Sleeve Gastrectomy Reveals the Plasticity of the Human Gastric Epithelium
Single-cell RNA-seq · human · 31 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)
- Samples
- 31 samples: 31 samples, each with a distinct title — no replicated groupsGroups are read from the sample names, not from the paper. Check the list below.
- 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.
- Paper
- Sleeve gastrectomy reveals the plasticity of the human gastric epitheliumNature communications · 2025
Read the full summary ↓
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE263018_RAW.tar (it can run to tens of gigabytes).
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.
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 PRJNA1095460 and SRA study SRP499374. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 31 sample titles: 31 distinct titles with little repetition. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.
- GSM8183355 Fundus (corpus), post-SG patient 5, scRNAseq
- GSM8183356 Antrum, naive patient 6, scRNAseq
- GSM8183357 Body (corpus), naive patient 6, scRNAseq
- GSM8183358 Fundus (corpus), naive patient 6, scRNAseq
- GSM8183359 Antrum, naive patient 7, scRNAseq
- GSM8183360 Body (corpus), naive patient 7, scRNAseq
- GSM8183361 Fundus (corpus), naive patient 7, scRNAseq
- GSM8183362 Body (corpus), naive patient 9, scRNAseq
- GSM8183363 Antrum, post-SG patient 10, scRNAseq
- GSM8183364 Body (corpus), post-SG patient 10, scRNAseq
- GSM8183365 Fundus (corpus), post-SG patient 10, scRNAseq
- GSM8183366 Antrum, naive patient 11, scRNAseq
- GSM8183367 Fundus (corpus), naive patient 11, scRNAseq
- GSM8183368 Antrum, naive patient 12, scRNAseq
- GSM8183369 Body (corpus), naive patient 12, scRNAseq
- GSM8183370 Fundus (corpus), naive patient 12, scRNAseq
- GSM8183371 Antrum, naive patient 14, scRNAseq
- GSM8183372 Body (corpus), naive patient 14, scRNAseq
- GSM8183373 Fundus (corpus), naive patient 14, scRNAseq
- GSM8183374 Antrum, post-SG patient 15, scRNAseq
- GSM8183375 Body (antrum), post-SG patient 15, scRNAseq
- GSM8183376 Fundus (corpus), post-SG patient 15, scRNAseq
- GSM8183377 Antrum, naive patient 17, scRNAseq
- GSM8183378 Body (corpus), naive patient 17, scRNAseq
- GSM8183379 Fundus (corpus), naive patient 17, scRNAseq
- GSM8183380 Antrum, post-SG patient 20, scRNAseq
- GSM8183381 Body (corpus), post-SG patient 20, scRNAseq
- GSM8183382 Fundus (corpus), post-SG patient 20, scRNAseq
- GSM8183383 Antrum, post-SG patient 21, scRNAseq
- GSM8183384 Body (corpus), post-SG patient 21, scRNAseq
- GSM8183385 Fundus (corpus), post-SG patient 21, scRNAseq
- GSE281124 Loss of METTL3 potentiates transposable element expression and drives inflammation-induced cell plasticity in the mammary epithelium 24 samples shares plasticity, epithelium
- GSE309705 Transcriptomic Profiling of Bronchial Epithelium Reveals Dysregulated Interferon and Inflammatory Responses to Rhinovirus in Exacerbation-Prone Pediatric Asthma 274 samples shares epithelium
- GSE309698 Transcriptomic Profiling of Bronchial Epithelium Reveals Dysregulated Interferon and Inflammatory Responses to Rhinovirus in Exacerbation-Prone Pediatric Asthma [RNA-Seq] 246 samples shares epithelium
- GSE143584 Gene expression from laser capture microdissected epithelium and stroma during pancreatic carcinogenesis 366 samples shares epithelium
- GSE309034 Combined BET Bromodomain and DNMT Inhibition Targets Lineage Plasticity in Prostate Cancer [BETi_DNMTi_human_RNAseq] 44 samples shares plasticity
- GSE324131 A single-cell and spatial atlas of prostate cancer reveals the combinatorial nature of gene modules underlying lineage plasticity and metastasis 38 samples shares plasticity
- GSE335480 Single-cell tracking reveals tumor-reactive T cell plasticity during melanoma TIL therapy 38 samples shares plasticity
- GSE275647 Role of Cancer-Associated Fibroblasts in Prognostic Stratification of Advanced-Stage Gastric Cancer through Interacting with Endothelial and Malignant Epithelial Cells [AGC RNA-seq] 216 samples shares gastric
Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.