GEO series

Unraveling epithelial cell heterogeneity in adamantinomatous craniopharyngioma using single-cell and spatially resolved analysis

GSE215932 Homo sapiens (human) Single-cell RNA-seq 10 samples Submitted 2024/06/26 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 10 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: 10 samples: 10 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: Multiomics integration-based immunological characterizations of adamantinomatous craniopharyngioma in relation to keratinizationCell death & disease · 2024

Summary (written by the submitters)

Adamantinomatous craniopharyngioma (ACP) is an intractable and histologically complex epithelial neoplasm with variable pathognomonic features, while its cellular composition, particularly the epithelial component, is poorly characterized. Herein, a single-nucleus transcriptome atlas specific to ACP was constructed by performing a single-nucleus RNA sequencing (snRNA-seq) survey of 55967 nuclei in 6 ACP samples from 3 patients.

Read the full summary ↓

We revealed 6 epithelial subtypes with diverse molecular functions in ACP by integrating snRNA-seq, spatial transcriptomic (ST) and immunostaining data. Additionally, we investigated the potential regulatory mechanism of ACP calcification by performing a trajectory analysis and transcription factor regulon analysis. Finally, we characterized the molecular characteristics of neurons and discovered potential epithelial cell–neuron crosstalk in ACP tissues. Overall, this large-scale single-nucleus atlas combined with ST provides, for the first time, unprecedented insights into epithelial cell heterogeneity within ACP and can serve as a resource for further investigation of tumor dynamics.

Published in

Multiomics integration-based immunological characterizations of adamantinomatous craniopharyngioma in relation to keratinization

Xu C, Wu J, Ye J et al. · Cell death & disease 2024 · PMID 38906852 · doi:10.1038/s41419-024-06840-1

This dataset

Open GSE215932 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 GSE215932_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 PRJNA891311 and SRA study SRP403062. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38906852) ↗

Study design

10 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 10 sample titles: 10 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.

Samples in this study

GSM6647597 P1_sample1, replicate 1, snRNAseq
GSM6647598 P1_sample2, replicate 2, snRNAseq
GSM6647599 P2_sample1, replicate 1, snRNAseq
GSM6647600 P2_sample2, replicate 2, snRNAseq
GSM6647601 P3_sample1, replicate 1, snRNAseq
GSM6647602 P3_sample2, replicate 2, snRNAseq
GSM6647603 P1_sample1, replicate 1, ST
GSM6647604 P1_sample2, replicate 2, ST
GSM6647605 P4_sample1, replicate 1, ST
GSM6647606 P5_sample1, replicate 1, ST

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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.