Differences in gene expression of liver cancer cells after co-culture with activated CD8+ T cells in vitro
RNA-seq (gene expression) · mouse · 10 samples
- What it is
- RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
- Organism
- Mus musculus (mouse)
- Samples
- 10 samples: 5 × Hepa1-6 cells, Ctrl vs 5 × Hepa1-6 cells, IKGroups 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.
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 GSE232024_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.
Also filed as BioProject PRJNA970660 and SRA study SRP436615. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 10 samples.
How we read this: 2 replicated groups read from 10 sample titles; they account for 10 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.
- GSM7310383 Hepa1-6 cells, Ctrl_1 Hepa1-6 cells, Ctrl
- GSM7310384 Hepa1-6 cells, Ctrl_2 Hepa1-6 cells, Ctrl
- GSM7310385 Hepa1-6 cells, Ctrl_3 Hepa1-6 cells, Ctrl
- GSM7310386 Hepa1-6 cells, Ctrl_4 Hepa1-6 cells, Ctrl
- GSM7310387 Hepa1-6 cells, Ctrl_5 Hepa1-6 cells, Ctrl
- GSM7310388 Hepa1-6 cells, IK_1 Hepa1-6 cells, IK
- GSM7310389 Hepa1-6 cells, IK_2 Hepa1-6 cells, IK
- GSM7310390 Hepa1-6 cells, IK_3 Hepa1-6 cells, IK
- GSM7310391 Hepa1-6 cells, IK_4 Hepa1-6 cells, IK
- GSM7310392 Hepa1-6 cells, IK_5 Hepa1-6 cells, IK
- GSE299133 Host MTDH orchestrates liver lipid metabolism and CD8+ T cells to suppress antitumor immunity [Hepatocytes] 18 samples shares cd8, liver
- GSE253492 Transcriptional Regulation by NFATc1 and NFATc2 in chronically activated CD8+ T cells 16 samples shares cd8, activated
- GSE290455 RNAseq of activated CD8+ T cells with and without Glutamine Synthetase 10 samples shares cd8, activated
- GSE313654 Activated Interferon Signaling Suppresses Age-Dependent Liver Cancer [RNAseq_MycRux] 58 samples shares activated, liver
- GSE329774 Metabolically and epigenetically reprogrammed splenic TRNP1hiCD8+ T cells exacerbate liver fibrosis 93 samples shares cd8, liver
- GSE281719 PD-1 Mediated Regulation of Unique Activated CD8+ T Cells by NK Cells in the Submandibular Gland [I15, I16] 60 samples shares cd8, activated
- GSE276107 Host MTDH orchestrates liver lipid metabolism and CD8+ T cells to suppress antitumor immunity 44 samples shares cd8, liver
- GSE248272 Therapeutically targeting perivascular macrophage effector function improves CD8+ T-cell infiltration and response to chemotherapeutic drugs in cancer 30 samples shares cd8
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.