Multiomic snRNAseq/snATACseq of human implantation sites, placenta and decidua
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Illumina NovaSeq 6000
ORGANISM(S): Homo sapiens
SUBMITTER:
PROVIDER: E-MTAB-12595 | biostudies-arrayexpress |
SECONDARY ACCESSION(S): E-MTAB-12421
REPOSITORIES: biostudies-arrayexpress
Similar Datasets
Project description:Comprehensive map of first- and second-trimester gonadal development in humans using a combination of single-cell and spatial transcriptomics, chromatin accessibility assays, and imaging.
2022-05-12 | E-MTAB-11708 | biostudies-arrayexpress
Project description:Comprehensive map of first- and second-trimester gonadal development in humans using a combination of single-cell and spatial transcriptomics, chromatin accessibility assays, and imaging.
2024-02-05 | E-MTAB-10570 | biostudies-arrayexpress
Project description:These samples are part of a study to provide a spatially resolved single-cell multiomics map of human trophoblast differentiation in early pregnancy. Here we profiled three human implantation sites (between 6 and 9 post-conceptional weeks, PCW) with snucRNAseq; five decidual and three placental samples from 8-13 PCW by scRNA-seq/snRNA-seq.
2023-02-09 | E-MTAB-12421 | 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
Project description:scRNA-seq of first and second trimester human gonads
2022-06-30 | E-MTAB-10551 | biostudies-arrayexpress
Project description:Comprehensive single-cell map of mouse gonadal development at stages E10.5, E11.5 and E12.5
2022-05-03 | E-MTAB-11480 | biostudies-arrayexpress
Project description:We developed cell2location, a principled and versatile Bayesian model that is designed to resolve fine-grained cell types in spatial transcriptomic data and create comprehensive cellular maps of diverse tissues. To validate cell2location in real tissue, we applied the model to data from the mouse brain, which features diverse neural cell types organised in a well characterised spatial architecture across brain areas, thus presenting a canonical use case to test spatial genomics. We generated matched single nucleus (sn, this submission) and Visium spatial RNA-seq (10X Genomics) profiles of adjacent mouse brain sections that contain multiple regions from the telencephalon and diencephalon. To assess the biological and intra-organ technical variation in spatial mapping, we assayed two mouse brains and serial tissue sections from each brain (total of 3 and 2 matched sections from two animals, respectively, and an extra section for snRNA-seq), creating a rich multi-modal and replicated transcriptomic dataset. Tissue processing. Brains of wild-type adult C57BL/6 mice (postnatal day 56, 1 female and 1 male) were dissected, snap frozen, embedded in optimal cutting temperature compound (Tissue-Tek) and stored at -80oC. Brain hemispheres were cryosectioned at -20oC using a cryostat (Leica, CM3050S). To assess tissue quality, RNA was extracted from test tissue sections using the RNeasy Pico Kit (Qiagen) and yielded high RIN values (9.6 and 9.7) on an Agilent Bioanalyser, indicating high RNA quality. For matched single nuclei and Visium RNA-seq experiments, brain hemispheres were cryosectioned to adjacent thick (200 µm) and thin (10 µm) coronal sections, respectively, and processed the same day. In total, four consecutive sets of thick and thin tissue sections were collected from each brain. Five sets of tissue sections yielded both good quality single nuclei and Visium data (three adjacent sections from mouse 1 and two sections from mouse 2) while one additional section from mouse 2 yielded good single nuclei; these were considered for analysis in this study. Visium spatial transcriptomics. Thin (10 µm) mouse brain sections were cryosectioned and mounted directly onto separate capture areas on 10X Visium Spatial Gene Expression slides (beta product version). Processing was done per manufacturer’s protocols. Briefly, sections were methanol-fixed, hematoxylin and eosin (H&E)-stained, and imaged on a NanoZoomer 2.0 slide scanner (Hamamatsu). Sections were then permeabilized and further processed to obtain cDNA libraries that were quality controlled using the Agilent Bioanalyser. The cDNA libraries were sequenced on the Illumina HiSeq 4000 system, aiming at 300 million raw reads per section with read lengths 28cy R1, 8cy i7 index, 0cy i5 index, 91cy read 2. 10X Visium spatial sequencing data was aligned to mouse pre-mRNA genome reference version mm10 using 10X SpaceRanger and mRNA count matrices were generated by adding intronic and exonic reads for each gene in each location. The paired histology H&E images were processed using 10X SpaceRanger to select locations covered by tissue by aligning pre-recorded spot locations with fiducial border spots in the histology image. This allows evaluating the correspondence between cell maps produced using our method and the known brain anatomy. This also allows identifying the number of nuclei in each spot using nuclear segmentation as described in Suppl. Methods and reported in Fig S8A-D. The histology image was used to manually annotate cortical layers in the primary somatosensory cortex (SSp) region using the lasso tool in the 10X Loupe browser.
2021-11-12 | E-MTAB-11114 | biostudies-arrayexpress
Project description:We sequenced human embryonic stem cells (hESCs), pre-mesendoderm cells (PreME) that acquire transient competence for PGCLC specification and cells at the mesendoderm (ME) stage when they are not longer PGC-competent.
2022-01-21 | E-MTAB-11135 | biostudies-arrayexpress
Project description:We performed single-cell RNA-sequencing to create a cell census of the human endometrium, which is the mucosal lining of the uterus that undergoes dynamic changes throughout the menstrual cycle. We integrated cells that were obtained from 3 superficial biopsies to selected samples that originated from 2 organ donors; this allowed a better representation of cell type diversity found within the full depth of the endometrium.
2021-07-21 | E-MTAB-10287 | biostudies-arrayexpress
Project description:Graft-versus-host-disease (GvHD) is the main complication of allogeneic hematopoietic stem cell transplantation (HSCT). Here we report studies of a patient with chronic GVHD (cGVHD) carrying persistent CD4+ T cell clonal expansion which harbored somatic mTOR, NFKB2, and TLR2 mutations. Functional analysis of the discovered mTOR mutation indicated a gain-of-function alteration and activation of both mTORC1 and mTORC2 signaling pathways leading to increased cell proliferation and decreased apoptosis. Single-cell RNA sequencing and real-time impedance measurements supported increased cytotoxicity of mutated CD4+ T cells. High throughput drug-sensitivity testing suggested mutations induce resistance to mTOR inhibitors but increase sensitivity for HSP90 inhibitors. Our findings suggest that somatic mutations may contribute to aberrant T cell proliferations and participate in the persistent immune activation in cGVHD paving the way for novel targeted therapies.
2020-04-12 | E-MTAB-8911 | biostudies-arrayexpress