3743 results for "RNA AND sequencing OR transcriptome"

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• Amplicon sequencing of long non-coding RNA associated with gastritis and gastric carcinogenesis

  This study aimed to identify dysregulations of long non-coding RNA (lncRNA) in gastric cancer, and to reveal molecular functions of lncRNAs associated with gastritis and gastric carcinogenesis. We also aimed to reveal the correlation between clinicopathological characteristics and lncRNA alterations. To this end, we will try to develop predictive markers of gastric cancer risk, which enables prevention and early detection of gastric cancer. We will also try to develop molecular markers which enable us to select treatment strategies for gastric cancer.

  Study JGAS000353

• Single cell RNA-sequencing (scRNA-seq) of the human hematopoietic stem cell compartment (CD34+CD38-CD45RA-)

  To identify a trajectory of human hematopoietic stem cell (HSC) activation single cell gene expression profiling of RNA from human HSC was performed by flow cytometry-based single cell deposition of CD34+CD38—CD45RA—cells from G-CSF-mobilized peripheral blood from 3 individual donors and 1 bone marrow donor in concert with single cell index-sorting (signals indexed: CD34, CD38, CD45RA, CD90, CD49f) and followed by SMARTseq2 based protocol. We show that INKA1 and CDK6/PAK4 expressing single cells segregate across alternative states of quiescence.

  Study EGAS00001004769

• RNA-sequencing of gluten-reactive and non-reactive T-cells from blood samples of treated CeD patients during a gluten-challenge

  Gluten-specific CD4+ T cells are drivers of celiac disease (CeD). Here we aimed to characterize such cells in blood of treated CeD patients during gluten challenge with comparison to similar cells of untreated disease. Treated CeD patients underwent a 3-day gluten-challenge with blood sampling at baseline and day 6 (d6). Blood cells were stained with HLA-DQ:gluten tetramers (Tetramer). CD4+, gut-homing Tetramer+ and Tetramer- effector-memory T (TEM) cells at d6 were subjected to bulk RNA-seq (n=7).

  Study EGAS00001004988

• Single-cell RNA and ATAC sequencing data analysis of human postmenopausal fallopian tube and ovary

  In this study, we used a combination of technologies, including Drop-seq, 10X single-cell RNA-seq (scRNA-seq), and single-cell assay on transposase accessible chromatin (scATAC-seq) to generate  a high-quality cell atlas that consists of four tissue sites from the reproductive tract of postmenopausal women. This atlas, by far the most comprehensive map of the postmenopausal female reproductive system to date, will pave the way to a better understanding of the pathophysiology of many diseases affecting older women.

  Study EGAS00001006780

• Precise reconstruction of the tumor microenvironment using bulk RNA-seq and a unique machine learning-based algorithm trained on artificial transcriptomes

  Cellular deconvolution algorithms virtually reconstruct tissue composition by analyzing the gene expression of complex tissues. Here, we present the decision tree machine learning algorithm, Kassandra, trained on a broad collection of > 9,400 tissue and blood sorted cell RNA profiles to accurately reconstruct the tumor microenvironment (TME). Bioinformatic correction for technical and biological variability, aberrant cancer cell expression inclusion, and the accurate quantification and normalization of transcript expression increased the stability and robustness of Kassandra. Performance was validated on over 4,000 H&E tissue slides and more than 1,000 normal and tumor tissues by comparison with cytometric, immunohistochemical or single-cell RNA-seq measurements. Kassandra accurately deconvolved stromal and immune elements of blood and tumors, revealing the role of the TME in tumor pathogenesis. Digital TME reconstruction revealed that the presence of PD1-positive CD8+ T cells strongly correlated with immunotherapy response and increased the predictive potential of established biomarkers, indicating that Kassandra could potentially be utilized in future clinical applications.

  Dataset EGAD00001008776

• Type 1 Diabetes Genetics Consortium (T1DGC): Case-only RNA-Seq Study

  Peripheral blood mononuclear cells (PBMC) from 82 T1D cases from the T1DGC study were fractionated by positive selection on magnetic beads into CD4+ T cells, CD8+ T cells and CD19+ B cells. RNA purification, library preparation and sequencing to an average of 50 million reads per sample were performed by the HudsonAlpha Genome Services Laboratory. The resulting dataset includes 82 subjects with data from at least one cell type; sequence data from all 3 cell types is available from 79 subjects. The individuals in this study can be linked to the phs000911 T1DGC ImmunoChip study via subject ids to obtain both genotypic and phenotypic information, including genotypes used for expression quantitative trait locus analysis.

  Study phs001426

• Resilience and Susceptibility Patterns in the Viral Immune Response Using RNA-Seq (ReSP VIRUS Study)

  This is a cross sectional study to investigate the relationship between nasal mucosa gene expression and clinical outcomes in infants with critical Respiratory Syncytial Virus (RSV) infection. We collected nasal mucosa samples from infants admitted to the pediatric intensive care unit with polymerase chain reaction confirmed RSV bronchiolitis. Samples were obtained by mucosal scrape biopsy of the inferior turbinate using a sterile plastic curette and bulk RNA-sequencing of mRNA was performed. Clinical data was extracted from the electronic health record including: demographics, medical history, duration of symptoms, time from admission to sample collection, hospital and intensive care unit length of stay, and type and duration of respiratory support.

  Study phs003053

• Single-cell RNA-seq data of the tumor microenvironment of lymphocyte-rich Hodgkin lymphoma and other Hodgkin lymphoma subtypes

  Lymphocyte-rich classic Hodgkin lymphoma (LR-CHL) is a rare subtype of Hodgkin lymphoma. Recent technical advances have allowed for the characterization of specific crosstalk mechanisms between malignant Hodgkin Reed-Sternberg (HRS) cells and different normal immune cells in the tumor microenvironment (TME) of CHL. However, the TME of LR-CHL has not yet been characterized at single cell resolution. Here, using single cell RNA sequencing, we examined the immune cell profile of 8 cell suspension samples of LR-CHL in comparison to 20 samples of the mixed cellularity (9 cases) and nodular sclerosis (11 cases) subtypes of CHL, as well as 5 reactive lymph node controls.

  Study EGAS00001005541

• Single-Cell Genomic and Transcriptomic Analysis of the Aging Human Brain

  We studied human brain aging using single-cell whole genome sequencing and single-nucleus RNA sequencing of the pre-frontal cortex from 19 donors ranging in age from 0.4 years to 104 years. All donors were free of neuropathology and none received a clinical neurological diagnosis prior to death. snRNA-seq libraries were prepared in batches that always included old and young donors to mitigate batch effects in the preparations. Neurons for single-cell WGS were collected from the same tissue region as the snRNA-seq for each donor but represent independent cells. Multiplexed error-robust fluorescent in-situ hybridization (MERFISH) data was also collected from pre-frontal cortex samples of a subset of the 19 donors. Transcriptomic results identified infant-specific cell clusters, and common-down regulation of cell-essential genes involved in ribosomes, transport, homeostasis and metabolism during aging, across all cell types. scWGS identified two-age associated mutational signatures, as well as gene-length and expression-level dependent somatic mutation rates in neurons. Raw sequencing data in fastq format for single-nucleus RNA-seq and single-cell DNA-seq, as well as cell and gene count matrix, transcript metadata, and cell metadata for MERFISH will be available in dbGaP.

  Study phs003445

• Genomic and Genetic Analysis of Brain Tumors and Analysis of Their Clinicopathological Significance

  The treatment outcome of glioma patients remains disappointing, and more studies are needed to improve therapeutic strategies. In particular, it is crucial to understand the mechanisms underlying resistance to treatment and malignant transformation, as well as the intratumoral heterogeneity and interaction of tumor cells and host immunity. Recent advances in omics analysis techniques, such as next-generation sequencing (NGS), have enabled us to obtain omics data more easily and accelerated our ability to integrate this information and precisely characterize cases. In our project, we have already collected more than 100 pairs of human glioma/normal tissue samples, of which some are multiple specimens collected from initial and recurrent tumors, and some include multiple specimens collected from different regions within a single tumor. We used our in-house algorithms, composed of alignment and variant calling programs, to analyze omics data from multiple platforms, such as whole-exome sequencing (WES), RNA sequencing (RNA-seq), and methylation array, with our original cohort of glioma patients. Using several informatics tools, the information about genetic and epigenetic status and expression signatures were analyzed. Those data are expected to provide key information for investigating the mechanisms discussed above. Through this project, we hope to improve strategies for personalized therapy based on comprehensive omics data.

  Study JGAS000004