Dynamics and single-cell transcriptomics of circulating immune cell populations in inflammatory bowel disease upon anti-integrin treatment
Single-cell RNA-seq · human · 29 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
- 29 samples: 25 distinct titles, 1 of them repeatedGroups 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
- Multimodal Profiling of Peripheral Blood Identifies Proliferating Circulating Effector CD4(+) T Cells as Predictors for Response to Integrin α4β7-Blocking Therapy in Inflammatory Bowel DiseaseGastroenterology · 2025
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 GSE261334_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 PRJNA1086513. Searching any of these in the dataset finder brings you back here.
How we read this: Read from 29 sample titles: 25 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.
- GSM8140673 PBMCs from healthy donor HC-1
- GSM8140674 PBMCs from healthy donor HC-5
- GSM8140675 PBMCs from healthy donor HC-2
- GSM8140676 PBMCs from healthy donor HC-3
- GSM8140677 PBMCs from healthy donor HC-4
- GSM8140678 PBMCs from IBD-1 at 0wk
- GSM8140679 PBMCs from IBD-1 at 6wk
- GSM8140680 PBMCs from IBD-9 at 0wk
- GSM8140681 PBMCs from IBD-9 at 6wk
- GSM8140682 PBMCs from IBD-10 at 0wk
- GSM8140683 PBMCs from IBD-10 at 6wk
- GSM8140684 PBMCs from IBD-2 at 0wk
- GSM8140685 PBMCs from IBD-2 at 6wk
- GSM8140686 PBMCs from IBD-3 at 0wk
- GSM8140687 PBMCs from IBD-3 at 6wk
- GSM8140688 PBMCs from IBD-4 at 0wk
- GSM8140689 PBMCs from IBD-4 at 6wk
- GSM8140690 PBMCs from IBD-5at 0wk
- GSM8140691 PBMCs from IBD-5 at 6wk
- GSM8140692 PBMCs from IBD-6 at 0wk
- GSM8140693 PBMCs from IBD-6 at 6wk
- GSM8140694 PBMCs from IBD-7 at 0wk
- GSM8140695 PBMCs from IBD-7 at 6wk
- GSM8140696 PBMCs from IBD-8 at 0wk
- GSM8140697 PBMCs from IBD-8 at 6wk
- GSM8140698 CD4 cells from IBD-2 and IBD-6 at 0 and 6wks and from healthy donor HC-4
- GSM8140699 CD4 cells from IBD-3 and IBD-4 at 0 and 6wks and from healthy donor HC-4
- GSM8140700 CD4 cells from IBD-10 and IBD-9 at 0 and 6wks and from healthy donor HC-1
- GSM8140701 CD4 cells from IBD7 and IBD-8 at 0 and 6wks and from healthy donor HC-2
- GSE282122 A longitudinal single-cell atlas of anti-tumour necrosis factor treatment in inflammatory bowel disease 216 samples shares single-cell, treatment, bowel
- 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
- 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
- GSE307215 Spatiotemporal Single-Cell Profiling Reveals T Cell Clonal Dynamics and Phenotypic Plasticity in Human Graft-versus-Host Disease 104 samples shares single-cell, dynamics
- GSE280982 Single-cell sequencing across radiation treatment timepoints in head and neck cancer 49 samples shares single-cell, treatment
- GSE287976 Single-Cell Omics Analysis of Human Basophils Reveals Two Transcriptionally Distinct Populations 12 samples shares single-cell, populations
- GSE305971 Integrative single-cell analysis reveals malignant archetypes and proliferative hierarchies in multiple myeloma 2105 samples shares single-cell
- GSE243013 A single-cell atlas of immune heterogeneity in anti-PD1-treated non-small cell lung cancer 243 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.