Single-cell RNA-sequencing of HNSCC-infiltrating T cells
Single-cell RNA-seq · human · 48 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
- 48 samples: 24 × chip 1 pool vs 24 × chip 2 poolGroups 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
- Single-cell TCR mapping reveals spatially coordinated T cell states in head and neck cancerScience immunology · 2026
Read the full summary ↓
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE287301_RAW.tar (it can run to tens of gigabytes).
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 PRJNA1212073 and SRA study SRP558177. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 48 samples.
How we read this: 2 replicated groups read from 48 sample titles; they account for 48 of them. 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.
- GSM8743442 Gene expression: chip 1 pool 1 chip 1 pool
- GSM8743443 Gene expression: chip 1 pool 2 chip 1 pool
- GSM8743444 Gene expression: chip 1 pool 3 chip 1 pool
- GSM8743445 Gene expression: chip 1 pool 4 chip 1 pool
- GSM8743446 Gene expression: chip 1 pool 5 chip 1 pool
- GSM8743447 Gene expression: chip 1 pool 6 chip 1 pool
- GSM8743448 Gene expression: chip 1 pool 7 chip 1 pool
- GSM8743449 Gene expression: chip 1 pool 8 chip 1 pool
- GSM8743450 Gene expression: chip 2 pool 1 chip 2 pool
- GSM8743451 Gene expression: chip 2 pool 2 chip 2 pool
- GSM8743452 Gene expression: chip 2 pool 3 chip 2 pool
- GSM8743453 Gene expression: chip 2 pool 4 chip 2 pool
- GSM8743454 Gene expression: chip 2 pool 5 chip 2 pool
- GSM8743455 Gene expression: chip 2 pool 6 chip 2 pool
- GSM8743456 Gene expression: chip 2 pool 7 chip 2 pool
- GSM8743457 Gene expression: chip 2 pool 16 chip 2 pool
- GSM8743458 CITE-seq: chip 1 pool 1 chip 1 pool
- GSM8743459 CITE-seq: chip 1 pool 2 chip 1 pool
- GSM8743460 CITE-seq: chip 1 pool 3 chip 1 pool
- GSM8743461 CITE-seq: chip 1 pool 4 chip 1 pool
- GSM8743462 CITE-seq: chip 1 pool 5 chip 1 pool
- GSM8743463 CITE-seq: chip 1 pool 6 chip 1 pool
- GSM8743464 CITE-seq: chip 1 pool 7 chip 1 pool
- GSM8743465 CITE-seq: chip 1 pool 8 chip 1 pool
- GSM8743466 CITE-seq: chip 2 pool 1 chip 2 pool
- GSM8743467 CITE-seq: chip 2 pool 2 chip 2 pool
- GSM8743468 CITE-seq: chip 2 pool 3 chip 2 pool
- GSM8743469 CITE-seq: chip 2 pool 4 chip 2 pool
- GSM8743470 CITE-seq: chip 2 pool 5 chip 2 pool
- GSM8743471 CITE-seq: chip 2 pool 6 chip 2 pool
- GSM8743472 CITE-seq: chip 2 pool 7 chip 2 pool
- GSM8743473 CITE-seq: chip 2 pool 16 chip 2 pool
- GSM8743474 TCR-seq: chip 1 pool 1 chip 1 pool
- GSM8743475 TCR-seq: chip 1 pool 2 chip 1 pool
- GSM8743476 TCR-seq: chip 1 pool 3 chip 1 pool
- GSM8743477 TCR-seq: chip 1 pool 4 chip 1 pool
- GSM8743478 TCR-seq: chip 1 pool 5 chip 1 pool
- GSM8743479 TCR-seq: chip 1 pool 6 chip 1 pool
- GSM8743480 TCR-seq: chip 1 pool 7 chip 1 pool
- GSM8743481 TCR-seq: chip 1 pool 8 chip 1 pool
+ 8 more — browse all 48 samples with per-sample file links →
- GSE269877 Single-cell RNA-sequencing of BCG naïve and recurrent non-muscle invasive bladder cancer reveals a CD6/ALCAM-mediated immune-suppressive pathway 38 samples shares rna-sequencing, single-cell
- GSE305971 Integrative single-cell analysis reveals malignant archetypes and proliferative hierarchies in multiple myeloma 2105 samples shares single-cell
- GSE334923 Single-cell RNA-seq atlas of peripheral blood and kidney graft immune cells in chronic antibody-mediated rejection 48 samples shares single-cell
- GSE294300 Single-cell gene expression profiles of cells from primary colorectal cancer and adjacent normal tissue [scRNA-Seq] 36 samples shares single-cell
- GSE243013 A single-cell atlas of immune heterogeneity in anti-PD1-treated non-small cell lung cancer 243 samples shares single-cell
- GSE263733 Single-cell transcriptome analysis reveals subtype-specific clonal evolution and microenvironmental changes in liver metastasis of pancreatic adenocarcinoma and their clinical implications 218 samples shares single-cell
- GSE282122 A longitudinal single-cell atlas of anti-tumour necrosis factor treatment in inflammatory bowel disease 216 samples shares single-cell
- GSE328275 Single-cell RNA sequencing of CD45+ immune cells across primary tumor, sentinel tumor-draining lymph node, and axillary lymph node in treatment-naive triple-negative breast cancer 28 samples shares single-cell
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.