11932 results for "cancer rna-seq"

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• single cell RNA-seq and ATAC-seq of human fetal forebrain tissue

  Oligodendrogenesis in the human central nervous system has been mainly observed at the second trimester of gestation, a much later developmental stage compared to mouse. Here we characterize the transcriptomic neural diversity in the human forebrain at post conceptual weeks (PCW) 8 to 10, using single-cell RNA-Seq. We find evidence of the emergence of a first wave of oligodendrocyte lineage cells as early as PCW 8, which we also confirm at the epigenomic level with single-cell ATAC-Seq. Using regulatory network inference, we predict key transcriptional events leading to the specification of oligodendrocyte precursor cells (OPCs). Moreover, by profiling the spatial expression of fifty key genes using In Situ Sequencing (ISS), we identify regions in the human ventral fetal forebrain where oligodendrogenesis first occurs. Our results indicate evolutionary conservation of the first wave of oligodendrogenesis between mouse and human and describe regulatory mechanisms involved in human OPC specification.

  Study EGAS00001006136

• COLONOMICS small RNA sequencing

  This dataset consists of SOLiD small RNA-seq of 250 colorectal samples: 100 tumor tissue samples, 100 normal tissue samples (adjacent to tumor sites) and 50 matched control samples of healthy individuals. CSfasta and qual files converted to single fastq files prior to uploading.

  Dataset EGAD00001004827

• 10x single-nucleus RNA-seq of postoperative human FCD II brain tissues

  This dataset contains 10x Genomics single-nucleus RNA sequencing data from postoperative brain tissues of patients with focal cortical dysplasia type II (FCD II). The samples were processed using the Chromium platform.

  Dataset EGAD50000001403

• GCP Challenges in Hepatology

  raw RNAseq data from blood plasma of patients diagnosed with liver disease. Total RNA libraries were prepared using the SMARTer Stranded Total RNA-Seq-Kit v3 - Pico Input Mammalian (Takara Bio). Libraries were paired-end sequenced (2x100) o a NovaSeq 6000 instrument using NovaSeq S2 or S1 kit (Illumina).

  Dataset EGAD50000000775

• Genomic (WGS) data for the validation of the glioblastoma progression study (GBMatch).

  43 low-coverage genomes derived from fresh-frozen glioblastoma tumor samples. These genomes have been produced for validation purposes and match the corresponding RRBS and RNA-seq profiles in that DNA and RNA was extracted from the same tumor samples.

  Dataset EGAD00001004077

• Transcriptome (RNA-seq) data for the validation of the glioblastoma progression study (GBMatch).

  37 transcriptomes derived from fresh-frozen glioblastoma tumor samples. These transcriptomes have been produced for validation purposes and match the corresponding RRBS and WGS profiles in that DNA and RNA was extracted from the same tumor samples.

  Dataset EGAD00001004076

• KNS42 and SF188 H3K36me3 chipSeq

  Chromatin immunoprecipitation (ChIP) was carried out employing antibodies against H3K36me3 and RNA polymerase II using the HistonePath and TranscriptionPath assays by ActiveMotif. Whole genome sequencing was carried out using an Illumina HiSeq2000 and data is provided as 6 BAM files. H3K36me3 chipseq RNA polymerase II chipseq and input coverage for each cell line.

  Dataset EGAD00001004119

• Dissociation of solid tumour tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses

  Background Single-cell RNA sequencing (scRNAseq) is a powerful tool for studying complex biological systems, such as tumour heterogeneity and tissue microenvironments. However, the sources of technical and biological variation in primary solid tumour tissues and patient-derived mouse xenografts for scRNAseq, are not well understood. Here, we used low temperature (6°C) protease and collagenase (37°C) to identify the transcriptional signatures associated with tissue dissociation across a diverse scRNAseq dataset comprising 128,481 cells from patient cancer tissues, patient-derived breast cancer xenografts and cancer cell lines.Results We observe substantial variation in standard quality control (QC) metrics of cell viability across conditions and tissues. From FACS sorted populations gated for cell viability, we identify a sub-population of dead cells that would pass standard data filtering practices, and quantify the extent to which their transcriptomes differ from live cells. We identify a further subpopulation of transcriptomically “dying” cells that exhibit up-regulation of MHC class I transcripts, in contrast with live and fully dead cells. From the contrast between tissue protease dissociation at 37°C or 6°C, we observe that collagenase digestion results in a stress response. We derive a core gene set of 512 heat shock and stress response genes, including FOS and JUN, induced by collagenase (37°C), which are minimized by dissociation with a cold active protease (6°C). While induction of these genes was highly conserved across all cell types, cell type-specific responses to collagenase digestion were observed in patient tissues. We observe that the yield of cancer and non-cancer cell types varies between tissues and dissociation methods.Conclusions The method and conditions of tumour dissociation influence cell yield and transcriptome state and are both tissue and cell type dependent. Interpretation of stress pathway expression differences in cancer single cell studies, including components of surface immune recognition such as MHC class I, may be especially confounded. We define a core set of 512 genes that can assist with identification of such effects in dissociated scRNA-seq experiments.

  Study EGAS00001003753

• Medulloblastoma dataset

  This dataset contains a tumor + normal DNA sequence data and tumor RNA seq data for a medulloblastoma patient.

  Dataset EGAD50000000384

• DAC Ghent University - Department of Movement and Sports Sciences

  DAC of Ghent University for RNA-seq data of human skeletal muscle biopsies collected at the Department of Movement and Sports Sciences

  Dac EGAC50000000263