|
| Status |
Public on May 12, 2022 |
| Title |
PSO Lesional Skin Patient 3 |
| Sample type |
SRA |
| |
|
| Source name |
Skin
|
| Organism |
Homo sapiens |
| Characteristics |
individual: patient 3
tissue: Skin punch biopsy
sequencing run / batch: 2
disease state: Psoriasis (PsO)
disease status: Lesional
pasi score: 6
|
| Treatment protocol |
Subjects were biologic-naïve (untreated)
|
| Growth protocol |
Three to four millimeter skin punch biopsies were obtained from lesional and non-lesional skin of healthy controls and subjects with psoriatic disease.
|
| Extracted molecule |
total RNA |
| Extraction protocol |
Tissues were embedded in OCT and frozen in liquid nitrogen-chilled isopentane within 5 minutes of devascularization. 10-µm cryosections were mounted onto the ST arrays (10X Visium) where reverse transcription of cellular transcripts took place in situ after tissue permeabilization.
|
| |
|
| Library strategy |
OTHER |
| Library source |
transcriptomic |
| Library selection |
other |
| Instrument model |
Illumina NovaSeq 6000 |
| |
|
| Description |
st_images.gz
Sample name: PSO_LES_P3
sample id v2: ST-17 L
|
| Data processing |
Sequencing output and the histology images were processed using space ranger software (10X Genomics). The space ranger mkfastq function was used for sample demultiplexing and to converting spatial barcodes and reads into FASTQ format.
The space ranger count function was used to align reads from FASTQ files to human genome (hg38) and then align microscopic slide image and transcriptome to generate barcode/UMI counts, feature spot matrices, cluster data, and perform gene expression analysis.
Assembly: hg38
Supplementary files format and content: Filtered feature-barcode matrices (filtered_feature_bc_matrix) containing only spot barcodes in MEX format for each sample. Spatial folder output from space ranger count function containing QC images for aligned fiducials and detetected tissue in jpg format, scalefactors_json.json, high and low resolution versions of the input image in png format, and tissue_positions_list.csv. All spatial outputs (Spatial image data) are stored in st_images.tar.gz.
Library strategy: spatial transcriptomics
|
| |
|
| Submission date |
May 02, 2022 |
| Last update date |
May 13, 2022 |
| Contact name |
Shruti Naik |
| E-mail(s) |
Shruti.Naik@nyulangone.org
|
| Organization name |
NYU Langone Health
|
| Department |
Pathology
|
| Lab |
Naik Lab
|
| Street address |
435 East 30th Street
|
| City |
New York |
| State/province |
NY |
| ZIP/Postal code |
10016 |
| Country |
USA |
| |
|
| Platform ID |
GPL24676 |
| Series (1) |
| GSE202011 |
Spatial transcriptomics stratifies psoriatic disease by emergent cellular ecosystems |
|
| Relations |
| BioSample |
SAMN28031794 |
| SRA |
SRX15104463 |
