Single cell studies of the cell cycle and some models.
Ontology highlight
ABSTRACT:
SUBMITTER: Mitchison JM
PROVIDER: S-EPMC550673 | biostudies-other | 2005
REPOSITORIES: biostudies-other
Similar Datasets
Project description:Blood and tissue are composed of many functionally distinct cell subsets. In immunological studies, these can be measured accurately only using single-cell assays. The characterization of these small cell subsets is crucial to decipher system-level biological changes. For this reason, an increasing number of studies rely on assays that provide single-cell measurements of multiple genes and proteins from bulk cell samples. A common problem in the analysis of such data is to identify biomarkers (or combinations of biomarkers) that are differentially expressed between two biological conditions (e.g. before/after stimulation), where expression is defined as the proportion of cells expressing that biomarker (or biomarker combination) in the cell subset(s) of interest. Here, we present a Bayesian hierarchical framework based on a beta-binomial mixture model for testing for differential biomarker expression using single-cell assays. Our model allows the inference to be subject specific, as is typically required when assessing vaccine responses, while borrowing strength across subjects through common prior distributions. We propose two approaches for parameter estimation: an empirical-Bayes approach using an Expectation-Maximization algorithm and a fully Bayesian one based on a Markov chain Monte Carlo algorithm. We compare our method against classical approaches for single-cell assays including Fisher's exact test, a likelihood ratio test, and basic log-fold changes. Using several experimental assays measuring proteins or genes at single-cell level and simulations, we show that our method has higher sensitivity and specificity than alternative methods. Additional simulations show that our framework is also robust to model misspecification. Finally, we demonstrate how our approach can be extended to testing multivariate differential expression across multiple biomarker combinations using a Dirichlet-multinomial model and illustrate this approach using single-cell gene expression data and simulations.
| S-EPMC3862207 | biostudies-literature
Project description:This experiment was provided by TAIR (http://arabidopsis.org). Effective analysis of gene expression during the cell cycle depends on achieving a good level of synchronisation. Until recently, analysis of cell cycle processes in plants has been hampered by the lack of synchronizable cell suspensions for Arabidopsis. We have recently developed a cell synchrony system for Arabidopsis cell suspensions MM1 and MM2d, and have developed two methods of synchronization. The first synchronizes cycling cells by blocking cells at the G1/S boundary using aphidicolin. The second uses sucrose removal and resupply to synchronize cells during re-entry into the cell cycle. Cell cycle synchrony in suspension cultured cells: cells can be reproducibly synchronized by blocking at the G1/S boundary or in early S phase using aphidicolin for 24 hr and then reversing the block by washing (Menges and Murray, 2002). On aphidicolin removal, the synchronous resumption of S phase and progression through the cell cycle occur and sequential RNA samples were taken at 2-3 hourly intervals over a 19 hour period. We have carried out a transcriptional profiling analysis with the aim to study gene expression during cell cycle progression after aphidicolin treatment of suspension-cultured cells using the near full genome ATH1 arrays (Menges et al., 2003). Experimenter name = Jim Murray Experimenter phone = 44 1223 334166 Experimenter fax = 44 1223 334162 Experimenter department = J Murray Laboratory Experimenter institute = University of Cambridge Experimenter address = Institute of Biotechnology Experimenter address = Tennis Court Road Experimenter address = Cambridge Experimenter zip/postal_code = CB2 1QT Experimenter country = United Kingdom Keywords: compound_treatment_design; time_series_design
2007-02-16 | GSE5747 | GEO
Project description:Deep generative models, such as variational autoencoders (VAEs) or deep Boltzmann machines (DBMs), can generate an arbitrary number of synthetic observations after being trained on an initial set of samples. This has mainly been investigated for imaging data but could also be useful for single-cell transcriptomics (scRNA-seq). A small pilot study could be used for planning a full-scale experiment by investigating planned analysis strategies on synthetic data with different sample sizes. It is unclear whether synthetic observations generated based on a small scRNA-seq dataset reflect the properties relevant for subsequent data analysis steps. We specifically investigated two deep generative modeling approaches, VAEs and DBMs. First, we considered single-cell variational inference (scVI) in two variants, generating samples from the posterior distribution, the standard approach, or the prior distribution. Second, we propose single-cell deep Boltzmann machines (scDBMs). When considering the similarity of clustering results on synthetic data to ground-truth clustering, we find that the [Formula: see text] variant resulted in high variability, most likely due to amplifying artifacts of small datasets. All approaches showed mixed results for cell types with different abundance by overrepresenting highly abundant cell types and missing less abundant cell types. With increasing pilot dataset sizes, the proportions of the cells in each cluster became more similar to that of ground-truth data. We also showed that all approaches learn the univariate distribution of most genes, but problems occurred with bimodality. Across all analyses, in comparing 10[Formula: see text] Genomics and Smart-seq2 technologies, we could show that for 10[Formula: see text] datasets, which have higher sparsity, it is more challenging to make inference from small to larger datasets. Overall, the results show that generative deep learning approaches might be valuable for supporting the design of scRNA-seq experiments.
| S-EPMC8087667 | biostudies-literature
Project description:Growth condition perturbation or gene function disruption are commonly used strategies to study cellular systems. Although it is widely appreciated that such experiments may involve indirect effects, these frequently remain uncharacterized. Here, analysis of functionally unrelated Saccharyomyces cerevisiae deletion strains reveals a common gene expression signature. One property shared by these strains is slower growth, with increased presence of the signature in more slowly growing strains. The slow growth signature is highly similar to the environmental stress response (ESR), an expression response common to diverse environmental perturbations. Both environmental and genetic perturbations result in growth rate changes. These are accompanied by a change in the distribution of cells over different cell cycle phases. Rather than representing a direct expression response in single cells, both the slow growth signature and ESR mainly reflect a redistribution of cells over different cell cycle phases, primarily characterized by an increase in the G1 population. The findings have implications for any study of perturbation that is accompanied by growth rate changes. Strategies to counter these effects are presented and discussed.
| S-EPMC4265054 | biostudies-literature
Project description:The dynamics of the late stages of the HIV-1 life cycle are poorly documented. Viral replication dynamics are typically measured in populations of infected cells, but asynchrony that is introduced during the early steps of HIV-1 replication complicates the measurement of the progression of subsequent steps and can mask replication dynamics and their variation in individual infected cells. We established microscopy-based methods to dynamically measure HIV-1-encoded reporter gene and antiviral gene expression in individual infected cells. We coupled these measurements with conventional analyses to quantify delays in the HIV-1 replication cycle imposed by the biphasic nature of HIV-1 gene expression and by the assembly-inhibiting property of the matrix domain of Gag. We further related the dynamics of restriction factor (APOBEC3G) removal to the dynamics of HIV-1 replication in individual cells. These studies provide a timeline for key events in the HIV-1 replication cycle, and reveal that the interval between the onset of early and late HIV-1 gene expression is only ~3 h, but matrix causes a ~6-12 h delay in the generation of extracellular virions. Interestingly, matrix delays particle assembly to a time at which APOBEC3G has largely been removed from the cell. Thus, a need to prepare infected cells to be efficient producers of infectious HIV-1 may provide an impetus for programmed delays in HIV-1 virion genesis. Our findings also emphasize the significant heterogeneity in the length of the HIV-1 replication cycle in homogenous cell populations and suggest that a typical infected cell generates new virions for only a few hours at the end of a 48 h lifespan. Therefore, small changes in the lifespan of infected cells might have a large effect on viral yield in a single cycle and the overall clinical course in infected individuals.
| S-EPMC4472667 | biostudies-literature
Project description:The fluorescent ubiquitination-based cell cycle indicator, also known as FUCCI, allows the visualization of the G1 and S/G2/M cell cycle phases of individual cells. FUCCI consists of two fluorescent probes, so that cells in the G1 phase fluoresce red and cells in the S/G2/M phase fluoresce green. FUCCI reveals real-time information about cell cycle dynamics of individual cells, and can be used to explore how the cell cycle relates to the location of individual cells, local cell density, and different cellular microenvironments. In particular, FUCCI is used in experimental studies examining cell migration, such as malignant invasion and wound healing. Here we present, to our knowledge, new mathematical models that can describe cell migration and cell cycle dynamics as indicated by FUCCI. The fundamental model describes the two cell cycle phases, G1 and S/G2/M, which FUCCI directly labels. The extended model includes a third phase, early S, which FUCCI indirectly labels. We present experimental data from scratch assays using FUCCI-transduced melanoma cells, and show that the predictions of spatial and temporal patterns of cell density in the experiments can be described by the fundamental model. We obtain numerical solutions of both the fundamental and extended models, which can take the form of traveling waves. These solutions are mathematically interesting because they are a combination of moving wavefronts and moving pulses. We derive and confirm a simple analytical expression for the minimum wave speed, as well as exploring how the wave speed depends on the spatial decay rate of the initial condition.
| S-EPMC5883567 | biostudies-literature
Project description:Time delays are known to play a crucial role in generating biological oscillations. The early embryonic cell cycle in the frog Xenopus laevis is one such example. Although various mathematical models of this oscillating system exist, it is not clear how to best model the required time delay. Here, we study a simple cell cycle model that produces oscillations due to the presence of an ultrasensitive, time-delayed negative feedback loop. We implement the time delay in three qualitatively different ways, using a fixed time delay, a distribution of time delays, and a delay that is state-dependent. We analyze the dynamics in all cases, and we use experimental observations to interpret our results and put constraints on unknown parameters. In doing so, we find that different implementations of the time delay can have a large impact on the resulting oscillations.
| S-EPMC5868829 | biostudies-literature
Project description:Transcription is a highly stochastic process. To infer transcription kinetics for a gene-of-interest, researchers commonly compare the distribution of mRNA copy-number to the prediction of a theoretical model. However, the reliability of this procedure is limited because the measured mRNA numbers represent integration over the mRNA lifetime, contribution from multiple gene copies, and mixing of cells from different cell-cycle phases. We address these limitations by simultaneously quantifying nascent and mature mRNA in individual cells, and incorporating cell-cycle effects in the analysis of mRNA statistics. We demonstrate our approach on Oct4 and Nanog in mouse embryonic stem cells. Both genes follow similar two-state kinetics. However, Nanog exhibits slower ON/OFF switching, resulting in increased cell-to-cell variability in mRNA levels. Early in the cell cycle, the two copies of each gene exhibit independent activity. After gene replication, the probability of each gene copy to be active diminishes, resulting in dosage compensation.
| S-EPMC4801054 | biostudies-literature
Project description:Chromosomes in proliferating metazoan cells undergo marked structural metamorphoses every cell cycle, alternating between highly condensed mitotic structures that facilitate chromosome segregation, and decondensed interphase structures that accommodate transcription, gene silencing and DNA replication. Here we use single-cell Hi-C (high-resolution chromosome conformation capture) analysis to study chromosome conformations in thousands of individual cells, and discover a continuum of cis-interaction profiles that finely position individual cells along the cell cycle. We show that chromosomal compartments, topological-associated domains (TADs), contact insulation and long-range loops, all defined by bulk Hi-C maps, are governed by distinct cell-cycle dynamics. In particular, DNA replication correlates with a build-up of compartments and a reduction in TAD insulation, while loops are generally stable from G1 to S and G2 phase. Whole-genome three-dimensional structural models reveal a radial architecture of chromosomal compartments with distinct epigenomic signatures. Our single-cell data therefore allow re-interpretation of chromosome conformation maps through the prism of the cell cycle.
| S-EPMC5567812 | biostudies-literature
Project description:Models for the control of global cell-cycle transcription have advanced from a CDK-APC/C oscillator, a transcription factor (TF) network, to coupled CDK-APC/C and TF networks. Nonetheless, current models were challenged by a recent study that concluded that the cell-cycle transcriptional program is primarily controlled by a CDK-APC/C oscillator in budding yeast. Here we report an analysis of the transcriptome dynamics in cyclin mutant cells that were not queried in the previous study. We find that B-cyclin oscillation is not essential for control of phase-specific transcription. Using a mathematical model, we demonstrate that the function of network TFs can be retained in the face of significant reductions in transcript levels. Finally, we show that cells arrested at mitotic exit with non-oscillating levels of B-cyclins continue to cycle transcriptionally. Taken together, these findings support a critical role of a TF network and a requirement for CDK activities that need not be periodic.
| S-EPMC5638368 | biostudies-literature