7190 results for "({!join from=dataset_studies to=id v=$q1}) OR ("transcriptome" OR "RNA-seq" OR "rna sequencing" OR "rna-sequencing")"

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• META-PRISM

  A pan-cancer cohort of 1031 patients resistant to systemic therapies or with no approved therapeutic options. It includes whole-exome sequencing of 571 tumor and matched-normal samples, and transcriptome sequencing of 947 tumor samples. Biopsies were taken at entry into precision medicine trials, often after diagnosed resistance. Comprehensive clinical information is available for all patients and include patient age at biospy, tumor primary site and histological subtype, biopsy site, treatments received prior to biopsy, blood assessment results at biopsy, metastatic sites at biopsy, and survival time from the biopsy date.

  Dataset EGAD00001009684

• miRNA expression data from primary tumors, metastasis and matched normals.

  MicroRNAs (miRs) have been recognized as promising biomarkers. It is unknown to what extent tumor-derived miRs are differentially expressed between primary colorectal cancers (pCRCs) and metastatic lesions, and to what extent the expression profiles of tumor tissue differ from the surrounding normal tissue. Next-generation sequencing (NGS) of 220 fresh-frozen samples, including paired primary and metastatic tumor tissue and non-tumorous tissue from 38 patients, revealed expression of 2245 known unique mature miRs and 515 novel candidate miRs. Unsupervised clustering of miR expression profiles of pCRC tissue with paired metastases did not separate the two entities, whereas unsupervised clustering of miR expression profiles of pCRC with normal colorectal mucosa demonstrated complete separation of the tumor samples from their paired normal mucosa. Two hundred and twenty-two miRs differentiated both pCRC and metastases from normal tissue samples (false discovery rate (FDR) <0.05). The highest expressed tumor-specific miRs were miR-21 and miR-92a, both previously described to be involved in CRC with potential as circulating biomarker for early detection. Only eight miRs, 0.5% of the analysed miR transcriptome, were differentially expressed between pCRC and the corresponding metastases (FDR <0.1), consisting of five known miRs (miR-320b, miR-320d, miR-3117, miR-1246 and miR-663b) and three novel candidate miRs (chr 1-2552-5p, chr 8-20656-5p and chr 10-25333-3p). These results indicate that previously unrecognized candidate miRs expressed in advanced CRC were identified using NGS. In addition, miR expression profiles of pCRC and metastatic lesions are highly comparable and may be of similar predictive value for prognosis or response to treatment in patients with advanced CRC.

  Dataset EGAD00001001644

• Cancer of Low survival and UnMet Need – Pathologist Initiated

  Despite significant advances in diagnostic cancer histopathology, a subset of tumors remain unable to be classified using WHO criteria. The resulting diagnostic uncertainty can result in inappropriate clinical management and negative patient outcomes. We investigated whether combining histopathology with whole genome and transcriptome sequencing (WGTS) could improve the classification of tumors that posed diagnostic dilemmas despite extensive histopathology and standard molecular work-up at a quaternary oncology center. We successfully sequenced 45 tumors from an initial set of 54 unclassified tumors (83% success rate). A confident diagnosis was made for 38/45 tumors (84%). Additionally, potential treatment targets were identified in 21/45 tumors (47%). Theoretical comparison with alternative assays demonstrated that WGTS was uniquely capable of detecting critical diagnostic findings in 15/38 tumors (39%). This work supports augmenting histopathology and standard molecular pathology with WGTS in the classification of difficult-to-diagnose tumors.

  Study EGAS00001008112

• LLDeep DAG2+ scRNA-seq in PBMCs

  Single cell transcriptomics of PBMCs of 47 donors from the Lifelines Deep cohort (general population, Northern part of the Netherlands). Cells of five or six different donors were pooled together in one sample pool, resulting in eight different sample pools. In total, 28.855 cells were captured and their transcriptomes were sequenced to an average depth of 74k. Genotype data was available for each donor, which allowed us to use the Demuxlet method that uses variable SNPs between the pooled individuals to determine which cell belongs to which individual. Since genotype information is lacking of 2 individuals, the transcriptome of only 45 individuals could be retrieved.

  Dataset EGAD00010001315

• RNA sequecing of primary B cells infected with Epstein-Barr virus (EBV) or stimulated with heat-inactivated EBV

  Naïve (CD27-IgD+) B cells were isolated from buffy coat preparations of healthy donors using CD19 magnetic beads, followed by reals of CD19 beads and incubation with IgD-biotin and anti-biotin magnetic beads. B cells were infected with EBV by spinoculation or stimulated with heat-inactivated EBV and control cells were left uninfected. RNA was extracted immediately after isolation in un-infected B cells. From EBV-infected B cells and B cells stimulated with heat-inactivated virus, RNA was extracted 24 and 96 hours after infection / stimulation.

  Dataset EGAD50000000305

• Loss of RREB1 in pancreatic beta cells reduces cellular insulin content and affects endocrine cell gene expression

  Genome-wide studies have uncovered multiple independent signals at the RREB1 locus associated with altered type 2 diabetes (T2D) risk and related glycemic traits. However, little is known about the function of the zinc finger transcription factor RREB1 in glucose homeostasis or how changes in its expression and/or function influence diabetes risk. We studied its function using three transcriptomic strategies, profiling gene expression when RREB1 is knocked out or knocked down in the Endo-betaH1 cell line, in addition to the in vitro differentiation in hiPSCs.

  Dataset EGAD00001009740

• scRNA-seq dataset, RCC

  scRNA-seq dataset for the study "Surgery in combination with immune checkpoint therapy as an effective treatment for patients with metastatic cancer." The dataset comprises 38 samples from 19 patients, with each pair includes a baseline (BL) sample and a post-biopsy or post-surgery sample. Each sample corresponds to an R1 (read 1) and an R2 (read 2) files (.fastq.gz) for paired reads. Beside procedure (biopsy or surgery), another important phenotype is response (PR and PD in this case, no SD for these patients), included in the description of the samples. The sequencing was performed using Illumina NovaSeq 6000 platform., as seen in the experiment description.

  Dataset EGAD50000000566

• Samples obtained within X-pand project

  This dataset contains five bone marrow samples and three mobilised peripheral blood ones from different healthy individuals. The three bone marrow samples have been sequence with the 10x multiome kit using Illumina sequencers. The three mobilised peripheral blood ones have been split in two aliquots, one for 10x multiome and the other for 10x CITE-seq. Both libraries types have been sequenced with an Illumina sequencer. Raw data has been processed with cell-ranger and map to the human genome (GRCh38) to obtain the bam files.

  Dataset EGAD50000001108

• Immune Landscape of Cervical Lymph Nodes in Multiple Sclerosis

  This study aimed to investigate the cellular compartment of deep cervical lymph nodes (dcLNs) during early MS before the influence of immunosuppressive treatments. We used ultrasound-guided FNAs followed by scRNA-seq,CITE-seq, TCR and BCR seq to characterize the immune landscape in MS patients compared to controls within the dcLNs.

  Study EGAS50000000843

• T-bet+ CXCR3+ B cells drive hyperreactive B-T cell interactions in multiple sclerosis

  scRNAseq, VDJseq and methylome of non-proliferating and autoproliferating B cells from HDs and MS patients, as well as snRNAseq of highly inflamed meningeal MS brain tissue datasets that have been generated and analyzed for the study "T-bet+ CXCR3+ B cells drive hyperreactive B-T cell interactions in multiple sclerosis".

  Study EGAS50000000845