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Spatially distinct reprogramming of the tumor microenvironment based on tumor invasion in diffuse-type gastric cancers

GSE167297 Homo sapiens (human) Single-cell RNA-seq 14 samples Submitted 2021/08/09 Platform GPL20301
At a glance

Single-cell RNA-seq · human · 14 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Homo sapiens (human)
Topic
Gastric cancerThis series is part of our curated briefing on it.
Samples
14 samples
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Summary (written by the submitters)
The invasive potential of gastric cancers (GCs) defines the hallmarks of malignancies; however, the cellular components and their molecular features associated with tumor invasiveness in GC microenvironments (TME) are poorly understood. In this study, we constructed a comprehensive atlas of 23,060 single cells obtained from surgically dissected superficial and deep layers of five diffuse-type GC along with matched normal.
Read the full summary ↓
As a result, seven major cell types were identified. Fibroblasts, endothelial cells, and myeloid cells were categorized as being enriched in the deep layers. Cell type-specific clustering further revealed that the superficial-to-deep layer transition is associated with enrichment in inflammatory endothelial cells and fibroblasts with upregulated CCL2 transcripts. This study reveals the spatial reprogramming of the TME that may underlie invasive tumor potential in diffuse-type GC. This TME profiling across tumor layers suggests new targets, such as CCL2, that can modify the TME to inhibit tumor progression in diffuse-type GC.
This dataset
Open GSE167297 in finder → Find more human RNA-seq datasets → Gastric cancer landscape →
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE167297_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA704101 and SRA study SRP307611. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 34385296) ↗
Samples in this study

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+ 14 more — browse all 14 samples with per-sample file links →

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