Source: 10x GenomicsURL: https://support.10xgenomics.com/single-cell-gene-expression/datasets/3.0.0/pbmc_10k_v3

This dataset contains single-cell RNA sequencing (scRNA-seq) data of 3,000 peripheral blood mononuclear cells (PBMCs) from a healthy donor, processed using the 10x Genomics Chromium platform. The raw data was obtained from 10x Genomics and subsequently aligned using Cell Ranger 8.0.1 with the GENCODE Release 47 (GRCh38.p14) reference genome.The dataset includes the following output files from the Cell Ranger pipeline:filtered_feature_bc_matrix.h5 – Filtered count matrix in HDF5 formatfiltered_feature_bc_matrix – Filtered gene-barcode matrix in directory formatraw_feature_bc_matrix – Raw gene-barcode matrix in directory formatraw_feature_bc_matrix.h5 – Raw count matrix in HDF5 formatThis dataset is valuable for researchers studying single-cell transcriptomics, immune cell profiling, and bioinformatics pipeline benchmarking.File format: HDF5 and Matrix Market (MTX)Reference Genome: GENCODE Release 47 (GRCh38.p14)Processing Pipeline: Cell Ranger 8.0.1For any questions or collaborations, please feel free to contact the uploader.

This dataset contains single cell RNA sequencing data of PBMC samples from 10 bladder cancer patients. cDNAs and single cell RNA libraries were prepared following manufacturer’s user guide (10x Genomics). Each library was sequenced in HiSeq4000 (Illumina) to achieve ~300 million reads following manufacturer’s sequencing specification.

This resource contains pre-processed A. thaliana root , the H. sapiens aortic valve datasets, PBMC Covid atlas and public 10x datasetse used in the paper, SCEMENT: Scalable and Memory Efficient Integration of Large-scale Single Cell RNA-sequencing Data. The raw datasets provided in the links below are pre-processed for quality control with respect to both cells and genes.

A. thaliana datasets are sourced from the following locations at Single-cell Gene expression Atlas and Gene Expression Omnibus (GEO):

1. E-GEOD-121619 : https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-121619/results">https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-121619/results
2. E-GEOD-152766 : https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-152766/results
3. E-GEOD-158761 : https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-158761/results
4. E-GEOD-123013 : https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-123013/results

H. sapiens datasets are obtained from the NCBI database : https://www.ncbi.nlm.nih.gov/bioproject/PRJNA562645/

1. GSE152766: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE152766
2. GSE158761: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE158761

All COVID atlas datasets are from: http://covid19.cancer-pku.cn . covid_atlas_data1.zip contains the h5ad files and covid_atlas_data2.zip contains the Seurat rds files.

PBMC datasets are from the following public sources:

References for the Datasets :

1. H. sapiens dataset: Kang Xu, Shangbo Xie,Yuming Huang,Tingwen Zhou, Ming Liu, Peng Zhu, Chunli Wang, Jiawei Shi, Fei Li,Frank W. Sellke and Nianguo Dong (2020) Cell-Type Transcriptome Atlas of Human Aortic Valves Reveal Cell Heterogeneity and Endothelial to Mesenchymal Transition Involved in Calcific Aortic Valve Disease.
2. E-GEOD-152766: Shahan R, Hsu C, Nolan TM, Cole BJ, Taylor IW et al. (2020) A single cell Arabidopsisroot atlas reveals developmental trajectories in wild type and cell identity mutants.
3. E-GEOD-121619: Jean-Baptiste K, McFaline-Figueroa JL, Alexandre CM, Dorrity MW, Saunders L et al. (2019) Dynamics of Gene Expression in Single Root Cells of Arabidopsis thaliana.
4. E-GEOD-123013: Ryu KH, Huang L, Kang HM, Schiefelbein J. (2019) Single-Cell RNA Sequencing Resolves Molecular Relationships Among Individual Plant Cells.
5. E-GEOD-158761: Gala HP, Lanctot A, Jean-Baptiste K, Guiziou S, Chu JC et al. (2020) A single cell view of the transcriptome during lateral root initiation in Arabidopsis thaliana.
6. COVID Atlas Reference: Xianwen Ren, Wen Wen, Xiaoying Fan et.al. (2021) COVID-19 immune features revealed by a large-scale single-cell transcriptome atlas
7. PBMC data are downloaded from respective links

Sample Procurement

Samples were collected directly from participants as part of ImYoo's "Single-cell immune profiling from self-collected capillary blood" study, approved by Advarra IRB (Protocol #Pro00057361).

Sample Processing

Whole capillary blood samples were self-collected from participants using the TAP II device. Samples were either processed shortly after collection, or if being stored for longer than 4 hours, were kept in a styrofoam cooler with ice packs. Cells were isolated using EasySep Direct Human PBMC Isolation Kit (STEMCELL Technologies Catalog #19654) and cryopreserved using CryoStor CS10 (STEMCELL Technologies Catalog #07930). Upon thawing, samples were labeled in accordance with the MULTI-seq protocol (https://www.nature.com/articles/s41592-019-0433-8) and then processed on a 10X Genomics Chromium, using either the Chromium Next GEM Single Cell 3' Kit v3.1 (10X Genomics Product Code 1000269) or Chromium Next GEM Single Cell 3’ HT Kit v3.1 (10X Genomics Product Code 1000370). DNA libraries were sequenced on either a NovaSeq 6000 or NextSeq 550.

Data Processing

Transcriptomic sequencing data was processed using Cell Ranger v7.0.1 with default parameters. Multiplexing oligo sequencing data was processed through a custom python script that counts the number of occurrences of each sample barcode sequence and assigns it to the corresponding cell barcode. Samples were demultiplexed using a custom algorithm that estimates the background sample barcode counts, and assigns each cell a probability of belonging to each sample. Cell typing was done as part of a larger dataset and consisted of iterative manual assignments of clusters to cell types. For each cell subtype detected, a new model was trained on just the cells of that type, and the process was repeated.

Metadata Fields

barcode: Original chromium cell barcode

Sample IDs: Unique ID for the experimental sample that was processed with 10x Chromium, could have come from the same biological sample (identified by original_sample_id)

Participant IDs: Unique ID for participant (here there are three participants: 2, 3 and 51)

Cell Barcoding Runs: Unique ID for the 10x Chromium cell barcoding run in which that sample was processed. Multiple samples can be processed in a cell barcoding run.

Lane: ID of which Chromium chip lane the cell came from

extraction_protocol: How the PBMCs were isolated from whole blood. In this dataset all samples were processed with the TAP device.

sample_processing_delay_seconds: The amount of time (in seconds) between when the blood was extracted from the participant and when PBMC isolation + cryopreservation was performed

cell_barcoding_delay_days: How long PBMC samples were stored in liquid nitrogen prior to being thawed and processed on 10x

cell_barcoding_protocol: Which single cell RNA sequencing experimental protocol was used. Here all samples were processed with 10x v3.1 chemistry.

run_lane_batch: Concatenation of columns Cell Barcoding Runs and Lane to provide a unique ID for experimental processing batch (i.e. the DNA library)

cell_type_level_1: Level 1 of a 4-tier PBMC ontology that does not provide a label for low quality cells - those were left as NaNs.

cell_type_level_2: Level 2 of a 4-tier PBMC ontology that does not provide a label for low quality cells - those were left as NaNs.

cell_type_level_3: Level 3 of a 4-tier PBMC ontology that does not provide a label for low quality cells - those were left as NaNs.

cell_type_level_4: Level 4 of a 4-tier PBMC ontology that does not provide a label for low quality cells - those were left as NaNs.

c1: Level 1 of a 4-tier PBMC ontology that also provides label for low quality cells, such as Debris, Doublets, experimental artifacts and others. These additional labels can be used for creating a junk detector.

c2: Level 1 of a 4-tier PBMC ontology that also provides label for low quality cells, such as Debris, Doublets, experimental artifacts and others. These additional labels can be used for creating a junk detector.

c3: Level 1 of a 4-tier PBMC ontology that also provides label for low quality cells, such as Debris, Doublets, experimental artifacts and others. These additional labels can be used for creating a junk detector.

c4: Level 1 of a 4-tier PBMC ontology that also provides label for low quality cells, such as Debris, Doublets, experimental artifacts and others. These additional labels can be used for creating a junk detector.

original_sample_id: Some samples were derived from the same originating whole blood sample. This field specifies the source of the whole blood sample.

10x pbmcs5000 cellsscRNA-seq

Single cell RNA-sequencing dataset of peripheral blood mononuclear cells (pbmc: T, B, NK and monocytes) extracted from two healthy donors.

Cells labeled as C26 come from a 30 years old female and cells labeled as C27 come from a 53 years old male. Cells have been isolated from blood using ficoll. Samples were sequenced using standard 3' v3 chemistry protocols by 10x genomics. Cellranger v4.0.0 was used for the processing, and reads were aligned to the ensembl GRCg38 human genome (GRCg38_r98-ensembl_Sept2019). QC metrics were calculated on the count matrix generated by cellranger (filtered_feature_bc_matrix). Cells with less than 3 genes per cells, less than 500 reads per cell and more than 20% of mithocondrial genes were discarded.

The processing steps was performed with the R package Seurat (https://satijalab.org/seurat/), including sample integration, data normalisation and scaling, dimensional reduction, and clustering. SCTransform method was adopted for the normalisation and scaling steps. The clustered cells were manually annotated using known cell type markers.

Files content:

- raw_dataset.csv: raw gene counts

- normalized_dataset.csv: normalized gene counts (single cell matrix)

- cell_types.csv: cell types identified from annotated cell clusters

- cell_types_macro.csv: cell macro types

- UMAP_coordinates.csv: 2d cell coordinates computed with UMAP algorithm in Seurat

we will guide you through the process of inferring enhancer regulons, known as eRegulons, and enhancer-driven gene regulatory networks or eGRNs, utilizing the STREAM tool. Our demonstration leverages a select subset of data from the publicly accessible 10x Genomics Multiome dataset, specifically focusing on human Peripheral Blood Mononuclear Cells (PBMCs). For those interested in the comprehensive dataset, the raw data captured by the 10X Genomics Multiome ATAC+GEX can be downloaded directly from https://www.10xgenomics.com/resources/datasets/pbmc-from-a-healthy-donor-granulocytes-removed-through-cell-sorting-10-k-1-standard-1-0-0.

• A public dataset created and provided by 10X Genomics, that consists in ~ 10,000 PBMC (peripheral bone marrow cells) from a human donor
• The experiment was performed with the 3’ capture kit v3
• The analysis was performed with Cell Ranger v3
• Mapping was performed on the GRCh38-2020-A manufacturer reference
• The structure of these Cell Ranger outputs are described here
• The sparse matrix format corresponds to the Market Exchange Format (MEX)
• For educational purpose (speed), the dataset columns were reduced to 1/5th (from 737,280 to 147,456 barcodes)

Gene expression (counts) scRNA-seq of co-cultured cancer- and immune cells treated with trifluridine and DMSO control assayed at two time-points (12h and 72h).

HCT116 were seeded in 6-well Nunc plates (50,000 cells/3mL/well) and precultured for 24 h before PBMCs were added at a 1:8 ratio. Co-cultures were treated with DMSO vehicle (0.1%) or FTD (3mM) for 12 h or 72 h. MACS Dead Cell Removal Kit (Miltenyi Biotec, Gladbach, DEU) was performed according to the manufacturer’s instructions on cells treated for 72 h to increase the viability of the samples before RNA-sequencing. The viability of the samples treated for 12 h was not subjected to Dead Cell Removal as the viability was already sufficient. All samples were washed in PBS with 0.04% BSA (2x1mL). Chromium Next GEM Single Cell 3’ library preparation and RNA-sequencing were performed by the SNP&SEQ Technology Platform (National Genomics Infrastructure (NGI), Science for Life Laboratory, Uppsala University, Sweden).

This data set contains processed data using Cell Ranger toolkit version 5.0.1 provided by 10x Genomics, for demultiplexing, aligning reads to the human reference genome GRCh38, and generating gene-cell unique molecular identifiers

The data published here contains single-cell RNA-sequencing (scRNAseq) data as obtained using the 3' scRNAseq using Chromium Single Cell 3’ Reagent from 10X Genomics on peripheral blood mononuclear cells (PBMC) from patients with colorectal cancer (CRC) and peritoneal metastases (PM). Sequencing was performed in a paired-ended fashion on the NovaSeq6000.

scRNAseq data generated with 10x genomics

scRNAseq and scTCRseq of serial peripheral blood mononuclear cell (PBMC) samples (n=72) taken at various timepoints before and during treatment (Week 0 (W0), Week 3 (W3), Week 6 (W6)). PBMC samples were pooled together into 37 pools, loading 2 or three samples per lane in the 10X Genomics chip, in equal proportions, according to a pre-designed pooling matrix.

数据可在 10x Genomics 网站上获得。链接: https://www.10xgenomics.com/datasets/pbmc-from-a-healthy-donor-granulocytes-removed-through-cell-sorting-10-k-1-standard-2-0-0, https://www.10xgenomics.com/datasets/frozen-human-healthy-brain-tissue-3-k-1-standard-1-0-0.

考虑到 10x 数据库通常需要登录才能访问,我们在此处上传了原始 10x 数据。

We performed single cell RNA- and TCR-sequencing (10x Genomics) on immune infiltrates (CD45+ cells) from 18 HNSCC patients enrolled in the IMCISION trial (Vos et al. 2021). Viable immune cells were isolated from pre-treatment and post-treatment primary tumor biopsies of 10 patients responding (1 partial pathological response and 9 major pathological responses) and 7 patients non-responding to anti-PD-1 and anti-CTLA4 combination immunotherapy. One patient treated with anti-PD-1 monotherapy (1 major pathological response) was included in the dataset. Bulk TCR-seq was performed on the PBMCs of responding patients, pre- and post-treatment.

Here we present the 1M-scBloodNL study, in which we performed single-cell RNA-seq on 120 individuals of the Northern Netherlands population cohort Lifelines. For each individual peripheral blood mononuclear cells (PBMCs) were sequenced in an unstimulated condition, and after 3 and 24 hour in vitro stimulation with C. albicans (CA), M. tuberculosis (MTB) and P. aeruginosa (PA), totalling approximately 1.3 million cells. scRNA-seq was conducted with the 10X Genomics 3'-end v2 (72 libraries) and v3 (33 libraries) technology. In general, each library contains PBMCs from 8 donors and 2 different stimulation-timepoint combinations. Donors were demultiplexed using a combination of SoupOrCell (https://www.nature.com/articles/s41592-020-0820-1) and genotype information to assign the correct donor to a donor-specific cell cluster.

This is the PBMC training dataset used for training Scaden models to perform deconvolution on PBMC RNA-seq datasets. It is compiled from four different PBMC scRNA-seq datasets downloaded from the 10X Genomics website (donorA, donorC, data6k, data8k).The datasets downloaded from 10X Genomics were processed and used to generate artificial bulk RNA-seq samples, which result in this dataset. A link to the 10X Genomics datasets site is provided.

Preprocessed and annotated scRNA-seq Seurat object of PBMC5K dataset of human PBMCs from 10X genomics.

Application of iDDN on single cell multiome data, as shown in the iDDN paper. The data is from 10x Genomics PBMC data.

10 samples (one baseline, 9 on-treatment). Fastq files containing 5'GEx data, prepared using 10x Genomics pipeline, sequenced on Illumina HiSeq4000.