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A Spatially Resolved Transcriptome Landscape during Thyroid Cancer Progression

GSE250521 Homo sapiens (human) Single-cell RNA-seq 25 samples Submitted 2025/02/16 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 25 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)
Topic
Thyroid cancerThis series is part of our curated briefing on it.
Samples
25 samples: 25 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.
Summary (written by the submitters)
Thyroid cancer ranks as the ninth most common cancer type in terms of incidence worldwide. Furthermore, the global incidence of thyroid cancer has been on the rise over the past decades.Spatial transcriptomics technology systematically profiles gene expression across tissue space by combing high-throughput RNA sequencing and imaging techniques.
Read the full summary ↓
The applications of ST have revealed high-resolution spatial architecture and cellular crosstalk in many tumor types, advancing the discovery of new targets for diagnosis and therapy. However, the spatial architecture in thyroid cancer and the structural differences in the TME among papillary thyroid cancer (PTC), locally advanced thyroid cancer (LPTC), and Anaplastic thyroid carcinoma (ATC) have been little investigated. Herein , we applied ST and scRNA-seq data to reveal the spatial difference of TME in PTC, LPTC, and ATC samples.
This dataset
Open GSE250521 in finder → Find more human RNA-seq datasets → Thyroid cancer landscape →
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 GSE250521_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 PRJNA1054236 and SRA study SRP478784. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
25 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 25 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.

Samples in this study
More Thyroid cancer datasets

Search all human RNA-seq datasets in GEO →

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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.