Longitudinal liquid biopsy identifies an early predictive biomarker of immune checkpoint blockade response in head and neck squamous cell carcinoma [bulk RNA-seq]
Single-cell RNA-seq · mouse · 178 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
- Mus musculus (mouse)
- Samples
- 178 samples: 178 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.
- Paper
- Longitudinal liquid biopsy identifies an early predictive biomarker of immune checkpoint blockade response in head and neck squamous cell carcinomaNature communications · 2025
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 GSE299686_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 PRJNA1276067 and SRA study SRP591692. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 178 sample titles: 178 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.
- GSM9043763 1_D4_7201
- GSM9043764 2_D4_7202
- GSM9043765 3_D4_7205
- GSM9043766 4_D4_7206
- GSM9043767 5_D4_7208
- GSM9043768 6_D4_7210
- GSM9043769 7_D4_7221
- GSM9043770 8_D4_7226
- GSM9043771 9_D4_7227
- GSM9043772 10_D4_7228
- GSM9043773 11_D4_7229
- GSM9043774 12_D4_311
- GSM9043775 13_D4_315
- GSM9043776 14_D4_322
- GSM9043777 15_D4_327
- GSM9043778 16_D4_7203
- GSM9043779 17_D4_7220
- GSM9043780 18_D4_7222
- GSM9043781 19_D4_304
- GSM9043782 20_D4_305
- GSM9043783 21_D4_308
- GSM9043784 22_D4_310
- GSM9043785 23_D4_313
- GSM9043786 24_D4_316
- GSM9043787 25_D4_320
- GSM9043788 26_D4_321
- GSM9043789 27_D4_323
- GSM9043790 28_D4_328
- GSM9043791 29_D4_329
- GSM9043792 30_D4_7218
- GSM9043793 31_D4_7223
- GSM9043794 32_D4_7224
- GSM9043795 33_D4_7225
- GSM9043796 34_D4_302
- GSM9043797 35_D4_306
- GSM9043798 36_D4_303
- GSM9043799 37_D4_309
- GSM9043800 38_D4_312
- GSM9043801 39_D4_314
- GSM9043802 40_D4_317
+ 138 more — browse all 178 samples with per-sample file links →
- GSE299683 Longitudinal liquid biopsy identifies an early predictive biomarker of immune checkpoint blockade response in head and neck squamous cell carcinoma [scRNA-seq] 125 samples shares longitudinal, identifies, predictive, checkpoint, biomarker, blockade, squamous, rna-seq
- GSE300288 Single-cell and spatial transcriptomics analysis of tobacco-associated lung adenocarcinoma development, and in response to targeting inflammation alone or in combination with immune checkpoint blockade in precancerous phase [scRNA-seq] 31 samples shares checkpoint, blockade, rna-seq
- GSE311344 T cells in B16 tumor model with ACT and checkpoint blockade - scRNA-seq, CITE-seq and TCRab [i35] 15 samples shares checkpoint, blockade, rna-seq
- GSE293874 Anchored screening identifies transcription factor blueprints underlying dendritic cell diversity and subset-specific anti-tumor immunity [mouse bulk RNA-seq] 167 samples shares identifies, rna-seq
- GSE98969 Single cell RNA-seq identifies a unique microglia type associated with Alzheimer’s disease [RNA] 97 samples shares identifies, rna-seq
- GSE334845 Discovery of Tcf7 regulators with clonally-resolved CRISPR screens identifies Trim28 as a mediator of CD8 T cell differentiation in tumors [RNA-Seq] 86 samples shares identifies, rna-seq
- GSE313732 Type I IFN Dynamics Orchestrate Protective ICAM1⁺ Neutrophil Heterogeneity as an Early Checkpoint for Survival in Lethal Viral Infection [RNA-Seq] 78 samples shares checkpoint, rna-seq
- GSE277171 A forward genetic screen identifies potassium channel essentiality in SHH medulloblastoma maintenance [RNA-seq] 70 samples shares identifies, rna-seq
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.