scholarly journals Bayesian inference of transcription dynamics from population snapshots of single-molecule RNA FISH in single cells

2017 ◽  
Author(s):  
Mariana Gómez-Schiavon ◽  
Liang-Fu Chen ◽  
Anne E. West ◽  
Nicolas E. Buchler
Keyword(s):  
Gene Expression ◽  
Single Molecule ◽  
Posterior Probability ◽  
Single Cells ◽  
Bayesian Posterior Probability ◽  
Model Parameters ◽  
Multiple Time ◽  
Stochastic Gene Expression ◽  
Time Points ◽  
Multiple Time Points

AbstractSingle-molecule RNA fluorescence in situ hybridization (smFISH) provides unparalleled resolution on the abundance and localization of nascent and mature transcripts in single cells. Gene expression dynamics are typically inferred by measuring mRNA abundance in small numbers of fixed cells sampled from a population at multiple time-points after induction. The sparse data that arise from the small number of cells obtained using smFISH present a challenge for inferring transcription dynamics. Here, we developed a computational pipeline (BayFish) to infer kinetic parameters of gene expression from smFISH data at multiple time points after induction. Given an underlying model of gene expression, BayFish uses a Monte Carlo method to estimate the Bayesian posterior probability of the model parameters and quantify the parameter uncertainty given the observed smFISH data. We tested BayFish on smFISH measurements of the neuronal activity inducible gene Npas4 in primary neurons. We showed that a 2-state promoter model can recapitulate Npas4 dynamics after induction and we inferred that the transition rate from the promoter OFF state to the ON state is increased by the stimulus.Author SummaryGene expression can exhibit cell-to-cell variability due to the stochastic nature of biochemical reactions. Single cell assays (e.g. smFISH) directly quantify stochastic gene expression by measuring the number of active promoters and transcripts per cell in a population of cells. The data are distributions and their shape and time-evolution contain critical information on the underlying process of gene expression. Recent work has combined models of stochastic gene expression with maximum likelihood methods to infer kinetic parameters from smFISH distributions. However, these approaches do not provide a probability distribution or likelihood of model parameters inferred from the smFISH data. This information is useful because it indicates which parameters are loosely constrained by the data and suggests follow up experiments. We developed a suite of MATLAB programs (BayFish) that estimate the Bayesian posterior probability of model parameters from smFISH data. The user specifies an underlying model of stochastic gene expression with unknown parameters (θ) and provides smFISH data (Y). BayFish uses a Monte Carlo algorithm to estimate the Bayesian posterior probability P(θ|Y) of model parameters. BayFish is easily modified and can be applied to other models of stochastic gene expression and smFISH data sets.

Gene expression analysis at multiple time-points identifies key genes for nerve regeneration

2016 ◽  
Vol 55 (3) ◽  
pp. 373-383 ◽  
Author(s):  
Bin Pan ◽  
Yi Liu ◽  
Jia-Yin Yan ◽  
Yao Wang ◽  
Xue Yao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Nerve Regeneration ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Multiple Time ◽  
Time Points ◽  
Key Genes ◽  
Multiple Time Points

scholarly journals Characterization of directed differentiation by high-throughput single-cell RNA-Seq

2014 ◽  
Author(s):  
Magali Soumillon ◽  
Davide Cacchiarelli ◽  
Stefan Semrau ◽  
Alexander van Oudenaarden ◽  
Tarjei S Mikkelsen
Keyword(s):  
Gene Expression ◽  
Disease Modeling ◽  
Single Cells ◽  
Adipogenic Differentiation ◽  
Digital Gene Expression ◽  
Multiple Time ◽  
Directed Differentiation ◽  
Time Points

Directed differentiation of cells in vitro is a powerful approach for dissection of developmental pathways, disease modeling and regenerative medicine, but analysis of such systems is complicated by heterogeneous and asynchronous cellular responses to differentiation-inducing stimuli. To enable deep characterization of heterogeneous cell populations, we developed an efficient digital gene expression profiling protocol that enables surveying of mRNA in thousands of single cells at a time. We then applied this protocol to profile 12,832 cells collected at multiple time points during directed adipogenic differentiation of human adipose-derived stem/stromal cells in vitro. The resulting data reveal the major axes of cell-to-cell variation within and between time points, and an inverse relationship between inflammatory gene expression and lipid accumulation across cells from a single donor.

scholarly journals Use of TanDEM-X and SRTM-C Data for Detection of Deforestation Caused by Bark Beetle in Central European Mountains

2021 ◽  
Vol 13 (15) ◽  
pp. 3042
Author(s):  
Kateřina Gdulová ◽  
Jana Marešová ◽  
Vojtěch Barták ◽  
Marta Szostak ◽  
Jaroslav Červenka ◽  
...  
Keyword(s):  
Bark Beetle ◽  
Synergistic Effects ◽  
Canopy Height ◽  
Surface Model ◽  
Multiple Time ◽  
Central European ◽  
Time Points ◽  
Bohemian Forest ◽  
Multiple Time Points ◽  
Surrounding Areas

The availability of global digital elevation models (DEMs) from multiple time points allows their combination for analysing vegetation changes. The combination of models (e.g., SRTM and TanDEM-X) can contain errors, which can, due to their synergistic effects, yield incorrect results. We used a high-resolution LiDAR-derived digital surface model (DSM) to evaluate the accuracy of canopy height estimates of the aforementioned global DEMs. In addition, we subtracted SRTM and TanDEM-X data at 90 and 30 m resolutions, respectively, to detect deforestation caused by bark beetle disturbance and evaluated the associations of their difference with terrain characteristics. The study areas covered three Central European mountain ranges and their surrounding areas: Bohemian Forest, Erzgebirge, and Giant Mountains. We found that vertical bias of SRTM and TanDEM-X, relative to the canopy height, is similar with negative values of up to −2.5 m and LE90s below 7.8 m in non-forest areas. In forests, the vertical bias of SRTM and TanDEM-X ranged from −0.5 to 4.1 m and LE90s from 7.2 to 11.0 m, respectively. The height differences between SRTM and TanDEM-X show moderate dependence on the slope and its orientation. LE90s for TDX-SRTM differences tended to be smaller for east-facing than for west-facing slopes, and varied, with aspect, by up to 1.5 m in non-forest areas and 3 m in forests, respectively. Finally, subtracting SRTM and NASA DEMs from TanDEM-X and Copernicus DEMs, respectively, successfully identified large areas of deforestation caused by hurricane Kyril in 2007 and a subsequent bark beetle disturbance in the Bohemian Forest. However, local errors in TanDEM-X, associated mainly with forest-covered west-facing slopes, resulted in erroneous identification of deforestation. Therefore, caution is needed when combining SRTM and TanDEM-X data in multitemporal studies in a mountain environment. Still, we can conclude that SRTM and TanDEM-X data represent suitable near global sources for the identification of deforestation in the period between the time points of their acquisition.

Meta-analysis of effect sizes reported at multiple time points: A multivariate approach

2012 ◽  
Vol 9 (5) ◽  
pp. 610-620 ◽  
Author(s):  
Thomas A Trikalinos ◽  
Ingram Olkin
Keyword(s):  
Random Effects ◽  
Treatment Effects ◽  
Multivariate Analyses ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Multiple Time ◽  
Time Points ◽  
Fixed And Random Effects ◽  
Multiple Time Points ◽  
Meta Analyses

Background Many comparative studies report results at multiple time points. Such data are correlated because they pertain to the same patients, but are typically meta-analyzed as separate quantitative syntheses at each time point, ignoring the correlations between time points. Purpose To develop a meta-analytic approach that estimates treatment effects at successive time points and takes account of the stochastic dependencies of those effects. Methods We present both fixed and random effects methods for multivariate meta-analysis of effect sizes reported at multiple time points. We provide formulas for calculating the covariance (and correlations) of the effect sizes at successive time points for four common metrics (log odds ratio, log risk ratio, risk difference, and arcsine difference) based on data reported in the primary studies. We work through an example of a meta-analysis of 17 randomized trials of radiotherapy and chemotherapy versus radiotherapy alone for the postoperative treatment of patients with malignant gliomas, where in each trial survival is assessed at 6, 12, 18, and 24 months post randomization. We also provide software code for the main analyses described in the article. Results We discuss the estimation of fixed and random effects models and explore five options for the structure of the covariance matrix of the random effects. In the example, we compare separate (univariate) meta-analyses at each of the four time points with joint analyses across all four time points using the proposed methods. Although results of univariate and multivariate analyses are generally similar in the example, there are small differences in the magnitude of the effect sizes and the corresponding standard errors. We also discuss conditional multivariate analyses where one compares treatment effects at later time points given observed data at earlier time points. Limitations Simulation and empirical studies are needed to clarify the gains of multivariate analyses compared with separate meta-analyses under a variety of conditions. Conclusions Data reported at multiple time points are multivariate in nature and are efficiently analyzed using multivariate methods. The latter are an attractive alternative or complement to performing separate meta-analyses.

Reduction in gene expression noise by targeted increase in accessibility at gene loci

2021 ◽  
Vol 118 (42) ◽  
pp. e2018640118
Author(s):  
LaTasha C. R. Fraser ◽  
Ryan J. Dikdan ◽  
Supravat Dey ◽  
Abhyudai Singh ◽  
Sanjay Tyagi
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Single Molecule ◽  
Single Cells ◽  
Global Changes ◽  
Messenger Rnas ◽  
Histone Acetyl Transferase ◽  
Gene Expression Noise ◽  
Expression Noise ◽  
Eukaryotic Genes

Many eukaryotic genes are expressed in randomly initiated bursts that are punctuated by periods of quiescence. Here, we show that the intermittent access of the promoters to transcription factors through relatively impervious chromatin contributes to this “noisy” transcription. We tethered a nuclease-deficient Cas9 fused to a histone acetyl transferase at the promoters of two endogenous genes in HeLa cells. An assay for transposase-accessible chromatin using sequencing showed that the activity of the histone acetyl transferase altered the chromatin architecture locally without introducing global changes in the nucleus and rendered the targeted promoters constitutively accessible. We measured the gene expression variability from the gene loci by performing single-molecule fluorescence in situ hybridization against mature messenger RNAs (mRNAs) and by imaging nascent mRNA molecules present at active gene loci in single cells. Because of the increased accessibility of the promoter to transcription factors, the transcription from two genes became less noisy, even when the average levels of expression did not change. In addition to providing evidence for chromatin accessibility as a determinant of the noise in gene expression, our study offers a mechanism for controlling gene expression noise which is otherwise unavoidable.

THE ROLE OF MEMORY PERSPECTIVE IN SOCIAL PHOBIA: IMMEDIATE AND DELAYED MEMORIES FOR ROLE-PLAYED SITUATIONS

2002 ◽  
Vol 30 (4) ◽  
pp. 415-425 ◽  
Author(s):  
Meredith E. Coles ◽  
Cynthia L. Turk ◽  
Richard G. Heimberg
Keyword(s):  
Social Phobia ◽  
Multiple Time ◽  
Behavioral Models ◽  
Social Situations ◽  
Time Points ◽  
Cognitive Behavioral Models ◽  
Multiple Time Points ◽  
Memory Perspective ◽  
Field Perspective

Cognitive-behavioral models (Clark & Wells, 1995; Rapee & Heimberg, 1997) and recent research suggest that individuals with social phobia (SP) experience both images (Hackmann, Surawy, & Clark, 1998) and memories (Coles, Turk, Heimberg, & Fresco, 2001; Wells, Clark, & Ahmad, 1998) of anxiety-producing social situations from an observer perspective. The current study examines memory perspective for two role-played situations (speech and social interaction) at multiple time points (immediate and 3 weeks post) in 22 individuals with generalized SP and 30 non-anxious controls (NACs). At both time points, SPs recalled the role-plays from a more observer/less field perspective than did NACs. Further, over time, the memory perspective of SPs became even more observer/less field while the memory perspective of NAC remained relatively stable.

The Silicon Peaches

2021 ◽  
pp. 31-42
Author(s):  
Dan Breznitz
Keyword(s):  
Information And Communication Technologies ◽  
Silicon Valley ◽  
Communication Technologies ◽  
Multiple Time ◽  
Rapid Rise ◽  
Time Points ◽  
Start Up ◽  
Information And Communication ◽  
Multiple Time Points ◽  
Start Ups

This chapter acknowledges that, for many regions, the idea of attracting cutting-edge tech start-ups is almost irresistible. Seemingly every community aspires to become the next Silicon Valley. But is that feasible? This chapter make these lessons concrete by elaborating on the rapid rise and, even faster and deeper, decline of America’s first Silicon Valley—Cleveland, Ohio. It then shows the near impossibility of trying to become the next Silicon Valley by analyzing the mysterious failure of Atlanta, Georgia—a city that diligently followed all the advice ever given to an aspiring new start-up hub, but somehow was always left only with the “potential.” We will see how at multiple time-points Atlanta’s companies were the leading innovators with the best products in the newest information and communication technologies (ICT), only to falter and be taken over by Silicon Valley companies without leaving any apparent impact on the region. It then brings in social-network research and the concept of embeddedness to explain why trying to recreate a Silicon Valley is a doomed (and expensive) enterprise.

scholarly journals Temporal gene expression in bovine corpora lutea after treatment with PGF2alpha based on serial biopsies in vivo

Reproduction ◽  
2001 ◽  
pp. 905-913 ◽  
Author(s):  
SJ Tsai ◽  
K Kot ◽  
OJ Ginther ◽  
MC Wiltbank
Keyword(s):  
Gene Expression ◽  
Corpus Luteum ◽  
Time Course ◽  
Regulatory Protein ◽  
Corpora Lutea ◽  
Multiple Time ◽  
Lh Receptor ◽  
Time Points ◽  
Multiple Biopsies ◽  
Fp Receptor

There is growing evidence to indicate that PGF(2alpha)-induced luteolysis involves altered gene expression in the corpus luteum. Concentrations of mRNA encoding nine different gene products were quantified at three time points from corpora lutea in situ. Serial luteal biopsies (2.1-5.5 mg per biopsy) were collected using an ultrasound-guided transvaginal method and mRNA concentrations were quantified with standard curve quantitative competitive RT-PCR. In the first experiment, three luteal biopsies were collected from three heifers and analysed in multiple assays to evaluate the repeatability of the methods. Concentrations of mRNA for glyceraldehyde 3-phosphate dehydrogenase (GAPDH), PGF(2alpha) receptor (FP receptor) and LH receptor were found to be highly repeatable between assays, between multiple biopsies and between animals (coefficients of variation 1.3-17.3%). In the second experiment, heifers on days 9-11 after ovulation were assigned randomly to receive saline only (n = 6), saline with biopsies taken at t = 0, 0.5 and 4.0 h after injection (n = 6), PGF(2alpha) only (n = 6) or PGF(2alpha) with biopsies taken at t = 0, 0.5 and 4.0 h after treatment (n = 7). Biopsy alone did not change corpus luteum diameter, serum progesterone concentrations or days to next ovulation within the saline- or PGF(2alpha)-treated groups. Concentrations of mRNA for steroidogenic acute regulatory protein, FP receptor, 3beta-hydroxysteroid dehydrogenase, cytosolic phospholipase A(2) and LH receptor were decreased at 4.0 h after PGF(2alpha) injection. In contrast, PGF(2alpha) increased mRNA concentrations for prostaglandin G/H synthase-2, monocyte chemoattractant protein-1 and c-fos but the time course differed for induction of these mRNAs. Concentrations of mRNA for GAPDH did not change after PGF(2alpha) treatment. In conclusion, the techniques allowed analysis of multiple, specific mRNAs in an individual corpus luteum at multiple time points without altering subsequent luteal function. Use of these techniques confirmed that luteolysis involves both up- and downregulation of specific mRNA by PGF(2alpha).

scholarly journals Optimizing the diagnostic power with gastric emptying scintigraphy at multiple time points

2011 ◽  
Vol 11 (1) ◽  
Author(s):  
Qingjiang Hou ◽  
Zhiyue Lin ◽  
Reginald Dusing ◽  
Byron J Gajewski ◽  
Richard W McCallum ◽  
...  
Keyword(s):  
Gastric Emptying ◽  
Multiple Time ◽  
Gastric Emptying Scintigraphy ◽  
Time Points ◽  
Diagnostic Power ◽  
Multiple Time Points
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