OCI-AML22 multiome datasets (scRNA-Seq/scATAC-Seq)
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1135479 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:The leukemia stem cell (LSC) compartment is a complex reservoir fuelling disease progression in acute myeloid leukemia (AML). The existence of heterogeneity within this compartment is well documented but prior studies have focused on genetic heterogeneity without being able to address functional heterogeneity. Understanding this heterogeneity is critical for the informed design of therapies targeting LSC, but has been hampered by LSC scarcity and the lack of reliable cell surface markers for viable LSC isolation. To overcome these challenges, we turned to the patient-derived OCI-AML22 cell model. This model includes functionally, transcriptionally and epigenetically characterized LSC broadly representative of LSC found in primary AML samples. Focusing on the pool of functionally assessed LSC (Boutzen et al, Leukemia, 2022), we performed single cell RNA-Seq/ATAC-Seq analysis. Using an integrated approach combining xenograft assays this single-cell analysis identified two LSC subtypes with distinct transcriptional, epigenetic and functional properties. These LSC subtypes differed in depth of quiescence, differentiation potential, repopulation capacity, sensitivity to chemotherapy and could be isolated based on CD112 expression. A majority of AML patient samples had transcriptional signatures reflective of either LSC subtype, and some even showed coexistence within an individual sample.
2024-10-02 | GSE272187 | GEO
Project description:Multiome scRNA- and scATAC-seq for SHH treated brain organoids
| PRJEB55517 | ENA
Project description:Thymic mimetic cells function beyond self-tolerance [scATAC-Seq Multiome]
| PRJNA991998 | ENA
Project description:Here, we performed multiome sequencing (snRNA-seq + snATAC-seq) of human fetal liver samples from 3 trisomy 21 (Ts21) and 3 healthy foetuses (median age 14 post-conception weeks). The data set is composed of approximately 60,000 CD45+ foetal liver cells.
2024-07-17 | E-MTAB-13070 | biostudies-arrayexpress
Project description:To investigate how SHH treatment influences patterning of early brain organoids, we performed multiome sequencing of brain organoids during early development
2022-09-01 | E-MTAB-12003 | biostudies-arrayexpress
Project description:BackgroundTechnical improvement in ATAC-seq makes it possible for high throughput profiling the chromatin states of single cells. However, data from multiple sources frequently show strong technical variations, which is referred to as batch effects. In order to perform joint analysis across multiple datasets, specialized method is required to remove technical variations between datasets while keep biological information.ResultsHere we present an algorithm named epiConv to perform joint analyses on scATAC-seq datasets. We first show that epiConv better corrects batch effects and is less prone to over-fitting problem than existing methods on a collection of PBMC datasets. In a collection of mouse brain data, we show that epiConv is capable of aligning low-depth scATAC-Seq from co-assay data (simultaneous profiling of transcriptome and chromatin) onto high-quality ATAC-seq reference and increasing the resolution of chromatin profiles of co-assay data. Finally, we show that epiConv can be used to integrate cells from different biological conditions (T cells in normal vs. germ-free mouse; normal vs. malignant hematopoiesis), which reveals hidden cell populations that would otherwise be undetectable.ConclusionsIn this study, we introduce epiConv to integrate multiple scATAC-seq datasets and perform joint analysis on them. Through several case studies, we show that epiConv removes the batch effects and retains the biological signal. Moreover, joint analysis across multiple datasets improves the performance of clustering and differentially accessible peak calling, especially when the biological signal is weak in single dataset.
| S-EPMC9338487 | biostudies-literature
Project description:Plasmodium-specific CD4+ T cells from mice infected with Plasmodium chabaudi chabaudi AS parasites were recovered at Days 0, 4, 7, and 32 to undergo processing and to generate scATAC-seq dataset. At Day 7, CXCR5+ and CXCR6+ cells were recovered separately. At Day 32, mice were administered with either saline or artesunate (intermittent artesunate therapy - IAT). scATAC-seq dataset was analysed to investigate epigenomic landscapes of CD4+ T cells from effector to memory states.
2020-10-14 | E-MTAB-9402 | biostudies-arrayexpress
Project description:Aging is a universal biological phenomenon linked to many diseases, such as cancer or neurodegeneration. However, the molecular mechanisms underlying aging, or how lifestyle interventions such as cognitive stimulation can ameliorate this process, are yet to be clarified. Here, we performed a multi-omic profiling, including RNA-seq, ATAC-seq, ChIP-seq, EM-seq, SWATH-MS and single cell Multiome scRNA and scATAC-seq, in the dorsal hippocampus of young and old mouse subjects which were subject to cognitive stimulation using the paradigm of environmental enrichment. In this study we were able to describe the epigenomic landscape of aging and cognitive stimulation.
2023-09-29 | PXD045567 | Pride
Project description:Multiome scRNA- and scATAC-seq of healthy and trisomy 21 (Down's syndrome) foetal liver
| PRJEB63257 | ENA
Project description:To study developmental trajectories in brain organoids, we conducted scRNA-seq and scATAC-seq in parallel on a dense timecourse of early development.
2022-08-18 | E-MTAB-11998 | biostudies-arrayexpress