12405 results for ""non-traditional+communal+storytelling+approach"+novel+segment+noted+in+critical+commentary"

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• Genomic Landscape of Apical Periodontitis

  We undertook the first genome-wide association (GWAS) study of apical periodontitis, enrolling 932 patients with deep carious lesions at UTHealth School of Dentistry and the University of Pittsburgh School of Dental Medicine. We used a case-control design to compare those with apical periodontitis to those without. Genotype data was generated for all participants and imputed using the TOPMed reference panel. We performed a single-variant association testing, a subphenotype analysis, and a genetically regulated expression association analysis, and generated a polygenic risk score and performed phenome-wide association analysis. We identified eight near-genome-wide significant loci for our primary outcome, eight genes where genetically regulated expression was associated with apical periodontitis status, and 33 phecodes that were associated with the generated polygenic risk score. We also replicated three loci previously reported in the literature. Genotype, phenotype, and results from the primary association analysis will be available via dbGaP.

  Study phs003252

• 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

• A tumor-associated photoreceptor signature unifies distinct central nervous system malignancies

  Pineoblastoma is a clinically aggressive childhood brain tumor composed of distinct molecular subgroups with divergent driver genes, demographics, and clinical outcomes. To identify developmental origins and mechanisms governing disease pathogenesis, we derive single-cell transcriptomes from pineal parenchymal tumors, aligning malignant cells with our atlas of pineal gland development to retrace cellular origins. Integrative computational analyses maps pineoblastoma origins to transient cycling pinealocyte progenitors during development. Lineage-specific perturbation of suspected drivers in the early pineal gland yields preclinical models representative of consensus molecular subgroups. Multi-omic characterization of patient tumors and these models uncovers a tumor-associated photoreceptor signature common to pineoblastoma, retinoblastoma, and Group 3 medulloblastoma. Transcriptional activity of this signature within respective cellular origins establishes a developmental basis for molecular similarities between entities. Photoreceptor signature constituents are selective dependencies across these anatomically distinct central nervous system malignancies, motivating future studies evaluating developmentally encoded programs of malignancy as potential therapeutic liabilities.

  Study EGAS50000001457

• STimage: Robust and interpretable prediction of gene markers and cell types from spatial transcriptomics data

  Spatial transcriptomics (ST) links tissue morphology with gene expression values, opening new avenues for digital pathology. Deep learning models are used to predict gene expression or classify cell types directly from images, offering significant clinical potential but still requiring improvements in interpretability and robustness. We present STimage as a comprehensive suite of models to predict spatial gene expression and classify cell types directly from standard H\&E images. STimage enhances robustness by estimating gene expression distributions and quantifying both data-driven (aleatoric) and model-based (epistemic) uncertainty using ensemble approach with foundation models. Interpretability is achieved through attribution analysis at single-cell resolution integrated with histopathological annotations, functional genes, and latent representations. We validated STimage across diverse datasets, demonstrating its performance across various platforms. STimage-predicted gene expression can stratify patient survival and predict drug response. By enabling molecular and cellular prediction from routine histology, STimage offers a powerful tool to advance digital pathology.

  Study EGAS50000001503

• Quick Guide for data submission

  Quick Guide This is a quick guide to submit data to the EGA. Please select data type to display the right detailed instructions. Sequence data Array-based data Get your submission account You first need to create your EGA user. Then, once your account has been verified by the Help Desk team, request your submission account and provide details of the data type and platform(s) in your submission. Register Study/DAC Use Submitter Portal to register your study, samples, Data Access Committee (DAC) and Policy. Upload data Encrypt your data files using the and upload it to your inbox using SFTP. Register experiments and runs Associate each data file to a registered sample and study by Linking files to samples. Details of the experimental procedure you followed must be provided. Finalise your submission Group your runs/analysis into datasets and link them to your new or existing DAC and policy using DAC Portal. Data request are done at a dataset level, thus files within a datasets must share release conditions. Release and admin When finalising the submission, set a released date to instruct our Helpdesk to release your study. All registered studies are automatically placed on hold until the named submission or DAC contact instructs our Helpdesk for the study to be released. Get your submission account Fill the submission form and provide details of the data type and platform(s) in your submission. Register Study/DAC Use EGA Programmatic submission to register your study, samples, Data Access Committee (DAC) and Policy. Upload data Encrypt your data files using the EgaCryptor and upload it using default FTP clients or Aspera. Complete the Array-Format (AF) spreadsheet Download the AF spreadsheet and complete all four sections. Return the spreadsheet to the EGA helpdesk. Release and admin Instruct our Helpdesk to release your study. All registered studies are automatically placed on hold until the named submission or DAC contact instructs our Helpdesk for the study to be released.

  Documentation submission/quickguide

• Uterine_Atlas_Endometriosis

  Uterine Atlas Endometriosis Endometriosis is a chronic inflammatory disease driven by oestrogen, affecting around 10% of women of child-bearing again world-wide. It is described by the presence of endometrium-like tissue outside of the uterus (ectopic endometriosis tissue) and debilitating chronic pain. There is a lack of understanding of endometriosis pathogenesis, as well as cellular composition of both the eutopic endometrium and ectopic endometriosis tissue of endometriosis patients.There are therefore two main aims for this study. Using single cell genomics and spatial methods we aim to: I) characterise endometrial cellular census in women with and without endometriosis II) characterise cellular census of ectopic endometriosis tissue Furthermore, we aim to establish endometrial organoids and endometriosis organoids from the samples collected and characterise these using single cell genomics and spatial methods. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001004725

• Whole_genome_sequencing_in_a_multiplex_Crohn_s_disease_family

  In 2009 we identified a four-generation family with over 700 members and 41 affected with Crohn's disease (CD). At the time we sequenced the exome of 6 affected individuals but did not identify any coding variants which appear to explain the high prevalence of disease. Since then we have collected DNA from a large number of additional family members, genotyped linkage arrays on the entire family to refine genomic regions shared by identity by descent and genotyped affected and unaffected members at known CD risk loci identified by Genome Wide Association Studies (GWAS). These analyses have confirmed that a significant unexplained excess of disease remains after accounting for all known genetic factors, and that several regions of the genome are shared by a large fraction of affected individuals. We therefore perform whole genomes sequencing from 8 individuals which will allow us to impute the complete sequence of nearly all the members of the two largest and most severely affected branches of the family.

  Study EGAS00001000060

• Searching for variants associated with endometriosis

  We explored regulatory mechanism of a SNP on chromosome 9p21 associated with endometriosis by leveraging “allele-specific” functional genomic approaches. By re-sequencing 1.29 Mb of 9p21 region and scrutinizing DNase-seq data from the ENCODE project, we prioritized rs17761446 as a candidate functional variant that was in perfect linkage disequilibrium with the original GWAS SNP (rs10965235) and located on DNase I hypersensitive site. Chromosome conformation capture followed by high-throughput sequencing revealed that the protective G allele of rs17761446 exerted stronger chromatin interaction with ANRIL promoter. We demonstrated that the protective allele exhibited preferential binding affinities to TCF7L2 and EP300 by bioinformatics and chromatin immunoprecipitation (ChIP) analyses. ChIP assays for histone H3 lysine 27 acetylation and RNA polymerase II reinforced the enhancer activity of the SNP site. The allele specific expression analysis for eutopic endometrial tissues and endometrial carcinoma cell lines showed that rs17761446 was a cis-regulatory variant where G allele was associated with increased ANRIL expression.

  Study EGAS00001001741

• Single cell RNA sequencing and Whole Genome Sequencing on different cells from the same sample for a triple negative patient derived xenograft and ovarian cancer cell lines.

  Measuring gene expression of genomically defined tumour clones at single cell resolution would associate functional consequences to somatic alterations, as a prelude to elucidating pathways driving cell population growth, resistance and relapse. In the absence of scalable methods to simultaneously assay DNA and RNA from the same single cell, independent sampling of cell populations for parallel measurement of single cell DNA and single cell RNA must be computationally mapped for genome-transcriptome association. Here we present clonealign, a robust statistical framework to assign gene expression states to cancer clones using single-cell RNA-seq and DNA-seq independently sampled from an heterogeneous cancer cell population. We apply clonealign to triple-negative breast cancer patient derived xenografts and high-grade serous ovarian cancer cell lines and discover clone-specific dysregulated biological pathways not visible using either DNA-Seq or RNA-Seq alone.

  Study EGAS00001003387

• Tracking the genomic evolution of esophageal adenocarcinoma through neoadjuvant chemotherapy

  Esophageal adenocarcinomas (EACs) are associated with dismal prognosis. Deciphering the evolutionary histories of this disease may shed light on therapeutically tractable targets and reveal changing mutational processes during the disease course and following neoadjuvant chemotherapy (NAC). We exome sequenced 40 tumor regions from 8 patients with operable EACs both before and after platinum-containing NAC. This revealed the evolutionary genomic landscape of EACs with the presence of heterogeneous driver mutations, parallel evolution, early genome doubling events and an association between high intratumor heterogeneity and poor response to NAC. Multi-region sequencing demonstrated a significant reduction in T>G mutations within a CTT context when comparing early and late mutational processes and the presence of a platinum signature with enrichment of C>A mutations within a CpC context following NAC. EACs are characterized by the presence of amplifications containing targetable oncogenes present before and after chemotherapy that may provide a rationale for future therapeutic approaches.

  Study EGAS00001001254