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• Tracing the origins of relapse in AML to stem cells

  In acute myeloid leukemia (AML), most patients relapse despite achieving remission. While relapse-specific mutations are sometimes detected at low frequency within bulk diagnosis samples, the cell type giving rise to relapse is unknown. Through combined genetic and functional analysis of purified subpopulations and xenografts from paired diagnosis/relapse samples, we identified two major patterns of relapse arising from therapy-resistant cells already present at diagnosis. In some cases relapse originated from rare leukemia stem cells with a primitive immunophenotype, while in other instances relapse developed from larger subclones of immunophenotypically-committed cells that retained strong stemness transcriptional signatures. The identification of distinct patterns of relapse should lead to improved methods for disease management and monitoring in AML. Moreover, the shared functional and transcriptional stemness properties that underlie both cellular origins of relapse underscore the importance of developing new therapeutic approaches that target stemness to prevent relapse.

  Study EGAS00001002225

• scRNAseq of colonic organoids derived from biopsies taken from healthy human individuals treated with IL22

  scRNAseq dataset of colonic organoids derived from epithelium from biopsies taken from three healthy human individuals. The organoids have either been grown in standard conditions (control) or treated with IL22 (treated). Includes 6 samples in total, one control from each individual (ctrl1, ctrl2, ctrl3) and one treated from each (treat1, treat2, treat3). The samples have been multiplexed using the antibody hashing technique. The 6 samples have been pooled into the one organoids sample. In order to analyse the raw files, they have to be demultiplexed first. Information necessary for demultiplexing, as well as which files belong to which sample, can be found in the map_file.csv, attached to each sample. Dataset includes raw Fastq files and processed csv count matrices. Fastq files are divided into HTO (hashtag) and RNA (transcriptome) files. HTO has one index (I1) and two read (R1, R2) files and RNA has two index (I1, I2) and two read (R1, R2) files. The fastq files are for the pooled (organoids) sample and need to be demultiplexed. Count matrices contain comma-separated values with cell barcodes as column names and gene names as row names. Since count matrices have been created after the demultiplexing step, there’s one matrix for each of the 6 individual samples. scRNA-seq data from human colon organoids was analysed in the same manner as for the Colitis dataset, apart from the following changes. Data was generated with the Cell Hashing technique, which uses oligo-tagged antibodies against surface proteins to barcode single cells. This allows for samples to be multiplexed together and run in a single experiment. The data was demultiplexed using the HTODemux() function from Seurat (Hao et al., 2021).

  Dataset EGAD00001010168

• Diagnostic utility of whole genome sequencing in adults with B-other acute lymphoblastic leukemia - RNA

  Genomic profiling at diagnosis of B-cell precursor Acute Lymphoblastic Leukemia (BCP-ALL) in adults is used to guide disease classification, risk stratification and treatment decisions. Patients for which diagnostic screening fails to identify disease defining or risk stratifying lesions are classified as B-other ALL. We screened a cohort of 652 BCP-ALL cases enrolled in UKALL14 to identify and perform whole genome sequencing (WGS) on paired tumor-normal samples. For 52 B-other patients we compared WGS findings to data from clinical and research cytogenetics. WGS identifies a cancer associated event in 51/52 cases, this includes an established subtype defining genetic alteration in 5/52 that were previously missed by standard-of-care genetics. Of the 47 true B-other ALL we identified a recurrent driver in 87% (41).  Complex karyotype by cytogenetics emerges as a heterogeneous group, underlied by distinct genetic alterations associated with either favorable (DUX4-r) or poor outcomes (MEF2D-r, IGK::BCL2). For a subset of 31 cases, we integrate findings from RNA-sequencing (RNA-seq) analysis to include fusion gene detection, and classification by gene expression. Compared to RNA-seq, WGS was sufficient to detect and resolve recurrent genetic subtypes, however RNA-seq can provide orthogonal validation of findings. In conclusion, we demonstrate that WGS can identify clinically relevant genetic abnormalities missed by standard-of-care testing and identify leukemia driver events in virtually all cases of B-other ALL.

  Dataset EGAD00001009305

• Open Targets 020 Epigenomes of Cell Lines (2018-10-23)

  There is currently a drive to establish cell based assay systems of greater human biological and disease relevance through the use of well characterised transformed cell lines, primary cells and complex cellular models (e.g. co-culture, 3D models). However, although the field is gaining valuable experience in running more non-standard & complex cell assays for target validation and compound pharmacology studies, there is the lack of a systematic approach to determine if this expansion in cell assay models is reflected in increased human biological and disease relevance. The increasing wealth of publically available transcriptomic, and epigenome (ENCODE and Epigenome Roadmap) data represents an ideal reference mechanism for determining the relationship between cell types used for target & compound studies to primary human cells and tissues from both healthy volunteers & patients. The CTTV020 epigenomes of cell line project aims to generate epigenetic and transcriptomic profiles of cell lines and compare these with existing and newly generated reference data sets from human tissue and cell types. The aim is to identify assay systems which will provide greater confidence in translating target biology and compound pharmacology to patients. Multiple cell types commonly used within research have been grouped according to biology. Examples include erythroid, lung epithelial, hepatocyte cell types and immortalised models of monocyte / macrophage biology. 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/ . This dataset contains all the data available for this study on 2018-10-23.

  Dataset EGAD00001004413

• Open Targets 020 Epigenomes of Cell Lines (2018-10-23)

  There is currently a drive to establish cell based assay systems of greater human biological and disease relevance through the use of well characterised transformed cell lines, primary cells and complex cellular models (e.g. co-culture, 3D models). However, although the field is gaining valuable experience in running more non-standard & complex cell assays for target validation and compound pharmacology studies, there is the lack of a systematic approach to determine if this expansion in cell assay models is reflected in increased human biological and disease relevance. The increasing wealth of publically available transcriptomic, and epigenome (ENCODE and Epigenome Roadmap) data represents an ideal reference mechanism for determining the relationship between cell types used for target & compound studies to primary human cells and tissues from both healthy volunteers & patients. The CTTV020 epigenomes of cell line project aims to generate epigenetic and transcriptomic profiles of cell lines and compare these with existing and newly generated reference data sets from human tissue and cell types. The aim is to identify assay systems which will provide greater confidence in translating target biology and compound pharmacology to patients. Multiple cell types commonly used within research have been grouped according to biology. Examples include erythroid, lung epithelial, hepatocyte cell types and immortalised models of monocyte / macrophage biology. 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/ . This dataset contains all the data available for this study on 2018-10-23.

  Dataset EGAD00001004416

• Distinct evolutionary trajectories of primary high grade serous ovarian cancers revealed through spatial mutational profiling

  High-grade serous ovarian cancer (HGSC) is characterized by poor outcome, often attributed to the emergence of treatment-resistant subclones. We sought to measure the degree of genomic diversity within primary, untreated HGSCs to examine the natural state of tumour evolution prior to therapy. We performed exome sequencing, copy number analysis, targeted amplicon deep sequencing and gene expression profiling on 31 spatially and temporally separated HGSC tumour specimens (six patients), including ovarian masses, distant metastases and fallopian tube lesions. We found widespread intratumoural variation in mutation, copy number and gene expression profiles, with key driver alterations in genes present in only a subset of samples (eg PIK3CA, CTNNB1, NF1). On average, only 51.5% of mutations were present in every sample of a given case (range 10.2 to 91.4%), with TP53 as the only somatic mutation consistently present in all samples. Complex segmental aneuploidies, such as whole-genome doubling, were present in a subset of samples from the same individual, with divergent copy number changes segregating independently of point mutation acquisition. Reconstruction of evolutionary histories showed one patient with mixed HGSC and endometrioid histology, with common aetiologic origin in the fallopian tube and subsequent selection of different driver mutations in the histologically distinct samples. In this patient, we observed mixed cell populations in the early fallopian tube lesion, indicating that diversity arises at early stages of tumourigenesis. Our results revealed that HGSCs exhibit highly individual evolutionary trajectories and diverse genomic tapestries prior to therapy, exposing an essential biological characteristic to inform future design of personalized therapeutic solutions and investigation of drug-resistance mechanisms

  Dataset EGAD00001000669

• Distinct evolutionary trajectories of primary high grade serous ovarian cancers revealed through spatial mutational profiling

  High-grade serous ovarian cancer (HGSC) is characterized by poor outcome, often attributed to emergence of treatment-resistant sub-clones. We sought to measure the degree of genomic diversity within primary, untreated HGSC to examine the natural state of tumor evolution prior to therapy. We performed exome sequencing, copy number analysis, targeted amplicon deep sequencing and gene expression profiling on thirty-one spatially and temporally separated HGSC tumor specimens (six patients) including ovarian masses, distant metastases, and fallopian tube lesions. We found widespread intra-tumoral variation in mutation, copy number, and gene expression profiles, with key driver alterations in genes present in only a subset of samples (e.g. PIK3CA, CTNNB1, NF1). On average, only 51.5% of mutations were present in every sample of a given case (range: 10.2% to 91.4%), with TP53 as the only somatic mutation consistently present in all samples. Complex segmental aneuploidies, such as whole genome doubling, were present in a subset of samples from the same individual, with divergent copy number changes segregating independently of point mutation acquisition. Reconstruction of evolutionary histories showed one patient with mixed HGSC and endometrioid histology with common etiologic origin in the fallopian tube and subsequent selection of different driver mutations in the histologically distinct samples. In this patient, we observed mixed cell populations in the early fallopian tube lesion, indicating diversity arises at early stages of tumorigenesis. Our results reveal that HGSC exhibit highly individual evolutionary trajectories and diverse genomic tapestries prior to therapy, exposing an essential biological characteristic to inform future design of personalized therapeutic solutions and investigation of drug resistance mechanisms.

  Dataset EGAD00001000974

• Open Targets 020 Epigenomes of Cell Lines (2018-10-23)

  There is currently a drive to establish cell based assay systems of greater human biological and disease relevance through the use of well characterised transformed cell lines, primary cells and complex cellular models (e.g. co-culture, 3D models). However, although the field is gaining valuable experience in running more non-standard & complex cell assays for target validation and compound pharmacology studies, there is the lack of a systematic approach to determine if this expansion in cell assay models is reflected in increased human biological and disease relevance. The increasing wealth of publically available transcriptomic, and epigenome (ENCODE and Epigenome Roadmap) data represents an ideal reference mechanism for determining the relationship between cell types used for target & compound studies to primary human cells and tissues from both healthy volunteers & patients. The CTTV020 epigenomes of cell line project aims to generate epigenetic and transcriptomic profiles of cell lines and compare these with existing and newly generated reference data sets from human tissue and cell types. The aim is to identify assay systems which will provide greater confidence in translating target biology and compound pharmacology to patients. Multiple cell types commonly used within research have been grouped according to biology. Examples include erythroid, lung epithelial, hepatocyte cell types and immortalised models of monocyte / macrophage biology. 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/ . This dataset contains all the data available for this study on 2018-10-23.

  Dataset EGAD00001004414

• Beacon v2

  Beacon v2: a tool for data discovery Motivation In the era of data-driven health research and personalised medicine, human genomic data has become extremely valuable. These are also identifiable data, as they carry information pointing to a specific individual as well as their own family; and as such, they must be protected. This makes data discovery particularly challenging: this is where ?Beacon? comes in. A ?Beacon? is an API aiming to enable the search of genomic variants and associated information without jeopardising the privacy of the dataset. Here, we refer to its current version, namely version 2 (v2). Definition Beacon v2 is a term that can refer to different aspects. The EGA is playing a central role in the following aspects: The Beacon v2 protocol is a Global Alliance for Health and Genomics standard. The Beacon v2 Reference Implementation (B2RI) is an ?out-of-the-box? Beacon instance developed with ELIXIR, which facilitates Beacon deployment. The EGA Beacon(s) are Beacons following the v2 standard and using the B2RI, deployed on top of data hosted at the EGA and allowing for their discovery. Resources Depending on whether you are visiting us a stakeholder (you need more general information about Beacon), a deployer /implementer (you want to have your own Beacon instance), or an EGA user (you want to query Beacon and start browsing data), you will be interested in the following resources: Your role Beacon aspect Documentation type Stakeholder Beacon v2 protocol Beacon website Beacon page on the GA4GH website Deployer/Implementer Beacon v2 protocol Read the docs: Beacon v2 standard technical description GitHub repository Beacon v2 standard Beacon v2 Reference Implementation Read the docs: B2RI technical description GitHub repository B2RI Guide to deploy Beacon using B2RI EGA user EGA Beacon EGA AF Browser

  Documentation about/projects-and-funders/beacon

• Evaluation of Ancestry Admixture among Chileans

  The purpose of this study is to investigate ancestry admixture among Chileans with and without gallbladder cancer.

  Study phs001385

• RNA-seq of murine osteosarcoma cell line genetically modified for CYR61

  Transcriptomic analysis of K7M2 murine osteosarcoma cells stably modified to overexpress or repress CYR61

  Study EGAS50000000526

• GEOCODE data access policy

  The GEOCODE Data Access Policy ensures the responsible use, security, and compliance of GEOCODE data. Access to GEOCODE data is restricted to authorized users based on role-based permissions, ensuring compliance with data protection regulations.

  Dac EGAC50000000574

• Primary Neuroblastoma Circle-seq

  We generated Circle-seq data for primary neuroblastoma to describe the landscape of extrachromosomal circular DNA in neuroblastoma.

  Study EGAS00001004797

• PREDICT

  PREDICT/Predicting individual response and resistance to VEGFR/mTOR pathway therapeutic intervention using biomarkers discovered through tumour functional genomics.

  Study EGAS00001000094

• Neuroblastoma tumors by bulk RNAseq, from Berlanga et al, 2022

  To prouve the relevance of the use of PDX models derived from patient's tumors.

  Study EGAS00001007161

• SEARCH FOR BACTERIA IN NEURAL TISSUE FROM AMYOTROPHIC LATERAL SCLEROSIS

  The study aims to find bacteria in neural tissue from patients with amyotrophic lateral sclerosis

  Study EGAS00001003295

• The_identification_of_genetic_vulnerabilities_in_head_and_neck_cancers_for_the_development_of_novel_therapies

  CRISPR/Cas9 lethality screens in a set of Asian head and neck cancer cell lines to identify novel targets.

  Study EGAS00001002204

• ImmunoAgeing_Colonies_WGS

  Aim to determine age at acquisition of clonal haematopoiesis mutations, clonal architecture, and dynamics of clonal expansions.

  Study EGAS00001004280

• G3BP2-KIT drives leukemia amenable to kinase inhibition in Ph-like ALL

  G3BP2-KIT drives leukemia amenable to kinase inhibition in Ph-like ALL: RNAseq for human sample

  Dataset EGAD00001007564

• WES for Patient 9 to 14 of NIBIT-M4 clinical trial

  WES for Patient 9 to 14 of NIBIT-M4 clinical trial

  Dataset EGAD00001009700

• Exome sequencing of Bilateral Anophthalmia cases- Pilot Study

  This pilot study aims to generate pilot data to inform future study designs by resequencing the whole exomes of 10 unrelated individuals diagnosed with Bilateral Anophthalmia.

  Dataset EGAD00001000348

• Neoadjuvant Breast Cancer Validations

  This study aims to re-sequence findings from whole genome studies using a bespoke pulldown method to validate mutations in those genomes sequenced.

  Dataset EGAD00001000663

• T2D-GENES Consortium: San Antonio Mexican American Family Studies (SAMAFS)

  The Type 2 Diabetes (T2D) Genetic Exploration by Next-generation sequencing in Ethnic Samples (T2D-GENES) Consortium is a collaborative international effort to identify genes influencing susceptibility to type 2 diabetes in multiple ethnic groups using next generation sequencing. To fulfill this objective, T2D-GENES Consortium undertook two large sequencing studies, called T2D-GENES Projects 1 and 2. Project 1 has carried out whole exome sequencing of 12,940 individuals, 6,504 with T2D and 6,436 non-diabetic controls, equally divided among five continental ancestry groups: Europeans, East Asians, South Asians, Hispanic Americans, and African Americans. The goal of Project 1 is to identify all genetic variants in the complete coding regions of the genomes (i.e., whole exome) by sequencing, including rare variants. Project 2 (i.e., SAMAFS substudy 2) is a pedigree-based study designed to identify low frequency or rare variants influencing susceptibility to T2D, using whole genome sequence information from approximately 600 individuals in 20 Mexican American T2D-enriched pedigrees from San Antonio, Texas, augmented with family-based imputation into approximately 440 additional family members. The major objectives of Project 2 are to identify low frequency or rare variants in and around known common variant signals for T2D, as well as to find novel low frequency or rare variants influencing susceptibility to T2D. Both T2D-GENES Projects 1 and 2 involve the San Antonio Mexican American Family Studies (SAMAFS), which are composed of four San Antonio, Texas-based family studies: the San Antonio Family Heart Study (SAFHS), San Antonio Family Diabetes/Gallbladder Study (SAFDGS), Veterans Administration Genetic Epidemiology Study (VAGES), and Family Investigation of Nephropathy and Diabetes - San Antonio (FIND-SA) Component and its extension called the Extended FIND [E-FIND]. The SAFHS and SAFDGS began in 1991 and have followed participants with extensive clinical phenotyping related to T2D for over 20 years. The VAGES was initiated in 1994 and a large battery of T2D-related phenotypic data has been obtained from its participants. The FIND-SA began in 2000, a part of the multicenter FIND study which was designed to identify genetic determinants of diabetic kidney disease; data from its participants related to T2D were used for this project. Non-overlapping subsets of SAMAFS participants are part of the T2D-GENES Projects 1 and 2, henceforth referred to as SAMAFS substudies 1 and 2, respectively. The SAMAFS substudies 1 and 2 are part of one of the five awards funded by NIDDK under a cooperative agreement award mechanism, which is governed by the Steering Committee of the T2D-GENES Consortium. Since Project 1 relies on population based subsets of cases and controls, 491 unrelated participants are drawn from the four SAMAFS as part of the T2D-GENES Project 1 Mexican American sample (i.e., SAMAFS substudy 1). The whole exome sequencing was performed at the Broad Institute. For Project 2, 1048 individuals are drawn from two SAMAFS (SAFHS and SAFDGS), representing 20 large families for substudy 2. The substudy 2 strategy is to sequence approximately 600 individuals at an average of 50x coverage across the entire genome, then impute genome wide genotypes for about 440 additional family members. The 600 sequenced individuals are specifically chosen for their value in imputing sequence information into other family members. By studying large pedigrees, we expect to find multiple individuals carrying each genetic variant, even if this variant is very rare in the population at large. Thus, a pedigree-based approach provides an excellent opportunity for identifying rare novel variants influencing risk of T2D and quantitative variation in T2D-related phenotypes. The whole genome sequencing has been done commercially by Complete Genomics, Inc. (CGI). The available sample of 1,048 includes 5 sequenced individuals who do not belong to any of the 20 large pedigrees. The final family data of 1,043 individuals includes whole genome sequence data for 607 individuals. After quality control, 590 sequenced individuals provide data for family based imputation using Merlin linkage analysis software into approximately 440 additional family members for whom chip based genotypes are available to indicate which parental haplotype is transmitted. The complete SAMAFS data including phenotype, genotype, sequence, other T2D-related trait data utilized for Projects 1 and 2 are available. These data can readily be viewed by clicking on the substudy title shown below or in the box: "Substudies", located on the right hand side of this parent or top study page phs000847.v1.p1, titled T2D-GENES Consortium: San Antonio Mexican American Family Studies (SAMAFS). phs000849 T2D-GENES Project 1: San Antonio Mexican American Family Studies (SAMAFS), Substudy 1: Whole Exome Sequencing phs000462 T2D-GENES Project 2: San Antonio Mexican American Family Studies (SAMAFS), Substudy 2: Whole Genome Sequencing in Pedigrees

  Study phs000847

• Sleep Heart Health Study (SHHS-BioLINCC)

  Data Access NOTE: Please refer to the “Authorized Access” section below for information about how access to the data from this accession differs from many other dbGaP accessions.Related StudiesParent cohort phenotype data can be accessed through ARIC-BioLINCC, Framingham-BioLINCC, and CHS-BioLINCC. Objectives To determine the cardiovascular and other consequences of sleep-disordered breathing and to test whether sleep-disordered breathing is associated with an increased risk of coronary heart disease, stroke, all-cause mortality and hypertension by examining subjects from well-characterized and established epidemiologic cohorts. Background Obstructive sleep apnea syndrome (OSA) is a potentially debilitating condition characterized by repetitive episodes of apnea while asleep, nocturnal oxygen desaturation, excessive daytime sleepiness, and loud disruptive snoring. Epidemiologic data from middle-aged adults indicate that OSA is common, with prevalence rates of 4% in men and 2% in women. Prior studies implicated OSA as a risk factor for the development of hypertension, ischemic heart disease, congestive heart failure, stroke and consequently premature death. Questions arose as to whether an increased propensity for cardiovascular and cerebrovascular diseases was limited to only those with frank OSA or whether more subtle forms of sleep-disordered breathing (SDB) would also confer elevated risk. Further evidence was also needed to clarify whether, SDB, including OSA, is an independent risk factor for the development of cardiovascular or cerebrovascular disease. Known cardiovascular and cerebrovascular disease risk factors such as obesity and smoking are commonly present in those with SDB; therefore, apparent associations between SDB and cardiovascular and cerebrovascular diseases may have resulted from the effects of these concomitant risk factors. Moreover, there was no understanding as to whether such factors as race, age, gender, and prevalent cardiovascular or cerebrovascular disease might interact with SDB to alter future cardiovascular and cerebrovascular disease risk. Mechanisms underlying any propensity to develop cardiovascular or cerebrovascular disease with SDB had not been firmly established (Quan, et al., 1997, PMID: 9493915). Participants Participants in SHHS were recruited from nine existing NHLBI epidemiological studies in which data on cardiovascular risk factors had been collected previously. The “parent” cohorts included: Two sites of the Atherosclerosis Risk in Communities Study (ARIC) Three sites of the Cardiovascular Health Study (CHS) The Framingham Offspring Cohort The Strong Heart Study (SHS) sites in South Dakota, Oklahoma, and Arizona The New York Hypertension Cohorts The Tucson Epidemiologic Study of Airways Obstructive Diseases and the Health and Environment Study From these parent cohorts, a sample of participants who met the inclusion criteria (age 40 years or older; no history of treatment of sleep apnea; no tracheostomy; no current home oxygen therapy) was invited to participate in the baseline examination of the SHHS, which included an initial polysomnogram (SHHS-1). Several cohorts over-sampled snorers in order to increase the study-wide prevalence of sleep-disordered breathing. In all, 6441 individuals were enrolled between November 1, 1995 and January 31, 1998. During exam cycle 3 (January 2001-June 2003), a second polysomnogram (SHHS-2) was obtained in 3295 of the participants. Due to sovereignty issues, Strong Heart Study participants are not included in the shared SHHS data. Data from a total of 5839 participants (1920 ARIC, 1249 CHS, 997 Framingham Offspring and OMNI 1, and 1673 from other studies), consenting to share data are available. Design The Sleep Heart Health Study added in-home polysomnography to the data collected in each of the parent studies at a baseline SHHS exam and a follow-up approximately 4 years later. Using the Compumedics PS polysomnograph, sleep studies were obtained in an unattended setting, usually in the homes of the participants, by trained and certified technicians. The recording montage consisted of: C3/A2 and C4/A1 EEGs, sampled at 125 Hz right and left electrooculograms (EOGs), sampled at 50 Hz a bipolar submental electromyogram (EMG), sampled at 125 Hz thoracic and abdominal excursions (THOR and ABDO), recorded by inductive plethysmography bands and sampled at 10 Hz "airflow" detected by a nasal-oral thermocouple (Protec, Woodinville, WA), sampled at 10 Hz finger-tip pulse oximetry (Nonin, Minneapolis, MN) sampled at 1 Hz ECG from a bipolar lead, sampled at 125 Hz for most SHHS-1 studies and 250 Hz for SHHS-2 studies Heart rate (PR) derived from the ECG and sampled at 1 Hz body position (using a mercury gauge sensor) ambient light (on/off, by a light sensor secured to the recording garment)This montage provides data on the occurrence of sleep-disordered breathing, sleep stages, heart rate, oximetry and on arousals. Each participant in the parent studies was also asked to complete the Sleep Habits Questionnaire which covers usual sleep pattern, snoring, and sleepiness.

  Study phs003637

• Functional Enhancer Elements Drive Subclass-Selective Expression From Mouse to Human Neocortex

  A complete epigenomic map of human brain cell types will be necessary to fully understand their genetic regulation, and to create the next generation of non-species-restricted genetic tools for their functional characterization. Here we performed both bulk and single-nucleus ATAC-seq studies of fresh human neocortical samples excised during neurosurgery, in order to discover the functional genomic elements that discriminate cell types. This dataset contains 78 bulk layer-dissected ATAC-seq samples, and 3660 single-nucleus ATAC-seq samples from fresh acutely isolated human neurosurgical samples. Furthermore, from these data we identified and validated several enhancers for use in AAV context. We also provide validation single cell RNA-seq for 456 human acutely labeled neurosurgical neurons labeled by 3 unique enhancer-AAV vectors, and single cell RNA-seq for 1483 mouse neurons labeled in vivo by 11 unique enhancer-AAV vectors. Together these datasets provide high-resolution maps of single human neocortical cell types, as well as proof of the functional utility of the genomic elements discovered. As a whole these data will be important to furnish the next generation of cell type-specific functional tools and therapies in neuroscience.  

  Study phs002292