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Correction of age-associated defects in dendritic cell functions enables CD4+T cells to eradicate tumors in the elderly

GSE260791 Mus musculus (mouse) RNA-seq (gene expression) 30 samples Submitted 2024/06/12 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 30 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
30 samples: 6 conditions, mostly with about 5 replicates eachGroups 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.
Summary (written by the submitters)
Defective host defenses later in life are associated with changes in immune system activity. The means to correct immune defects to ensure immunity in the elderly are undefined. In this study, we found that CD8+T cells, which are necessary for anti-tumor immunity in young mice, are not required to eradicate the same cancers later in life.
Read the full summary ↓
Rather, CD4+T cells drive anti-tumor immunity in elderly mice. The generation of anti-tumor CD4+T cells requires multiple dendritic cell (DC) activities that are elicited by immune agonists known as hyperactivators, but not by adjuvants that model those used clinically. DC hyperactivators correct age-associated defects in DC migration and T cell co-stimulation while enabling NLRP3 inflammasome activities within living cells. These combined activities enable DCs to induce TH1-skewed T cells that persist into old age and eliminate implantable tumors. These results raise the possibility of correcting age-associated immune defects through DC manipulation.
This dataset
Open GSE260791 in finder → Find more mouse RNA-seq datasets →
Download · which file do I need?

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.

1 · Just have a look

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.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE260791_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

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.

Raw reads on ENA ↗

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 PRJNA1083511 and SRA study SRP493191. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
6 conditions, mostly with about 5 replicates each
8wk_OVAposPBS_IFA_Mouse ×5 8wk_OVAposLPSposPGPC_IFA_Mouse ×5 90wk_PBS_Only_Mouse ×5 90wk_OVAposPBS_IFA_Mouse ×5 90wk_OVAposLPSposPGPC_IFA_Mouse ×5 8wk_PBS_Only_Mouse ×4 8wk-PBS-Only-Mouse ×1

Supports a between-group comparison across 29 samples.

How we read this: 6 replicated groups read from 30 sample titles; they account for 29 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.

Samples in this study
Datasets on a similar topic

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