Population growth is often ignored when quantifying gene expression levels across clonal cell populations. We develop a framework for obtaining the molecule number distributions in an exponentially ...growing cell population taking into account its age structure. In the presence of generation time variability, the average acquired across a population snapshot does not obey the average of a dividing cell over time, apparently contradicting ergodicity between single cells and the population. Instead, we show that the variation observed across snapshots with known cell age is captured by cell histories, a single-cell measure obtained from tracking an arbitrary cell of the population back to the ancestor from which it originated. The correspondence between cells of known age in a population with their histories represents an ergodic principle that provides a new interpretation of population snapshot data. We illustrate the principle using analytical solutions of stochastic gene expression models in cell populations with arbitrary generation time distributions. We further elucidate that the principle breaks down for biochemical reactions that are under selection, such as the expression of genes conveying antibiotic resistance, which gives rise to an experimental criterion with which to probe selection on gene expression fluctuations.
Growth pervades all areas of life from single cells to cell populations to tissues. 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 characterize (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.
Phenotypic switching in gene regulatory networks Thomas, Philipp; Popović, Nikola; Grima, Ramon
Proceedings of the National Academy of Sciences - PNAS,
05/2014, Letnik:
111, Številka:
19
Journal Article
Recenzirano
Odprti dostop
Noise in gene expression can lead to reversible phenotypic switching. Several experimental studies have shown that the abundance distributions of proteins in a population of isogenic cells may ...display multiple distinct maxima. Each of these maxima may be associated with a subpopulation of a particular phenotype, the quantification of which is important for understanding cellular decision-making. Here, we devise a methodology which allows us to quantify multimodal gene expression distributions and single-cell power spectra in gene regulatory networks. Extending the commonly used linear noise approximation, we rigorously show that, in the limit of slow promoter dynamics, these distributions can be systematically approximated as a mixture of Gaussian components in a wide class of networks. The resulting closed-form approximation provides a practical tool for studying complex nonlinear gene regulatory networks that have thus far been amenable only to stochastic simulation. We demonstrate the applicability of our approach in a number of genetic networks, uncovering previously unidentified dynamical characteristics associated with phenotypic switching. Specifically, we elucidate how the interplay of transcriptional and translational regulation can be exploited to control the multimodality of gene expression distributions in two-promoter networks. We demonstrate how phenotypic switching leads to birhythmical expression in a genetic oscillator, and to hysteresis in phenotypic induction, thus highlighting the ability of regulatory networks to retain memory.
Experiments serve different purposes in chemistry education ranging from illustrating a certain subject matter to providing data for school-based research projects. When students use these ...experiments to collect data and draw conclusions, it is important that these experiments are valid. In this study, a new experiment for the determination of the explosiveness of dusts using a Low-cost-Hartmann apparatus (LC-HA) was developed for high school students. Which similarities and differences can be found between the data of developers and prospective users of the experiment? What is the added value of designing and applying a round robin test to a high school experiment? A round robin test including developers (n = 5) and prospective users (n = 54) is carried out. Data are collected from experiments with two different flour dust concentrations. Accuracy, precision, and variance are calculated for each group and compared between groups. There are similarities and differences between the data of both groups. For the lower concentration of flour dust, there are significant differences between the results of the two groups. For the higher concentration, there are no significant differences. Thus, carrying out a round robin test including both developers and prospective users benefits both groups. The developers gain insights into the application of their method. The prospective users receive information about their performance and the validity of their data. In the future, participation in round robin procedures should also be used as an opportunity to promote students’ knowledge about and skills for validation.
Cellular proliferation depends on refilling the tricarboxylic acid (TCA) cycle to support biomass production (anaplerosis). The two major anaplerotic pathways in cells are pyruvate conversion to ...oxaloacetate via pyruvate carboxylase (PC) and glutamine conversion to α-ketoglutarate. Cancers often show an organ-specific reliance on either pathway. However, it remains unknown whether they adapt their mode of anaplerosis when metastasizing to a distant organ. We measured PC-dependent anaplerosis in breast-cancer-derived lung metastases compared to their primary cancers using in vivo 13C tracer analysis. We discovered that lung metastases have higher PC-dependent anaplerosis compared to primary breast cancers. Based on in vitro analysis and a mathematical model for the determination of compartment-specific metabolite concentrations, we found that mitochondrial pyruvate concentrations can promote PC-dependent anaplerosis via enzyme kinetics. In conclusion, we show that breast cancer cells proliferating as lung metastases activate PC-dependent anaplerosis in response to the lung microenvironment.
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•Lung metastases have higher PC-dependent anaplerosis than primary breast cancers•PC-dependent anaplerosis is a function of the in vivo microenvironment•Pyruvate to glutamine availability is increased in the in vivo lung microenvironment•Compartment-specific pyruvate distributions can be inferred from 13C labeling patterns
Christen et al. find that primary breast cancers and their resulting lung metastases use different modes of TCA cycle anaplerosis. Lung metastases increase PC-dependent anaplerosis in response to the in vivo lung microenvironment. Mechanistically, this response is mediated by alterations in PC expression and mitochondrial pyruvate concentrations.
Growth impacts a range of phenotypic responses. Identifying the sources of growth variation and their propagation across the cellular machinery can thus 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. Alongside we provide a theory to analyse stochastic chemical reactions coupled with cell divisions, enabling efficient parameter estimation, sensitivity analysis and hypothesis testing. The cell model recovers population-averaged data on growth-dependence of bacterial physiology and how growth variations in single cells change across conditions. We identify processes responsible for this variation and reconstruct the propagation of initial fluctuations to growth and other processes. Finally, we study drug-nutrient interactions and find that antibiotics can both enhance and suppress growth heterogeneity. Our results provide a predictive framework to integrate heterogeneous data and draw testable predictions with implications for antibiotic tolerance, evolutionary and synthetic biology.
Nucleotide modifications within RNA transcripts are found in every organism in all three domains of life. 6-methyladeonsine (m(6)A), 5-methylcytosine (m(5)C) and pseudouridine (Ψ) are highly abundant ...nucleotide modifications in coding sequences of eukaryal mRNAs, while m(5)C and m(6)A modifications have also been discovered in archaeal and bacterial mRNAs. Employing in vitro translation assays, we systematically investigated the influence of nucleotide modifications on translation. We introduced m(5)C, m(6)A, Ψ or 2'-O-methylated nucleotides at each of the three positions within a codon of the bacterial ErmCL mRNA and analyzed their influence on translation. Depending on the respective nucleotide modification, as well as its position within a codon, protein synthesis remained either unaffected or was prematurely terminated at the modification site, resulting in reduced amounts of the full-length peptide. In the latter case, toeprint analysis of ribosomal complexes was consistent with stalling of translation at the modified codon. When multiple nucleotide modifications were introduced within one codon, an additive inhibitory effect on translation was observed. We also identified the m(5)C modification to alter the amino acid identity of the corresponding codon, when positioned at the second codon position. Our results suggest a novel mode of gene regulation by nucleotide modifications in bacterial mRNAs.
Abstract
Motivation
Normalization of single-cell RNA-sequencing (scRNA-seq) data is a prerequisite to their interpretation. The marked technical variability, high amounts of missing observations and ...batch effect typical of scRNA-seq datasets make this task particularly challenging. There is a need for an efficient and unified approach for normalization, imputation and batch effect correction.
Results
Here, we introduce bayNorm, a novel Bayesian approach for scaling and inference of scRNA-seq counts. The method’s likelihood function follows a binomial model of mRNA capture, while priors are estimated from expression values across cells using an empirical Bayes approach. We first validate our assumptions by showing this model can reproduce different statistics observed in real scRNA-seq data. 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 fluorescence in situ hybridization measurements. Moreover, by using priors informed by dataset structures, bayNorm improves accuracy and sensitivity of differential expression analysis and reduces batch effect compared with other existing methods. Altogether, bayNorm provides an efficient, integrated solution for global scaling normalization, imputation and true count recovery of gene expression measurements from scRNA-seq data.
Availability and implementation
The R package ‘bayNorm’ is publishd on bioconductor at https://bioconductor.org/packages/release/bioc/html/bayNorm.html. The code for analyzing data in this article is available at https://github.com/WT215/bayNorm_papercode.
Supplementary information
Supplementary data are available at Bioinformatics online.
As a measure of the brain's temporal fine-tuning capacity, temporal resolution power (TRP) explained repeatedly a substantial amount of variance in psychometric intelligence. Recently, spatial ...suppression, referred to as the increasing difficulty in quickly perceiving motion direction as the size of the moving stimulus increases, has attracted particular attention, when it was found to be positively related to psychometric intelligence. Due to the conceptual similarities of TRP and spatial suppression, the present study investigated their mutual interplay in the relation to psychometric intelligence in 273 young adults to better understand the reasons for these relationships. As in previous studies, psychometric intelligence was positively related to a latent variable representing TRP but, in contrast to previous reports, negatively to latent and manifest measures of spatial suppression. In a combined structural equation model, TRP still explained a substantial amount of variance in psychometric intelligence while the negative relation between spatial suppression and intelligence was completely explained by TRP. Thus, our findings confirmed TRP to be a robust predictor of psychometric intelligence but challenged the assumption of spatial suppression as a representation of general information processing efficiency as reflected in psychometric intelligence. Possible reasons for the contradictory findings on the relation between spatial suppression and psychometric intelligence are discussed.
As a measure of the brain’s temporal fine-tuning capacity, temporal resolution power (TRP) explained repeatedly a substantial amount of variance in psychometric intelligence. Recently, spatial ...suppression, referred to as the increasing difficulty in quickly perceiving motion direction as the size of the moving stimulus increases, has attracted particular attention, when it was found to be positively related to psychometric intelligence. Due to the conceptual similarities of TRP and spatial suppression, the present study investigated their mutual interplay in the relation to psychometric intelligence in 273 young adults to better understand the reasons for these relationships. As in previous studies, psychometric intelligence was positively related to a latent variable representing TRP but, in contrast to previous reports, negatively to latent and manifest measures of spatial suppression. In a combined structural equation model, TRP still explained a substantial amount of variance in psychometric intelligence while the negative relation between spatial suppression and intelligence was completely explained by TRP. Thus, our findings confirmed TRP to be a robust predictor of psychometric intelligence but challenged the assumption of spatial suppression as a representation of general information processing efficiency as reflected in psychometric intelligence. Possible reasons for the contradictory findings on the relation between spatial suppression and psychometric intelligence are discussed.
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