Single-cell Multiomics Clonal Hematopoiesis Team Data Access Committee
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ABSTRACT:
PROVIDER: EGAC00001002735 | EGA |
REPOSITORIES: EGA
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Project description:Data Access Committee EGAC00001003380
| EGAC00001003380 | EGA
Project description:Hepatoblastomas (HB) display heterogeneous cellular phenotypes that influence the clinical outcome, but the underlying mechanisms are poorly understood. Here, we use a single-cell multiomic strategy to unravel the molecular determinants of this plasticity. We identify a continuum of HB cell states between hepatocytic (scH), liver progenitor (scLP) and mesenchymal (scM) differentiation poles, with an intermediate scH/LP population bordering scLP and scH areas in spatial transcriptomics. Chromatin accessibility landscapes reveal the gene regulatory networks of each differentiation pole, and the sequence of transcription factor activations underlying cell state transitions. Single-cell mapping of somatic alterations reveals the clonal architecture of each tumor, showing that each genetic subclone displays its own range of cellular plasticity across differentiation states. The most scLP subclones, overexpressing stem cell and DNA repair genes, proliferate faster after neo-adjuvant chemotherapy. These results highlight how the interplay of clonal evolution and epigenetic plasticity shapes the potential of HB subclones to respond to chemotherapy.
| S-EPMC11001886 | biostudies-literature
Project description:BACKGROUNDCurative gene therapies for sickle cell disease (SCD) are currently undergoing clinical evaluation. The occurrence of myeloid malignancies in these trials has prompted safety concerns. Individuals with SCD are predisposed to myeloid malignancies, but the underlying causes remain undefined. Clonal hematopoiesis (CH) is a premalignant condition that also confers significant predisposition to myeloid cancers. While it has been speculated that CH may play a role in SCD-associated cancer predisposition, limited data addressing this issue have been reported.METHODSHere, we leveraged 74,190 whole-genome sequences to robustly study CH in SCD. Somatic mutation calling methods were used to assess CH in all samples and comparisons between individuals with and without SCD were performed.RESULTSWhile we had sufficient power to detect a greater than 2-fold increased rate of CH, we found no detectable variation in rate or clone properties between individuals affected by SCD and controls. The rate of CH in individuals with SCD was unaltered by hydroxyurea use.CONCLUSIONSWe did not observe an increased risk for acquiring detectable CH in SCD, at least as measured by whole-genome sequencing. These results should help guide ongoing efforts and further studies that seek to better define the risk factors underlying myeloid malignancy predisposition in SCD and help ensure that curative therapies can be more safely applied.FUNDINGNew York Stem Cell Foundation and the NIH.
| S-EPMC8843701 | biostudies-literature
Project description: Not available
| S-EPMC8617438 | biostudies-literature
Project description:Knowledge of human fetal blood development and how it differs from adult blood is highly relevant to our understanding of congenital blood and immune disorders and childhood leukemia, of which the latter can originate in utero. Blood formation occurs in waves that overlap in time and space, adding to heterogeneity, which necessitates single-cell approaches. Here, a combined single-cell immunophenotypic and transcriptional map of first trimester primitive blood development is presented. Using CITE-seq (cellular indexing of transcriptomes and epitopes by sequencing), the molecular profile of established immunophenotype-gated progenitors was analyzed in the fetal liver (FL). Classical markers for hematopoietic stem cells (HSCs), such as CD90 and CD49F, were largely preserved, whereas CD135 (FLT3) and CD123 (IL3R) had a ubiquitous expression pattern capturing heterogenous populations. Direct molecular comparison with an adult bone marrow data set revealed that the HSC state was less frequent in FL, whereas cells with a lymphomyeloid signature were more abundant. An erythromyeloid-primed multipotent progenitor cluster was identified, potentially representing a transient, fetal-specific population. Furthermore, differentially expressed genes between fetal and adult counterparts were specifically analyzed, and a fetal core signature was identified. The core gene set could separate subgroups of acute lymphoblastic leukemia by age, suggesting that a fetal program may be partially retained in specific subgroups of pediatric leukemia. Our detailed single-cell map presented herein emphasizes molecular and immunophenotypic differences between fetal and adult blood cells, which are of significance for future studies of pediatric leukemia and blood development in general.
| S-EPMC10506049 | biostudies-literature
Project description:The Genetic Association Information Network (GAIN) Data Access Committee was established in June 2007 to provide prompt and fair access to data from six genome-wide association studies through the database of Genotypes and Phenotypes (dbGaP). Of 945 project requests received through 2011, 749 (79%) have been approved; median receipt-to-approval time decreased from 14 days in 2007 to 8 days in 2011. Over half (54%) of the proposed research uses were for GAIN-specific phenotypes; other uses were for method development (26%) and adding controls to other studies (17%). Eight data-management incidents, defined as compromises of any of the data-use conditions, occurred among nine approved users; most were procedural violations, and none violated participant confidentiality. Over 5 years of experience with GAIN data access has demonstrated substantial use of GAIN data by investigators from academic, nonprofit, and for-profit institutions with relatively few and contained policy violations. The availability of GAIN data has allowed for advances in both the understanding of the genetic underpinnings of mental-health disorders, diabetes, and psoriasis and the development and refinement of statistical methods for identifying genetic and environmental factors related to complex common diseases.
| S-EPMC3617375 | biostudies-literature
Project description:Data Access Committee EGAC00001002256
| EGAC00001002256 | EGA
Project description:Data Access Committee EGAC00001001420
| EGAC00001001420 | EGA
Project description:Genetic mutations associated with acute myeloid leukemia (AML) also occur in age-related clonal hematopoiesis, often in the same individual. This makes confident assignment of detected variants to malignancy challenging. The issue is particularly crucial for AML post-treatment measurable residual disease monitoring, where results can be discordant between genetic sequencing and flow cytometry. We show here, that it is possible to distinguish AML from clonal hematopoiesis and to resolve the immunophenotypic identity of clonal architecture. To achieve this, we first design patient-specific DNA probes based on patient's whole-genome sequencing, and then use them for patient-personalized single-cell DNA sequencing with simultaneous single-cell antibody-oligonucleotide sequencing. Examples illustrate AML arising from DNMT3A and TET2 mutated clones as well as independently. The ability to personalize single-cell proteogenomic assessment for individual patients based on leukemia-specific genomic features has implications for ongoing AML precision medicine efforts.
| S-EPMC8265308 | biostudies-literature
Project description:Resolving chromatin-remodeling-linked gene expression changes at cell-type resolution is important for understanding disease states. Here we describe MAGICAL (Multiome Accessibility Gene Integration Calling and Looping), a hierarchical Bayesian approach that leverages paired single-cell RNA sequencing and single-cell transposase-accessible chromatin sequencing from different conditions to map disease-associated transcription factors, chromatin sites, and genes as regulatory circuits. By simultaneously modeling signal variation across cells and conditions in both omics data types, MAGICAL achieved high accuracy on circuit inference. We applied MAGICAL to study Staphylococcus aureus sepsis from peripheral blood mononuclear single-cell data that we generated from subjects with bloodstream infection and uninfected controls. MAGICAL identified sepsis-associated regulatory circuits predominantly in CD14 monocytes, known to be activated by bacterial sepsis. We addressed the challenging problem of distinguishing host regulatory circuit responses to methicillin-resistant and methicillin-susceptible S. aureus infections. Although differential expression analysis failed to show predictive value, MAGICAL identified epigenetic circuit biomarkers that distinguished methicillin-resistant from methicillin-susceptible S. aureus infections.
| S-EPMC10653299 | biostudies-literature