← BioTransfer GEO Dataset Finder
GEO series

Insights into intestinal neuroendocrine tumors in relation to normal human EEC differentiation [RNAseq]

GSE277212 Homo sapiens (human) Single-cell RNA-seq 27 samples Submitted 2026/04/29 Platform GPL20795
At a glance

Single-cell RNA-seq · human · 27 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
27 samples: 10 conditions, mostly in duplicateGroups 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
Multiomic analyses delineate human neuroendocrine tumor cell states in relation to normal enteroendocrine cell ontogenyThe Journal of clinical investigation · 2026
Summary (written by the submitters)
Mutationally silent small intestinal neuroendocrine tumors (SI-NETs) characterized by few recurrent alterations, such as those that inactivate the cyclin-dependent kinase (CDK) inhibitor CDKN1B in <10% of cases are not well defined at the molecular level. Since the native enteroendocrine cells (EECs), which these tumors resemble, are scarce; the lack of reference data from normal EECs limits identification of gene and cis-element dysregulation in SI-NETs.
Read the full summary ↓
We established transcriptional landscapes (RNA-seq) in bulk cultures and at single-cell (sc) resolution during the trajectory of normal human EEC differentiation from ileal stem cells in vitro (PMID 38733993). We compared the findings in SI-NETs against the novel reference profiles of normal human EEC differentiation and maturation. SI-NETs correspond principally to mature EC cells, with discernible features of non-EC and progenitor cells; persistent expression of the latter genes likely contributes to malignant properties. Underlying this aberrant expression pattern we found diverse SI-NET enhancers. EECs normally differentiate through an intermediate HES6hi/ASCL1+ oscillatory state before NEUROD1+ pre-terminal cells emerge; ASCL1 expression then persists only in mature EC cells. In contrast, although SI-NETs express NEUROD1, they strictly lack ASCL1 and other genes normally co-expressed with ASCL1. Notably, ASCL1 and loci encoding transcription factors that define non-EC cell types (ISL1, ARX, PAX6, PDX1) are marked with H3K27me3, signifying epigenetic silencing. SI-NETs express CDKN1B but other CDK inhibitors, e.g., CDKN2A and CDKN1C, are epigenetically silenced, suggesting that tumor cells may especially depend on wild-type CDKN1B function. Chromatin and transcriptional features of SI-NETs, revealed for the first time with respect to normal EEC differentiation, will help identify tumorigenic pathways and candidate therapeutic targets.
Published in
Multiomic analyses delineate human neuroendocrine tumor cell states in relation to normal enteroendocrine cell ontogeny
Singh PN, Hadj Bachir E, Howe JR et al. · The Journal of clinical investigation 2026 · PMID 42166365 · doi:10.1172/JCI197772
This dataset
Open GSE277212 in finder → Find more human RNA-seq datasets →
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 GSE277212_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 PRJNA1161187 and SRA study SRP532758. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42166365) ↗
Study design
10 conditions, mostly in duplicate
DFCI SINET ×9 DFCI SINET2 ×2 DFCI SINET8 ×2 DFCI SINET10 ×2 DFCI SINET17 ×2 DFCI SINET18 ×2 DFCI SINET20 ×2 DFCI SINET23 ×2 +2 more

Supports a between-group comparison across 27 samples.

How we read this: 10 replicated groups read from 27 sample titles; they account for 27 of them. 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.

Samples in this study
Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

Share this dataset

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.