Estimating RNA dynamics using one time point for one sample in a single-pulse metabolic experiment
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA631723 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR11770267.fastq.gz | Fastqsanger.gz | |||
| SRR11770268.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 2 of 2 |
Similar Datasets
Project description:Over the past decade, experimental procedures such as metabolic labeling for determining RNA turnover rates at the transcriptome-wide scale have been widely adopted. Several computational methods to estimate RNA processing and degradation rates from such experiments have been suggested, but they all require several RNA sequencing samples. Here we present a method that can estimate RNA processing and degradation rates from a single sample. To this end, we use the Zeisel model and take advantage of its analytical solution, reducing the problem to solving a univariate non-linear equation on a bounded domain. The approach is computationally rapid and enables inference of rates that correlate well with previously published datasets. In addition to saving experimental work and computational time, having a sample-based rate estimation has several advantages. It does not require an error-prone normalization across samples and enables the use of replicates to estimate uncertainty and perform quality control. Finally the method and theoretical results described here are general enough to be useful in other settings such as nucleotide conversion methods.
2020-05-12 | GSE150286 | GEO
Project description:BackgroundOver the past decade, experimental procedures such as metabolic labeling for determining RNA turnover rates at the transcriptome-wide scale have been widely adopted and are now turning to single cell measurements. Several computational methods to estimate RNA synthesis, processing and degradation rates from such experiments have been suggested, but they all require several RNA sequencing samples. Here we present a method that can estimate those three rates from a single sample.MethodsOur method relies on the analytical solution to the Zeisel model of RNA dynamics. It was validated on metabolic labeling experiments performed on mouse embryonic stem cells. Resulting degradation rates were compared both to previously published rates on the same system and to a state-of-the-art method applied to the same data.ResultsOur method is computationally efficient and outputs rates that correlate well with previously published data sets. Using it on a single sample, we were able to reproduce the observation that dynamic biological processes tend to involve genes with higher metabolic rates, while stable processes involve genes with lower rates. This supports the hypothesis that cells control not only the mRNA steady-state abundance, but also its responsiveness, i.e., how fast steady state is reached. Moreover, degradation rates obtained with our method compare favourably with the other tested method.ConclusionsIn addition to saving experimental work and computational time, estimating rates for a single sample has several advantages. It does not require an error-prone normalization across samples and enables the use of replicates to estimate uncertainty and assess sample quality. Finally the method and theoretical results described here are general enough to be useful in other contexts such as nucleotide conversion methods and single cell metabolic labeling experiments.
| S-EPMC9034570 | biostudies-literature
Project description:Pulse chase measurements using thiouracil (DTU) labeling via UPRT and chasing with uracil Data from tachyzoites is labeled "DTU Pulse Chase". Two independent pulse chase experiments were performed in tachyzoites, pulse chase 1 and 2. Duplicate arrays at each timepoint were performed for pulse chase 2 (2 a and b). Data from bradyzoites are labeled "DTU Bradyzoite Pulse Chase". Two independent pulse chase experiments were performed in bradyzoites and a single set of arrays were performed for each experiment. Just one chase timepoint was used in the bradyzoite experiments, the 2 hour chase. An RNA stablity experiment design type examines stability and/or decay of RNA transcripts. Keywords: RNA_stability_design
2005-07-16 | GSE2947 | GEO
Project description:Pulse chase measurements using thiouracil (DTU) labeling via UPRT and chasing with uracil Data from tachyzoites is labeled "DTU Pulse Chase". Two independent pulse chase experiments were performed in tachyzoites, pulse chase 1 and 2. Duplicate arrays at each timepoint were performed for pulse chase 2 (2 a and b). Data from bradyzoites are labeled "DTU Bradyzoite Pulse Chase". Two independent pulse chase experiments were performed in bradyzoites and a single set of arrays were performed for each experiment. Just one chase timepoint was used in the bradyzoite experiments, the 2 hour chase. An RNA stablity experiment design type examines stability and/or decay of RNA transcripts. User Defined
2005-07-16 | E-GEOD-2947 | biostudies-arrayexpress
Project description:Pulse chase measurements using thiouracil (DTU) labeling via UPRT and chasing with uracil Data from tachyzoites is labeled "DTU Pulse Chase". Two independent pulse chase experiments were performed in tachyzoites, pulse chase 1 and 2. Duplicate arrays at each timepoint were performed for pulse chase 2 (2 a and b). Data from bradyzoites are labeled "DTU Bradyzoite Pulse Chase". Two independent pulse chase experiments were performed in bradyzoites and a single set of arrays were performed for each experiment. Just one chase timepoint was used in the bradyzoite experiments, the 2 hour chase.
2005-07-07 | E-SMDB-2949 | biostudies-arrayexpress
Project description:Reproduction requires pulsatile release of hypothalamic GnRH, which regulates expression of the pituitary gonadotropins FSH and luteinizing hormone (LH). Fshb expression shows an inverted U-shaped response to GnRH pulse frequency. Increasing GnRH pulse frequency beyond ~one pulse/2 hours, despite increasing the average GnRH concentration, induces progressively less Fshb. To clarify regulatory mechanisms underlying Fshb gene control, we developed three biologically inspired and topologically distinct mathematical models. The models represent: 1) parallel activation of Fshb inhibitory and stimulatory factors (e.g. inhibin α, VGF), 2) activation of a signaling component with a refractory period (e.g. G protein), and 3) inactivation of a factor needed for Fshb induction (e.g. GDF9). Simulations with all three models recapitulated the Fshb expression levels obtained in standard perifusion experiments at different GnRH pulse frequencies. Notably, simulations altering average concentration, pulse duration and frequency showed that the apparent frequency-dependent pattern of Fshb expression obtained with model 1 actually resulted from variations in average GnRH concentration. In contrast, models 2 and 3 showed “true” frequency sensing. To resolve which components of this GnRH signal induce Fshb, a massively parallel experimental system was developed. Analysis of over 4000 samples in ~40 experiments indicated that, while early genes Egr1 and Fos respond only to variations in GnRH concentration, Fshb induction is sensitive to GnRH pulse frequency changes, whilst maintaining the same average concentration. These results provide a framework for understanding the role of different regulatory factors in modulating the responses of the Fshb gene.
2017-04-07 | GSE85866 | GEO
Project description:Reproduction requires pulsatile release of hypothalamic GnRH, which regulates expression of the pituitary gonadotropins FSH and luteinizing hormone (LH). Fshb expression shows an inverted U-shaped response to GnRH pulse frequency. Increasing GnRH pulse frequency beyond ~one pulse/2 hours, despite increasing the average GnRH concentration, induces progressively less Fshb. To clarify regulatory mechanisms underlying Fshb gene control, we developed three biologically inspired and topologically distinct mathematical models. The models represent: 1) parallel activation of Fshb inhibitory and stimulatory factors (e.g. inhibin α, VGF), 2) activation of a signaling component with a refractory period (e.g. G protein), and 3) inactivation of a factor needed for Fshb induction (e.g. GDF9). Simulations with all three models recapitulated the Fshb expression levels obtained in standard perifusion experiments at different GnRH pulse frequencies. Notably, simulations altering average concentration, pulse duration and frequency showed that the apparent frequency-dependent pattern of Fshb expression obtained with model 1 actually resulted from variations in average GnRH concentration. In contrast, models 2 and 3 showed “true” frequency sensing. To resolve which components of this GnRH signal induce Fshb, a massively parallel experimental system was developed. Analysis of over 4000 samples in ~40 experiments indicated that, while early genes Egr1 and Fos respond only to variations in GnRH concentration, Fshb induction is sensitive to GnRH pulse frequency changes, whilst maintaining the same average concentration. These results provide a framework for understanding the role of different regulatory factors in modulating the responses of the Fshb gene.
2017-04-07 | GSE86130 | GEO
Project description:Reproduction requires pulsatile release of hypothalamic gonadotropin-releasing hormone (GnRH), which regulates expression of the pituitary gonadotropins follicle-stimulating hormone (FSH) and luteinizing hormone (LH). Fshb expression shows an inverted U-shaped response to GnRH pulse frequency. Increasing GnRH pulse frequency beyond ~one pulse/2 hours, despite increasing the average GnRH concentration, induces progressively less Fshb. To clarify regulatory mechanisms underlying Fshb gene control, we developed three biologically inspired and topologically distinct mathematical models. The models represent: 1) parallel activation of Fshb inhibitory and stimulatory factors (e.g. inhibin α, VGF), 2) activation of a signaling component with a refractory period (e.g. G protein), and 3) inactivation of a factor needed for Fshb induction (e.g. GDF9). Simulations with all three models recapitulated the Fshb expression levels obtained in standard perifusion experiments at different GnRH pulse frequencies. Notably, simulations altering average concentration, pulse duration and frequency showed that the apparent frequency-dependent pattern of Fshb expression obtained with model 1 actually resulted from variations in average GnRH concentration. In contrast, models 2 and 3 showed “true” frequency sensing. To resolve which components of this GnRH signal induce Fshb, a massively parallel experimental system was developed. Analysis of over 4000 samples in ~40 experiments indicated that, while early genes Egr1 and Fos respond only to variations in GnRH concentration, Fshb induction is sensitive to GnRH pulse frequency changes, whilst maintaining the same average concentration. These results provide a framework for understanding the role of different regulatory factors in modulating the responses of the Fshb gene.
2017-04-07 | GSE85869 | GEO
Project description:Reproduction requires pulsatile release of hypothalamic gonadotropin-releasing hormone (GnRH), which regulates expression of the pituitary gonadotropins follicle-stimulating hormone (FSH) and luteinizing hormone (LH). Fshb expression shows an inverted U-shaped response to GnRH pulse frequency. Increasing GnRH pulse frequency beyond ~one pulse/2 hours, despite increasing the average GnRH concentration, induces progressively less Fshb. To clarify regulatory mechanisms underlying Fshb gene control, we developed three biologically inspired and topologically distinct mathematical models. The models represent: 1) parallel activation of Fshb inhibitory and stimulatory factors (e.g. inhibin α, VGF), 2) activation of a signaling component with a refractory period (e.g. G protein), and 3) inactivation of a factor needed for Fshb induction (e.g. GDF9). Simulations with all three models recapitulated the Fshb expression levels obtained in standard perifusion experiments at different GnRH pulse frequencies. Notably, simulations altering average concentration, pulse duration and frequency showed that the apparent frequency-dependent pattern of Fshb expression obtained with model 1 actually resulted from variations in average GnRH concentration. In contrast, models 2 and 3 showed “true” frequency sensing. To resolve which components of this GnRH signal induce Fshb, a massively parallel experimental system was developed. Analysis of over 4000 samples in ~40 experiments indicated that, while early genes Egr1 and Fos respond only to variations in GnRH concentration, Fshb induction is sensitive to GnRH pulse frequency changes, whilst maintaining the same average concentration. These results provide a framework for understanding the role of different regulatory factors in modulating the responses of the Fshb gene.
2017-04-07 | GSE85865 | GEO