Multiome RNA+ATAC on human PBMCs (snATAC-seq data)
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PROVIDER: PRJEB52980 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:We generated Multiome RNA+ATAC data from the same cell from human PBMC. This served as a gold benchmark for a novel integration method for multi-omics data that we developed.
2021-12-02 | E-MTAB-11226 | biostudies-arrayexpress
Project description:We generated Multiome RNA+ATAC data from the same cell from human PBMC. This served as a gold benchmark for a novel integration method for multi-omics data that we developed.
2021-12-02 | E-MTAB-11225 | biostudies-arrayexpress
Project description:Multiome RNA+ATAC on human PBMCs (snRNA-seq data)
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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:mouse retina 10x Genomics Multiome ATAC+RNA data
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Project description:The 10x multiome (RNA+ATAC) kit was applied to CRL-1459 cells in culture at passage 10 and 15
2024-07-13 | E-MTAB-14220 | biostudies-arrayexpress
Project description:This study used droplet-based snATAC-seq to profile the chromatin accessibility landscape of 91,922 nuclei in the mouse cerebellum across eleven developmental stages, from the beginning of neurogenesis (e10.5) till adulthood (P63). The study included two biological replicates per stage, one from each sex. Cerebelli were dissected as whole or in two halves, nuclei were extracted and profiled using 10x single-cell ATAC reagent kit (v1.0) and a Chromium controller. Libraries were sequenced using paired-reads on Illumina NextSeq 550 and initial data processing was performed using Cellranger ATAC (1.1).
2021-09-07 | E-MTAB-9765 | biostudies-arrayexpress
Project description:Mouse retina is heterogeneous, composed of multiple neuronal and non-neuronal cell types. Among them, bipolar cells (BC), which connect photoreceptors (cones and rods) to inner retina, are traditionally dissected into rare subtypes of subtle functional and morphological differences. While high-resolution single-cell transcriptomic profiles of BCs are availabl, little is known about the corresponding single-cell chromatin landscapes. Although it is now possible to directly generate multiome data, there are often restrictions on cost, feasibility, and data quality. Therefore, integrating single-cell ATAC and RNA profiles obtained independently from the same retina sample may provide an exciting opportunity to comprehensively characterize these rare cell subtypes and discover transcription factors (TFs) important in establishing or maintaining the cell identities
2022-05-02 | GSE201402 | GEO
Project description:Background: A better understanding of the pancreatic adenocarcinoma (PDAC) immune microenvironment is critical to improving outcomes. Myeloid cells are of particular importance for PDAC progression. Circulating monocytes are recruited and hijacked by the tumor to become immune suppressive tumor-associated macrophages. Meanwhile, tissue resident macrophages are innately anti-inflammatory and pro-fibrotic. Open chromatin and transcription factor activity provide source and function data for myeloid cells. Thus, we explore single nuclear assay for transposase accessible chromatin (ATAC) sequencing (snATAC-Seq), a method to analyze open gene promoters and transcription factor binding, as an important means for discerning the myeloid composition in human PDAC tumors. Methods: Frozen pancreatic tissues (benign or PDAC) were digested into single nuclei suspensions. snATAC-Seq and sn-Multiome (ATAC + RNA) libraries were prepared using 10X Chromium technology and sequenced by an Illumina sequencer. Signac was used for preliminary analysis, clustering, and differentially accessible chromatin region identification. snMultiome data was used to validate cell annotations for the snATAC-Seq data. Results: Unique populations comprised the benign and tumor samples. Myeloid cell transcription factor activities were higher in tumor samples than benign pancreatic samples, enabling us to further stratify myeloid populations in PDAC tumors. GO enrichment analysis showed myeloid cells can activate immune response in tumors. Subcluster analysis revealed eight distinct myeloid populations. GO enrichment demonstrated unique functions of different myeloid populations, including IL-1b signaling (recruited monocytes) and intracellular protein transport (dendritic cells). Conclusions: These data suggest snATAC-Seq as a method for retrospective analysis of frozen human pancreatic tissues to identify myeloid populations. Understanding the source specific potential function of myeloid cells is important for patient prognosis in PDAC.
2024-02-15 | GSE241883 | GEO
Project description:Similar to other droplet-based single cell assays, single nucleus ATAC-seq (snATAC-seq) data harbor multiplets that confound downstream analyses. Detecting multiplets in snATAC-seq data is particularly challenging due to data sparsity and limited dynamic range (0 reads: closed chromatin, 1: open on in one parental chromosome allele, 2: open on in both alleles chromosomes). Yet, these unique data features offer an opportunity to identify multiplets. ATAC-DoubletDetector (https://ucarlab.github.io/ATAC-DoubletDetector/) AMULET (Atac MULtiplet Estimation Tool) exploits these unique features to detect multiplets by studying enumerates the number of regions with >2 uniquely aligned reads across the genome to effectively detect multiplets - an effective alternative to methods based on artificially-generated multiplets. We evaluated the method by generating snATAC-seq data (e.g., state-of-the-art ArchR). For benchmarking we generated snATAC-seq data and generated data fromeasured the efficacy of AMULET inm in two primary human tissues: peripheral human blood mononuclear cells (PBMCs) and pancreatic islet samples. AMULET detects had high multiplets with an estimated precision (estimated via donor-based multiplexing) and high recall (estimated via simulated doublets) compared to alternatives 0.57 precision and achieves 0.85 recall. When and was the most effective when a certain read depth is achieved (a certain read depth per nucleus is achieved samples are sequenced deeply (e.g., median read count per nucleus >20K25K) reads per nucleus in PBMCs), ATAC-DoubletDetector captured 85% of simulated doublets (i.e., recall), significantly outperforming ArchR (24%). For lower read depth, ATAC-DoubletDetector and ArchR produced complementary results. Moreover, ATAC-DoubletDetector was equally effective in identifying homotypic multiplets (i.e., multiplets from the same cell type), which are missed by simulation-based methods. Cell-specific marker peaks enabled accurate (85%) tracing of cellular origins of snATAC-seq multiplets. Accordingly, more abundant cells within a tissue are more likely to form multiplets and the majority of multiplets are homotypic. ATAC-DoubletDetector is a fast and effective multiplet detection/annotation tool for improved single cell epigenomic data analyses across diverse biological systems and conditions.
2021-08-11 | GSE165212 | GEO