14977 results for "cancer studies using rna-seq"

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• A unidirectional histone code in bidirectional promoters across cell types

  Recent studies indicate that thousands of genes are expressed from bidirectional promoters (BDPs). Gene regulation at BDPs is poorly understood, in particular how the cell is able to regulate them differently. Here we investigate the effect of histone Modifications in BDP regulation. In this study, we model the gene activity using different gene expression assays, such as RNA-Seq, GRO-cap, and CAGE around the BDP transcription start sites (TSSs) using different histone modifications in various cell types. We develop a new statistical approach that links histone modifications to gene expression at BDPs. It improves over previous methods, because it is able to capture spatial dependencies of histone modifications along a promoter and leads to more interpretable results. We predict a general histone code that is independent of transcript orientation, cell type, and promoter configuration. The histone code at BDPs reveals that promoters are regulated unidirectionally, such that the majority of histone marks associated with gene expression occur downstream of the gene's TSS. By contrasting associations of histone marks with steady-state levels of capped RNAs in CAGE and nascent capped RNAs in GRO-cap, we show which histone marks have a preference for initiating polymerases and actively elongating polymerases. Using single-cell data we show that the bidirectional histone signal of activating marks is often due averaging of a heterogeneous cell population. Our results have implications for other experiments where the relationship between histone modification and gene initiation or gene elongation is investigated on a genome-wide scale.

  Study EGAS00001001656

• COVID-19 Multiomics Atlas

  We provide a single-nuclei RNA-sequencing (snRNA-seq) dataset derived from four COVID-19 patients, generated using the 10x Chromium Next GEM Single Cell v3.1 kit. For our study, these were integrated with snRNA-seq data with 12 publicly available sc/snRNA-seq datasets, comprising organ donor lung samples (n=89) and COVID-19 lung tissue samples (n=51).

  Dataset EGAD00001015602

• Cross-tissue transcriptomic analysis of human secondary lymphoid organ residing ILC3 reveals a default quiescent state in the absence of inflammation

  A cross tissue transcriptional comparison of human ILC3 isolated from non-inflamed lymph nodes (LN) and spleens, inflamed tonsils and the peripheral blood (PB). Using uniform gating and high-purity cell-sorting, ILC3 were sorted as Lineage(CD3,CD19,CD34,CD14,CD94)-CD45intCD127+CRTH2-CD117+. From LN, spleen, tonsils and PB, NKp44- ILC3 were isolated and, in addition, NKp44+ ILC3 were sorted from spleen and tonsils. RNA quality was measured on a RNA 6000 Pico Kit (Agilent Technologies) using a 2100 Bioanalyzer (Agilent Technologies). 100 pg high-quality RNA was isolated and cDNA was generated using the SMART-Seq (v3) Ultra Low Input RNA Kit (Clontech) with 15 cycles of amplification. Amplified and Covaris-fragmented cDNA was quality-checked on a High Sensitivity DNA chip (Agilent Technologies) and further processed according the TruSeq Nano DNA Library Preparation Kit (Illumina). Data was processed as follows: SMARTer adapters were trimmed with cutadapt and the resulting sequences were aligned to the human RefSeq transcriptome using 14 TopHat2 (Kim et al., 2013). Normalization and quantification was performed using Cufflinks (Trapnell et al., 2012). FPKM counts were determined per gene with HTSeq-count.

  Study EGAS00001002636

• RNA-seq of human glioblastoma and matched gliomasphere cell lines (BLN panel)

  This dataset contains all RNA-seq runs for the BLN panel of cell lines and matched parental tumors. Tumor/cell line pairs have been authenticated using SNP profiles and all pairs were confirmed. Please note: The dataset also contains raw data from an early primary culture (BLN-1) where no stable cell line could be generated. Please also note different reference genomes.

  Dataset EGAD00001002893

• Evolution of four ER+ breast cancers

  The evolution of four breast cancers was analyzed using longitudinal samples collected over 2-15 years. Whole-genome sequencing and single-cell RNA-Seq were used to analyze evolution. We have deposited VCF files for SNV, indel, and structural variant calls from WGS data, and a text file showing transcripts per million (TPM) expression for the single-cell RNA-Seq data.

  Dataset EGAD00001003303

• Single-cell RNA-seq of patient-derived organoids across three generations

  Single-cell RNA-seq of first-, second-, and third-generation patient-derived organoids. Obtained using the 10X Genomics single-cell 3' expression solution (v3 chemistry). First- and second-generation PDOs from one patient and first-, second-, and third-generation PDOs from three additional patients.

  Dataset EGAD00001008431

• Using human induced pluripotent stem cells (iPSC) and iPSC-derived neurons to explore cellular phenotypes associated with schizophrenia.

  Schizophrenia and major mental illness are common devastating conditions. Whilst partially effective treatments are available, none are disease-modifying. Notwithstanding important insights provided by animal models there is a need for novel models of mental illness to develop more effective therapies. A study of sporadic schizophrenia-derived human induced pluripotent stem cells (hiPS) has demonstrated a neuronal phenotype representing a powerful proof of concept of in vitro disease modeling, even for diseases with complex causation such as schizophrenia. These techniques have been used here to examine the behaviour of neural stem cells and neurons derived from fibroblasts from subjects carrying NDE1 copy number variations (CNVs) associated with increased risk of major mental illness. Using hiPS from individuals with and without disease-associated CNVs we have investigated how disease risk is conferred at a cellular and molecular level through detailed comparative studies of neural progenitor proliferation, neuronal morphology, physiology, cytoskeletal organization, synaptic function, glutamate (NMDA) receptor expression and also RNA sequencing and proteomic studies of these patient-derived cells.

  Study EGAS00001002895

• HDAC inhibitors in synovial sarcoma cells

  Histone deacetylase (HDAC) inhibition has been shown in previous studies to disrupt the synovial sarcoma oncoprotein complex, resulting in apoptosis. To understand the molecular effects of HDAC inhibition, RNA-Seq transcriptome analysis was undertaken in six human synovial sarcoma cell lines. HDAC inhibition induced pathways of cell cycle arrest, neuronal differentiation and response to oxygen-containing species, effects also observed in other cancers treated with this class of drugs. More specific to synovial sarcoma, polycomb-group targets were reactivated including tumor suppressor CDKN2A, and pro-apoptotic transcriptional patterns were induced. Functional analyses revealed that ROS-mediated FOXO activation and pro-apoptotic factors BIK, BIM and BMF were important to apoptosis induction following HDAC-inhibition in synovial sarcoma

  Study EGAS00001002637

• CALGB/SWOG 80405: Genome-Wide Association Study of Patients with Advanced or Metastatic Colorectal Cancer Treated with First-Line Chemotherapy Combined with Cetuximab and/or Bevacizumab

  CALGB/SWOG 80405 (NCT00265850) was a randomized phase III study of bevacizumab, cetuximab, or the combination of these two monoclonal antibodies, in addition to systemic chemotherapy for patients with untreated metastatic adenocarcinoma of the colon or rectum. We prospectively collected germline DNA and conducted genome-wide association studies using clinical outcomes and molecular features.

  Study phs003428

• RNA sequencing in primary inflammatory (TPP) macrophages following deletion of a disease-associated gene desert at chr21q22, disruption of ETS2, or treatment of ETS2-edited macrophages with a HIF1α stabiliser.

  This dataset comprises raw RNA sequencing data (FASTQs) from primary inflammatory (TPP) macrophages that were unedited (non-targeting control, NTC), edited to delete the disease-associated chr21q22 enhancer region (n=5), or edited to disrupt ETS2 with 1 of 2 independent gRNAs (n=9). We also performed RNA-sequencing in NTC or ETS2-edited TPP macrophages that were treated for 12 hours with vehicle (DMSO) or roxadustat (30 uM, n=3). Macrophages were detached using Accutase and lysed. RNA was extracted from cell lysates using an AllPrep DNA/RNA Mini Kit (Qiagen). Sequencing libraries were prepared from 10ng RNA using the SMARTer Stranded Total RNA-Seq Kit v2 - Pico Input Mammalian (Takara) following the manufacturer’s instructions. The quality and molarity of all libraries was assessed using a BioAnalyzer 2100 and the libraries were sequenced on a NextSeq500. Raw data are provided as 50 bp paired-end Illumina reads.

  Dataset EGAD00001011338

• Spatial Transcriptomics Uncovers Tumor Microenvironment-Based Subtypes in Invasive Lobular Carcinoma

  Invasive lobular carcinoma (ILC) is the second most common histological subtype of breast cancer (BC); however, little is known about its tumor microenvironment (TME). Here, we investigated the spatial architecture of 43 hormone-receptor-positive, HER2-negative (HR+/HER2-) primary ILC samples, using spatial transcriptomics and detailed morphological annotation. By integrating morphology, spatial, and single-cell data, we mapped TME-related cell types, exploring their proximity to tumor cells and their enrichment in specific compartments. Clustering of spatial transcriptomics spots revealed significant inter- and intra-tumor heterogeneity, identifying pathways and cell types linked with disease outcomes. Morphological and gene expression data were finally integrated with a multimodal approach, leading to the identification of 4 new ILC subtypes based on TME heterogeneity. By using derived, specific gene expression signatures, these subtypes were identified and validated in external bulk RNA-seq/microarray datasets (SCAN-B and METABRIC), where differences in prognosis emerged.

  Study EGAS50000001001

• Prediction and quantification of splice events from RNA-seq data

  Analysis of splice variants from short read RNA-seq data remains a challenging problem. Here we present a novel method for the genome-guided prediction and quantification of splice events from RNA-seq data, which enables the analysis of unannotated and complex splice events. Splice junctions and exons are predicted from reads mapped to a reference genome and are assembled into a genome-wide splice graph. Splice events are identified recursively from the graph and are quantified locally based on reads extending across the start or end of each splice variant. We assess prediction accuracy based on simulated and real RNA-seq data, and illustrate how different read aligners (GSNAP, HISAT2, STAR, TopHat2) affect prediction results. We validate our approach for quantification based on simulated data, and compare local estimates of relative splice variant usage with those from other methods (MISO, Cufflinks) based on simulated and real RNA-seq data. In a proof-of-concept study of splice variants in 16 normal human tissues (Illumina Body Map 2.0) we identify 249 internal exons that belong to known genes but are not related to annotated exons. Using independent RNA samples from 14 matched normal human tissues, we validate 9/9 of these exons by RT-PCR and 216/249 by paired-end RNA-seq (2 x 250 bp). These results indicate that de novo prediction of splice variants remains beneficial even in well-studied systems. An implementation of our method is freely available as an R/Bioconductor package SGSeq.

  Study EGAS00001001026

• Enhancer profiling identifies epigenetic markers of endocrine resistance and reveals therapeutic options for metastatic castration-resistant prostate cancer patients (LuCaP cell line data)

  Androgen Receptor (AR) signaling inhibitors, including enzalutamide, are treatment options for patients with metastatic castration-resistant prostate cancer (mCRPC), but resistance inevitably develops. Using metastatic samples from a prospective phase II clinical trial, we epigenetically profiled enhancer/promoter activities with H3K27ac chromatin immunoprecipitation followed by sequencing, before and after AR-targeted therapy. We identified a distinct subset of H3K27ac-differentially marked regions that associated with treatment responsiveness. These data were successfully validated in mCRPC patient-derived xenograft models (PDX). In silico analyses revealed HDAC3 as a critical factor that can drive resistance to hormonal interventions, which we validated in vitro. Critically, we identified the pan-HDAC inhibitor, vorinostat to be effective in decreasing tumor cell proliferation both in vitro and in vivo. Moreover, we uncovered evidence for HDAC3 working together with Glucocorticoid Receptor (GR) as a potential mechanism for this therapeutic effect. These findings demonstrate rationale for new therapeutic strategies including HDAC inhibitors to improve patient outcome in advanced stages of mCRPC. Project Goal: We identified H3K27ac regions associated with resistance in patient samples. Extensive analysis of these regions suggested that HDAC3 may play a role in resistance. To test this, we analyzed ChIP-seq and RNA-seq from cell lines generated from PDX LuCaP samples (recapitulating resistant and non-resistant) to validate findings in patient samples. ChIP-seq samples were used to verify the signals identified from specific factors, such as H3K27ac, AR, FOXA1, GR and HDAC3, their association with resistance and possible mechanisms of action. RNA-seq samples were analyzed to determine the effect of AR signaling in these samples in specific genes in the H3K27ac resistance-associated regions.

  Study EGAS50000000917

• FASTQ files of the small RNA-Seq dataset from the POPS cohort

  Placental biopsies were collected within 30 minutes of birth and flash frozen in RNAlater(ThermoFisher). For each biopsy,total placental RNA was extracted from approximately 5 mg of tissue using the “mirVana miRNA Isolation Kit” (Ambion) followed by DNase treatment (“DNA-free DNA Removal Kit”, Ambion). RNA quality was assessed with the Agilent Bioanalyzer and all the samples with RIN values ≥ 7.0 were used in the downstream experiments. Total RNA-libraries were prepared from 300-500ng of total placental RNA with the “TruSeq Stranded Total RNA Library Prep Kit with Ribo-Zero Human/Mouse/Rat”(Illumina). Small RNA-libraries were prepared from 150ng of total placental RNA with the “NEBNext Multiplex Small RNA Library Prep Kit for Illumina”(New England Biolabs)and concentrated using the “QIAquick PCR purification kit”(Qiagen). Paired libraries were combined and size selected using the Pippin Prep and 3% Agarose Gel Cassette with marker F (Sage Science),pooled and sequenced (single-end, 50bp) using a Single End V4 cluster kit and HiSeq4000 instrument.

  Dataset EGAD00001004860