High-dimensional profiling uncovers heterogeneity, lineage-specific precursors and inflammation-induced changes in the mononuclear phagocyte compartment of the human intestine
Single-cell RNA-seq · human · 28 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
- 28 samples: 28 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
- Heterogeneity of the intestinal mononuclear phagocyte compartment in health and inflammatory bowel diseaseScience immunology · 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 GSE309239_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 PRJNA1334397. 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 28 sample titles: 28 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.
- GSM9263492 P1, cLP, scRNAseq, mononuclear phagocytes, GEX
- GSM9263493 P1, SILP, scRNAseq, mononuclear phagocytes, GEX
- GSM9263494 P2, cLP, scRNAseq, mononuclear phagocytes, GEX
- GSM9263495 P2, SILP, scRNAseq, mononuclear phagocytes, GEX
- GSM9263496 P3, cLP, scRNAseq, mononuclear phagocytes, GEX
- GSM9263497 P3, SILP, scRNAseq, mononuclear phagocytes, GEX
- GSM9263498 P4, cLP, multiome, mononuclear phagocytes, GEX
- GSM9263499 P4, cLP, multiome, mononuclear phagocytes, ADT
- GSM9263500 P4, SILP, multiome, mononuclear phagocytes, GEX
- GSM9263501 P4, SILP, multiome, mononuclear phagocytes, ADT
- GSM9263502 P5, cLP, multiome, mononuclear phagocytes, GEX
- GSM9263503 P5, cLP, multiome, mononuclear phagocytes, ADT
- GSM9263504 P6, cLP, multiome, mononuclear phagocytes, GEX
- GSM9263505 P6, cLP, multiome, mononuclear phagocytes, ADT
- GSM9263506 CDpat1, SILP, non-inflamed, scRNAseq, mononuclear phagocytes, GEX
- GSM9263507 CDpat1, SILP, inflamed, scRNAseq, mononuclear phagocytes, GEX
- GSM9263508 CDpat2, SILP, non-inflamed, scRNAseq, mononuclear phagocytes, GEX
- GSM9263509 CDpat2, SILP, inflamed, scRNAseq, mononuclear phagocytes, GEX
- GSM9263510 CDpat3, SILP, non-inflamed, scRNAseq, mononuclear phagocytes, GEX
- GSM9263511 CDpat3, SILP, inflamed, scRNAseq, mononuclear phagocytes, GEX
- GSM9263512 CDpat4, SILP, non-inflamed, scRNAseq, mononuclear phagocytes, GEX
- GSM9263513 CDpat4, SILP, non-inflamed, scRNAseq, mononuclear phagocytes, ADT
- GSM9263514 CDpat4, SILP, inflamed, scRNAseq, mononuclear phagocytes, GEX
- GSM9263515 CDpat4, SILP, inflamed, scRNAseq, mononuclear phagocytes, ADT
- GSM9263516 CDpat5, SILP, non-inflamed, scRNAseq, mononuclear phagocytes, GEX
- GSM9263517 CDpat5, SILP, inflamed, scRNAseq, mononuclear phagocytes, GEX
- GSM9263518 CDpat6, SILP, non-inflamed, scRNAseq, mononuclear phagocytes, GEX
- GSM9263519 CDpat6, SILP, inflamed, scRNAseq, mononuclear phagocytes, GEX
- GSE317520 Mitochondrial DNA Mutations Drive Tumor Heterogeneity in Papillary Thyroid Carcinoma 92 samples shares heterogeneity
- GSE309593 Longitudinal Analysis of Multiple Myeloma: Therapeutic Response and Immune Microenvironment Dynamics in the Context of Tumor Heterogeneity [CITE-Seq] 91 samples shares heterogeneity
- GSE256220 Lineage-specific canonical and non-canonical activity of EZH2 in advanced prostate cancer subtypes 87 samples shares lineage-specific
- GSE253904 High-Dimensional Single-Cell Multimodal Landscape of Human Carotid Atherosclerosis 24 samples shares high-dimensional
- GSE280617 H3K27me3 chromatin heterogeneity reveals variable cell responses to estrogen and endocrine treatment [RNA-seq] 18 samples shares heterogeneity
- GSE242352 Resolving spatial subclonal genomic heterogeneity of loss of heterozygosity and extrachromosomal DNA in gliomas 17 samples shares heterogeneity
- GSE253902 High-Dimensional Single-Cell Multimodal Landscape of Human Carotid Atherosclerosis [CITE-seq] 12 samples shares high-dimensional
- GSE256354 Hypomethylating agents induce epigenetic and transcriptional heterogeneity with implications for acute myeloid leukemia cell self-renewal 2765 samples shares heterogeneity
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.