Pre-existing skin-resident CD8 and γδ T cell circuits mediate immune response in Merkel cell carcinoma and Predict immunotherapy efficacy [scRNA_tissue]
Single-cell RNA-seq · human · 192 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
- 192 samples: 192 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
- Preexisting Skin-Resident CD8 and γδ T-cell Circuits Mediate Immune Response in Merkel Cell Carcinoma and Predict Immunotherapy EfficacyCancer discovery · 2024
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 GSE235090_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 PRJNA984316. 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 192 sample titles: 192 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.
- GSM7494647 MO_MCC_011, skin - RNA
- GSM7494648 MO_MCC_011, skin - ab VDJ
- GSM7494649 MO_MCC_057, lymph node - RNA
- GSM7494650 MO_MCC_057, lymph node - ADT
- GSM7494651 MO_MCC_057, lymph node - ab VDJ
- GSM7494652 MO_MCC_057, lymph node - gd VDJ
- GSM7494653 MO_MCC_059, lymph node - RNA
- GSM7494654 MO_MCC_059, lymph node - ADT
- GSM7494655 MO_MCC_059, lymph node - ab VDJ
- GSM7494656 MO_MCC_059, lymph node - gd VDJ
- GSM7494657 MO_MCC_061, skin - RNA
- GSM7494658 MO_MCC_061, skin - ab VDJ
- GSM7494659 MO_MCC_066, skin - RNA
- GSM7494660 MO_MCC_066, skin - ab VDJ
- GSM7494661 MMO_MCC_068skin_RNA,skin
- GSM7494662 MMO_MCC_068skin_ADT,skin
- GSM7494663 MMO_MCC_068skin_abVDJ,skin
- GSM7494664 MO_MCC_068skin_gdVDJ,skin
- GSM7494665 MO_MCC_068LN_RNA,skin
- GSM7494666 MO_MCC_068LN_ADT,skin
- GSM7494667 MO_MCC_068LN_abVDJ,skin
- GSM7494668 MO_MCC_068LN_gdVDJ,skin
- GSM7494669 MO_MCC_069, skin - RNA
- GSM7494670 MO_MCC_069, skin - ADT
- GSM7494671 MO_MCC_069, skin - ab VDJ
- GSM7494672 MO_MCC_069, skin - gd VDJ
- GSM7494673 MO_MCC_071, skin - RNA
- GSM7494674 MO_MCC_071, skin - ADT
- GSM7494675 MO_MCC_071, skin - ab VDJ
- GSM7494676 MO_MCC_071, skin - gd VDJ
- GSM7494677 MO_MCC_072, skin - RNA
- GSM7494678 MO_MCC_072, skin - ADT
- GSM7494679 MO_MCC_072, skin - ab VDJ
- GSM7494680 MO_MCC_072, skin - gd VDJ
- GSM7494681 MO_MCC_073, skin - RNA
- GSM7494682 MO_MCC_073, skin - ADT
- GSM7494683 MO_MCC_073, skin - ab VDJ
- GSM7494684 MO_MCC_073, skin - gd VDJ
- GSM7494685 MO_MCC_074, skin - RNA
- GSM7494686 MO_MCC_074, skin - ADT
+ 152 more — browse all 192 samples with per-sample file links →
- GSE235093 Pre-existing skin-resident CD8 and γδ T cell circuits mediate immune response in Merkel cell carcinoma and Predict immunotherapy efficacy 333 samples shares cd8, skin-resident, immunotherapy, pre-existing, circuits, efficacy, mediate, predict
- GSE235092 Pre-existing skin-resident CD8 and γδ T cell circuits mediate immune response in Merkel cell carcinoma and Predict immunotherapy efficacy [bulkRNA-seq] 85 samples shares cd8, skin-resident, immunotherapy, pre-existing, circuits, efficacy, mediate, predict
- GSE308745 The Cd4+ T cell population Partners with Tpex CD8 T cells to mediate Antitumor Immunity in the Tumor Microenvironment.[PB cohort2] 87 samples shares cd8, mediate
- GSE284015 GZMK-expressing tissue-resident memory CD8+ T cells mediate human intestinal acute graft-versus-host disease 26 samples shares cd8, mediate
- GSE295601 The Cd4+ T cell population Partners with Tpex CD8 T cells to mediate Antitumor Immunity in the Tumor Microenvironment. [P6] 18 samples shares cd8, mediate
- GSE271689 Enhancing Immunotherapy Outcomes: Spatial Multi-Omics Predictive Models for Non-Small Cell Lung Cancer 586 samples shares immunotherapy, predict
- GSE307931 Circulating transcriptome analysis finds gene signatures and immune cell activation and abundance predict response to immunotherapy in bladder cancer 164 samples shares immunotherapy, predict
- GSE300793 Spatial tumour-immune ecosystems shape the efficacy of anti-PD1 immunotherapy in primary cutaneous melanoma [RNAseq] 57 samples shares immunotherapy, efficacy
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.