15167 results for "cancer studies using rna-seq"

in 15.43 milliseconds.

• The Dynamic Landscape of Open Chromatin During Human Cortical Neurogenesis

  Non-coding regions comprise most of the human genome and harbor a significant fraction of risk alleles for neuropsychiatric diseases, yet their functions remain poorly defined. We created a high-resolution map of non-coding elements involved in human cortical neurogenesis by contrasting chromatin accessibility and gene expression in the germinal zone and cortical plate of the developing cerebral cortex. To obtain a high resolution depiction of chromatin structure and gene expression in developing human fetal cortex, we dissected the post-conception week (PCW) 15-17 human neocortex into two major anatomical divisions to distinguish between proliferating neural progenitors and post mitotic neurons: (1) GZ: the neural progenitor-enriched region encompassing the ventricular zone (VZ), subventricular zone (SVZ), and intermediate zone (IZ) and (2) CP: the neuron-enriched region containing the subplate (SP), cortical plate (CP), and marginal zone (MZ). Tissues were obtained from three independent donors and three to four technical replicates from each tissue were processed for ATAC-seq to define the landscape of accessible chromatin and RNA-seq for genome-wide gene expression profiling.

  Study phs001438

• Upper cortical layer-driven network impairment in schizophrenia

  Schizophrenia is one of the most wide-spread and complex mental disorders. To characterize the impact of schizophrenia, we performed single nucleus RNA sequencing (snRNA-seq) of >220,000 neurons from the dorsolateral prefrontal cortex of patients with schizophrenia and matched controls. Additionally, >115,000 neurons were analyzed topographically by immunohistochemistry. Compositional analysis of snRNA-seq data revealed a reduction in abundance of GABAergic neurons and a concomitant increase in principal neurons, most pronounced for upper cortical layer subtypes, which was substantiated by histological analysis. Many neuronal subtypes showed extensive transcriptomic changes, the most dramatic - in upper layer GABAergic neurons, including downregulation in energy metabolism and upregulation in neurotransmission. Transcription factor network analysis demonstrated a developmental origin of transcriptomic changes. Finally, Visium spatial transcriptomics further corroborated upper layer neuron vulnerability in schizophrenia. Overall, our results point towards general network impairment within upper cortical layers as a core substrate associated with schizophrenia symptomatology. Study is available on bioRxiv (https://doi.org/10.1101/2020.11.17.386458) and in upcoming publication.

  Study EGAS00001006495

• Genome Diversity in Africa Project: Uganda (2021-02-17)

  Globally, human populations show structured genetic diversity as a result of geographical dispersion, selection and drift. Understanding this genetic variation can provide insights into the evolutionary processes that shape both human adaptation and variation in disease. Populations from SSA have the highest levels of genetic diversity. This characteristic, in addition to historical genetic admixture, can lead to complexities in the design of studies assessing the genetic determinants of disease and human variation. However, such studies of African populations are also likely to provide new opportunities to discover novel disease susceptibility loci and variants and refine gene-disease association signals. A systematic assessment of genetic diversity within SSA would facilitate genomic epidemiological studies in the region. The Genome Diversity in Africa Project (GDAP) aims to produce a comprehensive catalogue of human genetic variation in SSA, including single nucleotide polymorphisms (SNPs), structural variants, and haplotypes. This resource will make a substantial contribution to understanding patterns of genetic diversity within and among populations in SSA, as well as providing a global resource to help design, implement and interpret genomic studies in SSA populations and studies comprising globally diverse populations, complementing existing genomic resources. Specifically, we plan to carry out high depth whole genome sequencing of up to 2000 individuals across Africa (25 individuals from each ethnolinguistic group). Our scientific objectives are to: 1) develop a resource that provides a comprehensive catalogue of genetic variation in populations from SSA accessible to the global scientific community; 2) characterise population genetic diversity, structure, gene flow and admixture across SSA; 3) develop a cost-efficient, next-generation genotype array for diverse populations across SSA; and 4) facilitate whole genome-sequencing association studies of complex traits and diseases by developing a reference panel for imputation and resource for enhancing fine-mapping disease susceptibility loci. These scientific objectives will be supported by cross-cutting operational activities, including network and management of the consortium, research ethics, and research capacity building in statistical genetics and bioinformatics . This dataset contains all the data available for this study on 2021-02-17.

  Dataset EGAD00001006976

• Efficient prediction of a spatial transcriptomics profile better characterizes breast cancer tissue sections without costly experimentation

  Spatial transcriptomics is an emerging technology requiring costly reagents and considerable skills, limiting the identification of transcriptional markers related to histology. Here, we show that predicted spatial gene-expressions in unmeasured regions and tissues can enhance biologists��� histological interpretations. We developed the Deep learning model for Spatial gene Clusters and Expression, DeepSpaCE and confirmed its performance using the spatial-transcriptome profiles and immunohistochemistry images of consecutive human breast cancer tissue sections. For example, the predicted expression patterns of SPARC, an invasion marker, highlighted a small tumor-invasion region that is difficult to identify using raw data of spatial transcriptome alone because of a lack of measurements. We further developed semi-supervised DeepSpaCE using unlabeled histology images and increased the imputation accuracy of consecutive sections, enhancing applicability for a small sample size. Our method enables users to derive hidden histological characters via spatial transcriptome and gene annotations, leading to accelerated biological discoveries without additional experiments.

  Study JGAS000290

• Analysis of IDHwt-glioblastoma samples from paired primary and recurrent tumor samples

  IDHwt-glioblastomas rapidly recur after initial treatment. To study genetic evolution in glioblastoma, we analyzed whole genomes of 21 pairs of primary and recurrent tumor samples using next generation sequencing. In addition we sequenced RNA from 16 tumor pairs and a panel of 50 glioma-associated genes in tumor pairs from 43 patients (including 14 of the 21 patients included in the WGS discovery set). Glioblastoma subtypes were determined using 450k/EPIC methylation arrays. This study was supported within the e:med program of the German Ministry of Education and Research (BMBF) by the collaborative research project ‘SYS-GLIO - Systems-based prediction of the biological and clinical behavior of gliomas’ (https://www.sys-med.de/de/demonstratoren/sys-glio/) This study is part of the Heidelberg Center for Personalized Oncology - HIPO-043

  Study EGAS00001003184

• Genomic Analysis of Nucleic Acid Sequences from Pancreatic Cancer

  Our study aims to perform multi-omics analysis of pancreatic ductal adenocarcinoma. We performed dual single-cell RNA sequencing and whole genome sequencing on fresh tumor specimens and dual bulk RNA-sequencing and 16S amplicon sequencing on frozen tumor specimens to identify human and non-human nucleic acid sequences. Additionally, we identified microbial sequences and found that bacterial taxonomic profiles from the same sample strongly correlated across technologies.

  Study phs003035

• Ductal keratin 15+ luminal progenitors in normal breast exhibit a basal-like breast cancer transcriptomic signature

  Normal breast luminal epithelial progenitors have been implicated as cell of origin in basal-like breast cancer, but their anatomical localization remains understudied. Here, we combine micro-collection of uncultured organoids and single cell mRNA sequencing (scRNA-seq) of FACS-sorted luminal epithelial cells with multicolor imaging to profile ducts and terminal ductal lobular units (TDLUs) and compare them with breast cancer subtypes. Unsupervised clustering reveals eleven distinct clusters and a differentiation trajectory starting with keratin 15+ (K15+) progenitors enriched in ducts. Spatial mapping of luminal progenitors is confirmed at the protein level by staining with critical duct markers. Comparison of the gene expression profiles of normal luminal cells with those of breast cancer subtypes reveals a strong correlation between normal breast K15+ ductal progenitors and basal-like breast cancer. We propose that K15+ basal-like breast cancers originate in ductal progenitors, which emphasizes the importance of not only lineages but also cellular position within the ductal-lobular tree.

  Study EGAS00001005963

• Mapping the breast cancer metastatic cascade onto circulating tumour DNA using genetic and epigenetic clonal tracking

  Dataset EGAD00001005507

• RNASeq files for GenomePaint paper

  RNASeq files for GenomePaint paper titled "Exploration of coding and non-coding variants in cancer using GenomePaint."

  Dataset EGAD00001006680

• Human skin cancer (BCC, SCC, melanoma) and healthy control skin

  Smart-seq2 single cell RNA sequencing of human BCC, SCC, melanoma (ALM) and healthy control skin samples.

  Dataset EGAD50000000540