Longitudinal liquid biopsy identifies an early predictive biomarker of immune checkpoint blockade response in head and neck squamous cell carcinoma [scRNA-seq]
Single-cell RNA-seq · mouse · 125 samples
- What it is
- Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
- Organism
- Mus musculus (mouse)
- Samples
- 125 samples: 125 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 GSE299683_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 PRJNA1276060. 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 125 sample titles: 125 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.
- GSM9043587 scRNAseq_SCAF3563_1_7246_T1
- GSM9043588 scRNAseq_SCAF3570_17_7246_T2
- GSM9043589 scRNAseq_SCAF3569_33_7246_T3
- GSM9043590 scRNAseq_SCAF3561_49_7246_T4
- GSM9043591 scRNAseq_SCAF3558_2_7248_T1
- GSM9043592 scRNAseq_SCAF3513_18_7248_T2
- GSM9043593 scRNAseq_SCAF3567_34_72248_T3
- GSM9043594 scRNAseq_SCAF3508_50_7248_T4
- GSM9043595 scRNAseq_SCAF3568_4_7251_T1
- GSM9043596 scRNAseq_SCAF3469_20_7251_T2
- GSM9043597 scRNAseq_SCAF3564_36_7251_T3
- GSM9043598 scRNAseq_SCAF3463_52_7251_T4_2
- GSM9043599 scRNAseq_SCAF3552_5_7252_T1
- GSM9043600 scRNAseq_SCAF3045_21_7252_2
- GSM9043601 scRNAseq_SCAF3566_37_7252_T3
- GSM9043602 scRNAseq_SCAF3560_53_7252_T4
- GSM9043603 scRNAseq_SCAF3403_10_7258_T1
- GSM9043604 scRNAseq_SCAF3565_26_7258_T2
- GSM9043605 scRNAseq_SCAF3472_42_7258_T3
- GSM9043606 scRNAseq_SCAF3562_58_7258_T4
- GSM9043607 scRNAseq_SCAF3467_15_7264_T1
- GSM9043608 scRNAseq_SCAF3046_31_7264_2
- GSM9043609 scRNAseq_SCAF3476_47_7264_T3
- GSM9043610 scRNAseq_SCAF3474_63_7264_T4
- GSM9043611 scRNAseq_SCAF3048_16_7265_1
- GSM9043612 scRNAseq_SCAF3517_32_7265_T2
- GSM9043613 scRNAseq_SCAF3466_48_7265_T3
- GSM9043614 scRNAseq_SCAF3461_64_7265_T4_2
- GSM9043615 scRNAseq_SCAF3521_6_7254_T1
- GSM9043616 scRNAseq_SCAF3406_22_7254_T2
- GSM9043617 scRNAseq_SCAF3464_38_7254_T3_2
- GSM9043618 scRNAseq_SCAF3522_54_7254_T4
- GSM9043619 scRNAseq_SCAF3473_7_7255_T1
- GSM9043620 scRNAseq_SCAF3477_23_7255_T2
- GSM9043621 scRNAseq_SCAF3047_39_7255_3
- GSM9043622 scRNAseq_SCAF3511_55_7255_T4
- GSM9043623 scRNAseq_SCAF3404_11_7259_T1
- GSM9043624 scRNAseq_SCAF3553_27_7259_T2
- GSM9043625 scRNAseq_SCAF3475_43_7259_T3
- GSM9043626 scRNAseq_SCAF3518_59_7259_T4
+ 85 more — browse all 125 samples with per-sample file links →
- GSE299686 Longitudinal liquid biopsy identifies an early predictive biomarker of immune checkpoint blockade response in head and neck squamous cell carcinoma [bulk RNA-seq] 178 samples shares longitudinal, identifies, predictive, checkpoint, biomarker, blockade, squamous
- 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, scrna-seq, blockade
- GSE311344 T cells in B16 tumor model with ACT and checkpoint blockade - scRNA-seq, CITE-seq and TCRab [i35] 15 samples shares checkpoint, scrna-seq, blockade
- GSE236889 Longitudinal scRNA-seq of murine T cell transfer colitis 64 samples shares longitudinal, scrna-seq
- GSE255423 Longitudinal scRNA-seq of murine Il10KO colitis 24 samples shares longitudinal, scrna-seq
- GSE245838 Anchored screening identifies transcription factor blueprints underlying dendritic cell diversity and subset-specific anti-tumor immunity [mouse scRNA-seq] 21 samples shares identifies, scrna-seq
- GSE290464 Noncanonical role of NCOR1 as a facilitator of DNA mismatch repair and its deficiency sensitizes cancers to immune checkpoint blockade therapy 18 samples shares checkpoint, blockade
- GSE299487 LY6D Identifies Persistent Tumor-Initiating Cells Driving Pancreatic Tumorigenesis [scRNA-seq] 12 samples shares identifies, scrna-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.