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Comparison of transcriptional profiles of CD62L+ and CD62L- subsets of in vivo primed CD44hiCD4+ T cells by exposure to ovalbumin plus papain

GSE192711 Mus musculus (mouse) RNA-seq (gene expression) 15 samples Submitted 2024/12/27 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 15 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
15 samples: 5 conditions, mostly in triplicateGroups 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)
Memory type 2 CD4+ T helper (Th2) cells play critical roles in allergic asthma, but their generation and maintenance remain poorly understood. Specially, roles of conventional dendritic cells (cDCs), including CD11b+CD103- cDC2s and CD11b-CD103+ cDC1s, in this process are still undefined. Here, we report that fate decision of memory Th2 cells was dictated by cDC subsets at priming stage.
Read the full summary ↓
To be specific, temporary depletion of CD103+ cDC1s during priming promoted the generation of allergen-specific circulating memory Th2 cells in exposure to ovalbumin plus papain, suggesting that precursors of memory Th2 cells emerged at priming stage. Subsequent data revealed that activated CD44hiCD62L+CD4+ T cells, which were elevated by cDC1 depletion during priming, exhibited memory-precursor-like transcriptional and functional properties. Taken together, CD103+ cDC1s reduced number of CD62L+ memory precursors, and thus restrained generation of memory Th2 cells.
This dataset
Open GSE192711 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 GSE192711_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 PRJNA792812 and SRA study SRP352730. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
5 conditions, mostly in triplicate
NaiveT ×3 WT_CD62L_neg_T ×3 WT_CD62L_pos_T ×3 Batf3_CD62L_neg_T ×3 Batf3_CD62L_pos_T ×3

Supports a between-group comparison across 15 samples.

How we read this: 5 replicated groups read from 15 sample titles; they account for 15 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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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.