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Spatially organized inflammatory myeloid-CD8+ T cell aggregates linked to Merkel-cell Polyomavirus driven Reorganization of the Tumor Microenvironment

GSE281260 Homo sapiens (human) Single-cell RNA-seq 146 samples Submitted 2024/11/11 Platform GPL18573
At a glance

Single-cell RNA-seq · human · 146 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
Merkel cell carcinomaThis series is part of our curated briefing on it.
Samples
146 samples: 146 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)
Merkel cell carcinoma (MCC) is an aggressive skin cancer with high propensity for metastasis, caused by Merkel cell polyomavirus (MCPyV), or chronic UV-light-exposure. How MCPyV modulates immune responses within the tumor microenvironment and how such are linked to patient outcomes remain unknown partly due to technical barriers to understanding the spatial organization of the tumor microenvironment at single-cell resolution.
Read the full summary ↓
We interrogated the cellular and transcriptional landscapes of 60 MCC-patients using Co-detection-by-indexing (CODEX) and targeted bulk RNA sequencing. Notably, we identified an enrichment of dysfunctional T cells spatially associating with CXCL9+ myeloid cells at the tumor invasive front as key feature of virus-positive MCC. While MCPyV-positivity and CD8+ T cell infiltration correlated with metastasis-free-survival, responses to immune checkpoint blockade were high regardless of virus-status. Instead, we found an enrichment of central memory T cells within tertiary-lymphoid-structures that associated with response to immune-checkpoint blockade. These findings highlight fundamental differences in the organization of the native tumor microenvironment of virus-positive MCC, that are linked to differential survival outcomes and could be used for personalized treatment strategies.
This dataset
Open GSE281260 in finder → Find more human RNA-seq datasets → Merkel cell carcinoma 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 GSE281260_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 PRJNA1182821 and SRA study SRP543647. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
146 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 146 sample titles: 146 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

+ 106 more — browse all 146 samples with per-sample file links →

More Merkel cell carcinoma 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.