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Immune landscape of viral- and carcinogen-drived head and neck cancer

GSE139324 Homo sapiens (human) Single-cell RNA-seq 63 samples Submitted 2019/11/13 Platform GPL18573
At a glance

Single-cell RNA-seq · human · 63 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
Head and neck cancerThis series is part of our curated briefing on it.
Samples
63 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)
Head and neck squamous cell carcinoma (HNSCC) arises through exposure to environmental carcinogens or malignant transformation by human papillomavirus (HPV). Here, we assessed the transcriptional profiles of 131,224 single cells from peripheral and intra-tumoral CD45+ immune populations from HPV– and HPV+ HNSCC and healthy donors. A spectrum of transcriptional signatures in immune lineages ranging from similar to highly divergent was present in the tumor microenvironments (TME) in HPV– and HPV+ HNSCC.
Read the full summary ↓
CD8+ and regulatory CD4+ T cells were similar in HPV– and HPV+ HNSCC, compared to significant differences between helper CD4+ T cells, B cells and myeloid cells. Further dissection of the major immune lineages identified unique cell states and differentiation trajectories. We contextualized our results by performing multispectral immunofluorescence and evaluating putative cell-cell communication based on spatial proximity, and found longer progression free survival in patients with enrichment of a CD4+ T follicular helper cell signature.
This dataset
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.

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 GSE139324_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA579320 and SRA study SRP226817. Searching any of these in the dataset finder brings you back here.

Samples in this study

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