Single cell RNA analysis of breast cancer bone metastases
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE190772 | GEO | 2022/02/08
REPOSITORIES: GEO
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2019-07-11 | GSE130318 | GEO
Project description:Primary prostate epithelial cells derived from a single patient were grown as organoids in vehicle or 10nM 1,25D (vitamin D). At day 8 and day 14 of culture, organoids were dissociated to single cells, and collected & prepped for RNA-seq using the 10x Genomics Chromium Genome Reagent kit v3 Chemistry. ~5,000 cells were collected from each sample and sequenced at a depth of ~50,000 genes per cells on a 2x150nt lane in a NovaSeq 6000. CellRanger alignment was performed to produce the RAW data files.
2020-06-01 | GSE142489 | GEO
Project description:We here systematically studied the interaction network of bone marrow cells. To this end, we micro-dissected many small interacting structures (cell doublets, triplets etc.) into single cells, and sequenced their mRNAs, to infer cell identity. After grouping the cells into cell types (based on the single-cell transcriptomes), we identified actual physical interactions that occurred more, or less, than what would be expected by chance. We compared the micro-dissected data to sorted hematopoietic stem cells. After mild dissociation of the bone marrow, we micro-dissected many small interacting structures (cell doublets, triplets etc.) into single cells, and sequenced their mRNAs. In addition, single hematopoietic stem cells were sorted (Lineage- Kit+ Sca1+ CD150+ CD48-) to sequence their transcriptome. In detail, the bone marrow was mildly flushed. Structures composed of about 10 to 20 cells were set apart. With needles and a micro-dissection microscope, we trimmed of smaller structures from these big structure. These smaller structures are mainly two to four cells attached together. These small structures were then further micro-dissected to single cells. The goal of doing these sequential dissections was to keep track of which cells were interacting with which. Each micro-dissected cell sample contains RNA-seq data for 96 single cells. Each single cells received a different barcode that allow us to entangle them, and to produce the processed .coutt.csv file. Each sorted hematopoietic stem cell sample represents a single cell. The processed data file expdata_BMJhscC.csv contains transcript counts for all analyzed cells.
2016-06-24 | E-GEOD-76983 | biostudies-arrayexpress
Project description:Purpose: Single-cell RNA sequencing has revolutionized cell-type specific gene expression analysis. The goals of this study are to compare cell specific gene expression patterns between retinal cell types originating from the fovea and the periphery of human eyes. Methods: Independent libraries were prepared for foveal and peripheral samples of neural retina from three donors using the 10x Chromium system. Libraries were sequenced on a HiSeq4000. Sequenced reads were mapped to the human genome build hg19 will CellRanger(v3.0.1) and filters removed cells likely to be doublets or cells with a high proportion of mitochondrial reads. Clustering of cells with similar expression profiles was performed with Seurat (v2.3.4). Results: Independent libraries were prepared for foveal and peripheral samples of neural retina from three donors using the 10x Chromium system. Libraries were sequenced on a HiSeq4000. Sequenced reads were mapped to the human genome build hg19 will CellRanger(v3.0.1) and filters removed cells likely to be doublets or cells with a high proportion of mitochondrial reads. Clustering of cells with similar expression profiles was performed with Seurat (v2.3.4). Conclusions: Our study generates a large atlas of human retinal transcriptomes at the single cell level. We identified the majority of expected neural and supportive cell types, and describe regional differences in gene expression between the fovea and the periphery. Our results show that that single-cell RNA sequencing can be performed on human retina after cryopreservation, and that cone photoreceptors and Muller cells demonstrate region-specific patterns of gene expression.
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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
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