell variability in both adult and embryonic stem cell populations, using both experimental and theoretical models to better understand stem cell biology at the single cell level.The first part of this thesis investigates how combinatorial control of signalling pathways, and transcription regulatory networks centred around Nanog can lend robustness to the pluripotent state, despite the observed variability in individual gene expression of core regulatory factors. In order to decouple functional variability from artefacts associated with reporter constructs, a novel theoretical framework is developed to model transcriptional co-regulation, and investigate how variability of regulatory inputs can coordinate stochastic gene expression, and in particular how extrinsic factors can regulate allelic expression in heterozygous knock-in reporter cell lines. This novel theoretical model captures the co-expression characteristics of pluripotency genes and explains the abnormal expression behaviour in widely used Nanog reporter cell lines, as well as illustrating general pitfalls when designing reporter cell lines for single-cell based assays.In the second part of this thesis, cell-to-cell variability in adult stem cell populations is examined. While functionally homogeneous embryonic stem cells are readily available, obtaining and purifying adult stem cells from primary tissue samples is a substantial challenge. This problem is particularly apparent for skeletal stem cells, which are exceptionally rare and cannot be reliably identified in situ through surface makers expression (although populations of cells enriched for skeletal stem cells may be obtained). Since these cells play a central role mediating hematopoietic stem cell activity, and are essential for bone regeneration and therefore skeletal tissue engineering strategies, understanding their molecular identity is a pressing current problem. To address this issue, current skeletal stem cell purification and recently developed high-throughput single cell profiling technologies that are able to quantify the expression of multiple transcripts in a large number of individual cells are combined. This method allows previously inaccessible detail on skeletal cell populations in situ to be obtained. To further investigate the role of cell-to-cell variability, the final part of this thesis explores how cells derived from bone marrow can be reverted to the pluripotent state, a process that is highly reliant on the individual cell fate and thereby strongly affected by cell-to-cell variability.In summary, this thesis gives examples of how variability affects the specification of cellular identities and contributes to the understanding of how variability is regulated across cell populations
Machine learning of stem cell identities from single-cell expression data via regulatory network archetypes
No full textThe molecular regulatory network underlying stem cell pluripotency has been intensively studied, and we now have a reliable ensemble model for the “average” pluripotent cell. However, evidence of significant cell-to-cell variability suggests that the activity of this network varies within individual stem cells, leading to differential processing of environmental signals and variability in cell fates. Here, we adapt a method originally designed for face recognition to infer regulatory network patterns within individual cells from single-cell expression data. Using this method we identify three distinct network configurations in cultured mouse embryonic stem cells—corresponding to naïve and formative pluripotent states and an early primitive endoderm state—and associate these configurations with particular combinations of regulatory network activity archetypes that govern different aspects of the cell's response to environmental stimuli, cell cycle status and core information processing circuitry. These results show how variability in cell identities arise naturally from alterations in underlying regulatory network dynamics and demonstrate how methods from machine learning may be used to better understand single cell biology, and the collective dynamics of cell communities
Single-cell pluripotency regulatory networks
No full textPluripotent stem cells (PSCs) are a popular model system for investigating development, tissue regeneration, and repair. Although much is known about the molecular mechanisms that regulate the balance between self-renewal and lineage commitment in PSCs, the spatiotemporal integration of responsive signaling pathways with core transcriptional regulatory networks are complex and only partially understood. Moreover, measurements made on populations of cells reveal only average properties of the underlying regulatory networks, obscuring their fine detail. Here, we discuss the reconstruction of regulatory networks in individual cells using novel single-cell transcriptomics and proteomics, in order to expand our understanding of the molecular basis of pluripotency, including the role of cell–cell variability within PSC populations, and ways in which networks may be controlled in order to reliably manipulate cell behaviorior
Heterogeneity and 'memory' in stem cell populations
No full textModern single cell experiments have revealed unexpected heterogeneity in apparently functionally 'pure' cell populations. However, we are still lacking a conceptual framework to understand this heterogeneity. Here, we propose that cellular memories – changes in the molecular status of a cell in response to a stimulus, that modify the ability of the cell to respond to future stimuli – are an essential ingredient in any such theory. We illustrate this idea by considering a simple age-structured model of stem cell proliferation that takes account of mitotic memories. Using this model we argue that asynchronous mitosis generates heterogeneity that is central to stem cell population function. This model naturally explains why stem cell numbers increase through life, yet regenerative potency simultaneously declines
Visualization and Clustering of High-Dimensional Transcriptome Data Using GATE
No full textThe potential gains from advances in high-throughput experimental molecular biology techniques are commonly not fully realized since these techniques often produce more data than can be easily organized and visualized. To address these problems, GATE (Grid-Analysis of Time-Series Expression) was developed. GATE is an integrated software platform for the analysis and visualization of high-dimensional time-series datasets, which allows flexible interrogation of time-series data against a wide range of databases of prior knowledge, thus linking observed molecular dynamics to potential genetic, epigenetic, and signaling mechanisms responsible for observed dynamics. This article provides a brief guide to using GATE effectively
Theory of cell fate
No full textCell fate decisions are controlled by complex intracellular molecular regulatory networks. Studies increasingly reveal the scale of this complexity: not only do cell fate regulatory networks contain numerous positive and negative feedback loops, they also involve a range of different kinds of nonlinear protein-protein and protein-DNA interactions. This inherent complexity and non-linearity makes cell fate decisions hard to understand using experiment and intuition alone. In this primer we will outline how tools from mathematics can be used to understand cell fate dynamics. We will briefly introduce some notions from dynamical systems theory, and discuss how they offer a framework within which to build a rigorous understanding of what we mean by a cell 'fate', and how cells change fate. We will also outline how modern experiments, particularly high-throughput single-cell experiments, are enabling us to test and explore the limits of these ideas, and build a better understanding of cellular identities
Modeling stem cell fates using non-Markov processes
No full textEpigenetic memories play an important part in regulating stem cell identities. Tools from the theory of non-Markov processes may help us understand these memories better and develop a more integrated view of stem cell fate and function.</p
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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