 |
 |
GEO help: Mouse over screen elements for information. |
|
| Status |
Public on Jun 01, 2022 |
| Title |
SZ16_GEX |
| Sample type |
SRA |
| |
|
| Source name |
CTCL PBMCs
|
| Organism |
Homo sapiens |
| Characteristics |
health status: Sezary Syndrome
|
| Extracted molecule |
total RNA |
| Extraction protocol |
Skin biopsies were minced and digested enzymatically (Whole Dissociation Skin Kit, Miltenyi Biotec) for 2 hours at 37°C and further dispersed using the Miltenyi gentleMACS Octo Dissociator. Normal skin biopsies were hashed together 2 at a time per protocol: https://www.biolegend.com/en-us/protocols/totalseq-b-or-c-with-10x-feature-barcoding-technology Normal Sample 1 and 2 are labeled with HTO1 and HTO2 respectively. Normal sample 3 and 4 are labeled with HTO9 and HTO10 respectively. PBMCs were acquired from whole blood samples using Ficoll. Experimental procedures followed established techniques using the Chromium Single Cell 5’ Library V1 kit (10x Genomics).
Experimental procedures followed established techniques using the Chromium Single Cell 5’ Library V1 kit +V(D)J Enrichment Kit, Human T Cell (10x Genomics).
Single Cell 10x Genomics 5' Library V1 Chemistry +V(D)J Enrichment Kit, Human T Cell
|
| |
|
| Library strategy |
RNA-Seq |
| Library source |
transcriptomic |
| Library selection |
cDNA |
| Instrument model |
Illumina NovaSeq 6000 |
| |
|
| Data processing |
Chromium scRNA-seq data produced by the 10X Chromium Platform were processed to generate sample-specific fastq files.
Processed reads were then examined by quality metrics, mapped to a reference human genome using RNA-seq aligner STAR and assigned to individual cells of origin according to the cell specific barcodes, using the Cell Ranger pipeline (10X Genomics).
To ensure that PCR amplified transcripts were counted only once, only single UMIs were counted for gene expression level 10. In this way, cell x gene UMI counting matrices were generated for downstream analyses.
Seurat, an R package developed for single-cell analysis, was used to identify distinct cell populations and visualize cell clusters in graphs. Specifically, the UMI matrix was filtered such that only cells expressing at least 200 genes were utilized in downstream analysis.
Unwanted sources of variation were regressed out of the data by constructing linear models to predict gene expression based on the number of UMIs per cell as well as the percentage of mitochondrial gene content.
Hashtags were demultiplexed based on the Seurat package, https://satijalab.org/seurat/articles/hashing_vignette.html
Genome_build: GrCh38A genome
Supplementary_files_format_and_content: H5 files for gene expression, CSV files for TCR outputs
|
| |
|
| Submission date |
Nov 12, 2021 |
| Last update date |
Jun 02, 2022 |
| Contact name |
Patrizia Fuschiotti |
| E-mail(s) |
paf23@pitt.edu
|
| Organization name |
University of Pittsburgh
|
| Street address |
200 Lothrop St
|
| City |
Pittsburgh |
| State/province |
PA |
| ZIP/Postal code |
15261 |
| Country |
USA |
| |
|
| Platform ID |
GPL24676 |
| Series (1) |
| GSE182861 |
Single-cell RNA sequencing unveils the clonal and transcriptional landscape of cutaneous T-cell lymphomas |
|
| Relations |
| BioSample |
SAMN23081039 |
| SRA |
SRX13118339 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSM5687769_SZ16raw_feature_bc_matrix.h5 |
49.0 Mb |
(ftp)(http) |
H5 |
SRA Run Selector |
| Processed data provided as supplementary file |
| Raw data are available in SRA |
|
|
|
|
 |
