Project description:CTCF ChIP-seq of 39 primary samples derived from human acute leukemias, namely AML, T-ALL and mixed myeloid/lymphoid leukemias with CpG Island Methylator Phenotype (CIMP). Due to patient confidentiality considerations, the raw data files for this dataset have been deposited to the EGA controlled-access archive under the accession numbers EGAS00001007094 (study); EGAD00001011059 (dataset).
Project description:H3K27ac ChIP-seq of 79 primary samples derived from human acute leukemias, namely AML, T-ALL and mixed myeloid/lymphoid leukemias with CpG Island Methylator Phenotype (CIMP). In addition, 4 samples derived from CD34+ cord blood cells of healthy donors were included. Due to patient confidentiality considerations, the raw data files for this dataset have been deposited to the EGA controlled-access archive under the accession numbers EGAS00001007094 (study); EGAD00001011060 (dataset).
Project description:This dataset is composed of the unique patients (276; at the Day 1 timepoint) that are present in the six other GEO datasets published by Hector Wong and the Genomics of Pediatric SIRS and Septic Shock Investigators. This dataset thus includes all unique patients from GSE4607, GSE8121, GSE9692, GSE13904, GSE26378, and GSE26440. These are only from the Day 1 timepoint.
Project description:Embryonic genome activation (EGA), a pivotal transcriptional event during preimplantation development, is accompanied by post-transcriptional regulation of maternal mRNAs. Disentangling the transcriptional output of the newly activated embryonic genome from concomitant post-transcriptional processing is important for decoding EGA dynamics.Here, using optimized low-input SLAM-seq (thiol(SH)-linked alkylation for the metabolic sequencing) in mouse embryos, we delineates the temporal hierarchy of EGA nascent transcription during mouse preimplantation embryogenesis and uncovers a mechanistic link between EGA and the first lineage specification, providing new insights into the regulatory architecture of early mammalian development.
Project description:In the past decades, the incidence of esophageal adenocarcinoma has increased dramatically in Western populations. Better understanding of disease etiology along with the identification of novel prognostic and predictive biomarkers are urgently needed to improve the dismal survival probabilities. Here, we performed comprehensive RNA (both coding and non-coding) profiling in various samples from 17 patients diagnosed with esophageal adenocarcinoma, high-grade dysplastic or non-dysplastic Barrett’s esophagus. Per patient, a blood plasma sample, and a healthy esophageal and disease tissue sample were included. In total, this comprehensive dataset consists of 102 RNA-seq libraries from 51 samples. The raw data for this study have been deposited to the controlled access archive EGA under submission EGAS00001004939.
Project description:In the past decades, the incidence of esophageal adenocarcinoma has increased dramatically in Western populations. Better understanding of disease etiology along with the identification of novel prognostic and predictive biomarkers are urgently needed to improve the dismal survival probabilities. Here, we performed comprehensive RNA (both coding and non-coding) profiling in various samples from 17 patients diagnosed with esophageal adenocarcinoma, high-grade dysplastic or non-dysplastic Barrett’s esophagus. Per patient, a blood plasma sample, and a healthy esophageal and disease tissue sample were included. In total, this comprehensive dataset consists of 102 RNA-seq libraries from 51 samples. The raw data for this study have been deposited to the controlled access archive EGA under submission EGAS00001004939.
Project description:In the past decades, the incidence of esophageal adenocarcinoma has increased dramatically in Western populations. Better understanding of disease etiology along with the identification of novel prognostic and predictive biomarkers are urgently needed to improve the dismal survival probabilities. Here, we performed comprehensive RNA (both coding and non-coding) profiling in various samples from 17 patients diagnosed with esophageal adenocarcinoma, high-grade dysplastic or non-dysplastic Barrett’s esophagus. Per patient, a blood plasma sample, and a healthy esophageal and disease tissue sample were included. In total, this comprehensive dataset consists of 102 RNA-seq libraries from 51 samples. The raw data for this study have been deposited to the controlled access archive EGA under submission EGAS00001004939.
Project description:Untargeted 1H NMR metabolomics is a robust and reproducible approach to study metabolism in biological samples, providing unprecedented insight into altered cellular processes associated with human diseases. Metabolomics is increasingly used alongside other -omics techniques, such as proteomics and transcriptomics, to detect instantaneous altered cellular function, for example the role of blood neutrophils in the inflammatory response. However, in some clinical settings e.g. pediatrics research, blood samples may be limited, restricting the amount of cellular material available for metabolomics analysis. In this study we wanted to establish the optimal 1D 1H NMR metabolomics pipeline for use with human neutrophil samples with low input material. We compared the effect of neutrophil isolation protocols using Ficoll-Paque or negative selection using antibody-labelled magnetic beads on neutrophil metabolite profiles. We also compared the effect of absolute cell counts ranging from 100,000 to 5 million neutrophils in total on the identities of metabolites that can be detected with increasing number of scans (NS) from 256 to 2,048. We found that variance in neutrophil profile was higher with N isolation method event though choice of isolation method did not significantly alter the metabolite profile of human neutrophils measured by 1D 1H NMR spectroscopy. The minimum number of cells required for detection of human metabolites was 400,000 at NS 256 for spectra acquired with a cryoprobe equipped 700MHz. Increasing the NS to 2,048 increased metabolite detection at the very lowest cell counts (<400,000 neutrophils), however this was associated with a significant increase in analysis time which would be rate-limiting for large studies. Application of a correlation reliability score (CRS) filtering method to the spectral bins preserved the essential discriminatory features of the PLS-DA models whilst improving the dataset robustness and analytical precision.