These data describe bulk RNAsequencing in wild-type cerbreal organoids sampled across a time series
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE181405 | GEO | 2021/08/30
REPOSITORIES: GEO
Similar Datasets
Project description:These data describe bulk RNAsequencing in wild-type cerbreal organoids sampled across a time series
| PRJNA751896 | ENA
Project description:During cortical development, distinct subtypes of glutamatergic neurons are sequentially born and differentiate from dynamic populations of progenitors. How progenitors and their daughter cells are temporally patterned remains unknown. Here, we trace the transcriptional trajectories of successive generations of apical progenitors (APs) and isochronic cohorts of their daughter neurons in the developing mouse neocortex using high temporal resolution parallel single-cell RNA sequencing. We identify and functionally characterize a core set of evolutionarily-conserved temporally patterned genes which drive APs from internally-driven states to more exteroceptive states, revealing a progressively increasing role for extracellular signals as corticogenesis unfolds. These embryonic age-dependent AP molecular states are reflected in their neuronal progeny as successive ground states, onto which essentially conserved early post-mitotic differentiation programs are applied. Thus, temporally unfolding molecular birthmarks present in progenitors act in their post-mitotic progeny as seeds for adult neuronal diversity.
2019-05-10 | GSE118953 | GEO
Project description:Rapid imaging techniques are increasingly used in functional MRI studies because they allow a greater number of samples to be acquired per unit time, thereby increasing statistical power. However, temporal correlations limit the increase in functional sensitivity and must be accurately accounted for to control the false-positive rate. A common approach to accounting for temporal correlations is to whiten the data prior to estimating fMRI model parameters. Models of white noise plus a first-order autoregressive process have proven sufficient for conventional imaging studies, but more elaborate models are required for rapidly sampled data. Here we show that when the "FAST" model implemented in SPM is used with a well-controlled number of parameters, it can successfully prewhiten 80% of grey matter voxels even with volume repetition times as short as 0.35 s. We further show that the temporal signal-to-noise ratio (tSNR), which has conventionally been used to assess the relative functional sensitivity of competing imaging approaches, can be augmented to account for the temporal correlations in the time series. This amounts to computing the t-score testing for the mean signal. We show in a visual perception task that unlike the tSNR weighted by the number of samples, the t-score measure is directly related to the t-score testing for activation when the temporal correlations are correctly modeled. This score affords a more accurate means of evaluating the functional sensitivity of different data acquisition options.
| S-EPMC6175228 | biostudies-literature
Project description:BACKGROUND: Periodogram analysis of time-series is widespread in biology. A new challenge for analyzing the microarray time series data is to identify genes that are periodically expressed. Such challenge occurs due to the fact that the observed time series usually exhibit non-idealities, such as noise, short length, and unevenly sampled time points. Most methods used in the literature operate on evenly sampled time series and are not suitable for unevenly sampled time series. RESULTS: For evenly sampled data, methods based on the classical Fourier periodogram are often used to detect periodically expressed gene. Recently, the Lomb-Scargle algorithm has been applied to unevenly sampled gene expression data for spectral estimation. However, since the Lomb-Scargle method assumes that there is a single stationary sinusoid wave with infinite support, it introduces spurious periodic components in the periodogram for data with a finite length. In this paper, we propose a new spectral estimation algorithm for unevenly sampled gene expression data. The new method is based on signal reconstruction in a shift-invariant signal space, where a direct spectral estimation procedure is developed using the B-spline basis. Experiments on simulated noisy gene expression profiles show that our algorithm is superior to the Lomb-Scargle algorithm and the classical Fourier periodogram based method in detecting periodically expressed genes. We have applied our algorithm to the Plasmodium falciparum and Yeast gene expression data and the results show that the algorithm is able to detect biologically meaningful periodically expressed genes. CONCLUSION: We have proposed an effective method for identifying periodic genes in unevenly sampled space of microarray time series gene expression data. The method can also be used as an effective tool for gene expression time series interpolation or resampling.
| S-EPMC1867827 | biostudies-other
Project description:Time series metasecretomes (weeks 1, 3, 5 and ten) of lignocellulose responsive microbiomes enriching on Spartina anglica biomass for 16 weeks in a natural UK salt marsh (Welwick, Humber estuary).
2019-05-29 | MSV000083872 | MassIVE
Project description:BackgroundPhysical forces, such as mechanical stress, are essential for tissue homeostasis and influence gene expression of cells. In particular, the fibroblast has demonstrated sensitivity to extracellular matrices with assumed adaptation upon various mechanical loads. The purpose of this study was to compare the vocal fold fibroblast genotype, known for its unique mechanically stressful tissue environment, with cellular counterparts at various other anatomic locales to identify differences in functional gene expression profiles.ResultsBy using RNA-seq technology, we identified differentially expressed gene programs (DEseq2) among seven normal human fibroblast primary cell lines from healthy cadavers, which included: vocal fold, trachea, lung, abdomen, scalp, upper gingiva, and soft palate. Unsupervised gene expression analysis yielded 6216 genes differentially expressed across all anatomic sites. Hierarchical cluster analysis revealed grouping based on anatomic site origin rather than donor, suggesting global fibroblast phenotype heterogeneity. Sex and age-related effects were negligible. Functional enrichment analyses based on separate post-hoc 2-group comparisons revealed several functional themes within the vocal fold fibroblast related to transcription factors for signaling pathways regulating pluripotency of stem cells and extracellular matrix components such as cell signaling, migration, proliferation, and differentiation potential.ConclusionsHuman fibroblasts display a phenomenon of global topographic differentiation, which is maintained in isolation via in vitro assays. Epigenetic mechanical influences on vocal fold tissue may play a role in uniquely modelling and maintaining the local environmental cellular niche during homeostasis with vocal fold fibroblasts distinctly specialized related to their anatomic positional and developmental origins established during embryogenesis.
| S-EPMC6480701 | biostudies-literature
Project description:MotivationIdentification of system-wide causal relationships can contribute to our understanding of long-distance, intercellular signaling in biological organisms. Dynamic transcriptome analysis holds great potential to uncover coordinated biological processes between organs. However, many existing dynamic transcriptome studies are characterized by sparse and often unevenly spaced time points that make the identification of causal relationships across organs analytically challenging. Application of existing statistical models, designed for regular time series with abundant time points, to sparse data may fail to reveal biologically significant, causal relationships. With increasing research interest in biological time series data, there is a need for new statistical methods that are able to determine causality within and between time series data sets. Here, a statistical framework was developed to identify (Granger) causal gene-gene relationships of unevenly spaced, multivariate time series data from two different tissues of Arabidopsis thaliana in response to a nitrogen signal.ResultsThis work delivers a statistical approach for modelling irregularly sampled bivariate signals which embeds functions from the domain of engineering that allow to adapt the model's dependence structure to the specific sampling time. Using Maximum-Likelihood to estimate the parameters of this model for each bivariate time series, it is then possible to use bootstrap procedures for small samples (or asymptotics for large samples) in order to test for Granger-Causality. When applied to the Arabidopsis thaliana data, the proposed approach produced 3,078 significant interactions, in which 2,012 interactions have root causal genes and 1,066 interactions have shoot causal genes. Many of the predicted causal and target genes are known players in local and long-distance nitrogen signaling, including genes encoding transcription factors, hormones, and signaling peptides. Of the 1,007 total causal genes (either organ), 384 are either known or predicted mobile transcripts, suggesting that the identified causal genes may be directly involved in long-distance nitrogen signaling through intercellular interactions. The model predictions and subsequent network analysis identified nitrogen-responsive genes that can be further tested for their specific roles in long-distance nitrogen signaling.AvailabilityThe method was developed with the R statistical software and is made available through the R package "irg" hosted on the GitHub repository https://github.com/SMAC-Group/irg where also a running example vignette can be found (https://smac-group.github.io/irg/articles/vignette.html). A few signals from the original data set are made available in the package as an example to apply the method and the complete Arabidopsis thaliana data can be found at: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE97500.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC8388030 | biostudies-literature
Project description:Pregnant C57Bl6N mice were treated with 0 (corn oil), 1.5, 3.0, or 6.0 ug/kg TCDD on gd14.5. Fetal hearts were collected on gd17.5. Hearts from each litter were pooled onto one chip. 4 replicates of each condition were run on affymetrix MG_U74Av2 chips, using standard affymetrix protocols and controls. Keywords: dose-response, 3 doses plus corn oil control, 4 replicates
2005-06-15 | GSE2812 | GEO
Project description:TODAY ProDIGY Exome Sequencing Study
| PRJNA432772 | ENA
Project description:TODAY ProDIGY Exome Sequencing Study
| PRJNA432773 | ENA