3930 results for "RNA AND sequencing OR transcriptome"

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• Transcriptome analysis of a novel human iPSC-derived 3D cortical tissue model - WT, APP KI, Abeta-treated, Abeta/Aducanumab-treated

  Single-cell RNA-seq analysis of human iPSC-derived microglia/astrocytes/neurons in a novel, human in vitro 3D cortical tissue model. Samples include distinct conditions (each as biological duplicates): - wild-type cells from cultures after 1 month (WT_1mo, cell lines: A18, KOLF) and 3 months (WT_3mo, cell lines: SA2, KOLF, A18) of culturing - knock-in cells from cultures carrying three familial AD mutations in the APP gene (Swedish, Iberian, Arctic; introduced by CRISPR/Cas9-mediated knock-in) after 3 months of culturing (KI_3mo, cell lines: SA2, A18) - subset of WT 1 month-old samples were exposed to 500nM synthetic Abeta42 for 2 consecutive feeds, and a subset of these subsequently treated with 1µg/ml Aducanumab antibody for 14 days

  Dataset EGAD50000002031

• RNA-seq of non-LPS treated (N), non-tolerized (NT), and tolerized (T) IFNg-primed macrophages pretreated with or without HDAC3i

  In this experiment we investigated the effect of HDAC3 inhibition on the transcriptome of IFNg-primed macrophages under different tolerization conditions. Peripheral blood mononuclear cells (PBMCs) were isolated from 3 healthy donors. PBMCs were isolated from whole blood of healthy donors using Ficoll gradient (Invitrogen). Monocytes (CD14+ cells) were positively selected from PBMCs using CD14 Microbeads according to the manufacturer’s instructions (Miltenyi Biotec). Monocytes were subsequently treated with or without 500 nM HDAC3i (ITF3100) for 30 minutes prior to overnight IFNg priming (50 ng/mL). Cells were then kept without LPS (non-LPS; N), treated with 10 ng/mL LPS once (non-tolerized; NT), or treated with LPS twice (tolerized; T; second LPS concentration: 100 ng/mL). In total, there were 18 samples included.

  Dataset EGAD00001005959

• RNA sequencing of NK cells in human lung

  Human lung tissue-resident NK cells (trNK cells) are likely to play important roles in viral infections, inflammation and cancer. However, knowledge about lung trNK cells is lacking but is fundamental for exploiting these cells in therapeutic approaches. Here we analysed the transcriptome of CD69+CD49a+CD103+CD16-CD56bright, CD69+CD49a+CD103-CD16-CD56bright, CD69+CD49a-CD103-CD16-CD56bright, CD69-CD49a-CD103-CD16-CD56bright, and CD56dimCD16+NKG2A+CD57- NK cells isolated from human lung.

  Study EGAS00001003544

• Integrative understanding of human immune system by functional genomics and development of intervention strategies for the prevention of autoimmune diseases

  To elucidate the feature of age-associated helper T (ThA) cells and its contribution to autoimmune diseases, various peripheral blood immune cell subsets from 136 systemic lupus erythematosus (SLE) and 89 healthy controls (HC) were collected (Naive_CD4, Th1, Th2, Th17, Tfh, Fr._I_nTreg, Fr._II_eTreg, Fr._III_T, ThA, Naive_CD8, Naive_B, SM_B, USM_B, DN_B, Plasmablast, NK, CD16p_Mono, CL_Mono, Neu, mDC, pDC, TEMRA_CD8, CM_CD8, EM_CD8, NC_Mono, Int_Mono, LDG). RNA-seq was performed with each cell subset samples.

  Study JGAS000627

• Integrative understanding of human immune system by functional genomics and development of intervention strategies for the prevention of autoimmune diseases

  To elucidate the regulation of gene expression in immune cell subsets and its contribution to autoimmune diseases, 24 peripheral blood immune cell subsets from 50 Systemic Sclerosis and 48 Healthy Controls were collected (Naive_CD4, Mem_CD4, Fr._I_nTreg, Fr._II_eTreg, Fr._III_T, Th1, Th2, Th17, Tfh, NK, Naive_CD8, EM_CD8, CM_CD8, TEMRA_CD8, Naive_B, USM_B, SM_B, DN_B, Plasmablast, CL_Mono, Int_Mono, NC_Mono, mDC, pDC). RNA-seq was performed with each immune cell subset samples. After gene expression quantification samples were filtered.

  Study JGAS000296

• Integrative understanding of human immune system by functional genomics and development of intervention strategies for the prevention of autoimmune diseases

  Various peripheral blood immune cell subsets from 89 healthy volunteers and 136 systemic lupus erhythematosus (SLE) donors were collected (Naive_CD4, Mem_CD4, Th1, Th2, Th17, Tfh, Fr._I_nTreg, Fr._II_eTreg, Fr._III_T, Naive_CD8, EM_CD8, CM_CD8, TEMRA_CD8, NK, Naive_B, USM_B, SM_B, DN_B, Plasmablast, CL_Mono (or CD16n_Mono), CD16p_Mono, Int_Mono, NC_Mono, mDC, pDC, Neu, LDG). 22 SLE donors were analyzed longitudinally. RNA-seq was performed with each immune cell subset samples. After gene expression quantification samples were filtered.

  Study JGAS000486

• Single-cell and spatial atlas of steatotic liver disease-related hepatocellular carcinoma

  This study consists of the spatial transcriptomics data generated by : 1. Visium CytAssist Spatial Gene Expression for FFPE (10x) for steatotic liver disease-associated hepatocellular carcinoma (SLD-HCC) (n=7) and non-SLD-HCC (n=5) 2. CosMxTM Human Universal Cell Characterization RNA Panel (1000-plex); NanoString, USA for n=4 SLD-HCC and n=4 non-SLD-HCC. The goal of this data is to compare SLD-HCC vs non-SLD-HCC as well as response to immunotherapy

  Study EGAS50000001034

• BAM files of total RNA-Seq data of POPS control samples (GRCh37)

  Placental biopsies (n = 64 female placentas, n = 67 male placentas) were selected from healthy pregnancies from the POPs cohort. A quality control process was also applied for the RNA-Seq datasets: reads were trimmed with Trim Galore!, which uses cutadapt internally and were mapped to the same version of human genome reference (hg19). TopHat2, a splice-aware mapper built on top of Bowtie2 short-read aligner, was used in the mapping process in which so-called two-pass (or two-scan) alignment protocol was applied to rescue unmapped reads from the initial mapping step. In the second mapping, previously unmapped reads were re-aligned to the exon-intron junctions detected in the first-mapping by TopHat2 and were combined across all 131 placenta samples. The initial and second mapped reads were merged by samtools

  Dataset EGAD00001003462

• RNA-Seq dataset for Genomic rearrangements in Pediatric Cancer

  RNA-Seq data for systematic gene fusion detection in Pediatric Cancer

  Dataset EGAD00001008152

• Global RNA sequencing data of human iPSC-derived microglia from frontotemporal dementia (FTD) patients

  Global RNA sequencing was performed in RNA samples extracted from human iPSC-derived microglia from healthy controls and sporadic (non-genetic) and genetic C9orf72 hexanucleotide repeat expansion-carrying patients with FTD.

  Study EGAS50000001688

• Characterization of Human Transcriptome by Computational and HTS Approaches

  We have deeply sequenced total RNA and whole exome of six different post-mortem human snap-frozen tissues (brain, liver, lung, striated muscle, kidney, heart) from three unrelated healthy Caucasian individuals (males, aged 47-54) with the aim to characterize post-transcriptional events due to alternative splicing and RNA editing. In addition, to facilitate the detection of RNA editing sites we have also resequenced the entire genome of each individual. Data are also used to investigate tissue-specific abundance of mitochondrial DNA from exomes and its correlation with mitochondrial transcription, mass and respiratory activity. Tissues were obtained from Cureline (South San Francisco, CA, USA). DNA and RNA were purified using the DNeasy Blood and Tissue Kit (Qiagen, Hilden, Germany) and the RNeasy Plus Mini Kit (Qiagen, Hilden, Germany), respectively. Exome capture was performed using the TruSeq Exome Enrichment Kit (Illumina, San Diego, CA) and the sequencing was carried out at IGA Technology Services in Udine (Italy) on Illumina HiSeq 2000 (generating for each tissue approximately 40 millions of 100bp paired-end reads). Strand-oriented RNA reads (2x100bp) were generated using the TruSeq Stranded Total RNA Sample Prep Kit (Illumina, San Diego, CA, USA) and sequenced at IGA Technology Services in Udine (Italy) on Illumina HiSeq 2000 platform. Whole genome resequencing was performed at Personal Genomics in Italy on an Illumina HiSeq 2000 platform (2x100bp) with a mean coverage of 30X. MiRNA-Seq was instead performed at IBBE Institute (Italy) using the TruSeq Small RNA Sample Prep Kit on Illumina MiSeq platform. Preliminary bioinformatics analyses have been performed to check quality using FASTQC software and inconsistent read regions have been trimmed using trim_galore. All reads were mapped onto the human reference genome (version hg19) using GSNAP.

  Study phs000870

• Raw RNA sequencing of hepatoblastoma PDX cell line HB-303-LEF

  Raw RNA sequencing of hepatoblastoma PDX cell line HB-303-LEF

  Study EGAS50000000928

• Single cell RNA sequencing of human cord Blood CD34 Cells

  We performed single cell RNA sequencing and ATAC e valuation of human umbilical cord blood. To explore the differentiation pathway for human lymphoid lineage, our target cells contain CD34-positive cord blood. That population is evaluated by single cell RNA sequencing and single cell ATAC.

  Study JGAS000528

• HuBMAP: High Resolution 3D Mapping of Cellular Heterogeneity Within Multiple Types of Mineralized Tissues

  The overall goal of the study was to generate 3D single cell maps of healthy human knee cartilage. Articular cartilage from the weightbearing and non-weightbearing regions of the femoral condyles in healthy knees from young donors (n=12) was analyzed by bulk RNA-sequencing (RNA-Seq), single nuclei RNA (snRNA-seq) and ATAC-sequencing and by spatial transcriptomic methods (GeoMx, MERFISH and Xenium). These multimodal data and additional single cell RNA-sequencing (scRNA-seq) data were integrated to generate a comprehensive molecular, genomic and function annotation of chondrocyte populations. Regulatory mechanisms were derived by linking motifs in expressed genes, transcription factors and chromatin organization.

  Study phs003721

• Deciphering molecular mechanisms underlying hematological malignancies and development of novel therapeutic approaches

  We aim to identify epigenomic features dysregulated in hematological malignancies through epigenetic analyses of hematological malignant cells. We also perform RNA sequencing to profile tumor cell-specific transcriptomes and target gene sequencing to profile somatic gene mutations in tumor cells. Regarding identified epigenomic abnormalities, we perform further analysis to determine whether they could be good candidates to translate into therapy development. To this end, we will modulate the activity of targets by using their activators and inhibitors in vitro and establish xenograft mouse models of hematological malignancies and manipulate their activity in vivo. Through these studies, we aim to develop new therapeutic agents or modalities that lead to improved patients��� prognosis.

  Study JGAS000603

• The Environmental Determinants of Diabetes in the Young Study (TEDDY)

  Study Overview The Environmental Determinants of Diabetes in the Young (TEDDY) Study is a longitudinal study that investigates genetic and genetic-environmental interactions, including gestational events, childhood infections, dietary exposures, and other environmental factors after birth, in relation to the development of islet autoimmunity and type 1 diabetes (T1D). A consortium of six clinical centers assembled to participate in the development and implementation of the study to identify environmental triggers for the development of islet autoimmunity and T1D in genetically susceptible individuals. Beginning in 2004, the TEDDY study screened over 400,000 newborns for high-risk HLA-DR, DQ genotypes from both the general population and families already affected by T1D. The TEDDY study enrolled around 8,676 participants across six clinical centers worldwide (Finland, Germany, Sweden and three in the United States) in the 15-year prospective follow-up. Participants are followed every three months for islet autoantibody (IA) measurements with blood sampling until four years of age and then at least every six months until the age of 15. After the age of four, autoantibody positive participants continue to be followed at three month intervals and autoantibody negative participants are followed at six-month intervals. In addition to the analysis of autoantibodies, additional data and sample collection are performed at each visit. Parents collect monthly stool samples in early childhood. The parents also fill out questionnaires at regular intervals in connection with study visits and record information about diet and health status in the child's TEDDY Book between visits. Continued long-term follow-up of the currently active TEDDY participants will provide important scientific information on early childhood diet, reported and measured infections, vaccinations, and psychosocial stressors that may contribute to the development of type 1 diabetes and islet autoimmunity. Additional information on the TEDDY study is available in the following articles: Rewers et al., 2008, PMID: 19120261 and Hagopian et al., 2006, PMID: 17130573. Details of the TEDDY protocol can be found in Hagopian et al., 2011, PMID: 21564455. TEDDY data currently available in dbGaP include: gene expression, SNPs, exome, microbiome (gut, nasal, and plasma), RNA sequencing, and whole genome sequencing. For more information on TEDDY Study version history please refer to TEDDY Study dbGaP README File. ImmunoChip SNP DNA from whole blood samples on study participants and their family members (mothers, fathers, and siblings) was obtained and used for SNP genotyping. Genotyping was performed by the Center for Public Health Genomics at the University of Virginia using the Illumina ImmunoChip SNP array, which contains around 196,000 SNPs from 186 regions associated with 12 autoimmune diseases (Hadley et al., 2015, PMID: 26010309). Data cleaning and validation included the removal of subjects with a low call rate (< 5% SNPs missing) and differences in reported sex and prior genotyping at the TEDDY HLA laboratory. Additionally, SNPs with a low call rate or Hardy-Weinberg equilibrium P value < 10-6, except for chromosome 6 due to HLA eligibility requirements, were removed from the final dataset (Törn et al., 2015, PMID: 25422107).TEDDY-T1DExome ArrayDNA from whole blood samples on study participants and their family members (mothers, fathers, and siblings) was obtained and used for genotyping. Genotyping was performed by the University of Virginia using the Illumina TEDDY-T1DExome array. The TEDDY-T1DExome array is a custom chip that contains 550,601 markers from the Infinium CoreExome-24 v1.1 BeadChip and an additional 90,214 tagSNPs specifically selected by the TEDDY investigators based on their associations with nutrients, vitamins, type 2 diabetes, autoimmune diseases, body-mass index, or other exposures and phenotypes measured by TEDDY study.The Illumina GenTrain2 algorithm was used for genotype calling. Sample quality control metrics included sample call rate, heterozygosity rate and concordance of gender between the information reported and genotyped. Gene Expression The TEDDY study collected peripheral blood for the extraction of total RNA from enrolled children starting at 3 months of age, and then at 3 month intervals up to 48 months and then biannually. Total RNA was extracted using a high throughput (96-well format) extraction protocol using magnetic (MagMax) beads technology at the TEDDY RNA Laboratory, Jinfiniti Biosciences in Augusta, GA. Purified RNA (200 ng) was further used for cRNA amplification and labeling with biotin using Target Amp cDNA synthesis kit (Epicenter catalog no. TAB1R6924). Labeled cRNA was hybridized to the Illumina HumanHT-12 Expression BeadChips based on the manufacturer's instructions. The HumanHT-12 Expression BeadChip provides coverage for more than 47,000 transcripts and known splice variants across the human transcriptome. Microbiome The TEDDY microbiome study aimed to characterize the longitudinal development of the microbiome, including bacteria, viruses and other microorganisms in the gut, plasma, and nasal cavity of prediabetic and diabetic subjects compared to autoantibody negative non-diabetic subjects. Stool samples used were collected monthly from 3 to 48 months, after which stool samples were collected every 3 months. Nasal swab samples were collected every 3 months starting at 9 months of age until 48 months, after which nasal swabs were collected every 6 months. Plasma samples were collected every 3 months starting at 3 months of age until 48 months, after which plasma samples were collected every 6 months. If the subject was autoantibody positive at 48 months then they remained on the 3 month collection interval for nasal swab and plasma samples. Samples underwent 16s rRNA gene sequencing, DNA and viral RNA metagenomics shotgun sequencing, and sequencing of the internal transcribed spacer (ITS) regions. Additional information on the TEDDY microbiome data is available in the following articles: Vatanen et al., 2018, PMID: 30356183, Stewart et al., 2018, PMID: 30356187, and Vehik et al., 2020, PMID: 31792456. RNA Sequencing The TEDDY study aimed to characterize the transcriptome in subjects with islet autoimmunity and type 1 diabetes compared to matched control subjects. Peripheral blood was collected to extract total RNA from enrolled children starting at 3 months of age, and then at 3 month intervals up to 48 months and then biannually. Total RNA was extracted using a high throughput (96-well format) extraction protocol using magnetic (MagMax) beads technology at the TEDDY RNA Laboratory, Jinfiniti Biosciences in Augusta, GA. Purified RNA was then sent to the Broad Institute for the generation of the TEDDY RNA sequencing (RNA-Seq) data. The RNA samples were prepped using Superscript III reverse transcriptase and Illumina's TruSeq Stranded mRNA Sample Prep Kit. The TruSeq libraries were run on the Illumina HiSeq2500 platform. Whole Genome Sequencing The TEDDY study aimed to conduct deep whole genome sequencing and examine the genomic variations in subjects with islet autoimmunity and type 1 diabetes compared to matched autoantibody negative and non-diabetic children. DNA from whole blood was obtained from TEDDY children for whole genome sequencing. The WGS data were generated on the Illumina HiSeq X Ten system.

  Study phs001442

• Neuroblastoma patient Total RNA Seq data

  Tumor Total RNA Seq data of primary neuroblastomas. This is an update of the „Berlin Neuroblastoma Dataset” (EGAS00001004022). This data was used for the analysis of circular RNA expression and regulation in neuroblastoma.

  Dataset EGAD00001008124

• Determining off-target effects of splice-switching antisense oligonucleotides using short read RNAseq in neuronally differentiated human induced pluripotent stem cells

  Neuronal cells derived from female healthy control hiPSC lines LUMCi003-A, LUMCi003-B and LUMCi023-A were transfected with in total four different splice-switching AONs targeting three different human transcripts. Untransfected samples as well as samples transfected with a scrambled AON were included as controls. Three days after transfection, RNA was isolated for sequencing using Illumina Novaseq6000 in order to perform differential gene expression analysis and differential splicing analysis to determine off-target effects. Sequencing was performed in two batches: one containing the samples from cell line LUMCi003-A and the other containing cell lines LUMCi003-B and LUMCi023-A.

  Dataset EGAD50000001742

• ICARUS-LUNG01 dataset

  A total of 46 frozen tumor biopsies underwent WES analysis, while 41 blood samples were used as germline controls.Tumor samples qualified for whole exome sequencing (WES) if they contained ≥10% tumor cells. For bulk RNA-seq, 20 paired baseline and on-treatment frozen tumor biopsies were analyzed. Tumor samples qualified if they contained ≥30% tumor cells. For GeoMx spatial transcriptomic study we used 7 patients from the ICARUS LUNG01 trial focusing on tumour and immune cell compartments. We selected 7 paired baseline and on-treatment patient samples for analysis. We selected up to 4 regions of interest (ROI) per sample tissue. We identified tumour and immune cell areas of illumination (AOI) using CK and CD45 antibodies respectively

  Dataset EGAD50000001014

• Tumor-derived cell lines as pharmacogenomic models to predict therapeutic vulnerabilities in hepatocellular carcinoma

  Hepatocellular carcinoma (HCC) is a heterogeneous aggressive malignancy with low efficacy of current therapies at advanced stages. We integrated molecular and pharmacological profiling of a large panel of liver cancer cell lines (LCCL) to assess their clinical relevance as HCC preclinical models and identify new effective therapies and biomarkers of response. Here, we performed multi-omic analysis including whole-exome, RNA and microRNA sequencing in a series 34 LCCL. Molecular profiles of LCCL and primary HCC were compared and we searched for molecular features associated with drug response. Our panel of LCCL faithfully recapitulated the most aggressive molecular “proliferation class” of HCC.

  Dataset EGAD00001004938

• Whole-genome-sequencing, Whole-exome-sequencing and RNA-sequencing in Sporadic ALS

  To identify the genetic cause and modifiers of Sporadic ALS, we performed Whole-genome-sequencing, Whole-exome-sequencing and RNA-sequencing of Sporadic ALS patients.

  Study JGAS000393

• RNA sequencing of 12 NSCLC samples from 4 patients at MDACC

  Multi-region RNA sequencing of 12 NSCLC samples from 4 patients at MDACC

  Study EGAS00001005951

• Five Mantle Cell Lymphoma Patients Before and After Failure of Standard Chemotherapy

  MCL cells were isolated from different tissues of five patients with MCL at diagnosis, and at the time of the first clinical relapse after failure of standard immunochemotherapy based on alternation of R(ituximab)-CHOP and R-high-dose-araC. All patients signed informed consent according to the Declaration of Helsinki. Four patients were diagnosed with classical nodal aggressive MCL, and all these patients experienced early relapse (i.e., progression of disease POD < 12 months). One patient was diagnosed with indolent non-nodal MCL and relapsed 9 years after initial MCL diagnosis. The dataset presents single cell RNA sequencing data as well as whole exome sequencing data for these five patients.

  Dataset EGAD50000001213

• Cell line data (RNAseq, ATACseq, ChIPseq)

  Content: 2 GB RTK I cell lines (LN229, ZH487) in two conditions (NT control and shSOX10). RNAseq: single replicates per condition, polyA+ RNA sequencing, SE. ATACseq: biological replicates per condition, SE. ChIPseq (histone H3 modifications, LN229 only): all marks for each condition were pooled and sequenced on two lanes for each pool. ChIPseq (BRD4 and SOX10): SOX10 libraries were sequenced on single lanes. BRD4 samples were multiplexed and sequenced in two lanes. ChIPseq input samples are also included. Data type and technology: RNAseq: SE 50bp sequenced on HiSeq2000/4000. ATACseq: SE 50bp sequenced on HiSeq2000/4000. ChIPseq: SE 50bp sequenced on HiSeq2000/4000.

  Dataset EGAD00001005493

• UCSF-CASCADE analysis of metastatic prostate cancer

  Targeted cancer therapy inevitably selects for tumor cells that harbor some form of therapy resistance. This phenomenon is a principle reason why advanced prostate cancer, which can be treated with agents targeting androgen signaling dependency and DNA repair failure, is a lethal condition. The influence of a patient's clinical history and disease evolution on how their disseminated tumors develop resistance has been difficulty to study, because few autopsy studies have been performed in heavily treated patients with DNA-repair deficient metastatic castration-resistant prostate cancer (mCRPC). Here, we assessed how resistance to targeted cancer therapies evolved in an autopsy cohort of 54 mCRPC tumors from six men. This dataset includes targeted sequencing, exome sequencing, and RNA-seq conducted on these biopsies.

  Dataset EGAD00001010275