A longitudinal single-cell atlas of anti-tumour necrosis factor treatment in inflammatory bowel disease
Single-cell RNA-seq · human · 216 samples
- What it is
- Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
- Organism
- Homo sapiens (human)
- Samples
- 216 samples: 216 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
- A longitudinal single-cell atlas of anti-tumour necrosis factor treatment in inflammatory bowel diseaseNature immunology · 2024
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 GSE282122_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 PRJNA1179412 and SRA study SRP541799. 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 216 sample titles: 216 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.
- GSM8636267 CID003352-2-reup
- GSM8636268 CID003353-1-reup
- GSM8636269 CID003354-1-reup
- GSM8636270 CID003355-1-reup
- GSM8636271 CID003356-1-reup
- GSM8636272 CID003357-1-reup
- GSM8636273 CID003359-1-reup
- GSM8636274 CID003360-1-reup
- GSM8636275 CID003361-1-reup
- GSM8636276 CID003362-1-reup
- GSM8636277 CID003363-1-reup
- GSM8636278 CID003364-1-reup
- GSM8636279 CID003365-1-reup
- GSM8636280 CID003366-1-reup
- GSM8636281 CID003367-2-reup
- GSM8636282 CID003368-1-reup
- GSM8636283 CID003369-1-reup
- GSM8636284 CID003370-1-reup
- GSM8636285 CID003371-2-reup
- GSM8636286 CID003372-1-reup
- GSM8636287 CID003373-1-reup
- GSM8636288 CID003374-1-reup
- GSM8636289 CID003375-1-reup
- GSM8636290 CID003376-1-reup
- GSM8636291 CID003377-1-reup
- GSM8636292 CID003378-1-reup
- GSM8636293 CID003379-1-reup
- GSM8636294 CID003380-1-reup
- GSM8636295 CID003381-1-reup
- GSM8636296 CID003382-1-reup
- GSM8636297 CID003383-1-reup
- GSM8636298 CID003384-1-reup
- GSM8636299 CID003385-1-reup
- GSM8636300 CID003386-1-reup
- GSM8636301 CID003665-1-reup
- GSM8636302 CID003666-1-reup
- GSM8636303 CID003667-1-reup
- GSM8636304 CID003669-1-reup
- GSM8636305 CID003670-1-reup
- GSM8636306 CID003671-1-reup
+ 176 more — browse all 216 samples with per-sample file links →
- GSE328275 Single-cell RNA sequencing of CD45+ immune cells across primary tumor, sentinel tumor-draining lymph node, and axillary lymph node in treatment-naive triple-negative breast cancer 28 samples shares single-cell, treatment
- GSE292623 A longitudinal single-cell and spatial multiomic atlas of pediatric high-grade glioma 18 samples shares longitudinal, single-cell
- GSE318490 Single-cell RNA-seq of tumor samples from six advanced HGSOC patients across treatment stages (pre-NACT, post-NACT and platinum-sensitive recurrence) 10 samples shares single-cell, treatment
- GSE196198 Longitudinal single-cell multiomics data for different severity of COVID-19 100 samples shares longitudinal, single-cell
- GSE254176 Dynamic changes of immune cells in systemic lupus erythematosus with nephritis using longitudinal peripheral blood single-cell RNA-seq 57 samples shares longitudinal, single-cell
- GSE280982 Single-cell sequencing across radiation treatment timepoints in head and neck cancer 49 samples shares single-cell, treatment
- GSE253661 Longitudinal single-cell transcriptomics of monocytes, Tfh and gd Tcells following a control human malaria infection. 16 samples shares longitudinal, single-cell
- GSE328048 Single-cell transcriptome reveals keratinocyte subclusters that contribute to altered differentiation and inflammatory responses in atopic dermatitis 116 samples shares single-cell
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.