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Anaplastic Transformation Model in Thyroid Cancer Revealed by Single Cell Lineage and Fate Analysis

GSE193581 Homo sapiens (human) Single-cell RNA-seq 32 samples Submitted 2023/04/14 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 32 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)
Topic
Thyroid cancerThis series is part of our curated briefing on it.
Samples
32 samples
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)
The deadliest anaplastic thyroid cancer (ATC) often transforms from indolent differentiated thyroid cancer (DTC), however the complex intra-tumor transformation process is poorly understood. We investigated an anaplastic transformation model by dissecting both cell lineage and cell fate transitions using single cell transcriptomes and genetic alterations data of patients with different subtypes of thyroid cancer.
Read the full summary ↓
The resulting model started from stress-responsive DTC cells to inflammatory ATC cells, to mitotic defective ATC cells and extended all the way to mesenchymal ATC cells. In parallel with tumor cell evolution, macrophages shifted from anti-tumor to tumor-promoting states and T cells reprogrammed from cytotoxic to exhausted states. Further, our analysis identified two important milestones: 1) diploid stage, where ATC cells were commonly diploids with non-RAS mutations and inflammatory phenotypes. 2) aneuploid stage, where ATC cells gained aneuploidy with frequent RAS mutations and mesenchymal phenotypes leading to the extreme lethal stage of ATC progression.
This dataset
Open GSE193581 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 GSE193581_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 PRJNA796654 and SRA study SRP354783. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 37053016) ↗
Samples in this study

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+ 32 more — browse all 32 samples with per-sample file links →

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