11979 results for "cancer rna-seq"

in 41.56 milliseconds.

• Reference epigenome ADMSC03 h3k9me3 ChIP-Seq data generated from KEP study

  ADMSC03 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007372

• Reference epigenome ADMSC03 input ChIP-Seq data generated from KEP study

  ADMSC03 input ChIP-Seq paired end data

  Dataset EGAD00001007373

• Reference epigenome ADMSC05 h3k27Ac ChIP-Seq data generated from KEP study

  ADMSC05 h3k27Ac ChIP-Seq paired end data

  Dataset EGAD00001007374

• Dataset for RNA sequencing glioblastoma data

  This dataset contains RNA sequencing data of 36 glioblastoma samples. Sequencing was performed on Illumina NovaSeq 6000 using TruSeq Stranded RNA Kit. The sequencing was always paired.

  Dataset EGAD50000000688

• Altered oligodendrocyte heterogeneity in Multiple sclerosis revealed by single nuclei RNA sequencing

  This study is currently hosted by the European Nucleotide Archive. To access the data contained within the Study please follow the link below: https://www.ebi.ac.uk/ena/browser/view/PRJEB39323 Oligodendrocyte (OL) pathology is increasingly implicated in neurodegenerative diseases, as they are involved in metabolic support of axons and functional cross-talk with other brain cells. Rodent OLs are heterogeneous, with developmental and biological differences, but the extent of heterogeneity in the normal human brain and its contribution to any changes to disease remains unknown. Here we performed single nuclei RNA-sequencing (snRNA-seq) from white matter (WM) areas of post mortem human brain both in control (Ctr) and multiple sclerosis (MS) patients. We identified several sub-clusters of oligodendroglia in the Ctr human WM, some similar to those in mouse, and defined new markers for these cell states. Strikingly, some of these sub-clusters were under-represented in MS tissue, while others were more prevalent than in controls. We found a lack of OL precursor cells (OPCs) and an OL subcluster in an intermediate stage of differentiation in MS lesions and in normal appearing white matter (NAWM), suggesting either depletion by the disease or by a regenerative response. The differences in mature OL sub-clusters indicate different functional states of OLs in MS tissue and, as this is similar in NAWM to lesions, that MS is a more diffuse brain disease than the focal demyelinating lesions suggest. We were also able to identify new putative markers of different MS lesion subtypes. Our findings of an altered heterogeneity of oligodendroglia in MS may have an important contribution to our understanding of disease progression and may alter therapeutic approaches to MS.

  Study EGAS00001003412

• HCA_Reproductive_Tract_Adult_RNA

  HCA Reproductive Tract Adult RNA

  Study EGAS00001006636

• reChIP-seq reveals widespread bivalency of H3K4me3 and H3K27me3 in CD4+ memory T-Cells

  Identification of genomic loci exhibiting the physical co-localization of two distinct DNA-associated proteins remains a technical challenge. To tackle this challenge we have developed a novel reChIP-seq approach that enriches for two distinct DNA-associated proteins in an unbiased and genome-wide manner. We illustrate the utility of our approach by identifying nucleosomes bivalently modified by H3K4me3 and H3K27me3 in primary human CD4+ central memory T-cells (TCMs). We unravel widespread bivalency of many unmethylated CpG-rich regions in TCMs overlapping with transcriptional start sites of developmental regulators. Furthermore, reChIP-seq uncovers two yet unobserved classes of bivalency, where either H3K4me3 or H3K27me3 is mixed with low but detectable bivalency. We provide evidence that bivalency is established by an interplay between genome and epigenome and thus attains stability. Our described reChIP-seq approach augments conventional ChIP-seq and is broadly applicable in the study of co-localization of DNA-associated proteins to unravel the intricate possibilities of combinatorial action.

  Study EGAS00001001568

• Spatial transcriptomic data of breast cancer

  Fastq files from spatial transcriptomic of breast cancer coming from 8 Breast cancer sections. Sample preparation: frozen BC samples were chosen based on tissue structure and RNA quality (RIN > 8). The “Visium Spatial Tissue Optimization Slide and Reagent Kit” (10X Genomics; #PN-1000193) was then used to optimize permeabilization conditions for BC tissues. Briefly, sections were fixed, stained and then permeabilized at different time points to capture mRNA, and the reverse transcription was performed to generate fluorescently labeled cDNA. The permeabilization time that resulted in the highest fluorescence signal with the lowest background diffusion was chosen. The best permeabilization time for BC tissue was 18 min. Cryostat sections of 10 μm of thickness were cut and placed on Visium Spatial Gene Expression slides (10X Genomics, PN-1000184). The slide was incubated for 1 min at 37°C, then fixed with methanol for 30 min at -20°C followed by Hematoxylin and Eosin (H&E) staining and images were taken under a high-resolution microscope. After imaging, the coverslip was detached by holding the slide in water and the slide was mounted in a plastic slide cassette. The spatial gene expression process, including tissue permeabilization, second strand synthesis and cDNA amplification, was performed according to the manufacturer’s instructions (10X Genomics; #CG000239). cDNA quality was next assessed using Agilent High sensitivity DNA Kit (Agilent, #5067-4626). The spatial gene libraries were constructed using Visium Spatial Library Construction Kit (10X Genomics, PN-1000184).

  Dataset EGAD50000000322

• Transcriptome of CD4+ T cells and CD8+ T cells in glioblastoma multiforme

  Patients included in this study were over 18 years of age and had a histology-confirmed diagnosis of glioblastoma multiforme (GBM). Exclusion criteria were the previous administration of any anti-tumor therapy including radiation therapy. All patients gave written informed consent. The study was approved by the local ethics committee (TUM Medical school) and conducted following the Declaration of Helsinki. During resection of the tumors, tumor tissue and tissue from normal appearing brain within the operative channel was collected. Blood was drawn during the surgical procedure. Single cell suspensions were prepared from the tumor tissue, the normal appearing brain, and the blood. CD4+ T cells and CD8+ T cells were sorted by flow cytometry. Only patients with a complete set of specimens (CD4+ tumor infiltrating lymphocytes (TIL), CD8+ TIL, CD4+ T cells from normal appearing brain, CD8+ T cells from normal appearing brain, blood-derived CD4+ and CD8+ T cells) containing a minimum of 1000 cells in each sorted sample were further analyzed (n=9). Total RNA was isolated from sorted cell populations using the RNAeasy Plus micro kit (Qiagen, 74034). Quality and integrity of total RNA was controlled on a Bioanalyzer 2100 (Agilent Technologies). Library preparation for bulk-sequencing of poly(A)-RNA was done as described previously (Parekh et al., 2016). Briefly, barcoded cDNA of each sample was generated with a Maxima RT polymerase (ThermoFisher Scientific, EP0742) using oligo-dT primer containing barcodes, unique molecular identifiers (UMIs) and an adaptor. Ends of the cDNAs were extended by a template switch oligo (TSO) and full-length cDNA was amplified with primers binding to the TSO-site and the adaptor. NEB UltraII FS kit was used to fragment cDNA. After end repair and A-tailing, a TruSeq adapter was ligated and 3'-end-fragments were finally amplified using primers with Illumina P5 and P7 overhangs. In comparison to previous descriptions (Parekh et al., 2016), the P5 and P7 sites were exchanged to allow sequencing of the cDNA in read 1 and barcodes and UMIs in read 2 to achieve a better cluster recognition. The library was sequenced on a NextSeq 500 (Illumina) with 59 cycles for the cDNA in read 1 and 16 cycles for the barcodes and UMIs in read 2. Data were processed using the published Drop-seq pipeline (v1.0) to generate sample- and gene-wise UMI tables (Macosko et al., 2015). Reference genome (GRCh38) was used for alignment. Transcript and gene definitions were used according to the Genecode Annotation Version 35.

  Study EGAS50000000156

• Non-coding RNAs Activated by the Wnt/Beta-catenin Signaling Pathway in Hepatoblastoma

  Hepatoblastoma (HB) is the most common pediatric liver tumor, affecting mostly children under 3 years of age. This rare tumor represents 1% of all pediatric cancers. Genetic studies have shown that HB is characterized by high frequency mutations of the CTNNB1 gene encoding beta-catenin (around 75%) and relative genomic stability. Here we have analyzed the transcriptional profile of 21 HBs compared to matched non-tumor livers by Cap Analysis of Gene Expression (CAGE), which provides accurate and quantitative profiling of all transcripts. CAGE analysis revealed strong upregulation of known Wnt target coding genes in most tumors analyzed, consistent with previous transcriptomic studies. To better define the Wnt-dependent transcriptional landscape of HB, we integrated CAGE data with TCF4 ChIP-seq data from a CTNNB1-mutated cancer cell line and with the FANTOM5 genomic coordinates of TCF/LEF binding motifs. Both TCF/LEF binding motifs and ChIP-seq peaks were strongly enriched in the immediate upstream region, not only for protein-coding genes, but also for non-coding transcripts. Among the selected 112 top Wnt target genes at FDR<1.0E-6 and fold change>8, we found clear over-representation (66%) of distant transcription start sites (TSSs) representing lncRNAs and enhancer RNAs, which raises the question of their role in HB pathogenesis. Analysis of the 112 promoters using CAGEd-oPOSSUM confirmed the predominant involvement of Tcf/Lef transcription factors, together with HNF4G, GATA2, Sox3 and Ets-related genes. Finally, the 112 Wnt target signature defined 3 tumor subclasses, T1, T2 and T3, characterized by progressive alteration of the non-coding part of the transcriptome and significant differences in clinical behavior.

  Study phs001433

• Circulating RNA profiles of healthy and preeclamptic pregnancies

  This prospective study investigated whether circulating RNA (C-RNA; cell free RNA present in the bloodstream) measurements could non-invasively monitor pregnancy status, with emphasis on detecting molecular markers associated with preterm preeclampsia. Whole blood samples were collected from pregnant women at the time of diagnosis with early-onset preeclampsia with severe features. Blood samples were also obtained normotypical control pregnancies and from patients diagnosed with late-onset preeclampsia for comparison. For all samples, C-RNA was extracted from 4 mL blood and whole transcriptome enrichment performed on cDNA libraries prior to sequencing to a target depth of 50M reads on an Illumina HiSeq 2000 platform. Throughout the course of this work, whole transcriptome RNA sequencing data was obtained from 289 C-RNA samples collected from 178 subjects. The complete dataset consists of samples from the following cohorts: Illumina Preeclampsia Cohort (iPEC): 40 pregnancies at the time of early-onset preeclampsia diagnosis and 73 gestational age-matched controls PEARL preeclampsia cohort: 12 early-onset and 12 late-onset preeclampsia patients PEARL healthy control cohort: 152 longitundinal samples collected from 41 healthy pregnancies

  Study phs002017

• ICGC MMML-seq Data Freeze March 2013 transcriptome sequencing

  ICGC MMML-seq Data Freeze March 2013 transcriptome sequencing

  Dataset EGAD00001000356

• ATAC-Seq of human stimulated and cultured CD4+ Treg cells

  This dataset includes whole genome sequence data for ATAC-seq (42 samples) of human stimulated and cultured CD4+ Treg cells.

  Dataset EGAD00001004829

• Reference epigenome KNIH001 miRNA-seq data generated from KEP study

  A KNIH001 miRNA-seq single end data for islet cells

  Dataset EGAD00001002760

• Reference epigenome Islet-derived-iPSC02 mRNA-Seq data generated from KEP study

  Islet-derived-iPSC02 mRNA-Seq paired end data

  Dataset EGAD00001007165

• Reference epigenome Pancreas-Islet02 h3k36me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet02 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007195

• Reference epigenome IPS-Fibroblast02 input ChIP-Seq data generated from KEP study

  IPS-Fibroblast02 input ChIP-Seq paired end data

  Dataset EGAD00001007276

• Reference epigenome KNIH002 miRNA-seq data generated from KEP study

  A KNIH002 miRNA-seq single end data for islet cells

  Dataset EGAD00001002761

• Reference epigenome KNIH005 miRNA-seq data generated from KEP study

  A KNIH005 miRNA-seq single end data for islet cells

  Dataset EGAD00001002764

• Reference epigenome KNIH003 miRNA-seq data generated from KEP study

  A KNIH003 miRNA-seq single end data for islet cells

  Dataset EGAD00001002762

• Reference epigenome KNIH004 miRNA-seq data generated from KEP study

  A KNIH004 miRNA-seq single end data for islet cells

  Dataset EGAD00001002763

• Reference epigenome Islet-derived-iPSC01 mRNA-Seq data generated from KEP study

  Islet-derived-iPSC01 mRNA-Seq paired end data

  Dataset EGAD00001007164

• Reference epigenome Islet-derived-MSC01 mRNA-Seq data generated from KEP study

  Islet-derived-MSC01 mRNA-Seq paired end data

  Dataset EGAD00001007160

• Reference epigenome Islet-derived-MSC02 mRNA-Seq data generated from KEP study

  Islet-derived-MSC02 mRNA-Seq paired end data

  Dataset EGAD00001007161

• Reference epigenome Islet-derived-MSC03 mRNA-Seq data generated from KEP study

  Islet-derived-MSC03 mRNA-Seq paired end data

  Dataset EGAD00001007162

• Reference epigenome KNIH005 mRNA-seq data generated from KEP study

  A KNIH005 mRNA-seq paired end data for islet cells

  Dataset EGAD00001002171

• Reference epigenome IPS02_N_NPC_mRNA-Seq data generated from KEP study

  A IPS02_N_NPC_mRNA-Seq paired end data for Neural progenitor cells(Nestin)

  Dataset EGAD00001003489

• Reference epigenome IPS03_N_ENeuron_smRNA-Seq data generated from KEP study

  A IPS03_N_ENeuron_smRNA-Seq single end data for Early neuron cells(Tuj1)

  Dataset EGAD00001003503

• Reference epigenome IPS02_N_NPC_smRNA-Seq data generated from KEP study

  A IPS02_N_NPC_smRNA-Seq single end data for Neural progenitor cells(Nestin)

  Dataset EGAD00001003502

• Reference epigenome KNIH001 mRNA-seq data generated from KEP study

  A KNIH001 mRNA-seq paired end data for islet cells

  Dataset EGAD00001002167

• McGill EMC Release 4 for assay "smRNA-seq"

  McGill EMC Release 4 for assay "smRNA-seq": Transcriptome profiling by high-throughput sequencing

  Dataset EGAD00001001292

• Reference epigenome IPS05_X_NPC_mRNA-Seq data generated from KEP study

  A IPS05_X_NPC_mRNA-Seq paired end data for Neural progenitor cells(Nestin)

  Dataset EGAD00001003492

• Oxford Human islet ATAC-seq dataset of 5 human islets

  ATAC-seq data for 5 non-diabetic human pancreatic islet samples

  Dataset EGAD00001003759

• McGill EMC Release 4 for assay "Bisulfite-seq"

  McGill EMC Release 4 for assay "Bisulfite-seq": Methylation profiling by high-throughput sequencing

  Dataset EGAD00001001289

• Reference epigenome KNIH002 mRNA-seq data generated from KEP study

  A KNIH002 mRNA-seq paired end data for islet cells

  Dataset EGAD00001002168

• Reference epigenome KNIH003 mRNA-seq data generated from KEP study

  A KNIH003 mRNA-seq paired end data for islet cells

  Dataset EGAD00001002169

• Reference epigenome IPS06_X_ENeuron_mRNA-Seq data generated from KEP study

  A IPS06_X_ENeuron_mRNA-Seq paired end data for Early neuron cells(Tuj1)

  Dataset EGAD00001003493

• Reference epigenome IPS03_N_ENeuron_mRNA-Seq data generated from KEP study

  A IPS03_N_ENeuron_mRNA-Seq paired end data for Early neuron cells(Tuj1)

  Dataset EGAD00001003490

• McGill EMC Release 4 for assay "mRNA-seq"

  McGill EMC Release 4 for assay "mRNA-seq": Transcriptome profiling by high-throughput sequencing

  Dataset EGAD00001001291

• Reference epigenome IPS05_X_NPC_smRNA-Seq data generated from KEP study

  A IPS05_X_NPC_smRNA-Seq single end data for Neural progenitor cells(Nestin)

  Dataset EGAD00001003505

• Reference epigenome KNIH006 mRNA-seq data generated from KEP study

  A KNIH006 mRNA-seq paired end data for beta cells

  Dataset EGAD00001002172

• Reference epigenome IPS06_X_ENeuron_smRNA-Seq data generated from KEP study

  A IPS06_X_ENeuron_smRNA-Seq single end data for Early neuron cells(Tuj1)

  Dataset EGAD00001003506

• Reference epigenome KNIH004 mRNA-seq data generated from KEP study

  A KNIH004 mRNA-seq paired end data for islet cells

  Dataset EGAD00001002170

• Reference epigenome KNIH006 miRNA-seq data generated from KEP study

  A KNIH006 miRNA-seq single end data for beta cells

  Dataset EGAD00001002765

• 4C-seq data of a primary AML with t(3;8)

  The viewpoints used in the 4C-seq data were either the EVI1 promoter or the MYC super-enhancer

  Dataset EGAD00001006818

• Reference epigenome Islet-derived-MSC04 mRNA-Seq data generated from KEP study

  Islet-derived-MSC04 mRNA-Seq paired end data

  Dataset EGAD00001007163

• Reference epigenome IPS-Fibroblast02 h3k27ac ChIP-Seq data generated from KEP study

  IPS-Fibroblast02 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007270

• Reference epigenome IPS-ENeuron02 h3k27ac ChIP-Seq data generated from KEP study

  IPS-ENeuron02 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007298

• Characterization of genetic intratumor heterogeneity in colorectal cancer and matching patient-derived spheroid cultures.

  Patient-derived in vitro cultures of colorectal cancer (CRC) may help guide treatment strategies prior to patient treatment. However, most previous studies have been performed on a single biopsy per tumor. The purpose of this study was to analyze multiple spatially distinct biopsies from CRCs and see how well intratumor heterogeneity (ITH) was recapitulated in matching patient-derived spheroids. Three to five biopsies were collected from six CRC tumors. Each biopsy was split in two; one half was used for spheroid culturing, the other half was used for direct DNA and RNA purification. For two of the patients, lymph node metastases were analyzed. Somatic mutations were called from whole exome sequencing data. Each tumor contained multiple mutations shared across all biopsies and spheroids, including major CRC drivers such as APC, KRAS, and TP53. At the same time, all tumors exhibited ITH on both mutation and copy number level. The concordance between biopsies and spheroids ranged between 40-70% for coding mutations. For three of the patients the biopsy and spheroid from matching areas clustered together, meaning that the spheroid resembled the area of origin more than the other areas. However, all biopsies and spheroids contained private mutations. Thereby, multiple cultures from spatially distinct sites of the tumor increase the insight into the genetic profile of the entire tumor. RNA sequencing based molecular subtyping was stable and seemed independent of genetic ITH. In conclusion, all examined CRC tumors contained genetic ITH on mutation and copy number level. Spheroid cultures partly reflected this ITH, meaning that multiple cultures from distinct sites of the tumor improved the representation of the different genetic subclones in the primary tumor. This should be taken in to account when establishing patient-derived models for drug screening.

  Study EGAS00001002684

• Characterization of Macrophage-Tropic HIV Infection of Central Nervous System Cells and the Influence of Inflammation

  In this study, we utilize macrophage-tropic and T cell-tropic HIV-1 Env proteins to establish accurate infection profiles for multiple CNS cells under basal and interferon alpha (IFN-α) or lipopolysaccharide (LPS)-induced inflammatory states. We found that macrophage-tropic viruses confer entry advantages in primary myeloid cells, including monocyte-derived macrophages, microglia, and induced pluripotent stem cell (iPSC)-derived microglia. Following 24-h IFN-α or LPS treatments, total RNA was harvested from 100,000 to 200,000 cells using an RNeasy RNA isolation kit (Qiagen, 74004). RNA was quantified using a NanoDrop spectrophotometer and TapeStation electrophoresis prior to library preparation. cDNA libraries were prepared using the KAPA RNA HyperPrep kit (Roche, 08098115702). Paired-end RNA sequencing was performed on a NovaSeq S4 platform at a read length of 100 bp, resulting in approximately 60 million reads per sample.

  Study phs003306