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| Status |
Public on May 30, 2023 |
| Title |
scRNA-seq_PDAC 3 |
| Sample type |
SRA |
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| Source name |
PDAC tumor tissue
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| Organism |
Homo sapiens |
| Characteristics |
tissue: human PDAC tumor
celltype: non-immune cells (CD45-)
feature: with Neural Hypertrophy and Schwann Cells Accumulation
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| Treatment protocol |
For PDAC tumor tissues, following resection in the operating room, small tissue blocks from ≥3 different sites of tumor were collected and immersed in tissue storage solution (Miltenyi), and transported to the research facility on ice immediately. On arrival, tissues were rinsed with PBS, and necrotic foci, hemorrhagic foci, and blood vessels were removed. Tissues were then minced in into small pieces (<1mm in diameter), and then transferred into 2ml digestion medium containing 100ul enzyme H, 50ul enzyme R, and 12.5ul enzyme A (Tumor Dissociation Kit, Miltenyi, 130-095-929) in RPMI-1640. Tissues were enzymatically digested on a shaker at 37℃ for 20min. The dissociated cells were collected at the interval of 10min to increase viability, and filtered through a 40μm nylon cell strainer to harvest single-cell suspension. Dead cells were removed by dead cell removal kit (Miltenyi). Single-cell RNA sequencing was performed on the single-cell suspensions with viability >70%, including CD45- non-immune filtrates from tumor tissues isolated by CD45 microbeads (Miltenyi).
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| Extracted molecule |
total RNA |
| Extraction protocol |
Single-cell capture was achieved by random distribution of a single-cell suspension across >200,000 microwells through a limited dilution approach. Beads with oligonucleotide barcodes were added to saturation so that a bead was paired with a cell in a microwell.
RNA libraries were prepared for sequencing using standard Illumina protocols
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| Library strategy |
RNA-Seq |
| Library source |
transcriptomic single cell |
| Library selection |
cDNA |
| Instrument model |
Illumina NovaSeq 6000 |
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| Data processing |
Basecalls performed using CASAVA version 1.8
Umi-tools was utilized to calculate the raw counts and cell whitelist by default parameter
We applied the Seurat [https://satijalab.org/seurat/] package for cell normalization and cell filtering considering the MT percentage, minimum and maximum gene numbers under following criteria: MT% < 20%; Cell Gene Number < Median Gene Number * 2; Cell Gene Number > 200. PCA and tSNE analysis was used for the single cell to cell relation description. Graphcluster and K-mean was utilized for cell clustering and based on the marker gene achieved some cluster was combined. Wilcox rank sum test was then used for marker gene analysis.
Assembly: GRCh38
Supplementary files format and content: Txt file and TSV file contain counts for each barcodes and genes
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| Submission date |
May 11, 2022 |
| Last update date |
May 30, 2023 |
| Contact name |
Meilin Xue |
| E-mail(s) |
xuemeilin@sjtu.edu.cn
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| Organization name |
Ruijin Hospital, Shanghai Jiao Tong University School of Medicine
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| Street address |
197 Ruijin 2nd Road
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| City |
Shanghai |
| State/province |
Shanghai |
| ZIP/Postal code |
200020 |
| Country |
China |
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| Platform ID |
GPL24676 |
| Series (1) |
| GSE202742 |
Schwann Cells Shape Tumor Cells and Cancer-Associated Fibroblasts in the Pancreatic Ductal Adenocarcinoma Microenvironment [scRNA-seq] |
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| Relations |
| BioSample |
SAMN28192580 |
| SRA |
SRX15234429 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSM6132067_CD450904.cellname.list.txt.gz |
38.0 Kb |
(ftp)(http) |
TXT |
| GSM6132067_CD450904.counts.tsv.gz |
10.4 Mb |
(ftp)(http) |
TSV |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data provided as supplementary file |
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