Human embryonic yolk sac CITE-seq data
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Illumina NovaSeq 6000
ORGANISM(S): Homo sapiens
SUBMITTER:
PROVIDER: E-MTAB-11549 | biostudies-arrayexpress |
SECONDARY ACCESSION(S): E-MTAB-11618E-MTAB-10552
REPOSITORIES: biostudies-arrayexpress
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Project description:In this study we used single cell multi-omics profiling to create an atlas of the human YS to gain insights into its haematopoietic, metabolic and nutritive functions during early embryonic development. This contains fetal liver CITE-seq (surface protein and cytosolic RNA content) data from six biological replicates. Pooled lanes were demultiplexed using SoupOrCell (for alignment and demultiplexing software and version numbers, please see accompanying manuscript and protocols within this accession). Raw count files provided are directly as output by alignment software, without any quality control applied. Quality control is described in accompanying manuscript methods. Metadata by barcode are provided as supplementary tables in accompanying manuscript.
2022-12-08 | E-MTAB-11613 | biostudies-arrayexpress
Project description:In this study we used single cell multi-omics profiling to create an atlas of the human YS to gain insights into its haematopoietic, metabolic and nutritive functions during early embryonic development. This contains embryonic liver CITE-seq (surface protein and cytosolic RNA content) data from three biological replicates. Pooled lanes were demultiplexed using SoupOrCell (for alignment and demultiplexing software and version numbers, please see accompanying manuscript and protocols within this accession). Raw count files provided are directly as output by alignment software, without any quality control applied. Quality control is described in accompanying manuscript methods. Metadata by barcode are provided as supplementary tables in accompanying manuscript.
2022-12-08 | E-MTAB-11618 | biostudies-arrayexpress
Project description:Synovial biopsies were taken using 14G Precisa Needle (HS Hospital Service, Italy) in ultrasound-guided protocol, and digested as described previously (Alivernini et al, 2020). Live CD45+ cells from the synovial tissue (ST) of healthy donors (n=3), patients with active RA (n=5) and patients in sustained remission (n=2) were sorted for single cell RNA (scRNA) or CITE (scCITE) sequencing using the 10x Genomics platform. Maximum 20,000 immune cells from tissue were sorted into Protein LoBind 1.5 ml Eppendorf tube containing 300 μl of RPMI media with 10% of FSC. Cells were loaded onto a Chromium Controller (10X Genomics) for single-cell partitioning, followed by library preparation using Single-Cell 3’ Reagent Kits v3.1. For 3 healthy ST and 4 ST from active RA (Figure S1), Totalseq Hashtag were used (Biolegend #394601, #394603, #394605, #394607, #394609) to combine 2 samples per run, and TotalSeq™-A Human Universal Cocktail (V1.0) was used to collect protein expression (CITEseq) data together with transcriptome data. Single-cell libraries were sequenced on the Illumina HiSeq 4000 system to a minimum depth of 50k reads/cell.
2024-12-06 | E-MTAB-14213 | biostudies-arrayexpress
Project description:To profile the cell states of interacting natural killer (NK) cells and blood cancer cells, we cultured 26 different cell lines representing diverse hematologic neoplasms either alone or with NK cells derived from three healthy human donors. After 24 h co-culture, we labeled the cells from each monoculture or co-culture condition with oligonucleotide-conjugated antibodies against ubiquitously expressed surface proteins (with different oligonucleotide for each mono- or co-culture), enabling multiplexing in the scRNA-seq using the cell hashing method. We additionally performed pooled CRISPR screens with a single-cell transcriptome readout using the CROP-seq platform in blood cancer cells cultured either alone or in the presence of NK cells to study the effects of perturbing genes that influenced sensitivity to NK cell killing in genome-scale CRISPR screens.
2023-07-24 | E-MTAB-13204 | biostudies-arrayexpress
Project description:This repository contains all the FASTQ files for the five data modalities (scRNA-seq, scATAC-seq, Multiome, CITE-seq+scVDJ-seq, and spatial transcriptomics) used in the article \\"An Atlas of Cells in The Human Tonsil,\\" published in Immunity in 2024. Inspired by the TCGA barcodes, we have named each fastq file with the following convention: [TECHNOLOGY].[DONOR_ID].[SUBPROJECT].[GEM_ID].[LIBRARY_ID].[LIBRARY_TYPE].[LANE].[READ].fastq.gz which allows to retrieve all metadata from the name itself. Here is a full description of each field: - TECHNOLOGY: scRNA-seq, scATAC-seq, Multiome, CITE-seq+scVDJ-seq, and spatial transcriptomics (Visium). We also include the fastq files associated with the multiome experiments performed on two mantle cell lymphoma patients (MCL). - DONOR_ID: identifier for each of the 17 patients included in the cohort. We provide the donor-level metadata in the file \\"tonsil_atlas_donor_metadata.csv\\", including the hospital, sex, age, age group, cause for tonsillectomy and cohort type for every donor. - SUBPROJECT: each subproject corresponds to one run of the 10x Genomics Chromium™ Chip. - GEM_ID: each run of the 10x Genomics Chromium™ Chip consists of up to 8 \\"GEM wells\\" (see https://www.10xgenomics.com/support/software/cell-ranger/getting-started/cr-glossary): a set of partitioned cells (Gel Beads-in-emulsion) from a single 10x Genomics Chromium™ Chip channel. We give a unique identifier to each of these channels. - LIBRARY_ID: one or more sequencing libraries can be derived from a GEM well. For instance, multiome yields two libraries (ATAC and RNA) and CITE-seq+scVDJ yields 4 libraries (RNA, ADT, BCR, TCR). - LIBRARY_TYPE: the type of library for each library_id. Note that we used cell hashing () for a subset of the scRNA-seq libraries, and thus the library_type can be \\"not_hashed\\", \\"hashed_cdna\\" (RNA expression) or \\"hashed_hto\\" (the hashtag oligonucleotides). - LANE: to increase sequencing depth, each library was sequenced in more than one lane. Important: all lanes corresponding to the same sequencing library need to be inputed together to cellranger, because they come from the same set of cells. - READ: for scATAC-seq we have three reads (R1, R2 or R3), see cellranger-atac's documentation. While we find these names to be the most useful, they need to be changed to follow cellranger's conventions. We provide a code snippet in the README file of the GitHub repository associated with the tonsil atlas to convert between both formats (https://github.com/Single-Cell-Genomics-Group-CNAG-CRG/TonsilAtlas/). Besides the fastq files, cellranger (and other mappers) require additional files, which we also provide in this repository: - cell_hashing_metadata.csv: as mentioned above, we ran cell hashing (10.1186/s13059-018-1603-1) to detect doublets and reduce cost per cell. This file provides the sequence of the hashtag oligonucleotides in cellranger convention to allow demultiplexing. - cite_seq_feature_reference.csv: similar to the previous file, this one links each protein surface marker to the hashtag oligonucleotide that identified it in the CITE-seq experiment. - V10M16-059.gpr and V19S23-039.gpr: these correspond to the two slides of the two Visium experiments performed in the tonsil atlas. They are needed to run spaceranger. - [GEM_ID]_[SLIDE]_[CAPTURE_AREA].jpg: 8 images associated with the Visium experiments. Here, GEM_ID refers to each of the 4 capture areas in each slide. - [TECHNOLOGY]_sequencing_metadata.csv: the GEM-level metadata for each technology. It includes the relationship between subproject, gem_id, library_id, library_type and donor_id. These are the other repositories associated with the tonsil atlas: - Expression and accessibility matrices: https://zenodo.org/records/10373041 - Seurat objects: https://zenodo.org/records/8373756 - HCATonsilData package: https://bioconductor.org/packages/release/data/experiment/html/HCATonsilData.html - Azimuth: https://azimuth.hubmapconsortium.org/ - Github: https://github.com/Single-Cell-Genomics-Group-CNAG-CRG/TonsilAtlas
2024-05-02 | E-MTAB-13687 | biostudies-arrayexpress
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2024-07-13 | E-MTAB-14214 | biostudies-arrayexpress
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2023-12-31 | E-MTAB-11590 | biostudies-arrayexpress
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2024-02-21 | E-MTAB-12508 | biostudies-arrayexpress
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