 |
 |
GEO help: Mouse over screen elements for information. |
|
| Status |
Public on Apr 22, 2020 |
| Title |
BALF, C144 (scRNA-seq) |
| Sample type |
SRA |
| |
|
| Source name |
BALF
|
| Organism |
Homo sapiens |
| Characteristics |
disease state: Coronavirus infected disease-19 (COVID-19)
patient group: mild
tissue: lung
sample type: BALF
cell subsets: Total cell
|
| Extracted molecule |
polyA RNA |
| Extraction protocol |
Total 11 µl of single cell suspension and 40 µl barcoded Gel Beads were loaded to Chromium Chip A to generate single-cell gel bead-in-emulsion (GEM). The poly-adenylated transcripts were reverse-transcribed later. The single-cell capturing and downstream library constructions were performed using the Chromium Single Cell 5’ library preparation kit according to the manufacturer’s protocol (10x Genomics). Full-length cDNA along with cell-barcode identifiers were PCR-amplified and sequencing libraries were prepared and normalized to 3 nM.
|
| |
|
| Library strategy |
RNA-Seq |
| Library source |
transcriptomic |
| Library selection |
cDNA |
| Instrument model |
BGISEQ-500 |
| |
|
| Description |
Single-cell RNAseq data
|
| Data processing |
The Cell Ranger Software Suite (Version 3.1.0) was used to perform sample de-multiplexing, barcode processing and single-cell 5’ UMI counting with human GRCh38 as the reference genome. Specifically, splicing-aware aligner STAR was used in FASTQs alignment. Cell barcodes were then determined based on distribution of UMI count automatically. Finally, gene-barcode matrix of all 6 donors and 8 previously reported healthy control was integrated with Seurat v3 to remove batch effect across different donors. Following criteria were then applied to each cell, i.e., gene number between 200 and 6000, UMI count above 1000 and mitochondrial gene percentage below 0.1.
The filtered gene-barcode matrix was normalized with LogNormalize methods in Seurat and analyzed by principal component analysis (PCA) using the top 2, 000 most variable genes. Then Uniform Manifold Approximation and Projection (UMAP) was performed on the top 50 principal components for visualizing the cells. Meanwhile, graph-based clustering was performed on the PCA-reduced data for clustering analysis with Seurat v3.
MAST in Seurat v3 was used to perform differential analysis. For each cluster, differentially-expressed genes (DEGs) were generated relative to all of the other cells.
The TCR sequences for each single T cell were assembled by Cell Ranger vdj pipeline (v3.1.0), leading to the identification of CDR3 sequence and the rearranged TCR gene. Cells with both TCR alpha and beta chains were kept and cells with only one TCR chain were discarded.
Genome_build: GRCh38 + COVID(MN908947)
Supplementary_files_format_and_content: h5, raw count matrix generated by Cell Ranger count; csv, tcr contig annotations generated by Cell Ranger vdj
|
| |
|
| Submission date |
Feb 25, 2020 |
| Last update date |
Apr 22, 2020 |
| Contact name |
Zheng Zhang |
| Organization name |
Shenzhen 3rd People's Hospital
|
| Street address |
No. 29, Bulan Road
|
| City |
Shenzhen |
| State/province |
Guangdong |
| ZIP/Postal code |
454171 |
| Country |
China |
| |
|
| Platform ID |
GPL23227 |
| Series (1) |
| GSE145926 |
Single-cell landscape of bronchoalveolar immune cells in COVID-19 patients |
|
| Relations |
| BioSample |
SAMN14207958 |
| SRA |
SRX7802549 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSM4339772_C144_filtered_feature_bc_matrix.h5 |
4.0 Mb |
(ftp)(http) |
H5 |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data provided as supplementary file |
|
|
|
|
 |
